{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Imports and Setup**"
      ],
      "metadata": {
        "id": "V9LrEKkcZtq3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lH4HmgpXZfMb",
        "outputId": "66d88486-e694-4f9c-ec80-ddd78d6a6507"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pandas Version : 2.2.2\n",
            "NumPy Version  : 2.0.2\n",
            "SHAP Version   : 0.52.0\n"
          ]
        }
      ],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "import os\n",
        "import time\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from IPython.display import display\n",
        "\n",
        "from sklearn.model_selection import (\n",
        "    train_test_split,\n",
        "    GridSearchCV,\n",
        "    cross_val_score\n",
        ")\n",
        "\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "from sklearn.pipeline import Pipeline\n",
        "\n",
        "from sklearn.impute import SimpleImputer\n",
        "\n",
        "from sklearn.preprocessing import (\n",
        "    StandardScaler,\n",
        "    OneHotEncoder,\n",
        "    LabelEncoder\n",
        ")\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from sklearn.metrics import (\n",
        "    accuracy_score,\n",
        "    precision_score,\n",
        "    recall_score,\n",
        "    f1_score,\n",
        "    roc_auc_score,\n",
        "    confusion_matrix,\n",
        "    classification_report\n",
        ")\n",
        "\n",
        "from sklearn.inspection import PartialDependenceDisplay\n",
        "\n",
        "import shap\n",
        "\n",
        "RANDOM_STATE = 42\n",
        "\n",
        "np.random.seed(RANDOM_STATE)\n",
        "\n",
        "plt.style.use(\"ggplot\")\n",
        "\n",
        "sns.set_theme(\n",
        "    style=\"whitegrid\",\n",
        "    context=\"notebook\"\n",
        ")\n",
        "\n",
        "plt.rcParams[\"figure.figsize\"] = (10, 6)\n",
        "plt.rcParams[\"axes.titlesize\"] = 16\n",
        "plt.rcParams[\"axes.labelsize\"] = 13\n",
        "plt.rcParams[\"xtick.labelsize\"] = 11\n",
        "plt.rcParams[\"ytick.labelsize\"] = 11\n",
        "\n",
        "pd.set_option(\"display.max_columns\", None)\n",
        "pd.set_option(\"display.max_rows\", 200)\n",
        "pd.set_option(\"display.width\", 200)\n",
        "pd.set_option(\"display.float_format\", \"{:.2f}\".format)\n",
        "\n",
        "print(f\"Pandas Version : {pd.__version__}\")\n",
        "print(f\"NumPy Version  : {np.__version__}\")\n",
        "print(f\"SHAP Version   : {shap.__version__}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load Dataset"
      ],
      "metadata": {
        "id": "wP1mr7eeZ8SC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"loans_full_schema.csv\")\n",
        "\n",
        "print(f\"Rows    : {df.shape[0]}\")\n",
        "print(f\"Columns : {df.shape[1]}\")\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "PHBaZjiGZ9tR",
        "outputId": "4e3998c1-857d-4d6f-8cee-6d62a1ca0f35"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Rows    : 10000\n",
            "Columns : 56\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0                emp_title  emp_length state homeownership  annual_income  verified_income  debt_to_income  annual_income_joint verification_income_joint  debt_to_income_joint  \\\n",
              "0           1  global config engineer         3.00    NJ      MORTGAGE       90000.00         Verified           18.01                  NaN                       NaN                   NaN   \n",
              "1           2   warehouse office clerk       10.00    HI          RENT       40000.00     Not Verified            5.04                  NaN                       NaN                   NaN   \n",
              "2           3                 assembly        3.00    WI          RENT       40000.00  Source Verified           21.15                  NaN                       NaN                   NaN   \n",
              "3           4         customer service        1.00    PA          RENT       30000.00     Not Verified           10.16                  NaN                       NaN                   NaN   \n",
              "4           5     security supervisor        10.00    CA          RENT       35000.00         Verified           57.96             57000.00                  Verified                 37.66   \n",
              "\n",
              "   delinq_2y  months_since_last_delinq  earliest_credit_line  inquiries_last_12m  total_credit_lines  open_credit_lines  total_credit_limit  total_credit_utilized  num_collections_last_12m  \\\n",
              "0          0                     38.00                  2001                   6                  28                 10               70795                  38767                         0   \n",
              "1          0                       NaN                  1996                   1                  30                 14               28800                   4321                         0   \n",
              "2          0                     28.00                  2006                   4                  31                 10               24193                  16000                         0   \n",
              "3          0                       NaN                  2007                   0                   4                  4               25400                   4997                         0   \n",
              "4          0                       NaN                  2008                   7                  22                 16               69839                  52722                         0   \n",
              "\n",
              "   num_historical_failed_to_pay  months_since_90d_late  current_accounts_delinq  total_collection_amount_ever  current_installment_accounts  accounts_opened_24m  months_since_last_credit_inquiry  \\\n",
              "0                             0                  38.00                        0                          1250                             2                    5                              5.00   \n",
              "1                             1                    NaN                        0                             0                             0                   11                              8.00   \n",
              "2                             0                  28.00                        0                           432                             1                   13                              7.00   \n",
              "3                             1                    NaN                        0                             0                             1                    1                             15.00   \n",
              "4                             0                    NaN                        0                             0                             1                    6                              4.00   \n",
              "\n",
              "   num_satisfactory_accounts  num_accounts_120d_past_due  num_accounts_30d_past_due  num_active_debit_accounts  total_debit_limit  num_total_cc_accounts  num_open_cc_accounts  \\\n",
              "0                         10                        0.00                          0                          2              11100                     14                     8   \n",
              "1                         14                        0.00                          0                          3              16500                     24                    14   \n",
              "2                         10                        0.00                          0                          3               4300                     14                     8   \n",
              "3                          4                        0.00                          0                          2              19400                      3                     3   \n",
              "4                         16                        0.00                          0                         10              32700                     20                    15   \n",
              "\n",
              "   num_cc_carrying_balance  num_mort_accounts  account_never_delinq_percent  tax_liens  public_record_bankrupt        loan_purpose application_type  loan_amount  term  interest_rate  installment  \\\n",
              "0                        6                  1                         92.90          0                       0              moving       individual        28000    60          14.07       652.53   \n",
              "1                        4                  0                        100.00          0                       1  debt_consolidation       individual         5000    36          12.61       167.54   \n",
              "2                        6                  0                         93.50          0                       0               other       individual         2000    36          17.09        71.40   \n",
              "3                        2                  0                        100.00          1                       0  debt_consolidation       individual        21600    36           6.72       664.19   \n",
              "4                       13                  0                        100.00          0                       0         credit_card            joint        23000    36          14.07       786.87   \n",
              "\n",
              "  grade sub_grade issue_month loan_status initial_listing_status disbursement_method  balance  paid_total  paid_principal  paid_interest  paid_late_fees  \n",
              "0     C        C3    Mar-2018     Current                  whole                Cash 27015.86     1999.33          984.14        1015.19            0.00  \n",
              "1     C        C1    Feb-2018     Current                  whole                Cash  4651.37      499.12          348.63         150.49            0.00  \n",
              "2     D        D1    Feb-2018     Current             fractional                Cash  1824.63      281.80          175.37         106.43            0.00  \n",
              "3     A        A3    Jan-2018     Current                  whole                Cash 18853.26     3312.89         2746.74         566.15            0.00  \n",
              "4     C        C3    Mar-2018     Current                  whole                Cash 21430.15     2324.65         1569.85         754.80            0.00  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bd661b76-890e-4022-a96a-222945829f6b\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>state</th>\n",
              "      <th>homeownership</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>verified_income</th>\n",
              "      <th>debt_to_income</th>\n",
              "      <th>annual_income_joint</th>\n",
              "      <th>verification_income_joint</th>\n",
              "      <th>debt_to_income_joint</th>\n",
              "      <th>delinq_2y</th>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <th>earliest_credit_line</th>\n",
              "      <th>inquiries_last_12m</th>\n",
              "      <th>total_credit_lines</th>\n",
              "      <th>open_credit_lines</th>\n",
              "      <th>total_credit_limit</th>\n",
              "      <th>total_credit_utilized</th>\n",
              "      <th>num_collections_last_12m</th>\n",
              "      <th>num_historical_failed_to_pay</th>\n",
              "      <th>months_since_90d_late</th>\n",
              "      <th>current_accounts_delinq</th>\n",
              "      <th>total_collection_amount_ever</th>\n",
              "      <th>current_installment_accounts</th>\n",
              "      <th>accounts_opened_24m</th>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <th>num_satisfactory_accounts</th>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <th>num_accounts_30d_past_due</th>\n",
              "      <th>num_active_debit_accounts</th>\n",
              "      <th>total_debit_limit</th>\n",
              "      <th>num_total_cc_accounts</th>\n",
              "      <th>num_open_cc_accounts</th>\n",
              "      <th>num_cc_carrying_balance</th>\n",
              "      <th>num_mort_accounts</th>\n",
              "      <th>account_never_delinq_percent</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>public_record_bankrupt</th>\n",
              "      <th>loan_purpose</th>\n",
              "      <th>application_type</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>initial_listing_status</th>\n",
              "      <th>disbursement_method</th>\n",
              "      <th>balance</th>\n",
              "      <th>paid_total</th>\n",
              "      <th>paid_principal</th>\n",
              "      <th>paid_interest</th>\n",
              "      <th>paid_late_fees</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>global config engineer</td>\n",
              "      <td>3.00</td>\n",
              "      <td>NJ</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>90000.00</td>\n",
              "      <td>Verified</td>\n",
              "      <td>18.01</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>38.00</td>\n",
              "      <td>2001</td>\n",
              "      <td>6</td>\n",
              "      <td>28</td>\n",
              "      <td>10</td>\n",
              "      <td>70795</td>\n",
              "      <td>38767</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>38.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1250</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "      <td>5.00</td>\n",
              "      <td>10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>11100</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>92.90</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>moving</td>\n",
              "      <td>individual</td>\n",
              "      <td>28000</td>\n",
              "      <td>60</td>\n",
              "      <td>14.07</td>\n",
              "      <td>652.53</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>27015.86</td>\n",
              "      <td>1999.33</td>\n",
              "      <td>984.14</td>\n",
              "      <td>1015.19</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>warehouse office clerk</td>\n",
              "      <td>10.00</td>\n",
              "      <td>HI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40000.00</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>5.04</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1996</td>\n",
              "      <td>1</td>\n",
              "      <td>30</td>\n",
              "      <td>14</td>\n",
              "      <td>28800</td>\n",
              "      <td>4321</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>8.00</td>\n",
              "      <td>14</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>16500</td>\n",
              "      <td>24</td>\n",
              "      <td>14</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>5000</td>\n",
              "      <td>36</td>\n",
              "      <td>12.61</td>\n",
              "      <td>167.54</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>4651.37</td>\n",
              "      <td>499.12</td>\n",
              "      <td>348.63</td>\n",
              "      <td>150.49</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>assembly</td>\n",
              "      <td>3.00</td>\n",
              "      <td>WI</td>\n",
              "      <td>RENT</td>\n",
              "      <td>40000.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>21.15</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>28.00</td>\n",
              "      <td>2006</td>\n",
              "      <td>4</td>\n",
              "      <td>31</td>\n",
              "      <td>10</td>\n",
              "      <td>24193</td>\n",
              "      <td>16000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>28.00</td>\n",
              "      <td>0</td>\n",
              "      <td>432</td>\n",
              "      <td>1</td>\n",
              "      <td>13</td>\n",
              "      <td>7.00</td>\n",
              "      <td>10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>4300</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>93.50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>other</td>\n",
              "      <td>individual</td>\n",
              "      <td>2000</td>\n",
              "      <td>36</td>\n",
              "      <td>17.09</td>\n",
              "      <td>71.40</td>\n",
              "      <td>D</td>\n",
              "      <td>D1</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>fractional</td>\n",
              "      <td>Cash</td>\n",
              "      <td>1824.63</td>\n",
              "      <td>281.80</td>\n",
              "      <td>175.37</td>\n",
              "      <td>106.43</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>customer service</td>\n",
              "      <td>1.00</td>\n",
              "      <td>PA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>30000.00</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>10.16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>25400</td>\n",
              "      <td>4997</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>15.00</td>\n",
              "      <td>4</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>19400</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>21600</td>\n",
              "      <td>36</td>\n",
              "      <td>6.72</td>\n",
              "      <td>664.19</td>\n",
              "      <td>A</td>\n",
              "      <td>A3</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>18853.26</td>\n",
              "      <td>3312.89</td>\n",
              "      <td>2746.74</td>\n",
              "      <td>566.15</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>security supervisor</td>\n",
              "      <td>10.00</td>\n",
              "      <td>CA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>35000.00</td>\n",
              "      <td>Verified</td>\n",
              "      <td>57.96</td>\n",
              "      <td>57000.00</td>\n",
              "      <td>Verified</td>\n",
              "      <td>37.66</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2008</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "      <td>16</td>\n",
              "      <td>69839</td>\n",
              "      <td>52722</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>6</td>\n",
              "      <td>4.00</td>\n",
              "      <td>16</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>32700</td>\n",
              "      <td>20</td>\n",
              "      <td>15</td>\n",
              "      <td>13</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>joint</td>\n",
              "      <td>23000</td>\n",
              "      <td>36</td>\n",
              "      <td>14.07</td>\n",
              "      <td>786.87</td>\n",
              "      <td>C</td>\n",
              "      <td>C3</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>21430.15</td>\n",
              "      <td>2324.65</td>\n",
              "      <td>1569.85</td>\n",
              "      <td>754.80</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset Overview"
      ],
      "metadata": {
        "id": "hPSlxbMaZ_2E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataset Information\n",
        "df.info()\n",
        "# Numerical Summary\n",
        "\n",
        "df.describe().T\n",
        "#Categorical Summary\n",
        "\n",
        "df.describe(include=\"object\").T\n",
        "# Random Sample\n",
        "display(df.sample(10, random_state=RANDOM_STATE))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "sw0qiRilaCQP",
        "outputId": "25a22649-41c3-4558-f5d7-dfffde62272c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 56 columns):\n",
            " #   Column                            Non-Null Count  Dtype  \n",
            "---  ------                            --------------  -----  \n",
            " 0   Unnamed: 0                        10000 non-null  int64  \n",
            " 1   emp_title                         9167 non-null   object \n",
            " 2   emp_length                        9183 non-null   float64\n",
            " 3   state                             10000 non-null  object \n",
            " 4   homeownership                     10000 non-null  object \n",
            " 5   annual_income                     10000 non-null  float64\n",
            " 6   verified_income                   10000 non-null  object \n",
            " 7   debt_to_income                    9976 non-null   float64\n",
            " 8   annual_income_joint               1495 non-null   float64\n",
            " 9   verification_income_joint         1455 non-null   object \n",
            " 10  debt_to_income_joint              1495 non-null   float64\n",
            " 11  delinq_2y                         10000 non-null  int64  \n",
            " 12  months_since_last_delinq          4342 non-null   float64\n",
            " 13  earliest_credit_line              10000 non-null  int64  \n",
            " 14  inquiries_last_12m                10000 non-null  int64  \n",
            " 15  total_credit_lines                10000 non-null  int64  \n",
            " 16  open_credit_lines                 10000 non-null  int64  \n",
            " 17  total_credit_limit                10000 non-null  int64  \n",
            " 18  total_credit_utilized             10000 non-null  int64  \n",
            " 19  num_collections_last_12m          10000 non-null  int64  \n",
            " 20  num_historical_failed_to_pay      10000 non-null  int64  \n",
            " 21  months_since_90d_late             2285 non-null   float64\n",
            " 22  current_accounts_delinq           10000 non-null  int64  \n",
            " 23  total_collection_amount_ever      10000 non-null  int64  \n",
            " 24  current_installment_accounts      10000 non-null  int64  \n",
            " 25  accounts_opened_24m               10000 non-null  int64  \n",
            " 26  months_since_last_credit_inquiry  8729 non-null   float64\n",
            " 27  num_satisfactory_accounts         10000 non-null  int64  \n",
            " 28  num_accounts_120d_past_due        9682 non-null   float64\n",
            " 29  num_accounts_30d_past_due         10000 non-null  int64  \n",
            " 30  num_active_debit_accounts         10000 non-null  int64  \n",
            " 31  total_debit_limit                 10000 non-null  int64  \n",
            " 32  num_total_cc_accounts             10000 non-null  int64  \n",
            " 33  num_open_cc_accounts              10000 non-null  int64  \n",
            " 34  num_cc_carrying_balance           10000 non-null  int64  \n",
            " 35  num_mort_accounts                 10000 non-null  int64  \n",
            " 36  account_never_delinq_percent      10000 non-null  float64\n",
            " 37  tax_liens                         10000 non-null  int64  \n",
            " 38  public_record_bankrupt            10000 non-null  int64  \n",
            " 39  loan_purpose                      10000 non-null  object \n",
            " 40  application_type                  10000 non-null  object \n",
            " 41  loan_amount                       10000 non-null  int64  \n",
            " 42  term                              10000 non-null  int64  \n",
            " 43  interest_rate                     10000 non-null  float64\n",
            " 44  installment                       10000 non-null  float64\n",
            " 45  grade                             10000 non-null  object \n",
            " 46  sub_grade                         10000 non-null  object \n",
            " 47  issue_month                       10000 non-null  object \n",
            " 48  loan_status                       10000 non-null  object \n",
            " 49  initial_listing_status            10000 non-null  object \n",
            " 50  disbursement_method               10000 non-null  object \n",
            " 51  balance                           10000 non-null  float64\n",
            " 52  paid_total                        10000 non-null  float64\n",
            " 53  paid_principal                    10000 non-null  float64\n",
            " 54  paid_interest                     10000 non-null  float64\n",
            " 55  paid_late_fees                    10000 non-null  float64\n",
            "dtypes: float64(17), int64(26), object(13)\n",
            "memory usage: 4.3+ MB\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "      Unnamed: 0                   emp_title  emp_length state homeownership  annual_income  verified_income  debt_to_income  annual_income_joint verification_income_joint  debt_to_income_joint  \\\n",
              "6252        6253        operation specialist        9.00    NC          RENT       65000.00         Verified           18.92                  NaN                       NaN                   NaN   \n",
              "4684        4685                receptionist        1.00    CA          RENT       37000.00  Source Verified           13.17                  NaN                       NaN                   NaN   \n",
              "1731        1732          massage therapoist       10.00    NV      MORTGAGE       60000.00     Not Verified            5.10                  NaN                       NaN                   NaN   \n",
              "4742        4743    senior software engineer       10.00    KS      MORTGAGE      100500.00  Source Verified           20.68                  NaN                       NaN                   NaN   \n",
              "4521        4522         executive director         5.00    LA          RENT       59000.00  Source Verified            1.02                  NaN                       NaN                   NaN   \n",
              "6340        6341                 underwriter       10.00    NJ      MORTGAGE       90000.00  Source Verified           16.77            150000.00           Source Verified                 10.58   \n",
              "576          577                         NaN         NaN    MS           OWN       22212.00  Source Verified            9.08                  NaN                       NaN                   NaN   \n",
              "5202        5203             hospital police        2.00    NY          RENT       28800.00  Source Verified           19.17                  NaN                       NaN                   NaN   \n",
              "6363        6364  electrical design engineer       10.00    FL      MORTGAGE       60000.00     Not Verified           13.91                  NaN                       NaN                   NaN   \n",
              "439          440                    teacher        10.00    NJ          RENT       61000.00     Not Verified           13.08                  NaN                       NaN                   NaN   \n",
              "\n",
              "      delinq_2y  months_since_last_delinq  earliest_credit_line  inquiries_last_12m  total_credit_lines  open_credit_lines  total_credit_limit  total_credit_utilized  num_collections_last_12m  \\\n",
              "6252          0                       NaN                  2004                   3                   9                  9              107464                  64183                         0   \n",
              "4684          0                     43.00                  2013                   2                   9                  6               21700                  13806                         0   \n",
              "1731          1                     14.00                  2000                   0                  17                 10               22838                  10286                         0   \n",
              "4742          0                     59.00                  2000                   6                  43                 22              149818                  74764                         0   \n",
              "4521          0                     36.00                  2005                   1                   7                  3               60921                  63440                         0   \n",
              "6340          5                     11.00                  1995                   0                  45                 20              334672                  52278                         0   \n",
              "576           0                       NaN                  2007                   0                  10                  4                9200                   4720                         0   \n",
              "5202          1                      3.00                  2004                   0                   7                  4               15980                   2872                         0   \n",
              "6363          0                       NaN                  2006                   1                  14                  8              255304                  31461                         0   \n",
              "439           2                     17.00                  1999                   1                  21                  5               96084                  69831                         0   \n",
              "\n",
              "      num_historical_failed_to_pay  months_since_90d_late  current_accounts_delinq  total_collection_amount_ever  current_installment_accounts  accounts_opened_24m  months_since_last_credit_inquiry  \\\n",
              "6252                             0                    NaN                        0                             0                             1                    7                              2.00   \n",
              "4684                             0                    NaN                        0                             0                             1                    4                             10.00   \n",
              "1731                             1                  28.00                        0                           742                             4                    3                             16.00   \n",
              "4742                             0                    NaN                        0                             0                             3                    5                              2.00   \n",
              "4521                             0                  36.00                        0                          1687                             1                    3                              9.00   \n",
              "6340                             0                    NaN                        0                             0                             3                    5                             16.00   \n",
              "576                              0                    NaN                        0                             0                             0                    0                               NaN   \n",
              "5202                             0                    NaN                        0                             0                             2                    1                               NaN   \n",
              "6363                             0                    NaN                        0                             0                             2                    2                              2.00   \n",
              "439                              0                  17.00                        0                             0                             2                    1                             10.00   \n",
              "\n",
              "      num_satisfactory_accounts  num_accounts_120d_past_due  num_accounts_30d_past_due  num_active_debit_accounts  total_debit_limit  num_total_cc_accounts  num_open_cc_accounts  \\\n",
              "6252                          9                        0.00                          0                          4              46500                      7                     7   \n",
              "4684                          6                        0.00                          0                          3              11800                      6                     5   \n",
              "1731                         10                        0.00                          0                          3               9300                     10                     6   \n",
              "4742                         22                        0.00                          0                          9              75000                     28                    19   \n",
              "4521                          3                        0.00                          0                          2               1000                      2                     2   \n",
              "6340                         20                        0.00                          0                          1               9200                     37                    15   \n",
              "576                           4                        0.00                          0                          1               2500                      5                     4   \n",
              "5202                          4                        0.00                          0                          1               1500                      4                     2   \n",
              "6363                          8                        0.00                          0                          2              30500                      7                     5   \n",
              "439                           5                        0.00                          0                          1               8900                      9                     2   \n",
              "\n",
              "      num_cc_carrying_balance  num_mort_accounts  account_never_delinq_percent  tax_liens  public_record_bankrupt        loan_purpose application_type  loan_amount  term  interest_rate  installment  \\\n",
              "6252                        4                  0                        100.00          0                       0               other       individual        10000    36           6.08       304.59   \n",
              "4684                        3                  1                        100.00          0                       0  debt_consolidation       individual        14400    36          15.04       499.47   \n",
              "1731                        5                  0                         29.40          0                       1  debt_consolidation       individual         1200    36          15.04        41.63   \n",
              "4742                       11                  4                         90.70          0                       0         credit_card       individual        40000    60           7.96       810.30   \n",
              "4521                        2                  1                         83.30          0                       0               other       individual         7500    36          13.59       254.85   \n",
              "6340                        2                  4                         84.40          0                       0  debt_consolidation            joint         6500    36          10.42       211.03   \n",
              "576                         4                  0                        100.00          0                       0  debt_consolidation       individual         5000    36          12.62       167.56   \n",
              "5202                        1                  0                         85.70          0                       0  debt_consolidation       individual         3000    36          17.09       107.10   \n",
              "6363                        3                  2                        100.00          0                       0  debt_consolidation       individual        12600    36           5.32       379.45   \n",
              "439                         1                  0                         81.00          0                       0  debt_consolidation       individual        13000    60          17.09       323.72   \n",
              "\n",
              "     grade sub_grade issue_month loan_status initial_listing_status disbursement_method  balance  paid_total  paid_principal  paid_interest  paid_late_fees  \n",
              "6252     A        A2    Jan-2018     Current                  whole                Cash  8717.45     1519.57         1282.55         237.02            0.00  \n",
              "4684     C        C4    Feb-2018     Current                  whole                Cash 13430.99     1486.38          969.01         517.37            0.00  \n",
              "1731     C        C4    Mar-2018     Current                  whole                Cash  1146.49       82.26           53.51          28.75            0.00  \n",
              "4742     A        A5    Mar-2018     Current                  whole           DirectPay 38354.23     2395.52         1645.77         749.75            0.00  \n",
              "4521     C        C2    Jan-2018     Current                  whole                Cash  6630.98     1268.59          869.02         399.57            0.00  \n",
              "6340     B        B3    Feb-2018     Current                  whole                Cash  5873.55      836.59          626.45         210.14            0.00  \n",
              "576      C        C1    Jan-2018     Current             fractional                Cash  4532.79      666.73          467.21         199.52            0.00  \n",
              "5202     D        D1    Jan-2018     Current             fractional                Cash  2668.83      532.65          331.17         201.48            0.00  \n",
              "6363     A        A1    Jan-2018  Fully Paid                  whole                Cash     0.00    12675.89        12600.00          75.89            0.00  \n",
              "439      D        D1    Jan-2018     Current                  whole                Cash 12287.09     1606.26          712.91         893.35            0.00  "
            ],
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              "\n",
              "  <div id=\"df-01eb90cb-ec33-47ed-88be-6bf138ab2d11\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>emp_title</th>\n",
              "      <th>emp_length</th>\n",
              "      <th>state</th>\n",
              "      <th>homeownership</th>\n",
              "      <th>annual_income</th>\n",
              "      <th>verified_income</th>\n",
              "      <th>debt_to_income</th>\n",
              "      <th>annual_income_joint</th>\n",
              "      <th>verification_income_joint</th>\n",
              "      <th>debt_to_income_joint</th>\n",
              "      <th>delinq_2y</th>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <th>earliest_credit_line</th>\n",
              "      <th>inquiries_last_12m</th>\n",
              "      <th>total_credit_lines</th>\n",
              "      <th>open_credit_lines</th>\n",
              "      <th>total_credit_limit</th>\n",
              "      <th>total_credit_utilized</th>\n",
              "      <th>num_collections_last_12m</th>\n",
              "      <th>num_historical_failed_to_pay</th>\n",
              "      <th>months_since_90d_late</th>\n",
              "      <th>current_accounts_delinq</th>\n",
              "      <th>total_collection_amount_ever</th>\n",
              "      <th>current_installment_accounts</th>\n",
              "      <th>accounts_opened_24m</th>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <th>num_satisfactory_accounts</th>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <th>num_accounts_30d_past_due</th>\n",
              "      <th>num_active_debit_accounts</th>\n",
              "      <th>total_debit_limit</th>\n",
              "      <th>num_total_cc_accounts</th>\n",
              "      <th>num_open_cc_accounts</th>\n",
              "      <th>num_cc_carrying_balance</th>\n",
              "      <th>num_mort_accounts</th>\n",
              "      <th>account_never_delinq_percent</th>\n",
              "      <th>tax_liens</th>\n",
              "      <th>public_record_bankrupt</th>\n",
              "      <th>loan_purpose</th>\n",
              "      <th>application_type</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>term</th>\n",
              "      <th>interest_rate</th>\n",
              "      <th>installment</th>\n",
              "      <th>grade</th>\n",
              "      <th>sub_grade</th>\n",
              "      <th>issue_month</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>initial_listing_status</th>\n",
              "      <th>disbursement_method</th>\n",
              "      <th>balance</th>\n",
              "      <th>paid_total</th>\n",
              "      <th>paid_principal</th>\n",
              "      <th>paid_interest</th>\n",
              "      <th>paid_late_fees</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>6252</th>\n",
              "      <td>6253</td>\n",
              "      <td>operation specialist</td>\n",
              "      <td>9.00</td>\n",
              "      <td>NC</td>\n",
              "      <td>RENT</td>\n",
              "      <td>65000.00</td>\n",
              "      <td>Verified</td>\n",
              "      <td>18.92</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2004</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>9</td>\n",
              "      <td>107464</td>\n",
              "      <td>64183</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>2.00</td>\n",
              "      <td>9</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>46500</td>\n",
              "      <td>7</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>other</td>\n",
              "      <td>individual</td>\n",
              "      <td>10000</td>\n",
              "      <td>36</td>\n",
              "      <td>6.08</td>\n",
              "      <td>304.59</td>\n",
              "      <td>A</td>\n",
              "      <td>A2</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>8717.45</td>\n",
              "      <td>1519.57</td>\n",
              "      <td>1282.55</td>\n",
              "      <td>237.02</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4684</th>\n",
              "      <td>4685</td>\n",
              "      <td>receptionist</td>\n",
              "      <td>1.00</td>\n",
              "      <td>CA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>37000.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>13.17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>43.00</td>\n",
              "      <td>2013</td>\n",
              "      <td>2</td>\n",
              "      <td>9</td>\n",
              "      <td>6</td>\n",
              "      <td>21700</td>\n",
              "      <td>13806</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>10.00</td>\n",
              "      <td>6</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>11800</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>14400</td>\n",
              "      <td>36</td>\n",
              "      <td>15.04</td>\n",
              "      <td>499.47</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>13430.99</td>\n",
              "      <td>1486.38</td>\n",
              "      <td>969.01</td>\n",
              "      <td>517.37</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1731</th>\n",
              "      <td>1732</td>\n",
              "      <td>massage therapoist</td>\n",
              "      <td>10.00</td>\n",
              "      <td>NV</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>60000.00</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>5.10</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>14.00</td>\n",
              "      <td>2000</td>\n",
              "      <td>0</td>\n",
              "      <td>17</td>\n",
              "      <td>10</td>\n",
              "      <td>22838</td>\n",
              "      <td>10286</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>28.00</td>\n",
              "      <td>0</td>\n",
              "      <td>742</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>16.00</td>\n",
              "      <td>10</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9300</td>\n",
              "      <td>10</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>29.40</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>1200</td>\n",
              "      <td>36</td>\n",
              "      <td>15.04</td>\n",
              "      <td>41.63</td>\n",
              "      <td>C</td>\n",
              "      <td>C4</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>1146.49</td>\n",
              "      <td>82.26</td>\n",
              "      <td>53.51</td>\n",
              "      <td>28.75</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4742</th>\n",
              "      <td>4743</td>\n",
              "      <td>senior software engineer</td>\n",
              "      <td>10.00</td>\n",
              "      <td>KS</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>100500.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>20.68</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>59.00</td>\n",
              "      <td>2000</td>\n",
              "      <td>6</td>\n",
              "      <td>43</td>\n",
              "      <td>22</td>\n",
              "      <td>149818</td>\n",
              "      <td>74764</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>2.00</td>\n",
              "      <td>22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>75000</td>\n",
              "      <td>28</td>\n",
              "      <td>19</td>\n",
              "      <td>11</td>\n",
              "      <td>4</td>\n",
              "      <td>90.70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>credit_card</td>\n",
              "      <td>individual</td>\n",
              "      <td>40000</td>\n",
              "      <td>60</td>\n",
              "      <td>7.96</td>\n",
              "      <td>810.30</td>\n",
              "      <td>A</td>\n",
              "      <td>A5</td>\n",
              "      <td>Mar-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>DirectPay</td>\n",
              "      <td>38354.23</td>\n",
              "      <td>2395.52</td>\n",
              "      <td>1645.77</td>\n",
              "      <td>749.75</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4521</th>\n",
              "      <td>4522</td>\n",
              "      <td>executive director</td>\n",
              "      <td>5.00</td>\n",
              "      <td>LA</td>\n",
              "      <td>RENT</td>\n",
              "      <td>59000.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>1.02</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>36.00</td>\n",
              "      <td>2005</td>\n",
              "      <td>1</td>\n",
              "      <td>7</td>\n",
              "      <td>3</td>\n",
              "      <td>60921</td>\n",
              "      <td>63440</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1687</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>9.00</td>\n",
              "      <td>3</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1000</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>83.30</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>other</td>\n",
              "      <td>individual</td>\n",
              "      <td>7500</td>\n",
              "      <td>36</td>\n",
              "      <td>13.59</td>\n",
              "      <td>254.85</td>\n",
              "      <td>C</td>\n",
              "      <td>C2</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>6630.98</td>\n",
              "      <td>1268.59</td>\n",
              "      <td>869.02</td>\n",
              "      <td>399.57</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6340</th>\n",
              "      <td>6341</td>\n",
              "      <td>underwriter</td>\n",
              "      <td>10.00</td>\n",
              "      <td>NJ</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>90000.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>16.77</td>\n",
              "      <td>150000.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>10.58</td>\n",
              "      <td>5</td>\n",
              "      <td>11.00</td>\n",
              "      <td>1995</td>\n",
              "      <td>0</td>\n",
              "      <td>45</td>\n",
              "      <td>20</td>\n",
              "      <td>334672</td>\n",
              "      <td>52278</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>5</td>\n",
              "      <td>16.00</td>\n",
              "      <td>20</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>9200</td>\n",
              "      <td>37</td>\n",
              "      <td>15</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>84.40</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>joint</td>\n",
              "      <td>6500</td>\n",
              "      <td>36</td>\n",
              "      <td>10.42</td>\n",
              "      <td>211.03</td>\n",
              "      <td>B</td>\n",
              "      <td>B3</td>\n",
              "      <td>Feb-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>5873.55</td>\n",
              "      <td>836.59</td>\n",
              "      <td>626.45</td>\n",
              "      <td>210.14</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>577</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MS</td>\n",
              "      <td>OWN</td>\n",
              "      <td>22212.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>9.08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "      <td>9200</td>\n",
              "      <td>4720</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2500</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>5000</td>\n",
              "      <td>36</td>\n",
              "      <td>12.62</td>\n",
              "      <td>167.56</td>\n",
              "      <td>C</td>\n",
              "      <td>C1</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>fractional</td>\n",
              "      <td>Cash</td>\n",
              "      <td>4532.79</td>\n",
              "      <td>666.73</td>\n",
              "      <td>467.21</td>\n",
              "      <td>199.52</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5202</th>\n",
              "      <td>5203</td>\n",
              "      <td>hospital police</td>\n",
              "      <td>2.00</td>\n",
              "      <td>NY</td>\n",
              "      <td>RENT</td>\n",
              "      <td>28800.00</td>\n",
              "      <td>Source Verified</td>\n",
              "      <td>19.17</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>3.00</td>\n",
              "      <td>2004</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>4</td>\n",
              "      <td>15980</td>\n",
              "      <td>2872</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1500</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>85.70</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>3000</td>\n",
              "      <td>36</td>\n",
              "      <td>17.09</td>\n",
              "      <td>107.10</td>\n",
              "      <td>D</td>\n",
              "      <td>D1</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>fractional</td>\n",
              "      <td>Cash</td>\n",
              "      <td>2668.83</td>\n",
              "      <td>532.65</td>\n",
              "      <td>331.17</td>\n",
              "      <td>201.48</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6363</th>\n",
              "      <td>6364</td>\n",
              "      <td>electrical design engineer</td>\n",
              "      <td>10.00</td>\n",
              "      <td>FL</td>\n",
              "      <td>MORTGAGE</td>\n",
              "      <td>60000.00</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>13.91</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2006</td>\n",
              "      <td>1</td>\n",
              "      <td>14</td>\n",
              "      <td>8</td>\n",
              "      <td>255304</td>\n",
              "      <td>31461</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2.00</td>\n",
              "      <td>8</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>30500</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>100.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>12600</td>\n",
              "      <td>36</td>\n",
              "      <td>5.32</td>\n",
              "      <td>379.45</td>\n",
              "      <td>A</td>\n",
              "      <td>A1</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>0.00</td>\n",
              "      <td>12675.89</td>\n",
              "      <td>12600.00</td>\n",
              "      <td>75.89</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>439</th>\n",
              "      <td>440</td>\n",
              "      <td>teacher</td>\n",
              "      <td>10.00</td>\n",
              "      <td>NJ</td>\n",
              "      <td>RENT</td>\n",
              "      <td>61000.00</td>\n",
              "      <td>Not Verified</td>\n",
              "      <td>13.08</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>17.00</td>\n",
              "      <td>1999</td>\n",
              "      <td>1</td>\n",
              "      <td>21</td>\n",
              "      <td>5</td>\n",
              "      <td>96084</td>\n",
              "      <td>69831</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>17.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>10.00</td>\n",
              "      <td>5</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>8900</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>81.00</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>debt_consolidation</td>\n",
              "      <td>individual</td>\n",
              "      <td>13000</td>\n",
              "      <td>60</td>\n",
              "      <td>17.09</td>\n",
              "      <td>323.72</td>\n",
              "      <td>D</td>\n",
              "      <td>D1</td>\n",
              "      <td>Jan-2018</td>\n",
              "      <td>Current</td>\n",
              "      <td>whole</td>\n",
              "      <td>Cash</td>\n",
              "      <td>12287.09</td>\n",
              "      <td>1606.26</td>\n",
              "      <td>712.91</td>\n",
              "      <td>893.35</td>\n",
              "      <td>0.00</td>\n",
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              "  </tbody>\n",
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              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
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              "\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-01eb90cb-ec33-47ed-88be-6bf138ab2d11 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-01eb90cb-ec33-47ed-88be-6bf138ab2d11');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
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          },
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    {
      "cell_type": "code",
      "source": [
        "# Dataset Structure\n",
        "numerical_columns = df.select_dtypes(include=np.number).columns.tolist()\n",
        "\n",
        "categorical_columns = df.select_dtypes(exclude=np.number).columns.tolist()\n",
        "\n",
        "dataset_summary = pd.DataFrame({\n",
        "    \"Property\": [\n",
        "        \"Number of Rows\",\n",
        "        \"Number of Columns\",\n",
        "        \"Numerical Features\",\n",
        "        \"Categorical Features\"\n",
        "    ],\n",
        "    \"Value\": [\n",
        "        df.shape[0],\n",
        "        df.shape[1],\n",
        "        len(numerical_columns),\n",
        "        len(categorical_columns)\n",
        "    ]\n",
        "})\n",
        "\n",
        "dataset_summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "_VPCctZ7aNpG",
        "outputId": "3cdc63fa-c238-4397-d37e-3c5ab369422d"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Property  Value\n",
              "0        Number of Rows  10000\n",
              "1     Number of Columns     56\n",
              "2    Numerical Features     43\n",
              "3  Categorical Features     13"
            ],
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Numerical Features</td>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>13</td>\n",
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              "\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('dataset_summary');\n",
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              "\n",
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              "  </div>\n"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset_summary",
              "summary": "{\n  \"name\": \"dataset_summary\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Property\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Number of Columns\",\n          \"Categorical Features\",\n          \"Number of Rows\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4981,\n        \"min\": 13,\n        \"max\": 10000,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          56,\n          13,\n          10000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Types Distribution\n",
        "dtype_count = df.dtypes.astype(str).value_counts().reset_index()\n",
        "\n",
        "dtype_count.columns = [\"Data Type\", \"Count\"]\n",
        "\n",
        "dtype_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "CxQL3QTMgjob",
        "outputId": "bbc2d75b-298d-42f4-90b8-f1ddffa5acf1"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Data Type  Count\n",
              "0     int64     26\n",
              "1   float64     17\n",
              "2    object     13"
            ],
            "text/html": [
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_d6ad4f1f-6162-45e7-b41f-66b1e6c3a57f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('dtype_count')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_d6ad4f1f-6162-45e7-b41f-66b1e6c3a57f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('dtype_count');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dtype_count",
              "summary": "{\n  \"name\": \"dtype_count\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Data Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"int64\",\n          \"float64\",\n          \"object\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 13,\n        \"max\": 26,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          26,\n          17,\n          13\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Types Visualization\n",
        "plt.figure(figsize=(6,6))\n",
        "\n",
        "# Create bar chart\n",
        "ax = sns.barplot(\n",
        "    data=dtype_count,\n",
        "    x=\"Data Type\",\n",
        "    y=\"Count\",\n",
        "    palette=\"Set2\"\n",
        ")\n",
        "\n",
        "# Add value labels\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{int(p.get_height())}\",\n",
        "        (\n",
        "            p.get_x() + p.get_width()/2,\n",
        "            p.get_height()\n",
        "        ),\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=11,\n",
        "        fontweight=\"bold\"\n",
        "    )\n",
        "\n",
        "# Add title\n",
        "plt.title(\"Distribution of Data Types\", fontsize=18)\n",
        "plt.xlabel(\"Data Type\")\n",
        "plt.ylabel(\"Number of Features\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "khKePd3oaTDO",
        "outputId": "d164e8d8-fd5f-4a66-ed67-2e5abb826ff5"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Cleaning and Preprocessing**"
      ],
      "metadata": {
        "id": "DeR8L551aV5e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Missing Value Analysis"
      ],
      "metadata": {
        "id": "0HMub5oAa1vy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Missing Values Summary\n",
        "missing = pd.DataFrame({\n",
        "    \"Missing Values\": df.isnull().sum(),\n",
        "    \"Percentage (%)\": (df.isnull().sum() / len(df)) * 100\n",
        "})\n",
        "\n",
        "missing = (\n",
        "    missing[missing[\"Missing Values\"] > 0]\n",
        "    .sort_values(\"Percentage (%)\", ascending=False)\n",
        ")\n",
        "\n",
        "missing\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "gzaVX0Usaatc",
        "outputId": "addd237e-2766-4fbf-ea3f-b9a9c388c9a1"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Missing Values  Percentage (%)\n",
              "verification_income_joint                   8545           85.45\n",
              "annual_income_joint                         8505           85.05\n",
              "debt_to_income_joint                        8505           85.05\n",
              "months_since_90d_late                       7715           77.15\n",
              "months_since_last_delinq                    5658           56.58\n",
              "months_since_last_credit_inquiry            1271           12.71\n",
              "emp_title                                    833            8.33\n",
              "emp_length                                   817            8.17\n",
              "num_accounts_120d_past_due                   318            3.18\n",
              "debt_to_income                                24            0.24"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a9021096-057c-4ab6-939d-f65f53ff405e\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Missing Values</th>\n",
              "      <th>Percentage (%)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>verification_income_joint</th>\n",
              "      <td>8545</td>\n",
              "      <td>85.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>annual_income_joint</th>\n",
              "      <td>8505</td>\n",
              "      <td>85.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income_joint</th>\n",
              "      <td>8505</td>\n",
              "      <td>85.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_90d_late</th>\n",
              "      <td>7715</td>\n",
              "      <td>77.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_delinq</th>\n",
              "      <td>5658</td>\n",
              "      <td>56.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>months_since_last_credit_inquiry</th>\n",
              "      <td>1271</td>\n",
              "      <td>12.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_title</th>\n",
              "      <td>833</td>\n",
              "      <td>8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>emp_length</th>\n",
              "      <td>817</td>\n",
              "      <td>8.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_accounts_120d_past_due</th>\n",
              "      <td>318</td>\n",
              "      <td>3.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>debt_to_income</th>\n",
              "      <td>24</td>\n",
              "      <td>0.24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-a9021096-057c-4ab6-939d-f65f53ff405e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a9021096-057c-4ab6-939d-f65f53ff405e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('missing')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_58ca40db-5bfa-4893-9adc-f113bb8ef1ed button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('missing');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "missing",
              "summary": "{\n  \"name\": \"missing\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"Missing Values\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3862,\n        \"min\": 24,\n        \"max\": 8545,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          318,\n          8505,\n          833\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Percentage (%)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.62810360058823,\n        \"min\": 0.24,\n        \"max\": 85.45,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          3.18,\n          85.05,\n          8.33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Missing Values Visualization\n",
        "plt.figure(figsize=(12,7))\n",
        "\n",
        "ax = sns.barplot(\n",
        "    data=missing.reset_index(),\n",
        "    x=\"Percentage (%)\",\n",
        "    y=\"index\",\n",
        "    palette=\"Reds_r\"\n",
        ")\n",
        "\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{p.get_width():.1f}%\",\n",
        "        (\n",
        "            p.get_width(),\n",
        "            p.get_y() + p.get_height()/2\n",
        "        ),\n",
        "        xytext=(6,0),\n",
        "        textcoords=\"offset points\",\n",
        "        va=\"center\",\n",
        "        fontsize=10,\n",
        "        fontweight=\"bold\"\n",
        "    )\n",
        "plt.title(\"Missing Values by Feature\", fontsize=18)\n",
        "plt.xlabel(\"Missing Percentage (%)\")\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 701
        },
        "id": "TsIo0VDuajcO",
        "outputId": "76cc0d7e-ffeb-4361-a0e8-478012d258d0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Overall Missing Values\n",
        "total_missing = df.isnull().sum().sum()\n",
        "\n",
        "total_values = df.size\n",
        "\n",
        "plt.figure(figsize=(6,6))\n",
        "\n",
        "plt.pie(\n",
        "    [total_missing, total_values - total_missing],\n",
        "    labels=[\"Missing\", \"Available\"],\n",
        "    autopct=\"%1.2f%%\",\n",
        "    explode=(0.08,0),\n",
        "    startangle=90\n",
        ")\n",
        "\n",
        "plt.title(\"Overall Missing Values\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "8HAuPPvVao0l",
        "outputId": "99d7f1cf-87eb-4427-b62a-42b74a203d90"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Duplicate Analysis"
      ],
      "metadata": {
        "id": "YJVAjOeXayer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check Duplicate Records\n",
        "duplicates = df.duplicated().sum()\n",
        "\n",
        "print(f\"Duplicate Rows : {duplicates}\")\n",
        "\n",
        "# Duplicate Records Visualization\n",
        "plt.figure(figsize=(6,6))\n",
        "\n",
        "plt.pie(\n",
        "    [duplicates, len(df)-duplicates],\n",
        "    labels=[\"Duplicate\",\"Unique\"],\n",
        "    autopct=\"%1.2f%%\",\n",
        "    explode=(0.10,0),\n",
        "    startangle=90\n",
        ")\n",
        "\n",
        "plt.title(\"Duplicate Record Analysis\")\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 541
        },
        "id": "YTA0w_7lazj-",
        "outputId": "3e8d80d2-af89-4d00-8e4f-52d274e0062e"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate Rows : 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Duplicate Records\n",
        "if duplicates > 0:\n",
        "    df = df.drop_duplicates().reset_index(drop=True)\n",
        "\n",
        "print(f\"Current Dataset Shape : {df.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AMGEGmUVbDSg",
        "outputId": "713a35e7-6575-42dd-b258-b7919ab67614"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current Dataset Shape : (10000, 56)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove Data Leakage Features\n",
        "leakage_features = [\n",
        "    \"paid_total\",\n",
        "    \"paid_principal\",\n",
        "    \"paid_interest\",\n",
        "    \"paid_late_fees\",\n",
        "    \"balance\"\n",
        "]\n",
        "\n",
        "df.drop(\n",
        "    columns=leakage_features,\n",
        "    inplace=True,\n",
        "    errors=\"ignore\"\n",
        ")\n",
        "\n",
        "print(f\"Dataset Shape : {df.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GuoHwxGCbErF",
        "outputId": "14cf5fb1-4fc9-49fd-f0cf-26a30f67efd6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Shape : (10000, 51)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature Type Summary\n",
        "numerical_columns = df.select_dtypes(include=np.number).columns.tolist()\n",
        "\n",
        "categorical_columns = df.select_dtypes(exclude=np.number).columns.tolist()\n",
        "\n",
        "feature_summary = pd.DataFrame({\n",
        "    \"Feature Type\": [\n",
        "        \"Numerical\",\n",
        "        \"Categorical\",\n",
        "        \"Total\"\n",
        "    ],\n",
        "    \"Count\": [\n",
        "        len(numerical_columns),\n",
        "        len(categorical_columns),\n",
        "        len(df.columns)\n",
        "    ]\n",
        "})\n",
        "\n",
        "feature_summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "HzFbu8uZbJkw",
        "outputId": "5b4d19ec-3d17-4ce8-9fd7-1835e2b3df7e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Feature Type  Count\n",
              "0    Numerical     38\n",
              "1  Categorical     13\n",
              "2        Total     51"
            ],
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              "      <th></th>\n",
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              "      <th>Count</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Numerical</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Categorical</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Total</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
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              "        document.querySelector('#df-28db55cc-2ec5-445c-81c3-0656e079ca7d button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-28db55cc-2ec5-445c-81c3-0656e079ca7d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_62571fc1-6200-485e-9e28-451f8991674c\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
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              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feature_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_62571fc1-6200-485e-9e28-451f8991674c button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feature_summary');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_summary",
              "summary": "{\n  \"name\": \"feature_summary\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Feature Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Numerical\",\n          \"Categorical\",\n          \"Total\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 13,\n        \"max\": 51,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          38,\n          13,\n          51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exploratory Data Analysis (EDA)**"
      ],
      "metadata": {
        "id": "xDhfOqCvbKtg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loan Status Distribution\n",
        "loan_status = (\n",
        "    df[\"loan_status\"]\n",
        "    .value_counts()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "loan_status.columns = [\"Loan Status\", \"Count\"]\n",
        "\n",
        "loan_status[\"Percentage\"] = (\n",
        "    loan_status[\"Count\"] / loan_status[\"Count\"].sum() * 100\n",
        ")\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "\n",
        "ax = sns.barplot(\n",
        "    data=loan_status,\n",
        "    x=\"Loan Status\",\n",
        "    y=\"Count\",\n",
        "    palette=\"viridis\"\n",
        ")\n",
        "\n",
        "\n",
        "for p, percent in zip(ax.patches, loan_status[\"Percentage\"]):\n",
        "    ax.annotate(\n",
        "        f\"{int(p.get_height()):,}\\n({percent:.1f}%)\",\n",
        "        (\n",
        "            p.get_x() + p.get_width()/2,\n",
        "            p.get_height()\n",
        "        ),\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=10,\n",
        "        fontweight=\"bold\"\n",
        "    )\n",
        "\n",
        "\n",
        "plt.title(\"Distribution of Loan Status\", fontsize=18)\n",
        "plt.xlabel(\"Loan Status\")\n",
        "plt.ylabel(\"Number of Loans\")\n",
        "\n",
        "plt.xticks(rotation=20)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "9DWy9onlbOPz",
        "outputId": "f6451777-4637-4e6f-9dc5-9988c33aca20"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loan Grade Distribution\n",
        "grade = (\n",
        "    df[\"grade\"]\n",
        "    .value_counts()\n",
        "    .sort_index()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "grade.columns = [\"Grade\", \"Count\"]\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "\n",
        "ax = sns.barplot(\n",
        "    data=grade,\n",
        "    x=\"Grade\",\n",
        "    y=\"Count\",\n",
        "    palette=\"Set2\"\n",
        ")\n",
        "\n",
        "\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{int(p.get_height())}\",\n",
        "        (\n",
        "            p.get_x()+p.get_width()/2,\n",
        "            p.get_height()\n",
        "        ),\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=10,\n",
        "        fontweight=\"bold\"\n",
        "    )\n",
        "\n",
        "\n",
        "plt.title(\"Distribution of Loan Grades\", fontsize=18)\n",
        "plt.xlabel(\"Loan Grade\")\n",
        "plt.ylabel(\"Number of Loans\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "revPU1FMbUSZ",
        "outputId": "ad9998bf-fdfe-4d2c-f858-853a7502861b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Annual Income Distribution\n",
        "annual_income = df[\"annual_income\"]\n",
        "\n",
        "mean_income = annual_income.mean()\n",
        "\n",
        "median_income = annual_income.median()\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "# Plot distribution\n",
        "sns.histplot(\n",
        "    annual_income,\n",
        "    bins=40,\n",
        "    kde=True,\n",
        "    color=\"steelblue\"\n",
        ")\n",
        "\n",
        "# Mean\n",
        "plt.axvline(\n",
        "    mean_income,\n",
        "    color=\"red\",\n",
        "    linestyle=\"--\",\n",
        "    linewidth=2,\n",
        "    label=f\"Mean = {mean_income:,.0f}\"\n",
        ")\n",
        "\n",
        "# Median\n",
        "plt.axvline(\n",
        "    median_income,\n",
        "    color=\"green\",\n",
        "    linestyle=\"--\",\n",
        "    linewidth=2,\n",
        "    label=f\"Median = {median_income:,.0f}\"\n",
        ")\n",
        "\n",
        "plt.title(\"Annual Income Distribution\", fontsize=18)\n",
        "plt.xlabel(\"Annual Income\")\n",
        "plt.ylabel(\"Frequency\")\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "1z4oWOmMbdtI",
        "outputId": "d2c025eb-cff5-454f-a438-5a136d1632f3"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Correlation Heatmap\n",
        "corr = df.select_dtypes(include=np.number).corr()\n",
        "\n",
        "plt.figure(figsize=(16,12))\n",
        "\n",
        "# Create heatmap\n",
        "sns.heatmap(\n",
        "    corr,\n",
        "    cmap=\"coolwarm\",\n",
        "    center=0,\n",
        "    linewidths=0.5\n",
        ")\n",
        "\n",
        "plt.title(\"Correlation Matrix of Numerical Features\", fontsize=18)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "hLRXdNvHbfXS",
        "outputId": "4975f283-b1a7-4338-b3ce-9b86f18f3dba"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1200 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Interest Rate by Loan Status\n",
        "interest_summary = (\n",
        "    df.groupby(\"loan_status\")[\"interest_rate\"]\n",
        "    .mean()\n",
        "    .reset_index()\n",
        ")\n",
        "\n",
        "interest_summary.columns = [\n",
        "    \"Loan Status\",\n",
        "    \"Average Interest Rate\"\n",
        "]\n",
        "plt.figure(figsize=(11,6))\n",
        "\n",
        "\n",
        "ax = sns.boxplot(\n",
        "    data=df,\n",
        "    x=\"loan_status\",\n",
        "    y=\"interest_rate\",\n",
        "    palette=\"Set3\"\n",
        ")\n",
        "\n",
        "\n",
        "means = df.groupby(\"loan_status\")[\"interest_rate\"].mean()\n",
        "\n",
        "for i, value in enumerate(means):\n",
        "    ax.text(\n",
        "        i,\n",
        "        value + 0.15,\n",
        "        f\"{value:.2f}\",\n",
        "        ha=\"center\",\n",
        "        fontsize=9,\n",
        "        fontweight=\"bold\",\n",
        "        color=\"red\"\n",
        "    )\n",
        "\n",
        "plt.title(\"Interest Rate by Loan Status\", fontsize=18)\n",
        "plt.xlabel(\"Loan Status\")\n",
        "plt.ylabel(\"Interest Rate (%)\")\n",
        "\n",
        "plt.xticks(rotation=20)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "ejeMvafnbmnX",
        "outputId": "32dc1074-d305-4c75-fdbc-972993118c7b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1100x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "# Plot home ownership distribution\n",
        "ax = sns.countplot(\n",
        "    data=df,\n",
        "    x=\"homeownership\",\n",
        "    order=df[\"homeownership\"].value_counts().index,\n",
        "    palette=\"Set2\"\n",
        ")\n",
        "\n",
        "# Add count labels\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{int(p.get_height())}\",\n",
        "        (\n",
        "            p.get_x() + p.get_width()/2,\n",
        "            p.get_height()\n",
        "        ),\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=10,\n",
        "        fontweight=\"bold\"\n",
        "    )\n",
        "\n",
        "plt.title(\n",
        "    \"Loan Distribution by Home Ownership\",\n",
        "    fontsize=18,\n",
        "    fontweight=\"bold\"\n",
        ")\n",
        "\n",
        "plt.xlabel(\"Home Ownership\")\n",
        "plt.ylabel(\"Number of Applicants\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "kiJs5EgO0YXb",
        "outputId": "8b7f5fd2-d7d9-45c8-e14f-393c7f118c3d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loan Status by Home Ownership\n",
        "plt.figure(figsize=(12,6))\n",
        "\n",
        "# Plot loan status by home ownership\n",
        "ax = sns.countplot(\n",
        "    data=df,\n",
        "    x=\"loan_status\",\n",
        "    hue=\"homeownership\"\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    \"Loan Status by Home Ownership\",\n",
        "    fontsize=18,\n",
        "    fontweight=\"bold\"\n",
        ")\n",
        "\n",
        "plt.xlabel(\"Loan Status\")\n",
        "plt.ylabel(\"Number of Applicants\")\n",
        "\n",
        "plt.xticks(rotation=20)\n",
        "\n",
        "plt.legend(title=\"Home Ownership\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "fZtpEyR90rf4",
        "outputId": "702b2fe9-db49-4d17-d81a-980f0ef7e596"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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FYVMaLXDkyBGtWrVKBw4c0JkzZxQVFWUxyuheMTExioyMfOBi1SVLljSHT8lKlSplsX3z5s1Uz3+QLl26qFixYho/fnyKoy3udeLECXXv3l0rV66Ur6+vTddLlpiYqA0bNmjTpk06duyYLl26pNjY2FSn0Fy6dClD17OHAgUKyNvb2zxi5tSpU2rXrp2qV68uPz8/+fn5qVy5cvL09JTRaFSuXLkyfM3y5cubw6dkFStWVPny5XXkyBHzvuTQIFmXLl0s1vZZunSpOYD69ddfLQKcp59+WkWLFs1wrRllz8/gvcqWLWuxzk7evHnl4eGhiIgI87569epZBDspLToeGRlp/nNK053SMgLxyJEjio6OtlpY/1GrXbu2Fi1aZN5ODpgOHz5snjL29NNP69SpUzpy5IhOnjypGzduKE+ePBajDpP7epjOnTtbbN//80t6dK9v3759Uz1WsWJF82sRFRWlVq1aqU6dOipZsqT58508gigt602l975tVb9+fXP4lKxRo0bKly+fxfv8/p8TAPCkI4ACAGSKy5cvW2wnPzHqfvcHL1evXjVPbZGkb7/9VtOmTbMYXfEwMTExDwyg7v8SLlmP/knv2jL3atCggRo0aKCQkBDt3LlTBw4c0KFDhyzWhUl269YtzZ49Wx9++KHN17t+/breeOMN/fvvv2k+53FYj0q6O3rmnXfeMb/ehw8f1uHDhy3alCtXTh07dlSHDh0eOrLtYVIb0eXj42MRQN3/d9WkSRMVKVLEPN0zPDxcu3btUlBQkNavX2/R9qWXXspQjfe6f9RKsosXL1p9Gb+fvT6DD2sv3Z0qe+8X8/tf55w5c1qdc+9nzNZANCkpSdeuXUtzAHXvtMlk165de+h597cxGAzy8PAwbwcFBclgMJh/Th09elRRUVEWYV716tWVP39+HTlyRCaTSfv27VPBggUt1jTKly+fypcv/9B67g9eMuv1dXFxeeADDYYMGaIePXqYRy+Gh4dbjBST7oaTL730krp3724xOiol6b1vW6X0c8JoNKpo0aIW7/OUfqYDwJOMRcgBAFnW9u3bNXXq1HSFT5Ie2j5fvnxW+9L7xLu0qFy5snr37q2pU6dqx44dCg4OVqdOnazaJT/lzVafffZZusInR0lpoev7H3d/vxdeeEHLli1T27ZtrRZxT3b06FF99NFH+vzzz+1Spy2cnZ2t/u6WLFmi69evW0y/y5cvnxo1amS36/r4+KT4jz3X4kovd3d3q333B4MptXGU9EzNLF26tNW+kJCQB55z/fp1i+l30t1pZvf+zPD09LQIYpKSkrR//36LAKpGjRqqUaOGeXvv3r0pjlJLy9ph9wfsjvj5lexBr2+BAgUeGApXr15dq1evVqdOnVSsWLEU24SHh2v8+PEWU6lT8yjvGwCQfoyAAgBkikKFCum///4zb9/753vduwCydPcLTfKXinXr1lkcy5EjhwYMGKA6derIw8NDBoNBe/bs0fDhw+1cvWP4+/vro48+0r///msx7Sa1pzqlRVxcnNUT/QICAtS/f3/5+fmZn+41evRoizWgMiqlL36xsbEW25cuXUrTvVWoUEFjx46VdHdEwenTpxUeHq6tW7da3NtPP/2k/v37pxggptX9QUJq+1MKwzp06KApU6bozp07kqSNGzeqTJkyFiMuWrVq9dBRHI+KPT6Dj8r9050MBoNWrlypvHnzPvTc9CwCXadOHTk5OVn8nW3YsEFDhw5NcTSNJK1atcoq1K5bt65Vu9q1a1usHbd3717z9LN8+fKpTJkyFiOw9uzZY/U+c9STEx31+qblfVKiRAl99NFHku6ul5b8+d69e7fF2mFbtmzR0aNHLaZ3ZpaUfk4kJSXp/PnzFvtSC80B4EnFCCgAQKaoWbOmxfbu3bsVFhZmse/8+fNWTz+6d4TA/dNG6tatq169eqlSpUry9fWVj4/PQ0cvPEq///67xo0bZ17TKCUmk8kqqEnpS4yLi4vFdmqjEG7cuGH1OPYBAwaoSZMmKlOmjHx8fOTh4WE1rS0lab2mlPIIl1OnTlls//TTTw+95v1/xwULFlT16tX10ksvaeLEiRZfkBMTE62m76TXkSNHrEaL/fvvvxbT7yTpqaeesjo3f/785gWTpbvh3+TJky3apGdBbEezx2fwUalVq5bFtslk0rZt21IdAebj46PY2FhduHDB6n37IPf/HUrSlStXNGbMmBTbnzhxQt99953FPmdn5xSnP94fHq1cudI8XSswMFAGg0EFCxY0r4d15MgRmxYgt8Wjen3vd//nO1++fHrqqafUunVrffbZZ1ZPebz/Z0hm2bZtm9UU1k2bNllMv5NS/jkBAE8yRkABAOwqtREkyQoVKiRXV1d17NhRy5cvN+9PSkpS9+7dzY+ADw8P1xdffGEVntw7zen+6RZ//fWXVq5cqSpVqigiIkI///yzFi9ebIe7so/o6Gj98MMPmj17tqpXr65nnnlGlSpVUuHChWU0GnX+/HktXrxYx48ftzgvpSeO3X/vO3fu1Pbt21WiRAkZDAblyJFDXl5e8vDwkLOzsxISEsxtf/jhB7m7u6tgwYI6deqUpk6dmqY1YNJ6Tenuui05cuQwjwaS7j6RKmfOnCpevLg2b96smTNnPvSabdq0kb+/v+rVq6fy5curSJEi5vWE1q1bp1u3blm0t8dC5G+++aaGDx+usmXL6sSJEymGD/c/sS1Zly5dtGzZMvP2ve/fSpUqPRajN5LZ4zP4qHh7e+v555+3GKX37bff6syZM2revLmKFCmihIQEXbhwQSEhIdqyZYv++ecf9e/f3ypoe5i3335bW7dutZgeumjRIp09e1adO3dWyZIlFRMTo+3bt+v777+3eg92795dJUqUsOq3Vq1aFqOr7g0w7g31atSoofDwcCUmJlo8+c3Ly8viCXL29Chf33slL1Bev359VaxYUT4+PsqVK5eioqK0detWnThxwqK9PT7f9hAXF6euXbtq6NCh8vX11d9//61x48ZZtHF2djY/SRQAcBcBFADArho2bPjA46tWrVL58uVVuXJlvf7665o9e7b52MWLFzVkyJBUz23fvr1FGNOkSROtXbvWvB0TE2M13c7Ly0tXrlxJ7204lMlk0t69ex/6NDHp7kiilEZTBAYGWtx7RESEevbsad6uVauW5s+fr5w5c+qZZ56x+GK5f/9+de3a1aK/tLxOab2mdPcx5U2aNNGaNWvMxy9fvqy33nrLos97F2ZOSVJSkvbs2WO1Fk5KfHx8UlzDJz2cnZ115cqVB74PX3jhhVRHNpQrV061atVK8ali9lx83B7s8Rl8lD744AOFhISYF3ZOSkrS0qVLtXTpUrtep2jRopo8ebLefPNNiwBo+/bt2r59+wPPbdq0aaqvX548eVSxYkUdOnTI6ti9AVTNmjUtQsxkjn7dH9Xrey+TyaR///1X//zzz0Pb5s6dO0Nhlz25ubkpLCxM/fr1S7VNt27dMnUtNgB4HDEFDwCQad577z316dMnTU8u69q1qz7++GOLfU2aNHngb5jLlStnXlvkcZAnTx45O6f9dz+FChXSzJkzLR5Xn6x169YqWrRomvp5//33zSOTUtK3b1/Vq1fvof2k55qSNHToUKu1ZZIZDAYNGjQoXf09SL58+TR+/PgMPwXvhRdeUNOmTVM9XrVqVY0ePfqBfdwf7kl3n8Z1/9Sux0FGP4OPko+Pj+bPn5+mp8BJd9cfetD7/kFq1Kih5cuXq1q1amlqnzt3bg0dOlQTJkx44GuZ0hS6nDlzqmLFiubt6tWrp/lce3qUr2965cyZU1988YXy5MnzSK73MF27dtWzzz6b6vGGDRtq0KBBj64gAMgiGAEFAMg0RqNRgwcPVtu2bbV48WLt2rVLZ8+eVXR0tNzc3FSsWDHVrFlTHTp0SPFR3gaDQePHj1ft2rW1ZMkSnTx5UgaDQT4+PmrWrJl69uypgwcPPvobS8Wzzz6rP//8Uzt27NCBAwd09OhRnTt3Tjdu3NDt27eVI0cOeXp6qkyZMnr22Wf14osvpvqFK2/evFq0aJGmTJmiHTt26NKlS1ZTpZL5+vpq1apVmjp1qrZs2aLLly+bR2Mkf5FKy0Lt6bmmJBUpUkTLli3TlClTtHXrVl27dk0eHh6qXr26evTooapVqz50dMWCBQvMizWHhobq2rVrun79ukwmkzw8PFSqVCnVrVtXr7zyitUUQVu4uLho/PjxWrVqlZYuXapjx44pPj5eJUqUUOvWrdWlS5eHLiLeoEEDFStWTOfOnTPva9KkSZoWdH7UMvoZfNRKlSql5cuX67ffftOGDRsUEhKiK1eu6M6dO8qdO7e8vb0VEBCgWrVqqUGDBimGt2lVsmRJLVy4UHv37tUvv/yiffv26eLFi7p586ZcXFyUP39+lStXTrVr11abNm3S9GS/2rVra8aMGRb7nnrqKYt1lHx9fVWkSBFdvHjR6lxHe5Svr3R3mt+ePXu0f/9+HTt2zPz5TkxMVJ48eVSyZEkFBQXp1Vdflbe3t53uMuNcXFw0ffp0LVmyRMuXL9fJkydlMplUunRpdejQQS+//HKanlYIAE8agym9z64GAADAA3Xr1k1//fWXeXvu3LmPJEAAYH8NGjSwCJT79++vAQMGZGJFAJA1MQUPAADAjjZt2qRdu3aZt0uVKpVp6yYBAAA8LpiCBwAAkEHz5s3T/PnzdfPmTatHsffr14/pOAAA4IlHAAUAAJBBkZGR+u+//6z2t2zZUi1btsyEigAAAB4vBFAAAAB2lDdvXpUoUUKvvvqq2rVrl9nlAAAAPBZYhBwAAAAAAAAOxSLkAAAAAAAAcCim4NnZgQMHZDKZ5OLiktmlAAAAAAAAOEx8fLwMBoMCAwMf2pYAys5MJpOY1QgAAAAAALK79OQfBFB2ljzyqXLlyplcCQAAAAAAgOOEhISkuS1rQAEAAAAAAMChCKAAAAAAAADgUARQAAAAAAAAcCg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mbdu2Wrx4sS5evCgPD49Hdh/IOpJ/1i5evFgrVqxQt27d9Pzzzys6Olr79+/XhAkTdODAAU2fPl3u7u4qUKCAcuTIIaPRqPfee0958uSRJD311FMqXLiwfv75Z73zzjuZfFfAo7Ft2zYVKVJEZcuW1YoVK3T+/HnNmTNHZcqUUXx8vDw9PSX934Oz/P39VadOHc2ePVuXLl1S4cKFM7P8VDECCtlKYmKi1W/pko0aNUq9evXSJ598ohYtWmjlypX68ssv5enpqfHjx5vny7Zp00YHDx5UWFiYJOv/UQMyy/3vbZPJZN53/2+wk/efP39ehw8fNi+of/LkSTk7Oyt//vySZF7Xo3PnzkpKStKePXtYYBnZQlJSkpydndW1a1etX7/eKnzavXu33n//fXl7e+uNN96Qq6urxowZoy+//FLx8fEqWrSoXF1ddfXqVXN/kuTj4yN/f38FBwdn2r0BtjAYDDIYDFZPrrt06ZIWLVqkpKQkubi4KDQ0VFWqVFGBAgXM4dOVK1cs+pHujoK6evUqD7BAqgwGg/7++299+eWXatKkiTp37ix/f39VqVJF3bt31zfffKPz589r/PjxMhgMmjt3rpo2barIyEjt2rXL3E+hQoVUv359BQcHW6xHBmR1qT1JdOfOnRo0aJCOHDmiqKgoHTp0SOXLl1e1atWUN29ec/iULHntvnLlysnDw0Pbtm17FOXbhAAK2Ury4spXr17V2bNnLb6wV69eXQkJCYqKitIbb7whDw8P1a9fX71799bevXsVEhIiSXruueeUO3dubd++/bFfxA1PhuDgYA0ZMkTvvvuuFi1apDt37ki6+z92RqNRMTEx+umnnzRixAitWLFC4eHh5t+GtGzZUjly5NAff/whSSpdurSSkpIUERGh+Ph4OTs7Ky4uTrly5VLFihX1119/McQdWV7yWn+SVLhwYcXGxmrVqlXm44mJiRo3bpy8vb01dOhQderUSVOmTFGnTp20bt067dixQwUKFFCZMmW0du1aSf/3G0Zvb2/Fx8dr7969TFnFYyspKcnqi01cXJyWLFmiy5cvW+wPDw9X0aJFdevWLUnSiy++qO+++06vv/66Xn31VdWuXVs9evTQ2LFjlZCQYP4sFCpUSAULFlRoaKik1L9I4cn2xx9/mJ+g6ObmJun/foH23HPPqW3bttqyZYu2bt0qSapTp45y5cplXnNPktzc3NSwYUOdPXtWBw4cyIzbAOzi3p+Tyb8Qu3DhgnlJmOSA1dvbWzExMapatary5Mkjb29v7dq1S//73//0zTffqE+fPho4cKCCg4MVExNjMQoqX758Onv27KO/uTQigEKWZDKZLH4Dkhw0/fbbb2rXrp0aN26svn37auTIkebfzNWvX1/58+dXdHS0eU0ck8mkoKAgeXl56ddff1VCQoLy5MmjBg0aaNOmTeYpe+fPn091ZBXgCGfOnNH777+vypUr69NPP5XBYNDZs2f18ccf65NPPtHFixeVkJCgyZMn64UXXtDcuXMVFRWladOmqWvXrubHsFapUkXFixfXv//+qwsXLihv3rzy9/fXrl27zKP8XF1dlZiYKHd3d127ds0cxgJZldFoVFRUlBYsWKDo6GgtWLBAw4cP14ULF2QwGBQZGakLFy6oadOmKlSokPm/J+3bt1fBggX1888/y2AwqEOHDtq2bZs2bdok6e5/M/744w+ZTCZFR0drxYoVmXmbgJXkdc6MRqPVCO7jx49r4sSJGjp0qDk0kiQPDw9dv35dxYoVk3R3mYKvv/5apUuXVqVKlfTee+/pqaee0pw5c7Ro0SKLa5UpU0YnT558BHeGx01MTIymTp2qQ4cOpXg8+Yv2P//8Iy8vL924ccN87N73ZqdOnZSYmKidO3dKkipXrix/f3/9888/5i/l0t2nVZcpU0Y///yzI24HcJikpCTzz+Z73/vJ4VOzZs308ccfS5J51GlkZKTy5s1r/kVXjx491KZNGx07dky//fabXF1dFRERoXfffVcrV6409+nn56fbt29bTZ1+nBBAIUu598Ob/ME6ffq0jEajDh8+rC+//FKVKlXShAkT9P/au+/AGs/+8ePvbBlCIlNEFllEJLLsxooGJfamdlE1SmuVao0q1aKqCEq1RSS1V2JvQeyRmrFChET2OOf3R37nbo6E9nm+Nfr4vP556pz73Oc+ea57XJ/ruj6ftm3bsn//fj799FNSU1OxtLSkVq1a6OjoaI2elC9fnqZNm7Jz504lWNWrVy+uXr3KlClT6Nu3L40aNeLcuXOv/geLt9bWrVtZt24dXbp0Yd++fUybNo0lS5YwcuRIoqOjOXz4MHl5eUqFro0bNzJv3jw2bdpERkYGK1asUNpz7dq1efjwIUeOHAGgd+/eJCUlsXLlSqBoVHz//v0kJSWRnJzM/v37yczMfG2/XYj/i2vXrnHs2DF69+7NzJkzuX//PsHBwdjY2CgPaQ8fPqRChQpKJ1wzcli1alWqVKnCjRs3SEtLo0OHDgQEBDBx4kTGjh3L8uXLWbRoEa1bt2b48OE4Ojq+tt8p3m4XLlwgNDS0RPBHsxw7Pj6euXPnsnXrVu7duwdA9erVWbx4MampqcyYMUP5jGZpnmZ2rYGBAeHh4Xz++edMmDCBtm3bMmzYMLy9vbWen0xNTcnPz6dSpUrKfsTbQ61W891337Fr164XLt23s7OjoKCgRKoAzXXX09OTypUrc/36de7fvw9AUFAQmZmZWsuILC0tady4MVFRUcpsPSHeJPfv32fOnDnKqgPN5AVdXV2l/R85coRLly4pfVp7e3umTp3K2bNnmTlzptK2Hz58SNmyZbXOk5EjR7JgwQI2btzI3LlzWbFiBQ4ODly5coX8/Hzl++zt7ZXg7Zs4gUICUOKNV1hYqIyiaE7e/Px8li5dSp06dViwYAEA3377LcbGxvTv35+6devSu3dvvvvuO27cuMGaNWsAqFu3LoWFhRw8eFDrO8LDw7l//z7nz59HpVLh6enJvHnzcHJywsXFhdWrV1OjRo1X+KvF20pzo6hbty4+Pj6kpaVhYGCAoaEhZcuWpUePHhgaGnL69GlMTEwYMWIEbdq04fr164wZM4batWuTlZXFvn37lBlODRo0wNjYWMmnEBoaSs+ePVm7di0RERGMGTOGL7/8kk8++YSZM2fSu3dvTE1NX9vfQIgXeVGuv8mTJyt5RQICAti6dStubm5UqVKFGjVqsHHjRqBohNDGxoZr166Rnp6Onp4eBQUF6OvrY2JiQn5+vlIVb9q0aXTt2pVz584RGRlJjRo16NChA4MGDZIk5OK10dfXJyMjg23btgFFwYD8/Hy2bNlC69atGTx4MMeOHWPWrFl06NCBW7duAeDl5cWYMWM4deoU33zzDfn5+Tx58oQKFSooHZ/igaSnT5+SmprKgQMHSEtLU9p8YWEhRkZGZGVlKefjm9jREf+8wsJC8vLyMDU1JSwsjIMHDyq58oovL9LR0UGlUuHo6EhycnKpS5Y1nfCaNWuSnp6utL06depgaWmpzIqCotna9erVo0GDBmRlZb3MnyjEf+XRo0esWLGCQ4cOAX8GWW/evMnnn39OUFAQY8eOZciQIYwaNYqLFy8CRZXYP/roI7Zs2cLixYsBqFixIvfu3StRGKhcuXIUFhaSk5PDhQsXyMvLw8HBAQMDA3R1dcnLy6NSpUoYGRlpHcOb5M2dmyXeaqVFjB89ekRubi59+/alc+fOxMfHM2TIEAIDA3n69ClJSUm0bdtWGZE+efIkixYt4tatWyQkJABQr1497OzsOHnyJJmZmUon28vLCxsbG1avXk2dOnUwMjKiadOmNG3a9NX/ePFWKz7SUbVqVU6cOMGjR4+oUKEC+fn5FBYWYmpqqow2+vn5sXnzZqZPn46HhwfTp0/HxsaGzp07c/78efz9/XF1dcXd3Z3ExESuXr2Km5sbAwYMwNXVlV27dnHv3j0GDx5Mw4YNX+dPF0KRk5PDw4cPS51hpLknJCcnk5WVhbOzs1ZS5OjoaAwNDRk2bBgmJiYAVKhQgZCQEPbs2cPZs2fx8fEhODiYn3/+mc2bN9OlSxf09PTIyMjg/PnzlC1bVqke4+joyIcffkjnzp2xtrZ+RX8BIUrSLOMwMDDA1dWVpk2bsnHjRgYPHoyuri4qlYoNGzYQGhpKu3btlPOncePG/PDDDwwfPhxbW1saNmzIqFGjWLx4MUZGRrzzzjskJyfj5OSkfNfly5dZvHixMsM8OTmZTp06ERgYCBSdh5ple1ZWVsCb2dER/zw9PT3lOty1a1d69erFlStXqFixYolZcLq6unh6emJubs6WLVuoWbMm+vr6Su4bPT098vPzefr0KVlZWVrX3SpVqhAXF8elS5fw9PQEICAggICAgFf7g4X4C5pAarVq1QgKCuLixYvcuXMHBwcHUlNTWb16Nbdu3WLChAkEBwdz9epVFi1axJw5c1i0aBFQlALg8ePHzJs3D39/f/T19bG1teXBgwc4OjqiVqt58OABa9euxcLCgsuXL7Nv3z7c3d157733lGMxNDTk6NGjDBo06LX8Lf4OCUCJN4bmZgTaDzEnTpxg6tSpVKhQgSFDhmBqasr06dN5//336datG1DUESlXrhwnTpwgNTWV6OhosrOzqVOnDvPmzVNuVkZGRgQGBrJ9+3bi4+Np2LAhKpWKMmXKMGLECAwNDZWIsRAvm2bGxbPUajV6enoEBARw6NAhdu3aRYcOHTAwMFCW2Gk6AQ8fPmT27Nk0aNCAESNGYGlpSX5+PnZ2dhw5coTGjRvj4OBA/fr1OXr0KHFxcbi5uQHQpEkTQkNDS62gJx0J8Tps2bKFX3/9ldu3b1O2bFm8vLzo1asX3t7eyhKO3bt3M2/ePG7cuIGdnR1OTk589dVXlCtXjpCQEFxdXXn06BE5OTnKbCYDAwNq1KiBvb09a9euxcfHh+bNm3PhwgWmT5/OgwcPCAoKYt++fdy/f5+vvvpK67jUarUEn8Rrp6urq1ybb9y4QfPmzYmJieH06dP4+flhZGREp06dqF27NiqViuXLl7Nu3Tru3LnD0aNHSUxMVDr47du3B+Dzzz8nMTERIyMjMjIyMDc3B8DCwoIqVapw6dIl2rdvT0REBOXKldM6nrJlyxIYGCiDF2+ZS5cusWzZMlJSUmjVqhW6urqcOHGCgIAAzMzMlO00z/UeHh40aNCAzZs306RJE+rUqVMiD86pU6cIDw9HrVZTWFiIvr4+TZs2xcXFBXt7+xLHUFhYWOLZRYjXpXhbbNiwIcuXLyc+Pl7Jq+fh4UGXLl1wdHQkKSmJI0eOcPbsWTIzM0lMTKRq1aoADBgwgFu3brFw4ULKly9P+fLllX6pjo4OxsbGJCYmkpSUhIWFBaNGjaJVq1bKd2ue39u2bUvlypVf4V/gPyMBKPHGKH4zysjIYNq0adja2lJQUICHhwedOnWiUqVKhIWFce7cOfz8/JTtbW1tsbGxIS4ujnv37jFy5Ejq1KmjjP5lZGQoMz/q1avHzz//TGxsLA0bNlQe5oqfwEK8Cprg0/MCUbVq1aJixYrExcVhZWVFZGQk8fHxtGzZktDQUKBoZmB6ejqNGjVSOsiateDXr18nMTERBwcH6tSpg4eHBzY2Nlrfoaenp1Sj0dxAJfgkXrXly5czY8YMypUrR1hYGG3btiUhIYGYmBhu3rzJL7/8gr6+PteuXeObb76hZs2ajB07lps3bzJ37lylSqSHhwf169dn06ZNXLhwgXr16int2dXVlaCgIHbt2sWUKVOoXLky06ZN47PPPmPr1q388ssvmJubM3ToUCXAqyG5bcSb4PHjxyxYsICoqCjc3d2xsbFBrVYTFxenPBOFhoZy8eJFRowYgVqtpkWLFnz55Zd07tyZkydPEhQUhKGhIXp6enTu3Jlr166xevVq7OzslBxQUFThrm/fvhgYGCivFZ+dDkW5orp27fpGJ7sV/7nCwkKlyu6zzp8/z8iRI7G0tKRZs2bs378fHR0dDh48SJs2bbQCUJrrppWVFQMGDGDPnj1MnTqVOXPm4O7uTkFBAenp6cyfPx9jY2PatGmjleO1YcOGzw1uSvBJvEn27t3LokWLsLCwUPquJ0+epHXr1lhaWtK6dWsePXrEyJEj2blzJ97e3nTu3Jn169ezZs0axo8fT15eHoaGhgwfPpzIyEiWLVuGvr6+sgRPpVJhbm7O+PHjKVu2rFJRUvOeZoCisLCQDh06YGFh8br+HH9JehnilcjOztb6d2l5Aq5du6as9c7OziYzM5MffviBhIQERo4cSc2aNbGysiI4OBgDAwPS0tK09lW/fn3s7e1p2bKlEqxSqVScP3+eOXPmEBcXB4C/vz+jR49mwIABL/Mni7dYTk4Op06dUqbkFq/YWNyZM2fo3LkzV65c0Xpd89Dm5OSEj48Phw8fZtSoUbi6urJmzRpmzZpF+fLlle0dHBxYv3492dnZpKSksGXLFvz9/bl+/Trnz5+noKAAGxsbIiMjadOmTYnj0EyDF+JV05wjmqVykyZNYsqUKURERPD555/z0UcfkZCQwIEDBwD45ptvUKlUDBo0CD8/Pzp06MDChQtJTU1l7dq1ALz77rvk5OQoS681bdvMzIzg4GAyMzOV8t4mJibMmjWLZcuWERUVxc6dO+nQoYOcD+KVyMjI4NKlS3+5nSavzo4dO9i0aROjR49mzJgx1KpVCxMTE7Zs2aIUjsjLy2PmzJlYWVkxf/58Bg0ahK+vL97e3hw5ckTJw6M59wYOHKgs/Xg214gm+KTJxVl8BpaGBJ/+dxTPt6pZ0qmh+e/Nmzfz5MkTvvrqK95//32++uorPv30Uy5cuKDksyltv+7u7kyYMIHs7Gw6derEgAEDmDVrFr169SI2NpYPPvgAHx+fUj9bPK+UEK/Cf1KI58yZM4wdOxYrKyvCwsJITk4mIyODkydPKtXUAVasWMH58+eZO3cuK1eupF+/fjg7OytVdg0NDYGigO3w4cOpX78+RkZGyjVb0zewsbHB2NhYq7Je8euynp7eGx18ApkBJV6ivLw81q5dy44dO5QIbuvWrQkICCjxAJOamsqkSZO4d+8esbGxWFtb06xZM3bv3k1ISIjW0gdXV1dq1KhBXFwcERERysNPkyZNuHbtGgsWLCApKQkfHx8uX77MgQMHsLa2pmXLlsp04A4dOrzSv4V4O2ja16effkpCQgLz58+nevXqz31AT09PJyEhARcXlxLvaUYzAgMDiYuLIzw8nJEjRyoPY5op6lWrVqVdu3ZMmzaNHj16kJ2dTWFhIWvWrKFPnz74+vpqnW8ybV28STRtsW3btkyZMoXLly/TtGlTpeMbGBiImZkZCQkJNGjQgHv37lGvXj2l6taRI0eIjIzkwoULWFlZUVBQgLe3Ny4uLpw4cYK7d+9SsWJFZZahh4cH5ubmxMTE8M477yjHUdoSDyFelsTERGbOnEl8fDyWlpa4uroyYsQIvL29S91eR0eHjIwMNm3ahIeHBx07dkRfX59atWphbGzM5MmTOXz4ME2aNOHx48fcuHGDDh06KMs6Hj9+jEql4s6dO1y8eBFnZ2flvlShQgU8PDwwNTUlNTW11HNB7hlvB00Hd+fOnURFRWFiYkJwcDCtW7fG2NiYzMxMLl++jJ+fn7K8R19fn27duvHzzz+ze/duGjRooCzjfFabNm2oXbs269evJzExkcuXLxMeHk6nTp2wtLR84TEJ8bLl5eWxZs0a1qxZQ/ny5fHz86Nbt27KTNPntcUlS5ZgZmbGuHHjsLW1pVWrVixfvpzZs2dz7Ngx3NzcePToEevXr+edd95RVjAYGhqSn5/PvXv3OH78OIGBgRQUFKCjo4ORkRFt2rThwoULytLn0vKr/Vv9e49cvNE2btxIeHg4y5Ytw8PDAx8fH9LT05VI8MaNG7VGSiwtLalfvz65ubmcPXsWAHd3dxwcHJTKLZoor5mZGe+++y5Hjx7l9u3byj4sLCz45JNPGDRoEA8ePGDFihWcPn2avn37snTpUvz8/ORGJl6Kn376iQ8++IAHDx4ARdUsCgsLOXfuHABJSUkMHz5cacsad+7coXLlyiVehz9vNL6+vjg7O3P8+HHl9eJT1PX09OjevbsS7HrvvfdYtWoV5ubm+Pn5lbhBSUdCvGk0wdR69eqxZ88erfMhOzubjIwM1Go1ubm56OjocOvWLT777DOCgoIYNGgQBgYGfP/998yaNUs5L8LCwrh586ZyDmrOJ1dXV3788UfmzJnz6n+oeOtpZpFERkaSmZnJ4sWL+fDDD3n69CmjRo3i5MmTAKXO+DAzM+PatWvUqlVLSeIM8M477+Dj40N0dDRQVCXY3t6eY8eOAUUj+Vu3bsXMzIzk5GT279+vVHjUHM/FixcpV66c1pIO8b+ttDamUqmYMmUKX3zxBaamphgaGjJlyhQmTpzIkydPMDU1JS0tDV1dXZ48eQIUXb91dXVp3rw5R48efeHzDBTN3hgwYABTp05l2bJlfPDBB1haWqJSqWSmk3itVq9ezdKlSwkNDaVWrVqsWrWKkSNHkpKS8tz+Y3Z2NqmpqVSrVg1bW1vlmtqpUyccHByIj48nOzubChUqKEEsTQXHuLg4nj59SpkyZfj+++8B7eJbhYWFpKWlUaFChVfw618tmQEl/jGaE+vMmTPMnz+f2rVrM2DAACpWrKicTFlZWWRkZDB69Gi6d+/OmDFjlCmHfn5+xMTEsGnTJnx8fLCzs6NevXps2rSpxKyN2rVro6enx/79+3F2dgb+nDEycOBA8vLyyMvL01qLLsTLUqZMGXbv3k2/fv2wtbWlcePGzJo1i/j4eFq3bo2hoSHx8fFMmDCBjz/+mBo1aiifzcvLK3XkT3Ozs7a2pmbNmqxdu5aTJ0/i7+9fIkm4rq4uTZo0oUmTJsprLxqtEeJV+rttsVu3bvTv35979+7h5uZGSkoKq1atwsbGhpYtW2JkZETlypXZsmULnp6efPLJJwQHByuzoZ4+fcrly5fx8PAgLCyM6dOnc+DAAZo2barcPwwMDJRqSkK8bMWv1ZolbKdOnWLnzp2MHTtWqehVr149unfvzvLly/Hx8dHKu6ShVqupUqUKiYmJyr91dHQwNzfHx8eHmJgYUlJSqFSpEi1btuSLL76gW7dumJiYcPXqVRYvXsydO3dwc3OjTJkyQFEHZ/369Rw8eJCQkBCtpd3if4smwKO5Furo6JCcnIyVlZXyWmxsLBs2bOCTTz4hPDwcU1NTWrVqxZQpU/j5558ZOnQodevWJTo6mgcPHlC+fHmlfVesWJFHjx5x7tw5vLy8njvYpaOjg1qtVp79NQGsf/NsDvHvVPzZ5PHjx8yfP59OnToxfPhwdHR0CAkJYdy4cXz77bd8+eWXpe5DR0cHAwMD8vPzycnJoUyZMhQUFGBsbEy1atU4deoUFy5coFatWrRv354ff/yRjIwMatasyc6dO2nZsiUWFhZK+9fV1UWtVnP//n1++eUXGjVq9Mr+Hq+SnO3iH6M5iZctWwbA8OHDcXR01IrkmpiYYGZmRseOHTl69CgPHz5UPu/p6Um1atXYt28fUFRdpW7duqSlpSmvaSLLlSpVIigoiJ9//lnJL1X85mVgYCDBJ/FSaWbkQVHpd3Nzcw4fPkx2dja6urrUrVuXc+fOcebMGWxtbZk/fz6FhYV88cUXSqJXS0tL0tLSnttWNe09KCgIExMTYmJi/tZxSfBJvAkKCwtRqVR/2RY194j69etTtmxZFi9eTLdu3WjQoAHXr19n6tSpyjLV8PBwKlasSMOGDWnXrh0VK1ZEpVJx8eJFZs+ezcGDB8nLy8POzo5p06YxYMAAORfEK5WRkcHy5csZOHAg06ZNY9OmTcCfz0gqlYrMzEwluXJBQQFWVla0bduW8+fPK/nJit9jNNsFBgZy4MABUlJSlGceY2NjcnNzycrKUnKldejQgdmzZ2NnZ0e5cuWYMWMGbm5uNGjQQKnKBEXPSmvWrKF27doMHTr0pf5dxOulmVmRnZ3N6dOnuXDhAg0bNmT37t3KNnFxcVStWpUOHTpgamoKgLm5Obm5ufz2228UFBTQsWNHUlJS2Lx5M1DUrvPz8zl69ChqtZpdu3aRkpLywmMpfk3W09OTa7R4JW7cuMHChQuZP38+CQkJWkUXrly5gpGREbVq1VLaY2BgIP369WPjxo2cOnWqxP40VdQrVqzI7du3uXz5svI6FK1guHv3LidOnACgb9++9OvXjwsXLrBkyRK8vLzo0aMHXbp0oVOnTsp+Hz16xFdffcXt27fp2rWrkiPzf4kEoMQ/6vHjx8THx9OwYUMqVKhAfn6+8l7x0ZBWrVqRmJiolXyzbNmy+Pn58fjxY/bu3QuAm5sb3t7eJTreRkZGtGzZkmrVqpV4SANZMy5evuLtuVy5ctSvX5/Y2FhSU1MBaNmyJU+ePFESIfv6+jJlyhSuX7/O9OnTycjIICUlBVtbW+7fv1/qd2g6GN7e3ri6uir5n140UigPc+JNoUlke//+fQ4fPkxeXt5zt9Uk6m/fvj1Hjx7F0tKSlStXsn79eurXr6+0+YYNG9KyZUtWrFjBxx9/zKpVq5g6dSofffQRiYmJ1KhRQ1mG17ZtW2V2lBAvW15eHuvWraNjx46sW7eOSpUqcenSJT7++GO++eYbJaltZmYm5ubmSrqB4kvprK2t2bBhA1Ayv4eBgQHvvvsuBgYGLFiwQLnXXL58mZs3b2JgYMCPP/6obBseHs7s2bOZNWsWQUFByn4036fpJP36669MnjxZK9em+HcrrdDPw4cPGT9+PIGBgcyYMYO5c+cCKEHL/Px8pYKWWq3m+++/p0mTJnTq1AknJyc++ugj8vLycHBwoEOHDqxevZrx48dz6NAhVq5ciaGhIYMHD/7L4JMQr5JarebAgQP06NGD1q1bs2fPHmJjY+nZsyffffedsp2xsTGpqalay0B1dXWJiIjA2NiYHTt2KEuXi+8bitJuZGdnK31VQ0NDUlNT2b59O+XKlePgwYOkpKRgYmLC8OHDWbFiBbt37+aTTz6hbNmyAFpLUC0tLZk0aRIHDhwgJCTkpf59XhdZgif+Uffu3SMrK0vpAD8v+bKfnx+Ojo7s2rWLkJAQZaTF09OT3NxcYmNjadiwIdbW1jRq1Ii5c+eSlpamJGKDopHw8PDwl/+jxFvn1KlTbNiwgSFDhiiJjZ9tyxcvXmTLli2MGjUKKEquOWDAAP744w8cHBzw9/enYsWKnDp1iocPH2JtbY2bmxtjx45l0aJFLF++HDs7O/Ly8qhYseILZy2ZmZnxzTffYGRk9NJ/uxD/Kc0sp2fbb3x8PLNnz+bcuXNYWFhgamrKqFGjlKWixdu8prPdunVrli1bRoMGDahVq5ay/+Jl30eOHImdnR3x8fFER0dTpkwZBg4cSJs2bSTHmXht0tLSWLhwoZJ+wNbWFgMDA6ZNm8aWLVvw8/MjNDQUc3NzbG1tOX78OI0aNVLabJUqVfDx8WHv3r2kpqZiaWlZ4r7g5ubGBx98wIIFC4iPj+edd97h5MmTODs7M2jQIG7cuFHiM5pBuuJLr+DfncBWlK54KXaAw4cPo6+vT2BgINu3bycuLo6pU6fi5eXFH3/8QWJiIlu2bGHYsGFYWlpSoUIFNm/ejJeXFy4uLrz33nuEh4dTpUoVAGUQYdiwYVSqVInffvuNzZs3Y2pqyocffkjnzp0ZNmzYa/v9QjzrwYMH7Nixg/LlyxMZGYmHhwdPnjxh/fr1zJ8/n969e2Nra4unpyeGhobcuHFD+axmhlOzZs3Yu3cvPXv21CrSoLmm1qlTh6ZNm7JkyRIKCwupW7cuFy9exM3NjSpVqpTIb6bJ6VRQUKAMGj+bVuNNr2L3fyUBKPGPcnV1JT8/n/T0dGUt7LPUajX6+vq0aNGCqKgoevTooeTkKF++PAYGBhw6dIiMjAzMzMzw8/PD09OTe/fuaQWghPgn3b59m59//pmNGzeSnp5OnTp1lOWdzwaf1Go1p0+fZvHixbRp0wY3NzeCg4OxsbFh79691KpVCzMzMxo3bsy6des4f/68UnGrdevWmJqaMnr0aDw9PcnJyflby5SMjIyUUU3pOIg3gSY3X2ntMT09nR9++AFzc3N++eUX0tLS+P333/n8888pLCwkLCxMa3tN3gNPT08qVarEgQMHaNKkCRYWFqWeG127dqVr165kZmYqAxhCvApJSUns2bOH9u3bY2xsrJWL6aOPPqJ+/fpazyotWrRg/fr1JCcnA0UzWq2trblw4YLynKM5l6pWrcq+fftISEh4bu6P7t274+TkxNq1a4mNjaVOnToMGDAAGxsb6tatW2J7Ccq+HTSzo1NTU8nMzGT58uWsWrWKnj17UqtWLbZv30716tVp0aIF+vr6uLu7o6ury+jRo4mNjaVjx45Ur14dGxsbgoKCmDZtmrLvnJwc9u7dy4kTJxg3bhxWVlYMGDCAsLAwMjIyqFatmtaxSLVd8aYwNzencePGODk5KTmDy5YtS8OGDYmMjOTatWvY2toCUKtWLfbs2UPXrl0xNDRUnj3Cw8OJiooiLS2tRJVQzfX/448/xsTEhK1bt7J+/Xrs7OyYPHkyISEhz31mf94kjbeB9GLEP6pMmTL4+vpy7tw5pUJd8aivZgkRQJcuXUhJSWHt2rXcuXOHBw8esHDhQry8vEhOTiYuLg4oWoP7+++/S+JY8VKkpKQwduxYmjRpwvnz5+nbty+bNm3i+++/x9HREYBVq1Yxd+5cZfRPR0eH4OBg7O3tlTwIhoaGvPvuu+zZs0fpaLRo0YKcnBylshEUdQaaN29O3759SUpKokKFCko1mb8iiTrFm0TTwTh37hwLFy5Ulk4DbN++nYMHDzJ8+HC8vb2pV68es2bNwsnJiV9//ZWMjIwSgSXNTI2IiAiOHz+uNRJZGrVaLcEn8cpcvXqVzz77jHfffZepU6eyc+dO4M/lTgYGBrRs2VIJPmmedWxtbUlLS1PK1hsaGhIQEMC9e/c4ePAg8Gfb9/T05OnTp1r3mmfp6ekRGhrK7Nmz2bJlCxMmTMDGxkbrWMT/LpVKpZV6QtPOdHR0uHr1KnXq1GH27Nlcv36dX3/9lYEDB6Krq8vt27fx9PREX19fK79kYGAg69evByA4OJjQ0FC2bdvGrl27uHTpEmfPnuWHH35g2bJlWFhYaC1DcnJyUoJPmmXUIEFP8epo2uPzKigaGxvTsGFDJfikcfjwYXx8fPDx8QGKrsthYWGcOHFCqdiuSZjv6OiIsbExf/zxB6B9ndVsAzB48GCWLVvGtm3b2LFjB3Xq1EFXVxeVSiXX5mdIT0b841q3bk1iYiJHjhwBtC8KmmmGx44dw9zcnOHDhxMdHc3AgQPp0qULWVlZfPzxx0RFRdG6dWtltpQQL4smKWeFChVYsWIFffr0wcnJCT09PeUhb/fu3WzZskW5+QDY2dlRt25dpUojFOU2u3v3LhcvXqSwsBBnZ2c8PDw4deqUUppYs22/fv1o06YN2dnZ2Nvby81J/GtorukHDhzgvffeo2fPnuzZs4cdO3aQlZWFWq3m4sWL1KhRQ6mGdODAAUaNGkV8fDy5ublKkLY4TaelY8eOpKamcvDgwRfODpRcZ+JVycvLY8OGDWzZsoXmzZvj4eGhDJIVr15UnOY8iYuLw8LCQivHkmZ235o1awCUimCVK1fm8ePHSmXU0jpVmnZvbGwMFHX8ZXbs26N4mfZny8O7ubnh5OTEtm3baNWqFX5+fspyH29vb86fP6+1LzMzM6pVq8bp06e5desWFhYWjBo1itDQUCZNmsSYMWPo2bMn27dv57333qNXr14lVjZo2qg8q4tXJSMjg2XLlvH+++8zY8YM8vLy/vJ5QPPsfebMGT7++GN++OEHDA0NiYuLU95r0KAB5cqVIyYmhoyMDKDoeltYWIiVlRWPHz8GSl5ni3+3lZWVklZDE5SVweOS5K8h/nHt2rXD1taWFStWcPv2ba2TrrCwkKNHjzJu3Diio6MZNGgQixYtUnImzJkzBx8fH2W2k3QwxMtmYWFBw4YNSU9P5/Dhw0BRO9XR0VEe8nr16kV6erpWFQxjY2MaN27MrVu3lNerVauGu7s7sbGxpKenA0VTd2/duqXMgtKcD2ZmZlSoUAGVSkVOTo7cnMQb5Xmj7FB0XU5NTWXevHk4OTmxYsUKFixYwPDhwzExMUFHR4fMzEwyMjIYPXo0wcHBDBkyhMzMTObOncvChQtxc3Mr8Z2aBz1ra2u6dOlCrVq15LwQbwRdXV1q1qzJzJkzlaTe586d4+7du1oj4M9+BuDkyZNUqVKFqlWrKtu5u7vTpk0bDh06xPr165XOzrJly7C3t1eCVX/nGUhfX1/Ok7fIpUuXGDt2LGFhYQwbNow5c+Zw/fp15f127doBaOWM1FRQjI+P59atW0p7MTQ0JCcnh4KCAmUGa5kyZfj6669Zu3YtH374IWvXrmXbtm3PrcYlz+niVTp27BhdunRh7dq1VK1aFUtLy+cW8ilO8zw/ceJEUlJSGDx4MGZmZkyZMoVx48aRkpKCjY0NXbp0Yf369coAAxTN8k5KSsLf3/9vH6eOjo4EZV9A/jLiH6ejo8PIkSOZOXMmgwcPZtCgQcpa84SEBGJiYvD19SUiIgIoWmIXGBj4mo9avM28vb1xdnYmJiaGOnXqoKenx+7du4mOjqZt27aEhoZSrlw5Tp48yXvvvadUrXB3d8fZ2ZkNGzYQEBAAFM0AjIyM5P79+1hYWNCkSROmTJnCH3/8oZVMGYpupB4eHq/lNwvxIsXb6aNHj5RRdI2oqCgSExMZNWoU1atXV17XJOwPCwtj/fr16OrqMmLECOrXr6+Uf8/IyODcuXNUqVKlxGi65nsnTZr0sn6aEP8xfX19QkNDlX/7+voSFxfHjh076N27NyqVqtRlR3fv3mXnzp188cUXgHZnvWPHjly9epXPP/+c6Ohoypcvz9GjR+nbty8uLi4v/0eJ106TluL333/HycmJWrVqUVBQoDUAVlxMTAyRkZHY29vz/vvvk5qayp49ezh16hQrVqwA4L333uObb77h3r17Sn4afX19mjVrxk8//cR3333HyJEjcXBw4OLFi1y9ehV7e3vWrVtHjx49lPxNdnZ22NnZKcdZWFgoHWrx2qjVajIzM1m0aBGurq58+umnWFhYlJprWKP4M7fmv3/66SfMzMyUtrxx40bGjh1LaGgozZs3p3fv3iQmJjJx4kQuXbpExYoViYmJoW3btlStWvWV/Na3gVxJxEvRqlUr7O3t+fbbbxk9ejRubm6kpqaSnZ1NREQEffr0KXUkRYjXwdnZmeDgYKKjoxk7dqyS2yMwMFDpNNevX599+/Zx4cIFgoODgaJSqQ4ODuzbt09JJtu8eXO+/vpr9u/fj5ubGxYWFkRHR2vduFJTU5UqRsOHD3/hDVSIl62wsBBdXV2tzvGlS5f46aefOHXqFBUqVCAgIICIiAglj8K9e/eUZLXFq25pHuoaNWpEpUqVcHJyom3bthgaGlJYWMjdu3dZsWIFenp6DBgwoETbl9F08apkZmZy7tw55Xr+d2gCrLVq1cLR0ZHdu3fTu3fv5+a8WbNmDVZWVtSvX7/Ee3p6eowZM4Z33nmHbdu2kZOTw5w5c6hdu/Z//ZvEv4umeui4cePo3Lkz1atXV2Yu5eXlkZ+fr+S5KywsxMDAgIiICPr27QsUBTgTExPZunUr8fHx+Pv7Y2dnh5eXF4cOHaJly5bY2tqiVqupWLEiw4cP5+uvv6ZHjx7Uq1ePs2fP4uXlRfPmzZk2bRpPnjyhfPnyWsdYPIglxKugKWKlCYZq2uCVK1c4ffo0W7Zs0VrSXJxarVYGBJ6tLAeUaN8tW7ZkwoQJPHr0CABTU1O+/PJLli9fzsmTJ9m2bRuNGjVi0KBBylJp8X8nVxPxUujq6hIUFMSPP/7InTt3uHLlCtbW1vJgJd5IRkZG+Pv7ExsbS3x8PNOnT8fb2xsHBwdlyUSrVq3YsmULhw8fVjos2dnZPH78mPv373Po0CGaNWuGg4MDvXv3xsfHB0NDQ9RqtRJ80ozAGBoaolKpmDNnjlIdT4hXqXjOGE3nOSMjA1NTU9avX8/ixYuxt7enV69epKamsnfvXmWUvbCwkAoVKih5nJ4XNBo6dCiRkZG0atWKRo0akZOTw/79+zE2NuaDDz6Qqqbitbh+/TqrVq1i1apVWFhYsHTpUjw9PUvMUC2NphNub29PzZo1+f333zl//jzVqlUr8fm0tDR27txJaGgolpaWZGdnc/v2bS5evEh4eDj6+vro6elRr1496tWrp/U9xYO64n9HQkICmzZtIicnBw8PD+rWrYurqyvh4eHEx8dz+/Zt1Go106dP59atW3h4eNC9e3dCQkLQ09PjnXfewczMjIMHDzJ37lxOnz6Nl5cXZmZmbNiwgRo1amBoaEjHjh2ZMWMGf/zxh1LhC4pmaFeuXJk1a9Zw9epVGjduzNChQ5kzZw6WlpakpqaW6KBLOxSvQl5eHjt37uT777+nQoUKrFy5Uml7mv+9evUqFhYWGBsbc/fuXX788UeSkpKoXbs2oaGhVKlSRWv24IEDB/j999/p1asXPj4+pV7jDxw4QF5enhLoValUmJmZMXToUJ4+faqseBD/LAlAiZdGrVZTtmxZPD09pYKdeONVr15dqXrXtGlTpYOuufH5+Pjg5+fH+vXrcXJywtfXl/Xr11O9enXy8/M5cOAAzZo1A+DTTz9V9lv84a14/qfPPvvslfwu8Xbbv38/iYmJ2NnZ4efnp5QQ1rRFtVrN2bNnGTFiBDVq1OCLL75QRtn79esHwP3797l69Spbtmzh+PHjBAYG4ubmRl5eHtu3bycsLEzrOx8+fIi1tTVt2rTBy8uLjRs3cvHiRQoKChgwYAAREREYGBi82j+EEBTNPl2yZAn79u0jICCA+/fvc+TIkRc+ozzbadH8OyAggG3btrF9+/ZSA1Dnzp3j/v37hIaGsm7dOmJiYoiPj6datWr4+/tTqVIlrZlTKpUKtVqNnp6edPr/R2gCiQcPHmTOnDlcvXqVgIAAzM3NWbduHSqVCldXV7p06ULPnj05ffo0x44dw8rKirp16/Lzzz8zadIkFixYgJubG2ZmZpw5c4bp06fj6enJ+PHjcXFx4fvvv2fz5s0MHjwYOzs72rZty7Rp0zh16hRBQUHK9ValUuHn54efn59yjOfPnyc2NpZatWrh6ur6uv5U4i32+PFjZs2axYEDBygoKCApKUl5jihe5TElJYVy5coRFxfHsmXLMDExwcHBgcjISFavXs3ixYtxcXFh5cqVzJs3j4KCAvz9/ZXBLs31OScnh/z8fC5fvszy5ctp0qQJ4eHhqNVqrWu4JvhUUFAgicT/YTrq59UtFEKIt4hKpWLBggUsWrSI3bt3KwnCNSVUdXV1OXfuHN9++y3nzp0jLy8PBwcHJk2ahJeXV4ly8Jqpw0K8KprOzp07d1i2bBkxMTGYmJjg4uLCyZMn6dy5M4MHD1ZGuhcuXIiOjg5ZWVno6+sTHh5O9erVKSwsxMzMjEOHDjF37lwSEhLw8PDg9u3bvPvuu3z55ZfcvXuXCRMmkJSUxPfff4+7uzu5ubmcPXuWFStWMH78eK2R99zcXK2kuEK8bI8ePUJHR0epKAdFVcN+/fVXnJ2dCQwMZPjw4VhaWjJ37twSS4yedw3XnGdPnjxh7NixJCcnEx0dXWK7AQMGsG/fPsqUKYOuri4tW7akW7dukvfvLaFpJzdv3uSTTz7BxsaGgQMH4urqiq6uLkZGRqSkpGBlZQVAvXr1UKvVBAcHM27cOKysrIiPj2f8+PEEBwczZcoUsrOz+frrr9mxYwfr1q1TrrFff/01kZGRzJ07l8aNG6Onp0fXrl25d+8eK1asUAbXAPLz8zl9+jT79+/nxo0b7N+/H1dXVyZPnqyVz0+Il+Xhw4ds3ryZ7t27o6+vT0pKCv369SMoKAhPT0++/vpr3n//fQYMGKAV/Dlx4gTdunXDy8sLFxcXJk6ciJmZGffv36dz587Ur1+fGTNmsGPHDtLS0qhXr54y6AZFAxBff/015ubmnDt3josXL1KrVi1GjBiBt7f3a/yLvH1kBpQQQlA0MuLn54eJiQnR0dH0799fCTxpRj2qV6/O3Llz2b9/P+XKlSMkJERrH8WXTUjwSbxqmk7xrFmzuHPnDp9++inBwcHo6+sreZ40I+EmJiYcOXKEx48f4+LiwuzZs7VyKpw5c4Zp06bh4eHBuHHjcHFxYf78+WzevJmHDx9SsWJFxo4dy8CBA+nSpQvvvPMO2dnZnDt3Dnd3d6X8sIYEn8SrkJ2dzbp161izZg3Z2dlYWFhQo0YNJkyYABSVyP7www+V7X18fDh48CAJCQkEBARozWJ63jIOTdW78uXLExgYyPLly9m9eze+vr7cunULT09PypQpg4+PD2XKlKF79+4EBQUp31k8R4n436V5Fli4cCG3bt1i6tSpJap/WllZKYHOjh07smDBAoKDg5WglJeXFw0bNiQqKoopU6ZgbGxMcnIyHh4eSmGIvLw8rl27BhQlKa9Xrx4mJiaMHj2aO3fuaAWfAAwMDDAxMeH69evo6Ogwe/ZsrQT7Qrxs69atUwbF9PX1sbS05Oeff8bMzIzMzEzi4uJYv349AwYM0BoY8PHxoXLlyly8eJFx48ZhYWGBSqXC0dGRnj17snTpUhISEpTVCM/S5G29cOECPj4+TJo0CXd391f1s0UxMpdMCCH+P3d3d/z9/dmyZQtQehDJxMSEsLAwJfhUvFS9LJsQr1NhYSHz5s1jz549fPTRR7Rp04bKlStTsWJFHB0dcXBwoGzZsqhUKsqUKUN4eDgPHz6kSZMmWFtbK8tO8/PziYmJ4cmTJ4wZM4YaNWpQtmxZZaQyISEBlUpF1apVWbBgAYMGDSIvLw8DAwO++OILlixZoiTvF+JVuXTpEu+//z6//vorDRo0YNSoUbi7u/Prr78SGRmpda3Oy8sDIDQ0VFlCDdrX8JUrVxIcHMywYcN48uSJVs4yzXaurq4YGRkxffp02rRpw9ChQ3nw4AEAH374IXPnzlWCTwUFBahUqudWOBP/e9LT0zl48CAtW7bEzc1NucYWp2lLmsrQaWlpynampqaEhISQnZ3N9u3bAfD09OTq1avMnj2bq1evsmTJEgwMDPjggw+4f/++Evz38/OjZcuWpR6Xp6cn3377Ld99950En8Qro2nXWVlZZGdnU6ZMGSXob2ZmhkqlwtTUlODgYG7evMnly5e1PmdoaKgs+b9x4waA0t6bNWtGWloaOTk5pX63ZsFX//79WbBgAZ9++inu7u5KhUfxaskMKCGE+P+sra0JCAhg5syZXLx4ES8vr+duq7lpSkdCvCmePHnC9u3b6dWrF3Xr1lVeLywsRKVSKbOfNDM86taty6+//srFixeBPztCBgYGPHjwAA8PD2UkPi8vj+vXrwNFo+wNGzbE0NBQyfEnSZPFq/bsMo7c3FyqV69Os2bN8Pf3R19fn+bNm6Ojo8PmzZvp3bu30k411YwCAwOpWLEip06dIj09HXNzc6XSnYWFBWPGjKFOnTpayzigKLCwfPly4uLiSEpKomrVqrRt25b27dsr5wxo53WSKmJvn1u3bpGZmYmxsfFzt9HV1UWtVuPo6IibmxsnT54kNTVVaUfu7u74+Pjw66+/EhYWRvv27UlOTmbdunWsW7cOExMTxowZQ1hYGB999JHWvp+XWF9y2YjXQVdXl8LCQnJzczE3Ny+xzFnzDOHr64utrS1r165lwoQJWu24Q4cOLFu2jOPHj9OhQwflWn7jxg2lsAOULOSg+W/NbOzCwkJ0dHTkOf41kSuQEEIUExgYyLBhw7Q6EaWRBzjxprl+/TqPHz/G2dkZKJrJBEUz+QwMDMjOzmbjxo2sW7cOKJrO7unpyfXr13nw4AE6OjrKZ6pXr641yh4ZGYm+vj6DBg3izp07JUYZJfgkXrV169Zx6NAhZQS8atWqjBkzhqCgICXYoxnd1sw6Kt5ONUHZ4OBg7t27x7Fjx5TPQFF57nbt2pUIPkHRObVnzx68vLzYtGkTGzduZNCgQVhZWVE8tap0bt5uNjY2ZGdnk56eriyDLo1mBkb79u05duyYsqQOwM7OjsaNG3P06FEyMzOxt7fnyy+/ZO7cuSxYsIA9e/YQHh6utLPisznkOUW8STTB+Pv376Ojo0N2drbW+8VnlgYFBSmz/jTXc5VKReXKlenSpQt79+5l6dKl3L17l6SkJFatWkVQUBA1atRQZpq+iJ6enpwfr5H85YUQohgfHx8GDx6slQ9HiDeZpsObl5dHYWEhDx8+BFBmPCUnJ9O3b1/8/PwYPXo0a9euJSkpCYCQkBBSUlI4cuSI1j7btWtH3bp1iYqKokuXLvz222+EhYXx4Ycfsn79eszNzV/hLxTiT89bxmFiYoKhoaFWAOjAgQOcP3+eUaNGKa89W3unYcOG6OrqcujQIYC/VaHR1NSU6OhoZsyYQZUqVVCr1RQUFMhMQKHFxsYGNzc3Ll++zL1794CS7Q/+7GC3a9eOrKwsTp06pQSS9PX18fb2RkdHh8OHDyufCQkJISAgANAOOknAU7wupS0xLU7T9j08PLh48SImJialng9ly5YlKCiIJ0+esH///hLvDxkyhO7duzNv3jz69OlD27ZtuX37Nn379sXIyEgCS/8C8v+QEEI8Q5MkVojXTaVS/WV+Ak2H18/PD319fW7duqU1QyknJwc/Pz/WrFnDyJEjefDgAWfPngWgQYMGGBsbc/ToUeDPzreNjQ1Tp05VRtn37t1LixYtpHMjXrvSlnEU73Do6Ohw/PhxmjZtSv/+/Xn69Cljx45lyJAhykw/zX4AqlWrRpUqVTh//jxJSUk8efJECRb8leJ5nfT19SX4JEoIDw/nzJkzJCQkAKUHoP744w8ePXqEubk5gYGBbN++nTt37ijvBwQEcPToUZo0aaL1Oc2+5LosXpfizyjFr8OltXPN+87Ozty5c4eHDx8qRR2K7w+KZmG7ubkRFRUFFA04aGZoly9fXhkM69mzJ3PnzmXbtm1aqQfEm00CUEII8QzNunAhXpW0tDSgaCS7eFJMzRKe/Pz8F3aK1Wo1xsbGBAcHc/z4ca5evaq85+TkxODBg6lRowYdOnRArVZz+vRpVCoVbm5ueHh4cOLECa5cuaLsS6P4KLsQb4IXLePQtF0bGxv69evH5s2bWbp0KVOnTuXw4cN89913ZGZmltinv78/Dx48YMaMGXTp0oXRo0eX2oF6lr6+vtwrxAt16tQJfX19fvnlF3Jyckp00v/44w/Gjh3Lxo0bAWjbti0PHjxQEuUDGBsbKwUkipOAp3hVnnc91DyjZGVlsWXLFnbv3k1eXt4L26a9vT3lypVj8+bNQOnLRp2dnQkODubIkSPs3buX2bNns3btWq3BtcqVK9O1a1dq165dYj/izSZ3TSGEEOI1efDgAV27dmXkyJHKa8WrZN28eZNhw4bRoEEDBg4cyKpVq3j69Cmg/UCoedjr1KkTN27cUHInPDsyaWFhgYmJCSdPnlRG2IODg7G3t1dGF6VTI16n/8syDk3bdXJyolOnTri5ueHs7Ey9evVo06YNJ0+e5ObNm8p+Hj58yG+//caOHTu4d+8ep06dIjAwkAkTJsh5IP4RlpaWDBw4kDNnzjB69GgSExN5+vQpqampnDhxgrlz51KhQgVatGgBFFXDO3DgAFWqVCmxLwl2ilctOTmZqKgoTp48CfxZdU4jKyuL6dOnU6dOHWbPns348ePp2bOnMsu6OM11ulKlSvj5+Sn5KJ8t0JCWlsaVK1dISUkhLS2NgQMHsnHjRsqWLaskHS9Oc8+QmYD/HlKSQwghhHhNzM3N8fDwYMuWLUr1LZVKxbx58yhTpgwZGRkYGBgwZswYtm/fzvfff09ubi59+vQpNd9Ms2bNCAgIYNmyZXh7e9O8eXOgKCF5amoqsbGxlC1bls6dO+Po6AgUjbh36NDhlf92ITSKV4t7dobIs228tGUctra2z82/pHm9Ro0axMTEYGtrCxQFq5KSkvjqq68ICAhg+fLlhISEvMRfKd5WvXv3xsrKih9++IE2bdrg7+9Peno6V69eJSQkhKFDhyp5JzXt9dkKYUK8Djk5Ofzyyy94eXlRq1Yt9PX1uXfvHtbW1ujr67Nt2zb27t3LuHHjaNCgAefPn2flypVMmjSJhQsXYmNjo7RpzfXZ2tqaFi1aMHr0aA4cOEC9evWAP9t+bGws48ePx8LCgl69etGhQ4dSA7IaEpj999FR/505xkIIIYR4KXbs2MEnn3zC5MmTad26NQATJ05k7dq1+Pv78+WXX+Lq6kpubi6jRo3i5s2bREdHl0iWrOmwXL58mdmzZ3PixAlcXFwICgoiOzubvXv3olKp6N69O507d8bMzOx1/FzxFvurJN1ZWVns2bMHY2Nj6tatW+pot8apU6cYNGgQAwcOpE+fPkoAtzSXL19m7NixmJqasmTJEqUUd25uLgYGBlodmIKCAnR1daVTI/5x169f5/LlyyQmJmJmZkZ4eLgSEBXiTTVmzBguXbpEzZo12bx5M35+fkyZMgVLS0v69OmDmZkZixYtUrZ/8OABDRo0YPjw4QwcOLDUa35GRgYjRowgPT2d2bNnU6lSJeUanpKSwpkzZ2jUqJGyvSY1wfOu8eLfRQJQQgghxGuUlJTEJ598gp6eHitXrgTg4sWLRERE8N577zFz5kxl27Vr1/Ldd98xceJEwsLCnjtK/vTpUzZu3MipU6d49OgRarWapk2b0r59+xd26oV4GZKTk9m/fz8uLi7UqlWrRLAoKyuL7777jtWrV1OhQgWys7OpXLky48ePx8fHR2tfmiDWw4cPmThxIklJSUouEY20tDQSExMBuHr1Klu3biU3N5cvvvii1JH0goICraWvQvzTnhd8LT77T4jXpbR2qFKpuHPnDn379uXWrVs4ODjQp08f6tati7OzM6mpqTRp0oSpU6fy7rvvcvXqVVasWEFcXBzZ2dmMGjWKiIgIjI2NS/3OK1eu8MEHH9CwYUM+++wz5fXi54oMCPxvkgCUEEII8RqpVCoWLFjAokWL2LVrF1ZWVhQWFtKuXTvs7e2ZPn065cuXByAxMZGJEydibW3NvHnzUKlUf/lglp2d/dwHQCFehZs3bzJixAi8vLyYOnUqgNYyjujoaBYtWkSfPn20lnGkp6eXWMZR3MaNGxk9ejRLlixRlnEAbNiwgXXr1nHz5k0KCgpo3rw5vXr1wtHR8S9nYQnxKmiqJ0pbFK9TadfDJ0+eKM8cd+7cITo6mi1btlClShXmzZun9bnQ0FBsbW15/Pgxt27dwtfXl1atWtGoUSPs7e3/8vs3b97MjBkz6NevH126dJEBsreEzGMTQgghXiNdXV38/PwwMTEhJiaG/v37o6enR3h4OEuXLiU5OVl5GHR1dcXX15etW7eSmpqKpaXlX3aoJfgkXjcnJyeqVKnC2bNn+eyzz0os44iKiqJy5cp07NgRADs7O3x8fGjQoAHR0dHPXcYRGhpK/fr1mTdvHs7OzlSqVAmAxo0bY21tjYmJCb6+vlqfkQ6/eBPIjA7xqmhmnJb2rKD596VLl1ixYgWnT5/GwsICX19fPvroIxwcHPjwww95+PAhp06d4uzZs/j4+KBWqykoKCAsLIzly5fTt29f2rZti5OTk/JdR48exdbWFmdn5xLHpDmWFi1akJuby++//46FhQXvvffeq/iTiNdMrn5CCCHEa+bu7k7NmjXZsGGD8tq7777L06dPOXHihFaVl4CAAHR0dPj999+Bv64aJsSrolKpSpTCVqlUJCUlkZCQwJUrVzh48CAjR45kwoQJVKxYkaysLC5dukRERARQtGRu0qRJtG3bFlNTU8zNzbVKbxdnZmbG6NGjSUlJYenSpcrrpqam1K5dWwk+FRQUyHkihHirpKamMnToUGXW6bPXZo1Vq1YxevRokpOT6datG3Xr1mXDhg18/vnnPH78GICGDRuSnZ3N/v37gaLAlYGBAe+++y5QNGhQuXJloGgG1a5du/j222+5dOlSqd9ZPBDWtm1bPv/8c1xcXJCFWW8HCUAJIYQQr5m1tTW1a9dWktQCODo64u/vT1xcHGlpacq2np6eODo6cvfuXUBKD4vXT9Np0NXVVdrjkydPlNd0dXVp1aoVLi4ueHt7061bN5ydnVGr1VhaWlKuXDl++uknwsLCaNmyJZcvX+aDDz5g06ZNdO3a9YWz+Nzd3Rk5ciQ7d+7kp59+Ii8vD/gzpwkUlfmWGSdCiLeJrq4uOjo67N69G6BEAm/N9TErK4s2bdoQGRlJ165d6dGjB9WqVePgwYOcOHECgJCQEGxtbTl58iRZWVno6OigUqnw9fWlX79+LF26lHbt2jF58mR69OjBuHHj8PDwKJHD73lcXFzw8fGRGapvCVmCJ4QQQrwBfH19sbGxISoqivHjxwNFI4Njx44lMTGRoKAgoCgw9eOPP2Jqavo6D1e8Zf5tyzgk4CSEeJuVL1+eRo0asWfPHg4fPkzt2rVLLVzStWtXTE1N2bdvHwsWLCAhIQFvb28ePnzI8ePHqV27Nqampvj7+xMbG8upU6eoW7cuhYWF6OrqMmrUKBo0aEBCQgJnz56lefPmdOzYEWtr69f0y8WbTu7OQgghxBvA1dWV4OBg4uLilNcaNGiAvb09ubm5WttK8Em8KrKMQwgh/p28vb1xdnZmzZo1Jd7TXD9NTU1JSEhg+vTpVKpUiV9++YXo6GgaN27MgQMHSEpKAqBRo0YAzJs3j7i4OD777DOWL1+Ojo4OQUFBDBgwgHnz5jFkyBCsra1RqVSy9FmUSgJQQgghxBvA3NwcX19f7t69y6FDhwCoUKECu3fvpn79+q/56MTbSpZxCCHEv5OzszMhISHs37+fgoKC5y7ZX7VqFbm5uYwfP1653latWpXbt28rAwB+fn4MGDCA3NxcJk+ezO3bt/H39wfQCvoXFhaiVquV5ddCPEtahRBCCPGGqFu3Lt9//z1BQUEyi0O8ETTLOB49esThw4eB0mdBde3alb59+7Jv3z46d+5MQEAADx48UJZxZGZmKss47ty5w6lTp7T2NWrUKL766itatmxJWloazZs3Z9OmTUyePBkHB4dX94OFEOJ/hJGREQEBARQWFrJ582ag5PU7KysLAAsLCywsLDAwMCA9PZ3jx4+Tm5vLoUOHSE1NBSAiIoIffviB3bt3s3LlSmrUqAFoz0bV09OTQQDxQpIDSgghhHhDODo64ujo+LoPQwgtxZdx1K5dW+u90pZxVKtWjTFjxuDv78+wYcM4cOAAEREReHp60qhRI2JjY5k3bx45OTnExsbi4eFB7969CQoKUnKdaWiWcMhIuhBC/Oe8vb3x8vIiKiqK1q1blwgOmZiY4OrqSmxsLJGRkdStW5c9e/ZQvXp1rK2tuX37ttZSOjs7O+DPQJYUQhH/KbmbCyGEEEKI55JlHEII8e/k4OBAnTp1OH36NA8ePNC6nmqCSC1btqR58+YsXryYLl26sGrVKgIDA5k9eza//PILVlZWJfarp6cnwSfxX5EZUEIIIYQQ4rk0yziioqLYvHkzrVu3LlFN6dllHECJZRwNGjTA0tKSiIgIateujZWVlVZOqWeXcQghhPi/0dXVxc/PDxMTEzZt2kSfPn3IzMzk8ePHnD17lpMnTxIeHs7UqVM5cuQIBgYGBAYGau1DpVLJQID4x0gASgghhBBCvJAs4xBCiH8nd3d3/P39iYmJISAggM2bNxMXF8ft27dxc3MjIiICXV1d6tSpo3ym+CCDBJ/EP0lHLVlOhRBCCCHEC6hUKhYsWMCiRYuIjY3FxsZGeU/TUUlKSmLBggXs3r2b3NxczMzMmDJlCqGhoa/xyIUQQkRGRvL1118DRQMAbdq0oUuXLtja2mptp1arJYm4eKkkACWEEEIIIf7SwYMHGTVqFAMGDHjuMg5fX19ZxiGEEG+YhIQEEhISCA4OxsvLS3ldpVKhVqtlFqp4ZSQAJYQQQggh/tLDhw+ZNGkSSUlJTJ06tcQyjq+//hpvb2+tzzybK0oIIcTrpykoIbOdxKsmASghhBBCCPG3yDIOIYT4d1Kr1Up1USFeFwlACSGEEEKIv0WWcQghhBDivyUBKCGEEEII8V+RZRxCCCGE+LskACWEEEIIIf42WcYhhBBCiP+GBKCEEEIIIYQQQgghxEslQ1dCCCGEEEIIIYQQ4qWSAJQQQgghhBBCCCGEeKkkACWEEEIIIYQQQgghXioJQAkhhBBCCCGEEEKIl0oCUEIIIYQQQgghhBDipZIAlBBCCCGEEEIIIYR4qSQAJYQQQgghhBBCCCFeKglACSGEEOKtdfToUTw8PIiMjHzdh/J/cunSJUaOHEnTpk3x8fEhODiYVq1a8dlnn3HhwgVlu/T0dObNm8fRo0f/z985b948YmNj/8/7EUIIIcTbQQJQQgghhBD/Yrt376Zt27YcO3aMZs2aMXHiRPr374+vry979uzh8OHDyrbp6enMnz+fY8eO/Z+/d/78+RKAEkIIIcTfpv+6D0AIIYQQQvz3vvnmG4yMjIiKisLOzk7rPZVKxZMnT17PgQkhhBBCFCMzoIQQQggh/objx4/z/vvvU6tWLWrUqEFERARr164tsd2ZM2f49NNPCQsLw9fXFz8/Pzp37szOnTtLbPvpp5/i4eHB06dPmTRpErVr18bHx4fOnTtz+vTpv3VcN27cwMXFpUTwCUBXVxdLS0ugaLlh48aNgaLZSx4eHnh4eNCoUSNl+1WrVtGnTx/q169P9erVqVevHh9//DG3b99Wtrl9+zYeHh4AxMTEKPvRvKZ5f968eSWOZ968eXh4eGjt7969e4wdO5bQ0FCqV69O7dq16dy5MzExMX/r9wshhBDi30FmQAkhhBBC/IVdu3YxdOhQrKyseP/99zEzM2Pz5s1MmDCB27dvM2LECGXbnTt3cu3aNZo3b46DgwNPnjwhJiaGoUOHMmvWLFq1alVi/3379sXS0pIhQ4bw5MkTli1bxoABA4iLi8PMzOyFx1a5cmX++OMPTp48ib+//3O3c3NzY+zYsUyfPp2mTZvStGlTAExNTZVtli5dSs2aNenRowfly5fnypUrREVFceTIETZu3IiFhQWWlpbMnDmTMWPGEBAQQMeOHf/TP6eioKCA999/n+TkZLp27YqzszMZGRlcvnyZ+Ph4IiIi/ut9CyGEEOLNIgEoIYQQQogXKCws5IsvvsDExIS1a9dia2sLQNeuXenZsyeLFi0iIiICZ2dnAD744ANGjRqltY8ePXrQpk0bfvjhh1IDUN7e3kyePFn5t5ubG8OHD2fTpk107tz5hcf34YcfMnz4cLp06YK7uzt+fn7UqFGDkJAQKlWqpGxnZWVFkyZNmD59Oh4eHrRu3brEvjZu3IiJiYnWa40bN6Z3795ERUXRv39/TExMaN26NWPGjMHR0bHU/fxdf/zxB9evX+fjjz+mf//+//V+hBBCCPHmkyV4QgghhBAvcP78ee7evUu7du2U4BOAoaEh/fr1Q6VSERcXp7xePICTnZ3N48ePyc7OJiQkhKtXr5KRkVHiO3r37q3175CQEABu3rz5l8fXvHlzfv75Z8LCwrh//z6rV69m/PjxNG7cmA8++IDU1NS//Vs1x65SqXj69Cmpqal4eHhQtmxZzpw587f383eVLVsWKFoe+OjRo398/0IIIYR4c8gMKCGEEEKIF9DkK6pSpUqJ96pWrQpAUlKS8tqjR4/49ttviYuLKzWokp6eXmJZnaOjo9a/LSwsAP52AvGAgAACAgJQq9XcuHGDo0eP8ssvv7Br1y5Gjx5NZGTk39rP4cOHWbBgAadPnyY3N1frvbS0tL+1j/+Eg4MDgwYNYtGiRdSrVw8vLy9CQkJo3rw5NWrU+Me/TwghhBCvjwSghBBCCCH+IWq1mj59+nD16lV69uxJ9erVKVu2LHp6eqxbt45NmzahUqlKfE5PT++5+/tP6Ojo4OLigouLCxEREbRo0YIDBw5w//79UpOUF3fmzBn69u1L5cqVGTVqFJUqVaJMmTLo6OgwYsSIv30sOjo6z32voKCgxGsjRoygffv27Nmzh/j4eKKiooiMjKRfv36MHj36b32nEEIIId58EoASQgghhHgBTR6lP/74o8R7mtc0M5guX77MpUuXGDJkCMOGDdPatrSKeS+TkZERXl5eJCUlkZycjJ2d3QuDQ5s2baKwsJDFixdrzcjKysoiPT39b39vuXLlgNJnTBWvfleco6MjPXr0oEePHuTm5tK3b1+WLFlCnz59qFChwt/+biGEEEK8uSQHlBBCCCHEC1SrVo2KFSsSHR3Nw4cPldfz8/OJjIxER0eHxo0bA6CrW/Ro9exsoStXrrBz586Xcnz79u0rdXZSamoqJ0+eRF9fHycnJ+DPHE+lBYeeNwvrxx9/LHXWlomJSalLBM3MzLC2tubIkSNax5WUlERsbKzWtk+fPiU/P1/rNSMjI1xdXZ97nEIIIYT4d5IZUEIIIYR46x0+fLhEziMoysXUpUsXJk6cyNChQ2nfvj0dO3bE1NSUrVu3kpCQwKBBg5QKeG5ublStWpUlS5aQk5ODi4sL169fZ/Xq1bi7u3P+/Pl//Ng/+ugjLC0tCQ0Nxc3NDX19fZKSkli/fj0pKSkMGTKE8uXLK7/HycmJzZs34+joiJWVFcbGxjRq1IgmTZqwfPly+vfvT6dOnTAwMODgwYNcvnxZyUlVXM2aNTl8+DCLFi2iYsWK6Ojo0KJFCwC6devGt99+S79+/WjSpAkPHjzgt99+o2rVqpw9e1bZx9GjR5k4cSLNmjXDxcUFU1NTzp07R1RUFL6+vkogSgghhBD/fjrq/zS5gBBCCCHE/4ijR4/Ss2fP577v4uLCtm3bADh27Bg//PADp0+fJj8/Hzc3N7p160aHDh20PnPnzh2++uorjh07RnZ2NlWrVmXgwIFcunSJ+fPnExcXpyzr+/TTT4mJieHy5cslvtvDw4OIiAhmzJjxwt+wdetW9u3bx5kzZ3jw4AFZWVmUK1cOb29vOnToQFhYmNb2Z86cYdq0aVy6dIns7GwcHBzYtWsXALGxsSxYsIBr165hZGREnTp1GD16NN27d8fBwYGVK1cq+7lx4wZTpkwhISGBzMxMAOV3FBQU8M0337BhwwbS0tKoUqUKH374IefPn9f6GyQlJbF48WKOHz9OcnIyKpUKe3t7mjdvTp8+fZQqeUIIIYT495MAlBBCCCGEEEIIIYR4qSQHlBBCCCGEEEIIIYR4qSQAJYQQQgghhBBCCCFeKglACSGEEEIIIYQQQoiXSgJQQgghhBBCCCGEEOKlkgCUEEIIIYQQQgghhHipJAAlhBBCCCGEEEIIIV4qCUAJIYQQQgghhBBCiJdKAlBCCCGEEEIIIYQQ4qWSAJQQQgghhBBCCCGEeKkkACWEEEIIIYQQQgghXioJQAkhhBBCCCGEEEKIl0oCUEIIIYQQQgghhBDipZIAlBBCCCGEEEIIIYR4qf4fWkk+LnfQgpIAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Employment Length by Loan Status\n",
        "plt.figure(figsize=(14,6))\n",
        "\n",
        "# Plot employment length\n",
        "sns.countplot(\n",
        "    data=df,\n",
        "    x=\"emp_length\",\n",
        "    hue=\"loan_status\"\n",
        ")\n",
        "\n",
        "plt.title(\n",
        "    \"Employment Length Across Loan Status\",\n",
        "    fontsize=18,\n",
        "    fontweight=\"bold\"\n",
        ")\n",
        "\n",
        "plt.xlabel(\"Employment Length\")\n",
        "plt.ylabel(\"Number of Applicants\")\n",
        "\n",
        "plt.xticks(rotation=45)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "ZWmlow4u023K",
        "outputId": "068b310c-74e8-4cfb-de35-1a204fe66200"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "bias_summary = pd.crosstab(\n",
        "    df[\"homeownership\"],\n",
        "    df[\"loan_status\"],\n",
        "    normalize=\"index\"\n",
        ") * 100\n",
        "\n",
        "bias_summary.round(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "OOMwsYQZ09dM",
        "outputId": "aaf3f3ea-67c9-4bb5-ff17-0d76f708cfdb"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "loan_status    Charged Off  Current  Fully Paid  In Grace Period  Late (16-30 days)  Late (31-120 days)\n",
              "homeownership                                                                                          \n",
              "MORTGAGE              0.08    93.88        4.70             0.58               0.33                0.42\n",
              "OWN                   0.07    92.31        5.17             0.89               0.59                0.96\n",
              "RENT                  0.05    94.09        3.94             0.70               0.36                0.86"
            ],
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              "\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>loan_status</th>\n",
              "      <th>Charged Off</th>\n",
              "      <th>Current</th>\n",
              "      <th>Fully Paid</th>\n",
              "      <th>In Grace Period</th>\n",
              "      <th>Late (16-30 days)</th>\n",
              "      <th>Late (31-120 days)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>homeownership</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MORTGAGE</th>\n",
              "      <td>0.08</td>\n",
              "      <td>93.88</td>\n",
              "      <td>4.70</td>\n",
              "      <td>0.58</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OWN</th>\n",
              "      <td>0.07</td>\n",
              "      <td>92.31</td>\n",
              "      <td>5.17</td>\n",
              "      <td>0.89</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RENT</th>\n",
              "      <td>0.05</td>\n",
              "      <td>94.09</td>\n",
              "      <td>3.94</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.36</td>\n",
              "      <td>0.86</td>\n",
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              "            style=\"display:none;\">\n",
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              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4e20285b-a621-4d12-ae51-9c328b0567e8 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4e20285b-a621-4d12-ae51-9c328b0567e8');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"bias_summary\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"homeownership\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"MORTGAGE\",\n          \"OWN\",\n          \"RENT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Charged Off\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.015275252316519466,\n        \"min\": 0.05,\n        \"max\": 0.08,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.08,\n          0.07,\n          0.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Current\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9727452561350947,\n        \"min\": 92.31,\n        \"max\": 94.09,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          93.88,\n          92.31,\n          94.09\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fully Paid\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6206716791777545,\n        \"min\": 3.94,\n        \"max\": 5.17,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4.7,\n          5.17,\n          3.94\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"In Grace Period\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15631165450257808,\n        \"min\": 0.58,\n        \"max\": 0.89,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.58,\n          0.89,\n          0.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Late (16-30 days)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1422439219556791,\n        \"min\": 0.33,\n        \"max\": 0.59,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.33,\n          0.59,\n          0.36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Late (31-120 days)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.28728615235220323,\n        \"min\": 0.42,\n        \"max\": 0.96,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.42,\n          0.96,\n          0.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Preprocessing Pipeline**"
      ],
      "metadata": {
        "id": "JtYA6FHfbzBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define Features and Target\n",
        "X = df.drop(\"loan_status\", axis=1)\n",
        "\n",
        "y = df[\"loan_status\"]\n",
        "\n",
        "print(f\"Feature Matrix : {X.shape}\")\n",
        "print(f\"Target Vector  : {y.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVXqXa1Eb0ze",
        "outputId": "a7253856-69cb-4543-8610-ecfcb5784a6e"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Feature Matrix : (10000, 50)\n",
            "Target Vector  : (10000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Encode Target Variable\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "y = label_encoder.fit_transform(y)\n",
        "\n",
        "target_mapping = pd.DataFrame({\n",
        "    \"Loan Status\": label_encoder.classes_,\n",
        "    \"Encoded Value\": label_encoder.transform(label_encoder.classes_)\n",
        "})\n",
        "\n",
        "target_mapping"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "TbGX0bAKb8J1",
        "outputId": "be8319e8-ab0d-473b-ff34-a238a9698b54"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Loan Status  Encoded Value\n",
              "0         Charged Off              0\n",
              "1             Current              1\n",
              "2          Fully Paid              2\n",
              "3     In Grace Period              3\n",
              "4   Late (16-30 days)              4\n",
              "5  Late (31-120 days)              5"
            ],
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              "\n",
              "  <div id=\"df-c57e1a7e-62a6-433b-866f-590f6b35664c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Loan Status</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Charged Off</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Current</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fully Paid</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>In Grace Period</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Late (16-30 days)</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Late (31-120 days)</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c57e1a7e-62a6-433b-866f-590f6b35664c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-c57e1a7e-62a6-433b-866f-590f6b35664c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-c57e1a7e-62a6-433b-866f-590f6b35664c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_e1c18895-706f-4ee1-b748-c0fd73edf06e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('target_mapping')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e1c18895-706f-4ee1-b748-c0fd73edf06e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('target_mapping');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "target_mapping",
              "summary": "{\n  \"name\": \"target_mapping\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"Loan Status\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"Charged Off\",\n          \"Current\",\n          \"Late (31-120 days)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Encoded Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 0,\n        \"max\": 5,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0,\n          1,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Train-Test Split\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X,\n",
        "    y,\n",
        "    test_size=0.20,\n",
        "    random_state=RANDOM_STATE,\n",
        "    stratify=y\n",
        ")\n",
        "\n",
        "print(f\"Training Features : {X_train.shape}\")\n",
        "print(f\"Testing Features  : {X_test.shape}\")\n",
        "print(f\"Training Labels   : {y_train.shape}\")\n",
        "print(f\"Testing Labels    : {y_test.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wSj_jLyRcAR2",
        "outputId": "9fe0277e-287a-4d3e-fbfb-828f598b2d82"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Features : (8000, 50)\n",
            "Testing Features  : (2000, 50)\n",
            "Training Labels   : (8000,)\n",
            "Testing Labels    : (2000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Identify Feature Types\n",
        "numerical_features = X_train.select_dtypes(\n",
        "    include=np.number\n",
        ").columns.tolist()\n",
        "\n",
        "categorical_features = X_train.select_dtypes(\n",
        "    exclude=np.number\n",
        ").columns.tolist()\n",
        "\n",
        "feature_summary = pd.DataFrame({\n",
        "    \"Feature Type\": [\n",
        "        \"Numerical\",\n",
        "        \"Categorical\"\n",
        "    ],\n",
        "    \"Count\": [\n",
        "        len(numerical_features),\n",
        "        len(categorical_features)\n",
        "    ]\n",
        "})\n",
        "\n",
        "feature_summary"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "nqAMTDGCcBd3",
        "outputId": "4299f05f-6db8-408f-c2ed-7df382e9ab8e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Feature Type  Count\n",
              "0    Numerical     38\n",
              "1  Categorical     12"
            ],
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              "\n",
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              "      <th>0</th>\n",
              "      <td>Numerical</td>\n",
              "      <td>38</td>\n",
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              "      <th>1</th>\n",
              "      <td>Categorical</td>\n",
              "      <td>12</td>\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
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              "      [theme=dark] .colab-df-generate {\n",
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              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('feature_summary')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('feature_summary');\n",
              "      }\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_summary",
              "summary": "{\n  \"name\": \"feature_summary\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Feature Type\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Categorical\",\n          \"Numerical\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 12,\n        \"max\": 38,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          12,\n          38\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Numerical Pipeline\n",
        "numeric_transformer = Pipeline(\n",
        "    steps=[\n",
        "        (\"imputer\", SimpleImputer(strategy=\"median\")),\n",
        "        (\"scaler\", StandardScaler())\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "mk2tuG3acD-G"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Categorical Pipeline\n",
        "categorical_transformer = Pipeline(\n",
        "    steps=[\n",
        "        (\"imputer\", SimpleImputer(strategy=\"most_frequent\")),\n",
        "        (\"encoder\", OneHotEncoder(handle_unknown=\"ignore\"))\n",
        "    ]\n",
        ")"
      ],
      "metadata": {
        "id": "oEkUbO5mcGCl"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create Preprocessing Pipeline\n",
        "preprocessor = ColumnTransformer(\n",
        "    transformers=[\n",
        "        (\"num\", numeric_transformer, numerical_features),\n",
        "        (\"cat\", categorical_transformer, categorical_features)\n",
        "    ]\n",
        ")\n",
        "\n",
        "preprocessor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "qF7VZ1wRcLsM",
        "outputId": "d510ab34-9f43-46d2-ef38-9e4294f2717a"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ColumnTransformer(transformers=[('num',\n",
              "                                 Pipeline(steps=[('imputer',\n",
              "                                                  SimpleImputer(strategy='median')),\n",
              "                                                 ('scaler', StandardScaler())]),\n",
              "                                 ['Unnamed: 0', 'emp_length', 'annual_income',\n",
              "                                  'debt_to_income', 'annual_income_joint',\n",
              "                                  'debt_to_income_joint', 'delinq_2y',\n",
              "                                  'months_since_last_delinq',\n",
              "                                  'earliest_credit_line', 'inquiries_last_12m',\n",
              "                                  'total_credit_lines', 'open_credit...\n",
              "                                  'num_cc_carrying_balance', ...]),\n",
              "                                ('cat',\n",
              "                                 Pipeline(steps=[('imputer',\n",
              "                                                  SimpleImputer(strategy='most_frequent')),\n",
              "                                                 ('encoder',\n",
              "                                                  OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                 ['emp_title', 'state', 'homeownership',\n",
              "                                  'verified_income',\n",
              "                                  'verification_income_joint', 'loan_purpose',\n",
              "                                  'application_type', 'grade', 'sub_grade',\n",
              "                                  'issue_month', 'initial_listing_status',\n",
              "                                  'disbursement_method'])])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
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              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
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              "  overflow: hidden;\n",
              "  padding: 0;\n",
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              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
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              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;,\n",
              "                                  &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;,\n",
              "                                  &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;,\n",
              "                                  &#x27;months_since_last_delinq&#x27;,\n",
              "                                  &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;,\n",
              "                                  &#x27;total_credit_lines&#x27;, &#x27;open_credit...\n",
              "                                  &#x27;num_cc_carrying_balance&#x27;, ...]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;encoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;,\n",
              "                                  &#x27;verified_income&#x27;,\n",
              "                                  &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;,\n",
              "                                  &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;,\n",
              "                                  &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;,\n",
              "                                  &#x27;disbursement_method&#x27;])])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for ColumnTransformer</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;,\n",
              "                                  &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;,\n",
              "                                  &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;,\n",
              "                                  &#x27;months_since_last_delinq&#x27;,\n",
              "                                  &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;,\n",
              "                                  &#x27;total_credit_lines&#x27;, &#x27;open_credit...\n",
              "                                  &#x27;num_cc_carrying_balance&#x27;, ...]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;encoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;,\n",
              "                                  &#x27;verified_income&#x27;,\n",
              "                                  &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;,\n",
              "                                  &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;,\n",
              "                                  &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;,\n",
              "                                  &#x27;disbursement_method&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;, &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;, &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;, &#x27;months_since_last_delinq&#x27;, &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;, &#x27;total_credit_lines&#x27;, &#x27;open_credit_lines&#x27;, &#x27;total_credit_limit&#x27;, &#x27;total_credit_utilized&#x27;, &#x27;num_collections_last_12m&#x27;, &#x27;num_historical_failed_to_pay&#x27;, &#x27;months_since_90d_late&#x27;, &#x27;current_accounts_delinq&#x27;, &#x27;total_collection_amount_ever&#x27;, &#x27;current_installment_accounts&#x27;, &#x27;accounts_opened_24m&#x27;, &#x27;months_since_last_credit_inquiry&#x27;, &#x27;num_satisfactory_accounts&#x27;, &#x27;num_accounts_120d_past_due&#x27;, &#x27;num_accounts_30d_past_due&#x27;, &#x27;num_active_debit_accounts&#x27;, &#x27;total_debit_limit&#x27;, &#x27;num_total_cc_accounts&#x27;, &#x27;num_open_cc_accounts&#x27;, &#x27;num_cc_carrying_balance&#x27;, &#x27;num_mort_accounts&#x27;, &#x27;account_never_delinq_percent&#x27;, &#x27;tax_liens&#x27;, &#x27;public_record_bankrupt&#x27;, &#x27;loan_amount&#x27;, &#x27;term&#x27;, &#x27;interest_rate&#x27;, &#x27;installment&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;, &#x27;verified_income&#x27;, &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;, &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;, &#x27;disbursement_method&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Preview Processed Data\n",
        "X_train_processed = preprocessor.fit_transform(X_train)\n",
        "\n",
        "X_test_processed = preprocessor.transform(X_test)\n",
        "\n",
        "print(f\"Processed Training Data : {X_train_processed.shape}\")\n",
        "print(f\"Processed Testing Data  : {X_test_processed.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3DrNI-AcMkQ",
        "outputId": "0becf3d5-4959-4dd4-ea3d-4aa53a7ad1ef"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processed Training Data : (8000, 4086)\n",
            "Processed Testing Data  : (2000, 4086)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Development**"
      ],
      "metadata": {
        "id": "cplDAagbcPxK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Logistic Regression"
      ],
      "metadata": {
        "id": "x75e1x-JcW64"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Build Model Pipeline\n",
        "logistic_pipeline = Pipeline(\n",
        "    steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\n",
        "            \"classifier\",\n",
        "            LogisticRegression(\n",
        "                random_state=RANDOM_STATE,\n",
        "                max_iter=5000,\n",
        "                class_weight=\"balanced\"\n",
        "            )\n",
        "        )\n",
        "    ]\n",
        ")\n",
        "\n",
        "logistic_pipeline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "sB1c7XX4cTFc",
        "outputId": "c908ac54-bb28-47bc-95bc-c28a3838c0c4"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='median')),\n",
              "                                                                  ('scaler',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['Unnamed: 0', 'emp_length',\n",
              "                                                   'annual_income',\n",
              "                                                   'debt_to_income',\n",
              "                                                   'annual_income_joint',\n",
              "                                                   'debt_to_income_joint',\n",
              "                                                   'delinq_2y',\n",
              "                                                   'months_since_last_delinq',\n",
              "                                                   'earliest_credit_line',\n",
              "                                                   'inquiries_last_12m',\n",
              "                                                   '...\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('encoder',\n",
              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                                  ['emp_title', 'state',\n",
              "                                                   'homeownership',\n",
              "                                                   'verified_income',\n",
              "                                                   'verification_income_joint',\n",
              "                                                   'loan_purpose',\n",
              "                                                   'application_type', 'grade',\n",
              "                                                   'sub_grade', 'issue_month',\n",
              "                                                   'initial_listing_status',\n",
              "                                                   'disbursement_method'])])),\n",
              "                ('classifier',\n",
              "                 LogisticRegression(class_weight='balanced', max_iter=5000,\n",
              "                                    random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;encoder&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=5000,\n",
              "                                    random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;encoder&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=5000,\n",
              "                                    random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;,\n",
              "                                  &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;,\n",
              "                                  &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;,\n",
              "                                  &#x27;months_since_last_delinq&#x27;,\n",
              "                                  &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;,\n",
              "                                  &#x27;total_credit_lines&#x27;, &#x27;open_credit...\n",
              "                                  &#x27;num_cc_carrying_balance&#x27;, ...]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;encoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;,\n",
              "                                  &#x27;verified_income&#x27;,\n",
              "                                  &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;,\n",
              "                                  &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;,\n",
              "                                  &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;,\n",
              "                                  &#x27;disbursement_method&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;, &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;, &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;, &#x27;months_since_last_delinq&#x27;, &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;, &#x27;total_credit_lines&#x27;, &#x27;open_credit_lines&#x27;, &#x27;total_credit_limit&#x27;, &#x27;total_credit_utilized&#x27;, &#x27;num_collections_last_12m&#x27;, &#x27;num_historical_failed_to_pay&#x27;, &#x27;months_since_90d_late&#x27;, &#x27;current_accounts_delinq&#x27;, &#x27;total_collection_amount_ever&#x27;, &#x27;current_installment_accounts&#x27;, &#x27;accounts_opened_24m&#x27;, &#x27;months_since_last_credit_inquiry&#x27;, &#x27;num_satisfactory_accounts&#x27;, &#x27;num_accounts_120d_past_due&#x27;, &#x27;num_accounts_30d_past_due&#x27;, &#x27;num_active_debit_accounts&#x27;, &#x27;total_debit_limit&#x27;, &#x27;num_total_cc_accounts&#x27;, &#x27;num_open_cc_accounts&#x27;, &#x27;num_cc_carrying_balance&#x27;, &#x27;num_mort_accounts&#x27;, &#x27;account_never_delinq_percent&#x27;, &#x27;tax_liens&#x27;, &#x27;public_record_bankrupt&#x27;, &#x27;loan_amount&#x27;, &#x27;term&#x27;, &#x27;interest_rate&#x27;, &#x27;installment&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;, &#x27;verified_income&#x27;, &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;, &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;, &#x27;disbursement_method&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content \"><pre>LogisticRegression(class_weight=&#x27;balanced&#x27;, max_iter=5000, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Hyperparameter tuning"
      ],
      "metadata": {
        "id": "Mey-CHuEce65"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameter Grid\n",
        "param_grid_lr = {\n",
        "    \"classifier__C\": [0.01, 0.1, 1, 10, 100],\n",
        "    \"classifier__solver\": [\"lbfgs\", \"saga\"],\n",
        "    \"classifier__penalty\": [\"l2\"]\n",
        "}\n",
        "\n",
        "param_grid_lr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qVFKDD87ca8G",
        "outputId": "c373fd51-ef99-4665-c6f1-2facbccf5d4b"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'classifier__C': [0.01, 0.1, 1, 10, 100],\n",
              " 'classifier__solver': ['lbfgs', 'saga'],\n",
              " 'classifier__penalty': ['l2']}"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Grid Search\n",
        "grid_lr = GridSearchCV(\n",
        "    estimator=logistic_pipeline,\n",
        "    param_grid=param_grid_lr,\n",
        "    cv=5,\n",
        "    scoring=\"f1_weighted\",\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "grid_lr.fit(X_train, y_train)\n",
        "\n",
        "lr_training_time = time.time() - start_time"
      ],
      "metadata": {
        "id": "eH1yOiRQcnBR"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Hyperparameters\n",
        "print(\"Best Parameters\")\n",
        "print(grid_lr.best_params_)\n",
        "\n",
        "print(\"\\nBest Cross Validation Score\")\n",
        "print(f\"{grid_lr.best_score_:.4f}\")\n",
        "\n",
        "print(f\"\\nTraining Time : {lr_training_time:.2f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OWq4PmvAcp3T",
        "outputId": "02d5a4b2-c5da-409f-8507-68ece3189473"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters\n",
            "{'classifier__C': 100, 'classifier__penalty': 'l2', 'classifier__solver': 'lbfgs'}\n",
            "\n",
            "Best Cross Validation Score\n",
            "0.8528\n",
            "\n",
            "Training Time : 904.53 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Model\n",
        "best_lr = grid_lr.best_estimator_\n",
        "\n",
        "best_lr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "3HNI2mpWcsJN",
        "outputId": "83dd252b-7c7c-4c7b-ef63-e5154cdba510"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='median')),\n",
              "                                                                  ('scaler',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['Unnamed: 0', 'emp_length',\n",
              "                                                   'annual_income',\n",
              "                                                   'debt_to_income',\n",
              "                                                   'annual_income_joint',\n",
              "                                                   'debt_to_income_joint',\n",
              "                                                   'delinq_2y',\n",
              "                                                   'months_since_last_delinq',\n",
              "                                                   'earliest_credit_line',\n",
              "                                                   'inquiries_last_12m',\n",
              "                                                   '...\n",
              "                                                                  ('encoder',\n",
              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                                  ['emp_title', 'state',\n",
              "                                                   'homeownership',\n",
              "                                                   'verified_income',\n",
              "                                                   'verification_income_joint',\n",
              "                                                   'loan_purpose',\n",
              "                                                   'application_type', 'grade',\n",
              "                                                   'sub_grade', 'issue_month',\n",
              "                                                   'initial_listing_status',\n",
              "                                                   'disbursement_method'])])),\n",
              "                ('classifier',\n",
              "                 LogisticRegression(C=100, class_weight='balanced',\n",
              "                                    max_iter=5000, random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-3 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-3 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-3 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-3 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-3 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-3 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-3 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-3 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                  (&#x27;encoder&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 LogisticRegression(C=100, class_weight=&#x27;balanced&#x27;,\n",
              "                                    max_iter=5000, random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                  (&#x27;encoder&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 LogisticRegression(C=100, class_weight=&#x27;balanced&#x27;,\n",
              "                                    max_iter=5000, random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;,\n",
              "                                  &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;,\n",
              "                                  &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;,\n",
              "                                  &#x27;months_since_last_delinq&#x27;,\n",
              "                                  &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;,\n",
              "                                  &#x27;total_credit_lines&#x27;, &#x27;open_credit...\n",
              "                                  &#x27;num_cc_carrying_balance&#x27;, ...]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;encoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;,\n",
              "                                  &#x27;verified_income&#x27;,\n",
              "                                  &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;,\n",
              "                                  &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;,\n",
              "                                  &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;,\n",
              "                                  &#x27;disbursement_method&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;, &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;, &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;, &#x27;months_since_last_delinq&#x27;, &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;, &#x27;total_credit_lines&#x27;, &#x27;open_credit_lines&#x27;, &#x27;total_credit_limit&#x27;, &#x27;total_credit_utilized&#x27;, &#x27;num_collections_last_12m&#x27;, &#x27;num_historical_failed_to_pay&#x27;, &#x27;months_since_90d_late&#x27;, &#x27;current_accounts_delinq&#x27;, &#x27;total_collection_amount_ever&#x27;, &#x27;current_installment_accounts&#x27;, &#x27;accounts_opened_24m&#x27;, &#x27;months_since_last_credit_inquiry&#x27;, &#x27;num_satisfactory_accounts&#x27;, &#x27;num_accounts_120d_past_due&#x27;, &#x27;num_accounts_30d_past_due&#x27;, &#x27;num_active_debit_accounts&#x27;, &#x27;total_debit_limit&#x27;, &#x27;num_total_cc_accounts&#x27;, &#x27;num_open_cc_accounts&#x27;, &#x27;num_cc_carrying_balance&#x27;, &#x27;num_mort_accounts&#x27;, &#x27;account_never_delinq_percent&#x27;, &#x27;tax_liens&#x27;, &#x27;public_record_bankrupt&#x27;, &#x27;loan_amount&#x27;, &#x27;term&#x27;, &#x27;interest_rate&#x27;, &#x27;installment&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;, &#x27;verified_income&#x27;, &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;, &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;, &#x27;disbursement_method&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>LogisticRegression</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(C=100, class_weight=&#x27;balanced&#x27;, max_iter=5000,\n",
              "                   random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction\n",
        "start_time = time.time()\n",
        "\n",
        "y_pred_lr = best_lr.predict(X_test)\n",
        "\n",
        "y_prob_lr = best_lr.predict_proba(X_test)\n",
        "\n",
        "lr_prediction_time = time.time() - start_time\n",
        "\n",
        "print(f\"Prediction Time : {lr_prediction_time:.4f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "42BVf7VccvAB",
        "outputId": "4087dd76-65fb-49a2-d310-1f147fb95c24"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction Time : 0.0575 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance Metrics\n",
        "lr_accuracy = accuracy_score(y_test, y_pred_lr)\n",
        "\n",
        "lr_precision = precision_score(\n",
        "    y_test,\n",
        "    y_pred_lr,\n",
        "    average=\"weighted\"\n",
        ")\n",
        "\n",
        "lr_recall = recall_score(\n",
        "    y_test,\n",
        "    y_pred_lr,\n",
        "    average=\"weighted\"\n",
        ")\n",
        "\n",
        "lr_f1 = f1_score(\n",
        "    y_test,\n",
        "    y_pred_lr,\n",
        "    average=\"weighted\"\n",
        ")\n",
        "\n",
        "lr_auc = roc_auc_score(\n",
        "    y_test,\n",
        "    y_prob_lr,\n",
        "    multi_class=\"ovr\"\n",
        ")\n",
        "\n",
        "lr_results = pd.DataFrame({\n",
        "    \"Metric\": [\n",
        "        \"Accuracy\",\n",
        "        \"Precision\",\n",
        "        \"Recall\",\n",
        "        \"F1-Score\",\n",
        "        \"ROC-AUC\"\n",
        "    ],\n",
        "    \"Score\": [\n",
        "        lr_accuracy,\n",
        "        lr_precision,\n",
        "        lr_recall,\n",
        "        lr_f1,\n",
        "        lr_auc\n",
        "    ]\n",
        "})\n",
        "\n",
        "lr_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ULb4Kx3AcxjA",
        "outputId": "f36c0fa6-043b-4755-8b5c-22167ba4449f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Metric  Score\n",
              "0   Accuracy   0.82\n",
              "1  Precision   0.88\n",
              "2     Recall   0.82\n",
              "3   F1-Score   0.85\n",
              "4    ROC-AUC   0.52"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-09443709-183f-4635-af51-0446a5b5c4ce\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Metric</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Precision</td>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Recall</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F1-Score</td>\n",
              "      <td>0.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ROC-AUC</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-09443709-183f-4635-af51-0446a5b5c4ce')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-09443709-183f-4635-af51-0446a5b5c4ce button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-09443709-183f-4635-af51-0446a5b5c4ce');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_447abc3a-6e4f-4a8f-8f78-821c7e6cb2a1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('lr_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_447abc3a-6e4f-4a8f-8f78-821c7e6cb2a1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('lr_results');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lr_results",
              "summary": "{\n  \"name\": \"lr_results\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Metric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Precision\",\n          \"ROC-AUC\",\n          \"Recall\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14642006940699206,\n        \"min\": 0.5217893815604477,\n        \"max\": 0.8821887190822518,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8821887190822518,\n          0.5217893815604477,\n          0.8225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report\n",
        "print(\n",
        "    classification_report(\n",
        "        y_test,\n",
        "        y_pred_lr,\n",
        "        target_names=label_encoder.classes_\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9CIPUCKWc0SA",
        "outputId": "4bbdb6de-ab36-49fe-8551-1f9b5667e63a"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    precision    recall  f1-score   support\n",
            "\n",
            "       Charged Off       0.00      0.00      0.00         1\n",
            "           Current       0.94      0.87      0.91      1875\n",
            "        Fully Paid       0.03      0.07      0.04        90\n",
            "   In Grace Period       0.04      0.08      0.05        13\n",
            " Late (16-30 days)       0.00      0.00      0.00         8\n",
            "Late (31-120 days)       0.00      0.00      0.00        13\n",
            "\n",
            "          accuracy                           0.82      2000\n",
            "         macro avg       0.17      0.17      0.17      2000\n",
            "      weighted avg       0.88      0.82      0.85      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred_lr)\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "\n",
        "ax = sns.heatmap(\n",
        "    cm,\n",
        "    annot=True,\n",
        "    fmt=\"d\",\n",
        "    cmap=\"Blues\",\n",
        "    linewidths=0.5,\n",
        "    cbar=True\n",
        ")\n",
        "\n",
        "\n",
        "plt.title(\"Logistic Regression Confusion Matrix\", fontsize=18)\n",
        "plt.xlabel(\"Predicted Class\")\n",
        "plt.ylabel(\"Actual Class\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "HS2wcT9tc5KM",
        "outputId": "b98fcdaa-982c-4905-dfc9-653d655a207a"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cross Validation\n",
        "cv_scores_lr = cross_val_score(\n",
        "    best_lr,\n",
        "    X_train,\n",
        "    y_train,\n",
        "    cv=5,\n",
        "    scoring=\"f1_weighted\"\n",
        ")\n",
        "\n",
        "cv_results_lr = pd.DataFrame({\n",
        "    \"Fold\": range(1, 6),\n",
        "    \"F1-Score\": cv_scores_lr\n",
        "})\n",
        "\n",
        "cv_results_lr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "7qipi8KGc-K7",
        "outputId": "c3a4f640-8ef1-4722-cdbe-2c393b7be8fc"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Fold  F1-Score\n",
              "0     1      0.86\n",
              "1     2      0.85\n",
              "2     3      0.85\n",
              "3     4      0.86\n",
              "4     5      0.85"
            ],
            "text/html": [
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              "\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fold</th>\n",
              "      <th>F1-Score</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
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              "      <td>0.86</td>\n",
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              "      <td>0.85</td>\n",
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              "      <td>0.85</td>\n",
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              "      <td>0.86</td>\n",
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              "      <td>0.85</td>\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6fc4765f-2dff-462a-9f53-93f1955b4f6a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-6fc4765f-2dff-462a-9f53-93f1955b4f6a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_ce1f2322-74c7-450a-990b-a584e0cf41c5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cv_results_lr')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ce1f2322-74c7-450a-990b-a584e0cf41c5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('cv_results_lr');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cv_results_lr",
              "summary": "{\n  \"name\": \"cv_results_lr\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Fold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.004315086329084703,\n        \"min\": 0.846300709000418,\n        \"max\": 0.8576560670799048,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.846300709000418,\n          0.8528190440929676,\n          0.8522910110026254\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Average Cross Validation Score\n",
        "print(f\"Mean Score : {cv_scores_lr.mean():.4f}\")\n",
        "print(f\"Standard Deviation : {cv_scores_lr.std():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XVxqNqLrc_Du",
        "outputId": "a967aa62-a1f5-4a17-d0e7-4e5a63e57bda"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Score : 0.8530\n",
            "Standard Deviation : 0.0039\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Overfitting Analysis\n",
        "train_pred_lr = best_lr.predict(X_train)\n",
        "\n",
        "train_accuracy_lr = accuracy_score(\n",
        "    y_train,\n",
        "    train_pred_lr\n",
        ")\n",
        "\n",
        "test_accuracy_lr = accuracy_score(\n",
        "    y_test,\n",
        "    y_pred_lr\n",
        ")\n",
        "\n",
        "overfitting_lr = pd.DataFrame({\n",
        "    \"Dataset\": [\n",
        "        \"Training\",\n",
        "        \"Testing\"\n",
        "    ],\n",
        "    \"Accuracy\": [\n",
        "        train_accuracy_lr,\n",
        "        test_accuracy_lr\n",
        "    ]\n",
        "})\n",
        "\n",
        "overfitting_lr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "RPEUyUaidB-i",
        "outputId": "a30adc57-387b-462d-ce4b-bad56f897c01"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Dataset  Accuracy\n",
              "0  Training      0.91\n",
              "1   Testing      0.82"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-16326c26-dd9e-4d94-87dc-95b7a2414060\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Training</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Testing</td>\n",
              "      <td>0.82</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-16326c26-dd9e-4d94-87dc-95b7a2414060')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-16326c26-dd9e-4d94-87dc-95b7a2414060 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-16326c26-dd9e-4d94-87dc-95b7a2414060');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_0e55ac20-72cf-47f7-a0fe-0699dec6dc7b\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('overfitting_lr')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_0e55ac20-72cf-47f7-a0fe-0699dec6dc7b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('overfitting_lr');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "overfitting_lr",
              "summary": "{\n  \"name\": \"overfitting_lr\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Dataset\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Testing\",\n          \"Training\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06072279483439479,\n        \"min\": 0.8225,\n        \"max\": 0.908375,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.8225,\n          0.908375\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training vs Testing Accuracy\n",
        "plt.figure(figsize=(6,5))\n",
        "\n",
        "\n",
        "ax = sns.barplot(\n",
        "    data=overfitting_lr,\n",
        "    x=\"Dataset\",\n",
        "    y=\"Accuracy\",\n",
        "    palette=\"Set2\"\n",
        ")\n",
        "\n",
        "\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{p.get_height():.3f}\",\n",
        "        (\n",
        "            p.get_x() + p.get_width()/2,\n",
        "            p.get_height()\n",
        "        ),\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=10,\n",
        "        fontweight=\"bold\"\n",
        "    )\n",
        "\n",
        "plt.ylim(0, 1.05)\n",
        "\n",
        "plt.title(\"Training vs Testing Accuracy\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "Iu3bg1ANdFrS",
        "outputId": "e3edcf23-35cc-4e2a-fb3e-3f2a60e40cd0"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest Model"
      ],
      "metadata": {
        "id": "mRtvVSoOdJm8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build Model Pipeline\n",
        "rf_pipeline = Pipeline(\n",
        "    steps=[\n",
        "        (\"preprocessor\", preprocessor),\n",
        "        (\n",
        "            \"classifier\",\n",
        "            RandomForestClassifier(\n",
        "                random_state=RANDOM_STATE,\n",
        "                class_weight=\"balanced\"\n",
        "            )\n",
        "        )\n",
        "    ]\n",
        ")\n",
        "\n",
        "rf_pipeline"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 358
        },
        "id": "sXOnDJaudOYh",
        "outputId": "7f1305ce-d749-47ae-8388-4c5692d29298"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='median')),\n",
              "                                                                  ('scaler',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['Unnamed: 0', 'emp_length',\n",
              "                                                   'annual_income',\n",
              "                                                   'debt_to_income',\n",
              "                                                   'annual_income_joint',\n",
              "                                                   'debt_to_income_joint',\n",
              "                                                   'delinq_2y',\n",
              "                                                   'months_since_last_delinq',\n",
              "                                                   'earliest_credit_line',\n",
              "                                                   'inquiries_last_12m',\n",
              "                                                   '...\n",
              "                                                                   SimpleImputer(strategy='most_frequent')),\n",
              "                                                                  ('encoder',\n",
              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                                  ['emp_title', 'state',\n",
              "                                                   'homeownership',\n",
              "                                                   'verified_income',\n",
              "                                                   'verification_income_joint',\n",
              "                                                   'loan_purpose',\n",
              "                                                   'application_type', 'grade',\n",
              "                                                   'sub_grade', 'issue_month',\n",
              "                                                   'initial_listing_status',\n",
              "                                                   'disbursement_method'])])),\n",
              "                ('classifier',\n",
              "                 RandomForestClassifier(class_weight='balanced',\n",
              "                                        random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-4 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-4 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-4 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-4 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-4 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-4 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-4 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;encoder&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;,\n",
              "                                        random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></div></label><div class=\"sk-toggleable__content \"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                   SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                                  (&#x27;encoder&#x27;,\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;,\n",
              "                                        random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;,\n",
              "                                  &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;,\n",
              "                                  &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;,\n",
              "                                  &#x27;months_since_last_delinq&#x27;,\n",
              "                                  &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;,\n",
              "                                  &#x27;total_credit_lines&#x27;, &#x27;open_credit...\n",
              "                                  &#x27;num_cc_carrying_balance&#x27;, ...]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;encoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;,\n",
              "                                  &#x27;verified_income&#x27;,\n",
              "                                  &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;,\n",
              "                                  &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;,\n",
              "                                  &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;,\n",
              "                                  &#x27;disbursement_method&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;, &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;, &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;, &#x27;months_since_last_delinq&#x27;, &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;, &#x27;total_credit_lines&#x27;, &#x27;open_credit_lines&#x27;, &#x27;total_credit_limit&#x27;, &#x27;total_credit_utilized&#x27;, &#x27;num_collections_last_12m&#x27;, &#x27;num_historical_failed_to_pay&#x27;, &#x27;months_since_90d_late&#x27;, &#x27;current_accounts_delinq&#x27;, &#x27;total_collection_amount_ever&#x27;, &#x27;current_installment_accounts&#x27;, &#x27;accounts_opened_24m&#x27;, &#x27;months_since_last_credit_inquiry&#x27;, &#x27;num_satisfactory_accounts&#x27;, &#x27;num_accounts_120d_past_due&#x27;, &#x27;num_accounts_30d_past_due&#x27;, &#x27;num_active_debit_accounts&#x27;, &#x27;total_debit_limit&#x27;, &#x27;num_total_cc_accounts&#x27;, &#x27;num_open_cc_accounts&#x27;, &#x27;num_cc_carrying_balance&#x27;, &#x27;num_mort_accounts&#x27;, &#x27;account_never_delinq_percent&#x27;, &#x27;tax_liens&#x27;, &#x27;public_record_bankrupt&#x27;, &#x27;loan_amount&#x27;, &#x27;term&#x27;, &#x27;interest_rate&#x27;, &#x27;installment&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content \"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content \"><pre>[&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;, &#x27;verified_income&#x27;, &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;, &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;, &#x27;disbursement_method&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content \"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content \"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-34\" type=\"checkbox\" ><label for=\"sk-estimator-id-34\" class=\"sk-toggleable__label  sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content \"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Hyperparameter Grid\n",
        "param_grid_rf = {\n",
        "    \"classifier__n_estimators\": [100, 200, 300],\n",
        "    \"classifier__max_depth\": [10, 20, 30, None],\n",
        "    \"classifier__min_samples_split\": [2, 5, 10],\n",
        "    \"classifier__min_samples_leaf\": [1, 2, 4],\n",
        "    \"classifier__max_features\": [\"sqrt\", \"log2\"]\n",
        "}\n",
        "\n",
        "param_grid_rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IeAad5lKdSD8",
        "outputId": "fa80e608-60c1-4a2d-c704-b801cdaddc15"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'classifier__n_estimators': [100, 200, 300],\n",
              " 'classifier__max_depth': [10, 20, 30, None],\n",
              " 'classifier__min_samples_split': [2, 5, 10],\n",
              " 'classifier__min_samples_leaf': [1, 2, 4],\n",
              " 'classifier__max_features': ['sqrt', 'log2']}"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Grid Search\n",
        "grid_rf = GridSearchCV(\n",
        "    estimator=rf_pipeline,\n",
        "    param_grid=param_grid_rf,\n",
        "    cv=5,\n",
        "    scoring=\"f1_weighted\",\n",
        "    n_jobs=-1\n",
        ")\n",
        "\n",
        "start_time = time.time()\n",
        "\n",
        "grid_rf.fit(X_train, y_train)\n",
        "\n",
        "rf_training_time = time.time() - start_time"
      ],
      "metadata": {
        "id": "lHkSyhSRdWTZ"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Hyperparameters\n",
        "print(\"Best Parameters\")\n",
        "print(grid_rf.best_params_)\n",
        "\n",
        "print(\"\\nBest Cross Validation Score\")\n",
        "print(f\"{grid_rf.best_score_:.4f}\")\n",
        "\n",
        "print(f\"\\nTraining Time : {rf_training_time:.2f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uq9CPbRQdXYD",
        "outputId": "238ebdf7-62ff-44bc-ece2-4916fed01858"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Parameters\n",
            "{'classifier__max_depth': 20, 'classifier__max_features': 'sqrt', 'classifier__min_samples_leaf': 2, 'classifier__min_samples_split': 2, 'classifier__n_estimators': 200}\n",
            "\n",
            "Best Cross Validation Score\n",
            "0.9075\n",
            "\n",
            "Training Time : 2171.41 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Best Model\n",
        "best_rf = grid_rf.best_estimator_\n",
        "\n",
        "best_rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "ybm2-Q3NdbCv",
        "outputId": "6d05b28e-805f-49cd-d9e7-6d60c78982e1"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num',\n",
              "                                                  Pipeline(steps=[('imputer',\n",
              "                                                                   SimpleImputer(strategy='median')),\n",
              "                                                                  ('scaler',\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  ['Unnamed: 0', 'emp_length',\n",
              "                                                   'annual_income',\n",
              "                                                   'debt_to_income',\n",
              "                                                   'annual_income_joint',\n",
              "                                                   'debt_to_income_joint',\n",
              "                                                   'delinq_2y',\n",
              "                                                   'months_since_last_delinq',\n",
              "                                                   'earliest_credit_line',\n",
              "                                                   'inquiries_last_12m',\n",
              "                                                   '...\n",
              "                                                                   OneHotEncoder(handle_unknown='ignore'))]),\n",
              "                                                  ['emp_title', 'state',\n",
              "                                                   'homeownership',\n",
              "                                                   'verified_income',\n",
              "                                                   'verification_income_joint',\n",
              "                                                   'loan_purpose',\n",
              "                                                   'application_type', 'grade',\n",
              "                                                   'sub_grade', 'issue_month',\n",
              "                                                   'initial_listing_status',\n",
              "                                                   'disbursement_method'])])),\n",
              "                ('classifier',\n",
              "                 RandomForestClassifier(class_weight='balanced', max_depth=20,\n",
              "                                        min_samples_leaf=2, n_estimators=200,\n",
              "                                        random_state=42))])"
            ],
            "text/html": [
              "<style>#sk-container-id-5 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-5 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-5 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-5 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-5 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-5 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-5 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-5 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=20,\n",
              "                                        min_samples_leaf=2, n_estimators=200,\n",
              "                                        random_state=42))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-35\" type=\"checkbox\" ><label for=\"sk-estimator-id-35\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>Pipeline</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                                   SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                                  (&#x27;scaler&#x27;,\n",
              "                                                                   StandardScaler())]),\n",
              "                                                  [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;,\n",
              "                                                   &#x27;annual_income&#x27;,\n",
              "                                                   &#x27;debt_to_income&#x27;,\n",
              "                                                   &#x27;annual_income_joint&#x27;,\n",
              "                                                   &#x27;debt_to_income_joint&#x27;,\n",
              "                                                   &#x27;delinq_2y&#x27;,\n",
              "                                                   &#x27;months_since_last_delinq&#x27;,\n",
              "                                                   &#x27;earliest_credit_line&#x27;,\n",
              "                                                   &#x27;inquiries_last_12m&#x27;,\n",
              "                                                   &#x27;...\n",
              "                                                                   OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                                  [&#x27;emp_title&#x27;, &#x27;state&#x27;,\n",
              "                                                   &#x27;homeownership&#x27;,\n",
              "                                                   &#x27;verified_income&#x27;,\n",
              "                                                   &#x27;verification_income_joint&#x27;,\n",
              "                                                   &#x27;loan_purpose&#x27;,\n",
              "                                                   &#x27;application_type&#x27;, &#x27;grade&#x27;,\n",
              "                                                   &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;,\n",
              "                                                   &#x27;initial_listing_status&#x27;,\n",
              "                                                   &#x27;disbursement_method&#x27;])])),\n",
              "                (&#x27;classifier&#x27;,\n",
              "                 RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=20,\n",
              "                                        min_samples_leaf=2, n_estimators=200,\n",
              "                                        random_state=42))])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-36\" type=\"checkbox\" ><label for=\"sk-estimator-id-36\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>preprocessor: ColumnTransformer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;median&#x27;)),\n",
              "                                                 (&#x27;scaler&#x27;, StandardScaler())]),\n",
              "                                 [&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;,\n",
              "                                  &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;,\n",
              "                                  &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;,\n",
              "                                  &#x27;months_since_last_delinq&#x27;,\n",
              "                                  &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;,\n",
              "                                  &#x27;total_credit_lines&#x27;, &#x27;open_credit...\n",
              "                                  &#x27;num_cc_carrying_balance&#x27;, ...]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
              "                                                  SimpleImputer(strategy=&#x27;most_frequent&#x27;)),\n",
              "                                                 (&#x27;encoder&#x27;,\n",
              "                                                  OneHotEncoder(handle_unknown=&#x27;ignore&#x27;))]),\n",
              "                                 [&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;,\n",
              "                                  &#x27;verified_income&#x27;,\n",
              "                                  &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;,\n",
              "                                  &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;,\n",
              "                                  &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;,\n",
              "                                  &#x27;disbursement_method&#x27;])])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-37\" type=\"checkbox\" ><label for=\"sk-estimator-id-37\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>num</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;Unnamed: 0&#x27;, &#x27;emp_length&#x27;, &#x27;annual_income&#x27;, &#x27;debt_to_income&#x27;, &#x27;annual_income_joint&#x27;, &#x27;debt_to_income_joint&#x27;, &#x27;delinq_2y&#x27;, &#x27;months_since_last_delinq&#x27;, &#x27;earliest_credit_line&#x27;, &#x27;inquiries_last_12m&#x27;, &#x27;total_credit_lines&#x27;, &#x27;open_credit_lines&#x27;, &#x27;total_credit_limit&#x27;, &#x27;total_credit_utilized&#x27;, &#x27;num_collections_last_12m&#x27;, &#x27;num_historical_failed_to_pay&#x27;, &#x27;months_since_90d_late&#x27;, &#x27;current_accounts_delinq&#x27;, &#x27;total_collection_amount_ever&#x27;, &#x27;current_installment_accounts&#x27;, &#x27;accounts_opened_24m&#x27;, &#x27;months_since_last_credit_inquiry&#x27;, &#x27;num_satisfactory_accounts&#x27;, &#x27;num_accounts_120d_past_due&#x27;, &#x27;num_accounts_30d_past_due&#x27;, &#x27;num_active_debit_accounts&#x27;, &#x27;total_debit_limit&#x27;, &#x27;num_total_cc_accounts&#x27;, &#x27;num_open_cc_accounts&#x27;, &#x27;num_cc_carrying_balance&#x27;, &#x27;num_mort_accounts&#x27;, &#x27;account_never_delinq_percent&#x27;, &#x27;tax_liens&#x27;, &#x27;public_record_bankrupt&#x27;, &#x27;loan_amount&#x27;, &#x27;term&#x27;, &#x27;interest_rate&#x27;, &#x27;installment&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-38\" type=\"checkbox\" ><label for=\"sk-estimator-id-38\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;median&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-39\" type=\"checkbox\" ><label for=\"sk-estimator-id-39\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>StandardScaler</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.StandardScaler.html\">?<span>Documentation for StandardScaler</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>StandardScaler()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-40\" type=\"checkbox\" ><label for=\"sk-estimator-id-40\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>cat</div></div></label><div class=\"sk-toggleable__content fitted\"><pre>[&#x27;emp_title&#x27;, &#x27;state&#x27;, &#x27;homeownership&#x27;, &#x27;verified_income&#x27;, &#x27;verification_income_joint&#x27;, &#x27;loan_purpose&#x27;, &#x27;application_type&#x27;, &#x27;grade&#x27;, &#x27;sub_grade&#x27;, &#x27;issue_month&#x27;, &#x27;initial_listing_status&#x27;, &#x27;disbursement_method&#x27;]</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-41\" type=\"checkbox\" ><label for=\"sk-estimator-id-41\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>SimpleImputer</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(strategy=&#x27;most_frequent&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-42\" type=\"checkbox\" ><label for=\"sk-estimator-id-42\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>OneHotEncoder</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.preprocessing.OneHotEncoder.html\">?<span>Documentation for OneHotEncoder</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>OneHotEncoder(handle_unknown=&#x27;ignore&#x27;)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-43\" type=\"checkbox\" ><label for=\"sk-estimator-id-43\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(class_weight=&#x27;balanced&#x27;, max_depth=20,\n",
              "                       min_samples_leaf=2, n_estimators=200, random_state=42)</pre></div> </div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Prediction\n",
        "start_time = time.time()\n",
        "\n",
        "y_pred_rf = best_rf.predict(X_test)\n",
        "\n",
        "y_prob_rf = best_rf.predict_proba(X_test)\n",
        "\n",
        "rf_prediction_time = time.time() - start_time\n",
        "\n",
        "print(f\"Prediction Time : {rf_prediction_time:.4f} seconds\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RJW0GCjFdbyo",
        "outputId": "34cfa9ae-eb86-47e1-89ef-fcf5a80fb5e6"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction Time : 0.2329 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance Metrics\n",
        "rf_accuracy = accuracy_score(y_test, y_pred_rf)\n",
        "\n",
        "rf_precision = precision_score(\n",
        "    y_test,\n",
        "    y_pred_rf,\n",
        "    average=\"weighted\"\n",
        ")\n",
        "\n",
        "rf_recall = recall_score(\n",
        "    y_test,\n",
        "    y_pred_rf,\n",
        "    average=\"weighted\"\n",
        ")\n",
        "\n",
        "rf_f1 = f1_score(\n",
        "    y_test,\n",
        "    y_pred_rf,\n",
        "    average=\"weighted\"\n",
        ")\n",
        "\n",
        "rf_auc = roc_auc_score(\n",
        "    y_test,\n",
        "    y_prob_rf,\n",
        "    multi_class=\"ovr\"\n",
        ")\n",
        "\n",
        "rf_results = pd.DataFrame({\n",
        "    \"Metric\": [\n",
        "        \"Accuracy\",\n",
        "        \"Precision\",\n",
        "        \"Recall\",\n",
        "        \"F1-Score\",\n",
        "        \"ROC-AUC\"\n",
        "    ],\n",
        "    \"Score\": [\n",
        "        rf_accuracy,\n",
        "        rf_precision,\n",
        "        rf_recall,\n",
        "        rf_f1,\n",
        "        rf_auc\n",
        "    ]\n",
        "})\n",
        "\n",
        "rf_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "p52lCBZDddw1",
        "outputId": "9b66c255-7425-4c8d-d094-01d4ac76a080"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Metric  Score\n",
              "0   Accuracy   0.94\n",
              "1  Precision   0.88\n",
              "2     Recall   0.94\n",
              "3   F1-Score   0.91\n",
              "4    ROC-AUC   0.56"
            ],
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              "      <th></th>\n",
              "      <th>Metric</th>\n",
              "      <th>Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.94</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Precision</td>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
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              "      <th>2</th>\n",
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              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F1-Score</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ROC-AUC</td>\n",
              "      <td>0.56</td>\n",
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              "\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('rf_results')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
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              "\n",
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              "        google.colab.notebook.generateWithVariable('rf_results');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "rf_results",
              "summary": "{\n  \"name\": \"rf_results\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Metric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Precision\",\n          \"ROC-AUC\",\n          \"Recall\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15943190335676774,\n        \"min\": 0.5625912011802939,\n        \"max\": 0.937,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8788769384692345,\n          0.5625912011802939,\n          0.937\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Classification Report\n",
        "print(\n",
        "    classification_report(\n",
        "        y_test,\n",
        "        y_pred_rf,\n",
        "        target_names=label_encoder.classes_\n",
        "    )\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHCXj8L8dggA",
        "outputId": "68ed96a7-ac98-4025-feb6-f88286242625"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                    precision    recall  f1-score   support\n",
            "\n",
            "       Charged Off       0.00      0.00      0.00         1\n",
            "           Current       0.94      1.00      0.97      1875\n",
            "        Fully Paid       0.00      0.00      0.00        90\n",
            "   In Grace Period       0.00      0.00      0.00        13\n",
            " Late (16-30 days)       0.00      0.00      0.00         8\n",
            "Late (31-120 days)       0.00      0.00      0.00        13\n",
            "\n",
            "          accuracy                           0.94      2000\n",
            "         macro avg       0.16      0.17      0.16      2000\n",
            "      weighted avg       0.88      0.94      0.91      2000\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion Matrix\n",
        "cm = confusion_matrix(y_test, y_pred_rf)\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "\n",
        "ax = sns.heatmap(\n",
        "    cm,\n",
        "    annot=True,\n",
        "    fmt=\"d\",\n",
        "    cmap=\"Greens\",\n",
        "    linewidths=0.5,\n",
        "    cbar=True\n",
        ")\n",
        "\n",
        "plt.title(\"Random Forest Confusion Matrix\", fontsize=18)\n",
        "plt.xlabel(\"Predicted Class\")\n",
        "plt.ylabel(\"Actual Class\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "0-CxBOx9dis9",
        "outputId": "6c3deed8-64b6-47f0-cf42-d00cb7953b0c"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cross Validation\n",
        "cv_scores_rf = cross_val_score(\n",
        "    best_rf,\n",
        "    X_train,\n",
        "    y_train,\n",
        "    cv=5,\n",
        "    scoring=\"f1_weighted\"\n",
        ")\n",
        "\n",
        "cv_results_rf = pd.DataFrame({\n",
        "    \"Fold\": range(1, 6),\n",
        "    \"F1-Score\": cv_scores_rf\n",
        "})\n",
        "\n",
        "cv_results_rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KjllQW60dpB5",
        "outputId": "8e5ce329-bdd8-4751-93db-2ed3b7a9437e"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Fold  F1-Score\n",
              "0     1      0.91\n",
              "1     2      0.91\n",
              "2     3      0.91\n",
              "3     4      0.91\n",
              "4     5      0.91"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-07ffe0c3-87d4-4c42-915e-f9465b0f808c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Fold</th>\n",
              "      <th>F1-Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07ffe0c3-87d4-4c42-915e-f9465b0f808c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-07ffe0c3-87d4-4c42-915e-f9465b0f808c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-07ffe0c3-87d4-4c42-915e-f9465b0f808c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_9dedb76b-db08-4505-a35a-b589e5756d15\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cv_results_rf')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_9dedb76b-db08-4505-a35a-b589e5756d15 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('cv_results_rf');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cv_results_rf",
              "summary": "{\n  \"name\": \"cv_results_rf\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Fold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          2,\n          5,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F1-Score\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0005374265346036674,\n        \"min\": 0.9072385409941899,\n        \"max\": 0.9084547572104061,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0.9072580645161291,\n          0.9084547572104061,\n          0.9072385409941899\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Average Cross Validation Score\n",
        "print(f\"Mean Score : {cv_scores_rf.mean():.4f}\")\n",
        "print(f\"Standard Deviation : {cv_scores_rf.std():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pss0zAl0dp4M",
        "outputId": "24f4c4c5-1eec-4908-d8f8-9bb10f9b36ab"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Score : 0.9075\n",
            "Standard Deviation : 0.0005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Overfitting Analysis\n",
        "train_pred_rf = best_rf.predict(X_train)\n",
        "\n",
        "train_accuracy_rf = accuracy_score(\n",
        "    y_train,\n",
        "    train_pred_rf\n",
        ")\n",
        "\n",
        "test_accuracy_rf = accuracy_score(\n",
        "    y_test,\n",
        "    y_pred_rf\n",
        ")\n",
        "\n",
        "overfitting_rf = pd.DataFrame({\n",
        "    \"Dataset\": [\n",
        "        \"Training\",\n",
        "        \"Testing\"\n",
        "    ],\n",
        "    \"Accuracy\": [\n",
        "        train_accuracy_rf,\n",
        "        test_accuracy_rf\n",
        "    ]\n",
        "})\n",
        "\n",
        "overfitting_rf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "yXiDUr1Ndr8p",
        "outputId": "8d9dac3d-fb82-461b-94fc-f61495cc04a2"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Dataset  Accuracy\n",
              "0  Training      1.00\n",
              "1   Testing      0.94"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-33ebeb2b-efb3-443b-8d8e-60cf73ca0279\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Dataset</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Training</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Testing</td>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33ebeb2b-efb3-443b-8d8e-60cf73ca0279')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-33ebeb2b-efb3-443b-8d8e-60cf73ca0279 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-33ebeb2b-efb3-443b-8d8e-60cf73ca0279');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_86567d4c-5ee8-42fc-af07-86bdc37d59bd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('overfitting_rf')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_86567d4c-5ee8-42fc-af07-86bdc37d59bd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('overfitting_rf');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "overfitting_rf",
              "summary": "{\n  \"name\": \"overfitting_rf\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Dataset\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Testing\",\n          \"Training\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Accuracy\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04180768843765461,\n        \"min\": 0.937,\n        \"max\": 0.996125,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0.937,\n          0.996125\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Training vs Testing Accuracy\n",
        "plt.figure(figsize=(6,5))\n",
        "\n",
        "# Create bar chart\n",
        "ax = sns.barplot(\n",
        "    data=overfitting_rf,\n",
        "    x=\"Dataset\",\n",
        "    y=\"Accuracy\",\n",
        "    palette=\"Set2\"\n",
        ")\n",
        "\n",
        "# Add labels\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{p.get_height():.3f}\",\n",
        "        (\n",
        "            p.get_x() + p.get_width()/2,\n",
        "            p.get_height()\n",
        "        ),\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=10,\n",
        "        fontweight=\"bold\"\n",
        "    )\n",
        "\n",
        "plt.ylim(0, 1.05)\n",
        "\n",
        "plt.title(\"Training vs Testing Accuracy\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "YXcFo7UPduXQ",
        "outputId": "43a79f58-88fb-49b7-efd6-f58b0284b271"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Interpretation and Comparison**"
      ],
      "metadata": {
        "id": "nEVtVCEIdyEK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest Feature Importance\n",
        "rf_model = best_rf.named_steps[\"classifier\"]\n",
        "\n",
        "feature_names = best_rf.named_steps[\n",
        "    \"preprocessor\"\n",
        "].get_feature_names_out()\n",
        "\n",
        "feature_importance = pd.DataFrame({\n",
        "    \"Feature\": feature_names,\n",
        "    \"Importance\": rf_model.feature_importances_\n",
        "})\n",
        "\n",
        "feature_importance = feature_importance.sort_values(\n",
        "    by=\"Importance\",\n",
        "    ascending=False\n",
        ")\n",
        "\n",
        "feature_importance.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "cIcPsHTvd3Ex",
        "outputId": "cd2f32f5-3d8c-4f37-d3f2-8920255c9a9b"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  Feature  Importance\n",
              "12                num__total_credit_limit        0.03\n",
              "37                       num__installment        0.03\n",
              "26                 num__total_debit_limit        0.03\n",
              "3                     num__debt_to_income        0.03\n",
              "0                         num__Unnamed: 0        0.03\n",
              "8               num__earliest_credit_line        0.03\n",
              "36                     num__interest_rate        0.03\n",
              "21  num__months_since_last_credit_inquiry        0.03\n",
              "2                      num__annual_income        0.02\n",
              "10                num__total_credit_lines        0.02\n",
              "27             num__num_total_cc_accounts        0.02\n",
              "11                 num__open_credit_lines        0.02\n",
              "22         num__num_satisfactory_accounts        0.02\n",
              "13             num__total_credit_utilized        0.02\n",
              "34                       num__loan_amount        0.02\n",
              "29           num__num_cc_carrying_balance        0.02\n",
              "25         num__num_active_debit_accounts        0.02\n",
              "30                 num__num_mort_accounts        0.02\n",
              "1                         num__emp_length        0.02\n",
              "31      num__account_never_delinq_percent        0.02"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aaf582cc-dda1-412a-8ac6-c59ab35322b7\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Importance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>num__total_credit_limit</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>num__installment</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>num__total_debit_limit</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>num__debt_to_income</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>num__Unnamed: 0</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>num__earliest_credit_line</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>num__interest_rate</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>num__months_since_last_credit_inquiry</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>num__annual_income</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>num__total_credit_lines</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>num__num_total_cc_accounts</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>num__open_credit_lines</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>num__num_satisfactory_accounts</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>num__total_credit_utilized</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>num__loan_amount</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>num__num_cc_carrying_balance</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>num__num_active_debit_accounts</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>num__num_mort_accounts</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>num__emp_length</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>num__account_never_delinq_percent</td>\n",
              "      <td>0.02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aaf582cc-dda1-412a-8ac6-c59ab35322b7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-aaf582cc-dda1-412a-8ac6-c59ab35322b7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-aaf582cc-dda1-412a-8ac6-c59ab35322b7');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "feature_importance",
              "summary": "{\n  \"name\": \"feature_importance\",\n  \"rows\": 4086,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4086,\n        \"samples\": [\n          \"cat__emp_title_program coordinator \",\n          \"cat__emp_title_secondary marketing/capital markets\",\n          \"cat__emp_title_cnc machinist \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Importance\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0019408029629211464,\n        \"min\": 0.0,\n        \"max\": 0.030631262220173025,\n        \"num_unique_values\": 459,\n        \"samples\": [\n          0.0008386984434395041,\n          0.009191223970451353,\n          6.769298785138099e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Top 20 Important Features\n",
        "top_features = feature_importance.head(20)\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "\n",
        "ax = sns.barplot(\n",
        "    data=top_features,\n",
        "    x=\"Importance\",\n",
        "    y=\"Feature\",\n",
        "    palette=\"viridis\"\n",
        ")\n",
        "\n",
        "\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{p.get_width():.3f}\",\n",
        "        (\n",
        "            p.get_width(),\n",
        "            p.get_y() + p.get_height()/2\n",
        "        ),\n",
        "        xytext=(5,0),\n",
        "        textcoords=\"offset points\",\n",
        "        va=\"center\",\n",
        "        fontsize=9\n",
        "    )\n",
        "\n",
        "plt.title(\"Top 20 Feature Importances - Random Forest\", fontsize=18)\n",
        "plt.xlabel(\"Importance Score\")\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "n4nwCOKDd605",
        "outputId": "71e8bf7d-3db1-4e78-d3ee-11cf98669ffa"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic Regression Feature Importance\n",
        "lr_model = best_lr.named_steps[\"classifier\"]\n",
        "\n",
        "coef = np.abs(lr_model.coef_).mean(axis=0)\n",
        "\n",
        "lr_importance = pd.DataFrame({\n",
        "    \"Feature\": feature_names,\n",
        "    \"Coefficient\": coef\n",
        "})\n",
        "\n",
        "lr_importance = lr_importance.sort_values(\n",
        "    by=\"Coefficient\",\n",
        "    ascending=False\n",
        ")\n",
        "\n",
        "lr_importance.head(20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "kcK6SbkTd_I1",
        "outputId": "7ce48b7a-6a28-482c-9737-444ac968ecc9"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Feature  Coefficient\n",
              "773      cat__emp_title_construction manger         5.71\n",
              "1714        cat__emp_title_insurance broker         5.39\n",
              "3243       cat__emp_title_senior supervisor         4.63\n",
              "3511      cat__emp_title_staff coordinator          4.62\n",
              "2557              cat__emp_title_pipewelder         4.51\n",
              "2836               cat__emp_title_ramp lead         4.47\n",
              "2802                 cat__emp_title_qc lead         4.46\n",
              "2515        cat__emp_title_personal trainer         4.27\n",
              "3348     cat__emp_title_site superintendent         4.26\n",
              "3417             cat__emp_title_sqd leader          4.20\n",
              "705     cat__emp_title_command master chief         4.17\n",
              "1858             cat__emp_title_lead driver         4.10\n",
              "2584        cat__emp_title_police detective         4.02\n",
              "619            cat__emp_title_cjief officer         3.96\n",
              "2548                   cat__emp_title_pilot         3.94\n",
              "3656       cat__emp_title_teacher assistent         3.93\n",
              "2819         cat__emp_title_quality manager         3.92\n",
              "1216  cat__emp_title_emergency truck driver         3.85\n",
              "2410          cat__emp_title_owner/operater         3.78\n",
              "672     cat__emp_title_clinical team leader         3.76"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a8f2ae5b-c661-405c-995b-ec4a189e8277\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Feature</th>\n",
              "      <th>Coefficient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>773</th>\n",
              "      <td>cat__emp_title_construction manger</td>\n",
              "      <td>5.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1714</th>\n",
              "      <td>cat__emp_title_insurance broker</td>\n",
              "      <td>5.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3243</th>\n",
              "      <td>cat__emp_title_senior supervisor</td>\n",
              "      <td>4.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3511</th>\n",
              "      <td>cat__emp_title_staff coordinator</td>\n",
              "      <td>4.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2557</th>\n",
              "      <td>cat__emp_title_pipewelder</td>\n",
              "      <td>4.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2836</th>\n",
              "      <td>cat__emp_title_ramp lead</td>\n",
              "      <td>4.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2802</th>\n",
              "      <td>cat__emp_title_qc lead</td>\n",
              "      <td>4.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2515</th>\n",
              "      <td>cat__emp_title_personal trainer</td>\n",
              "      <td>4.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3348</th>\n",
              "      <td>cat__emp_title_site superintendent</td>\n",
              "      <td>4.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3417</th>\n",
              "      <td>cat__emp_title_sqd leader</td>\n",
              "      <td>4.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>705</th>\n",
              "      <td>cat__emp_title_command master chief</td>\n",
              "      <td>4.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1858</th>\n",
              "      <td>cat__emp_title_lead driver</td>\n",
              "      <td>4.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2584</th>\n",
              "      <td>cat__emp_title_police detective</td>\n",
              "      <td>4.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>cat__emp_title_cjief officer</td>\n",
              "      <td>3.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2548</th>\n",
              "      <td>cat__emp_title_pilot</td>\n",
              "      <td>3.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3656</th>\n",
              "      <td>cat__emp_title_teacher assistent</td>\n",
              "      <td>3.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2819</th>\n",
              "      <td>cat__emp_title_quality manager</td>\n",
              "      <td>3.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1216</th>\n",
              "      <td>cat__emp_title_emergency truck driver</td>\n",
              "      <td>3.85</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2410</th>\n",
              "      <td>cat__emp_title_owner/operater</td>\n",
              "      <td>3.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>672</th>\n",
              "      <td>cat__emp_title_clinical team leader</td>\n",
              "      <td>3.76</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8f2ae5b-c661-405c-995b-ec4a189e8277')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a8f2ae5b-c661-405c-995b-ec4a189e8277 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a8f2ae5b-c661-405c-995b-ec4a189e8277');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "lr_importance",
              "summary": "{\n  \"name\": \"lr_importance\",\n  \"rows\": 4086,\n  \"fields\": [\n    {\n      \"column\": \"Feature\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4086,\n        \"samples\": [\n          \"cat__emp_title_math teacher\",\n          \"cat__emp_title_auto advisor supervisor\",\n          \"cat__emp_title_chemical officer\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Coefficient\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6336228576766355,\n        \"min\": 0.0,\n        \"max\": 5.70911955953856,\n        \"num_unique_values\": 4084,\n        \"samples\": [\n          0.6171697148680587,\n          0.4231703247600644,\n          0.09151578714725149\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Top Logistic Regression Features\n",
        "top_lr = lr_importance.head(20)\n",
        "\n",
        "plt.figure(figsize=(12,8))\n",
        "\n",
        "\n",
        "ax = sns.barplot(\n",
        "    data=top_lr,\n",
        "    x=\"Coefficient\",\n",
        "    y=\"Feature\",\n",
        "    palette=\"coolwarm\"\n",
        ")\n",
        "\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{p.get_width():.3f}\",\n",
        "        (\n",
        "            p.get_width(),\n",
        "            p.get_y() + p.get_height()/2\n",
        "        ),\n",
        "        xytext=(5,0),\n",
        "        textcoords=\"offset points\",\n",
        "        va=\"center\",\n",
        "        fontsize=9\n",
        "    )\n",
        "\n",
        "plt.title(\"Top Logistic Regression Features\", fontsize=18)\n",
        "plt.xlabel(\"Average Absolute Coefficient\")\n",
        "plt.ylabel(\"Feature\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 801
        },
        "id": "0VIyexW1eCA-",
        "outputId": "33582f55-c33a-4ed5-eaae-4004b38457e8"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(X_train_processed))\n",
        "print(X_train_processed.dtype)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gipKJoimtm-b",
        "outputId": "bde8bc3d-a820-4ce1-bed5-71784afa681e"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'scipy.sparse._csr.csr_matrix'>\n",
            "float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SHAP Analysis\n",
        "# Prepare data\n",
        "X_train_processed = best_rf.named_steps[\n",
        "    \"preprocessor\"\n",
        "].transform(X_train)\n",
        "\n",
        "# Convert sparse matrix to dense array\n",
        "X_train_processed = X_train_processed.toarray()\n",
        "\n",
        "# Random Forest model\n",
        "rf_model = best_rf.named_steps[\"classifier\"]\n",
        "\n",
        "# SHAP explainer\n",
        "explainer = shap.TreeExplainer(rf_model)\n",
        "\n",
        "# Calculate SHAP values\n",
        "shap_values = explainer.shap_values(X_train_processed)"
      ],
      "metadata": {
        "id": "3dttbBdReFUH"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(type(shap_values))\n",
        "print(np.array(shap_values).shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2U6lek4xvwq",
        "outputId": "f7bd7763-396e-4263-daa0-f060be45323e"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'numpy.ndarray'>\n",
            "(8000, 4086, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_id = target_mapping.loc[\n",
        "    target_mapping[\"Loan Status\"] == \"Charged Off\",\n",
        "    \"Encoded Value\"\n",
        "].iloc[0]\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "shap.summary_plot(\n",
        "    shap_values[:, :, class_id],\n",
        "    X_train_processed,\n",
        "    feature_names=feature_names,\n",
        "    max_display=20\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951
        },
        "id": "1TBj8OD8x57d",
        "outputId": "cfc1350c-4440-43cc-8e15-f1390b3a69e3"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 2 Axes>"
            ],
            "image/png": 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E8uXLsdlsAWk/++wz8vLyGDt2LMHBwaxevZpXX32VJk2acNVVV1WmW7FiBYWFhYwcOZImTZqQlZXFsmXLuPfee3njjTfo2bNnQL4Oh4N77rmHXr16cf/997Nz506WL1+O0+kkLCyMHTt2MGnSJDweDx988AF/+ctfWLFiRUD5nnrqKb788kuGDx/OmDFjcLvdrF69mvvuu48XXniBoUOHVqb929/+xrp16xgyZAgDBw4kNTWVhx9+uDKgrGrHjh3cfffdjB49mlmzZp2wLvfu3YvL5aJbt27VzlUEGTt37mxYAYfHC6P/CSnlS8du3geXP4NWokPBTSnB5BJHGEdbni6CKCMCE0d7IhRUPOVhRnGQyg/BFxFTlkWf/D/QyncXV/Diq6G1bMCFAwUNPS5MOFzBxB3JpyDEisHjJS6rgMLQINZGHBNsQLXGtc7nw6uVBxAVNA3V40WnaVg9Puw6H9lBJv97fZr/8X9lWo422n0auKucU/AHIlUbt4oCPl9gW9qr+V+rlAcbyjF5H3OdinyqHj/e3Paq76lK06rnd944eVl+yoA7vvTx3kgd96z1BQQbAMUumPFt4LEyLzy/SaOJVePBPjVf43+HfNy79ujP8N0/NIw6H/Ov0NWYXlS3N19j0gpf5ddk+X4Nu8fH19dJHTYmEnAIcRIul4v3338fg8E/hGL48OGMGzeOTz755KwDji5duvDII48EHFu0aBFZWVkkJiYSFOR/Etu3b1+mTJlSOZb/fNKxY0ceffTRytdt2rTh0UcfZc2aNUyYMCEgbWZmJosXL668r3HjxjF69GgSExMDAo4nnngCiyVwZ+gJEyYwadIk3nnnnWoBR0FBAVOnTmXq1KmVx4qLi1m7di0dO3bknXfeQa/3/7pr3bo1M2fODCjft99+y+rVq3n88ccZP358ZR6TJ0/m1ltv5aWXXuKSSy5BURQ2bNjAunXrqgUQvXr14qGHHjqTKqyUnZ0NQExMTLVzFccq0tQ3p9OJ11vDZnvHUDfvw5wSuE+FUuJAw4wCFBGOCVf199XQInZipZm6n6iSXCiBUtWM1z+YCYBCwlEpxsjROvKhYsNOLmYOm3uyrXsbskIj0Hm9JCRn0uFgJg6TgdxQG0pNT+2rHLN5fUR5PBQqCgXGo0Oqgrw+0qwWOpY6QFEosZmPNshrWrWq4lDVQKTiuE8rn9dR/frVK0mp3vCv2otyPCdaSOtE7z2vgozT98UBH3a7k2X7DJzOsJjPdnu4q3P1jSMBPk3SAYEN4yV7ffx3cPVhew2Fw+EI+G9dW7JLxeMLbI6uPaRxpMBOsLF2rmG1WmsnI0A7xc9Ow/621D4JOIQ4ieuuu64y2AB/w69FixakpKScdd5TpkwJeN2zZ08WLVrEqFGjKhvlAO3atcNms3H48OGzvmZtu+GGGwJeVww/qql+xowZE3BfZrOZbt268fvvvwekqxps2O12XC4XOp2Orl27smPHjmr56nQ6rr/++oBjPXr04Ntvv2XChAmVwQZQGaxULd+qVauw2WwMGzaMgoKCgHyGDBnCm2++yeHDh2nZsiXr1q0D4Oabbw5IN2zYMFq2bMmhQ4eq1ceWLVuqlbkmZeXjTqp+3ioYjcaANPWtpp9DTYxFBXRVQKnWyFXR8GLEiaOGXomaeioUIMp3dMiUxefGh6EyfT5RtOEwTkKwY8aFiWyao8OHgo9tnRLICg0DwKvTsbttHEciQnCWz8foWFBEpKOMXIu58hphHi+Rbi+qAsFeH3qfj0ifj8P4l8g1aBoGDfINeor1OoI9XvSnujRuTcmqHvP5ThwcnPhk7dO0Bh10BKlOkpJ2E2PsSFqZ6ZTfZ/UWkZR0qMZzamkMEBtwLFwtIylpz9kU9byQnJx8Tq7jzQ8DWgYcC9F7SN6bVDny8GwdOxRXnHsScAhxEjUNYQoNDSUzM7PW8w4O9u+EXNPQnJCQEAoLC6sdP5dqmqx87D2EhYUB1FjW49XlsWlTU1OZO3cuGzZsoLg4cJB3TWWIiorCZApsQISEhADV67LieNVrJicnU1payhVXXFEt7wp5eXm0bNmStLQ0VFWlZcuW1dK0bt26WsBxOsxmf0PX7XZXO+dyuQLS1LeuXbueUg8HncA7dQf6976rPOQZ0R1l8xGUgmLCySaXGLwo6Cob0Bp6HPgb1GrlMR2B19OqBCVlWPEPvPJymHYUEx6QVo+T7MiQasUrCbJgcPufXus1jXt27OV/zZtyxGYhzOXGZdCjU1T0iooDjSy9jiK9EZ2mYfNpVISGeUYDifFNmHYogxb5JWyPCTs6D+PYh+O68uFNKnBMJwcVk5g93qPxhFIesVWNL5Ty9x4bBFQdLqWV/0/lkKvy7o8TDanyAeox72nAQcZRGi8P09GpVSee1atM/1qr9qQ63KRR6PSvWlbBqtd4eqiNTtGdasz1ry1hRbZGaon/PTpF4+9D9HRqU3P6hsDhcJCcnEyrVq2q9TTXhTbt4ZMsH7/lHP0+/60/dOvScOtQVCcBhxAnoao1P2IJWJv/BH+QT9Qo0+lqHqN6vOPaiYZXnAWTyXTCYKaia/3YRj2cXlmPl7Yqu93OHXfcgcPhYMqUKSQkJGCz2VAUhXfffbfG/SiO9zM60bmq5dM0jfDwcJ599tnj5tO2bduTlv1sRUdHA5CVlVXtXMWxijT1rabPwnG9/QCM6w8b90KvNugnDEDLKIQPfkLdmUrCkQLydhtwZIHB6SDMl46RUrwoaFgow0wOMURzBHPAvA4fXvS4sQAKOryUEloefAQKIp/4tGwMDg2H1ciRpmH4dCrKMb0RwW43V2Vm4zQYKDMaK9vmTp3KothoSss/w15FoQiNEMCh01FaPvdij81ChtlM2+wi0kOtlOl1aMcGFgr+hrxe9c/hqCiCqpTHV+UHVPzzNSqO+zgagxkqltPV/K+P9zuoIujQAg4cU5bAQwYFNDRibNC3qUJSrn8CdYdwOFAE+/I1vJqCx+vDoykoaCiKgkdTarHPRaseFFW7x2MjMLDpwVU+r96k9yfpEgWvX67johh/eHjLRdCrmca7O3z8kqXh9cFlLRT+1FtHegks+N3LnjyF7jFw90U6WoUef4njVlb4dZrGwp0auQ6Nie2PXqehs1gstToU6XiswPobND5M0jhYqDGyjcqQeGmeNjbyExWiFlQ8NS8qKqp2Li0t7VwX57Q1a9aM5ORkCgoKKnsoqjp48CA2m63Gc7Vt06ZNZGdn89RTTzF27NiAc/PmzauTazZv3pzDhw/TrVu3k/6BjYuLw+fzcejQoWpByMGDB8+qHAkJCRiNRrZv317tXMUQps6dO5/VNeqFqsK1A/z/yinxEfCof6U3HVBTGFUZKi78CW3q16TRjpYkYcEfAJdhwEFU5Z4coRSTR0vMlOImMCAqJoJ2SUcDudj0fHZc1BzVG9jF4DYa8CkKZQZDwIJPJm8Nw5sUhVy9Dq3KZoC7Q4MI8nj44CYTF3XRs/jTRDp37kybtgmYrEH0/z8XO7I0/+pTOrU8qMC/lqyqHJ3HoYBZ1XjlKgW7R8cNXVVigk4esP+e5cOs14ix+IOCMLOKx+dfcvZEKy3VFbvLh9Wo4tO0gOsf+/qM8rbbSUpKolOnTqfdMO4erfDypdXrM9wMsy87vaZRpEXhz70bQy9Q/bEZFe68SOqwMZOdxoWoBTabjcjISDZv3hzw5Dw1NbVyzP/5rGK53Q8//LDauZ9//pkDBw4wZMiQE/Yk1JaKXpBje0g2bNhwyvMGTteoUaPw+Xy89tprNZ7PzT06b6BitaqFCxcGpFm3bl2Nw6nKyspITk4mJyen2rljWa1WhgwZwtatW9mz5+gYcLvdzueff06LFi3o0qXLKd1TY2K8eRBttaeJf7o7BbY4NLz4cFFM9DEbAIIRNznWCOxmf6PRB5QGGSjCig/wlScPK7DTe+teDN6j452MHjden4+fYyKr9VoqQEQNQ92qPnXX+Xx0D/KyY14zBvQORl9l8reqgNWoY/u9FrRZFhyPm1k/Vcem6XqWT9GxbILCb7fqcD1iQHvChPaEGcfjFu7sY+bPAwynFGwAdI9RaR+hI8yiJ9KqQ6cqmPRKvQQbAFaj/3fGsdevr/IIIeqH9HAIUUsmTZrEvHnzmDFjBkOHDiUnJ4fPPvuMtm3bsnPnzvou3gmNHTuWNWvW8M4777B792769u2LyWRi9+7drFy5ksjIyHO2OlaPHj2IjIxk9uzZZGRkEBMTw549e1i1ahUJCQns27ev1q95+eWXM2bMGD755BN27drFkCFDCAsLIysri99//53U1FSWLVsGwMCBAxkyZAgrV66ksLCQQYMGkZqaypIlS2jbti379weut3k6y+KCf5nhzZs3c//993PDDTdgs9lYunQp2dnZzJ49u/Ft+ncagmaNIGjWCJy2ezDa8wimgLJj5mro8FFqU9kV3wKDy4M9yIzB66XD9jQcRiMoCgaPlyC7E5PDyyW//Y4vSMFhMLGxZQdaNtXzdngIHUrLMFYddgdEuD2kVBnS7tXr0Aw6bE43No+X1sUl/OXR6vOvamLWKwxqLn+ChRAXBvltJ0QtmTZtGiUlJaxatYqtW7fSunVrnnzySZKSks77gEOv1/Paa6+xaNEivvrqK+bPn4/X6yUmJobx48dz2223nXTTutoSHBzMa6+9xpw5c0hMTMTr9dKxY0deeeUVli1bVicBB8DTTz9Nnz59WLp0Ke+++y5ut5vIyEg6duzIfffdF5D2X//6F/PmzWPNmjVs2rSJtm3b8p///Ic1a9ZUCzhOV/PmzXnrrbd49dVXK8vRsWNH5syZQ//+/c8q78bCeOS/2K9/G8eXu1G8gctPOrDQvCCLXfGtcJn1XLb/F1rnpAMK6b4o9phb4NbrsJsNeFUFl0/FpfnYHd8anQaX/60zw+KDmfSYk37ZhZh8Ghrg1qn0K7ET7fHwfWQYpSYDmkEPikJwmZMueYVEdQlhWPtaWsdTCHGeunAf+pwNRaurWahCCCFEHcqNf47iNB8WXKj4cGCmiGBCyCTT0JRwrZRYT+BQtl3mFqQbY3AbdOD1EVLqJDs2GHf/WEY+153odsGVaS+7cTc2VceG6HCal7lAg73BVkrKAw3/nAuNJkV2ts8KIzo88Bme2+3m448/pnPnziQkJBAaGnpO6uVCcTZzOMTxSb2emKZMPXkiQNHer+OSNCwyh0MIIUSDFLrhfmyUkEUUmTShkFAUPKTREo/bSrinuNp7Ijz+hR3sQWYUvQ+L24PB5WXaJxcHBBsAsYqPpNAgeucX80tYML9EhFBi0JdvEKj5V5ryacTovdWCDSGEEEfJb0ghGqD8/PyT7oHg8XgCNryridlsDtiIT4iGRB8fSlThLAyj32H/jx4UTYeCila+87MTQ8AyugAO1YRHr5ITE4yj0EdUQQmGMi8+r4aqCxwq8cQDsUx6u4RUi4nL03NIs5nxKgrJVhMuvR69z0urIjsfPx5xzu5ZCFG/ZKfxMyMBhxAN0NSpU8nIyDhhml69erFt27YTpjnVicxCnK+UEAth399LcdgcIgvL8HJ0D4QjRGPDjr58E4wSg5kN7bqQGxZCUGExKColBgsuk75asAHQqX8Yn1j1PPRmLjuCLQR7NaKdLrr7yjBE6OnT1cI9E2OwGGWwgBBCnIgEHEI0QP/4xz9wOp0nTBMSElLjviBVnS+byAlxtpwRQcQVbiKPVjgwoqFShpndtKUlKYRRQJjbTrOCNLwef++g3utB79RwRx1/E8NO3YL44lXpBRRCiLMhAYcQDVCPHj3quwhCnFeGfH0NyT0O0KZkOwYSyNTFUGow0cp1hEhfLhpw0NaMHKN/4rbV7cBa7CO3pZFxHw+p38ILIUQjJwGHEEKIBs/aNoLOxc/iaP1nDMlFmJVQdsfEkEooYe6WuFQDdp2ZkYd+RlE1rB4nB6+9nI6J16AaZEiUEOJUyeyMMyG/ZYUQQjQa5vnTCSUPh8dGWHEZKAoFxhDsegudCg8R7HMQ5CmjTGeg5T8vkWBDCCHOAenhEEII0WgoV3TH2DaCVvuT0OUnEFTqpMxooL3zIM3dRwD/ruE7Q1txccdzs5mlEEJc6CTgEEII0agY9v2X6MUbCXluDcW/FpHqaskRYnEQhAEX+yObMPDba+u7mEKIBkmGVJ0JCTiEEEI0OrqJ/bFO7I8ViEwtpPCHNJSWkVg7htEx4virUgkhhKh9EnAIIYRo1PTxoUROCa3vYgghxAVLAg4hhBBCCCFOwanuNC4CyfIcQgghhBBCiDojAYcQQgghhBCizkjAIYQQQgghhKgzModDCCHEBemzH0t59d0c4u1Oigx69oSauGegjj9NaVrfRRNCnLdkDseZkB4OIYQQF5w1G0p5780M+uQW0dThpH1RKVem5vGPrQZG/CcPr1er7yIKIUSjIT0cQgghLihP3reFkG37adOkE0khQQB0KC7FoEErj5td2Xqunrobd6iJm29qyvRBlnousRBCNGwScAghhLhgrPjrDzwybzEbmrXg8UuuxqP6O/q/1DRu3ptMXHY+f/5hR+Wgia+SW/PCpR3Z9XBQ/RVaCHHekL7PMyNDqoQQQjR6mqYx/rUCmr73I6qm8eCVYyuDDQCvotB+3wHu+W4jnhAD+eFWVJ+XsVv3UJTl5Na3Cuqv8EII0cBJD4cQQohG74FFxZT8UkRomR2A3ZHR1dKsT2iNxWIDIC3YxuJeHYjKLebazUm8HtaP/83MYv8LkRh0unNadiGEaOgk4BBCCNFo7UnzMHl+IUkOHWXR4eyKak5MaSnBbg95usA/gU3KXAAcCgvm1YHd8epUaAs6r4/I4lIsXo0rp+9i4ZPNiUsIqY/bEULUO1ml6kxIwCGEEKJR8vo0hr5YQKbZBDrArOfnpu35ql1n8kJtUOYBRUHxadz463a6FpSCovBd62b+YKMiH51K08JibvlpB1avjwVTU3hk3RVYjDIqWQghToX8thRCCNEobdzrJMtoAAVau5z0zM7nuxaxvNG7B+gUsOjA6SK4qISpW36jeW4eAGWGGp7F+RQiiu2YHU7ichxsj53FvntWntsbEkKIBkoCDiGEEI1OVqaL//z3CF2KS5l4KINBadnk+xR+bhaDV1FAUcBigEgrRVHBzBnQj9j0HBJSMhh0ILVafiO37icqvwzwD7H6I+Ii4ucvZN+ja8/1rQkhRIMjQ6qEEEI0GmVujSf+L4fDPxUS7Pagheso1qksjmsCHp8/0NApoGng08CoA5OebwZ2olNxMV1Ss+m7P51SVcd37VuABtds2sPYrftw61VyIvx7cpQGW1neZjSt523A8+RQ9DZDPd+5EOJc0GQOxxmRHg4hLiCzZs2iT58+9V2MszJmzBjuvPPOkx47WzXV1fz58+nTpw/p6em1ei2om3u40JS5fQycmYluVRrNiu2sjY9hR5NIvoxr4g8wFMBqAJ3qX0zf46t8r91sZP7lfbCbjQAM25vC01+s5+lV6+lcUsi+dtF4dRr9cn9jcPZmmpekcyQ0moOWBD5r/SmZn+2rn5sWQogGQHo4hKhDixYtIjg4mDFjxpwX+VxIGkOdNYZ7OFee+9rBU9+4idSbeLNDS4r0enw6BRxu8JYHFooCdjdYFVAV8AF4saBx5Zb9JOQXkdUsBk1RCCoqIbio1P8+TaMoxExecxNDDh7E5HWTUJzC+ma9yfQ1w2MxkHT7NxTfu4QWm+/C1CK0vqpBCCHOSxJwCFGHPvroI2JjY8+6wVhb+TRWn332GYoS2M1dF3V22223ccstt2A0Gmstzwrn6h4am4/WFPHaZ4XsDrXiM5nJtpTvkaFp4Pb6h00dy+sDtTydR+P2NVu5+OARfuvXBmd5kjyzCXwaUbmFeExGHDYLB4NaMrfprQzZ9wtdM/fRN30nX3SwsrrLQHoezqLkSCZ7LvmIqw/eharIsAshGif5bp8JCTiEEHWqrKwMvV6PXl93v27qIgCoSV3ex7m6h/p2KMONpkGrZkfnPOw95GL3ES9Du5sJNvv/mNtdGolbytiyy4nq9LIv2clOhx67ouA06umSX0yG0YTe6yXPaMRzbGChVPRi1BBwHOObbq1o7XEHHLOWltErKY3g0jI0ckiPDWd3u1jcOgNmLQ8LRwAYv/trfmsRxaJ+/blpg48eyXuYefVyjhijiCzz0t+h4RiZhSO6JbvWZxC3fxfxbYIhLoKcAhV7syiSzVaiix00CdMREWeuVr68VAdep4/otrbTre7jKyiFT3+CpBT/EDOzEexO2JMG8RHg0WBXGuQVQ2mZP3hTVf+/ltHQKhr+twNK7FBSBlYzXNwRcktgfwa0iAK3BwrsEG4Fgx5sFogK9l8vLRdCbGBUITrU//MKtoDLAy/fCul58L/t/ryv7gWd4iGpfDJ/p/jj35fdCTsOQ0JTiAg+evy3g7D9EIztByHWo8edbvgtGVrH+MsBUFgK3/0BJgMM7eKvm/NRmQt+PwRtmkDUMfvCZOZDSg70aO2veyHqmXwKRb1YsWIFzzzzDPPmzWPXrl0sXryYrKwsYmNjmT59OqNHjwYgPT2dsWPHcscdd3DXXXcF5DF//nwWLFjA8uXLadasGeAfd79y5UrWrl3L7Nmz+eGHH3C73fTt25fHHnuMqKgolixZwqJFi0hPTyc2NpYHHniAYcOG1fo9Voz/z8jICJgLULW869at4/3332fPnj0oikK7du2YOnVqQHlOls+GDRtYtmwZO3fuJCcnB4PBQJcuXZg+fTq9e/eutfspKSnhvffe49tvvyU9PR2LxUKrVq2YNGkSV155JXC0/r/++mvmzJnD+vXryc/PZ9myZTRr1oySkhLefvttvvnmG44cOYLNZqNfv37ce++9xMcHNiIyMzOZPXs2P//8MwC9evVi5syZNZZtzJgxxMbG8uabb55SnZ2pmj5zFcc++eQTli5dyldffUVJSQndu3fnkUceoVWrVnzzzTe89dZbJCcnExERwa233sr48ePr5R7qS4nDx9Pz8vh1t39zvW4JRp68PYxZs3PIOuTvV3hVrzJpaiTREXrum5+PXlWJ8PjYZ9BTpJj9sw5V/wpTG8JCjgYTVqN/TsaxFBUUzd/bUXkM0FeZvujzcSjYQl5IYEP/oj8OEVxaVvmWuIx88sNsWAyFdDuSVCU7jce+WULTDBMxJbn0KNnCsC+/BeDD7hfzbpeRXPfuLjJmr2Zk9pfYNDsOvZnl7a8mM6gpsJcSkxHNaARFod2AcCY82Q69UcXt9LL86ST2/+xfrrdphyAm/LsrtoizbAD/czH8bdGZv/9wDvyQFHispAy+/PXo6/zSo/8/Pf/08n/jq8DXM9+FUCsU+neJZ2gXWPYoGI550rxiM0yd4w+mzEZ44Wa46woY/DfYXD7HRlX8Ac2fRsO32+H6lyC7CIx6eGoSxEXAnfP8ARZAkBlW/s1/zfPJ2t9g8suQW+wv+z+mwF+v9Z977AN4cRl4vBAbDkv+CgM61G95xQVPAg5Rr+bOnYvT6WT8+PEYjUYWL17MrFmziI+Pp0ePHmec74wZM4iJieHuu+8mJSWFxMREHn74YS699FKWLl3KuHHjMBqNJCYm8sgjj7BkyRLi4uJq78aAv//977z88suEhYUxffr0yuPh4eEAfPrppzz//PO0atWK22+/HYCVK1fy0EMP8fjjj1c2SE+Wz4oVKygsLGTkyJE0adKErKwsli1bxr333ssbb7xBz549z/peiouLue222zhw4ADDhw9n4sSJeL1edu/ezY8//lgZcFS47777iIyM5LbbbsPhcGC1WikpKWH69OlkZmYyduxY2rRpQ05ODosXL+aWW25h4cKFxMbGVl7vzjvv5MiRI4wfP542bdqwbds27rrrLpxOZ01FDHCyOqsLs2bNwmKxcOutt1JQUMAHH3zAAw88wN13382cOXOYOHEiISEhLFu2jH/+85+0adPmhJ/x+riHuvTxmpLKYANg+z4X/56XWxlsAIR5fCxYVMCuMDPdy9wcCrJSjI8ilKMBhU8DD/6GI/iXttWp/uNVezM0DXy+8tEP5atSKQrxDgepNpP/tcsLXo0SReVARAgty5z+J+1AZF5xtXsILbQTasmpdtziLeOipBS6eLcRxtH33fj7ela360GJyQBhQezN70gP9zY2xPUpDzb8gpwunIDLZGLvhny2LMtkwHXN2LYkvTLYAMjcXcL3/5fM1X9tf8r1Xk1S6tkFG/WlItgAf+/Dv5bAUxOOHitzwa2v+YONitd/fgcy8o8GG+D/jMx8F24eCre86g82wN+z8sQi0Ov8DfUKJWX+fPe/XvnZqHceL0x71R9sgL/sjyyEsX39x/695GjajHyYPhd2zqmfsjZCskrVmZGAQ9Qrl8vF+++/j8HgH14xfPhwxo0bxyeffHJWAUeXLl145JFHAo4tWrSIrKwsEhMTCQoKAqBv375MmTKFpUuXcv/995/x9WoycuRI5s2bR0REBCNHjgw4V1RUxJw5c4iPj+fdd9+tLM/EiRO58cYbmT17NiNGjCA4OPiE+QA88cQTWCyWgGMTJkxg0qRJvPPOO7UScMydO5cDBw4EBEIVfD5ftfRt27blH//4R8CxF198kbS0NN555x3atz/aYBozZgyTJ09m/vz5zJo1C4D333+f9PR0nnrqKcaOHQvAddddx0svvcRHH3100vKerM7qQmRkJC+//HLlPIywsDBefPFFXnjhBRITE2na1N/AvOKKKxg1atRJP+P1cQ+nw+l04vV6T56w3I59ZdWOpaS4qh0Lc3nx2L0UG/WEuT0cMOiqZ+bTjg6jrtgRvCIAqejN8FSZKF4ZdEDP1Gwyw4P9p1XV32uiV0m1WWhtL0Mrz6Yo2EJkfknAZUuCzJRYm3IsOzYUr55gCqud65N+AIe+OUEuD0eMMeAmINiooPN4weT//4e2F9J9VBgpv1XvGUj9vQC73V7t+KnS/e/Xiss0aN4fd+Jw+L8XDocDZV82ltxjgkSfD+/yTVT7BHl9OL/YjOlw9eAxINiocPAI9tQsiAyufq4eKAeOYEnPq3bc+e12lPwSqvV/JaViT8+GsFMbkudwOAL+2xhYrdaTJxJ1SgIOUa+uu+66ymADICYmhhYtWpCSknJW+U6ZMiXgdc+ePVm0aBGjRo2qbNwDtGvXDpvNxuHDh8/qeqdr48aNOBwOJk+eHFCeoKAgJk+ezEsvvcTGjRu5/PLLT5pX1WDDbrfjcrnQ6XR07dqVHTt2nHVZfT4fX331Fa1bt64WbACoavXVtW+66aaA15qmsXr1anr27ElMTAwFBQUB5e/atSsbNmyoPLZu3ToiIyMZNWpUQD7Tpk07pYCjPlx//fUBk74rgolLLrmkMtgAfw9Fy5Ytz/ozXt9O97MVag4BAhs8wcFuihyBzcFCvYpiUIi0u9gfbMPs9VFS0wLumgaUz9HQlde7qoBOxepy49A0tGpPpDV+iY3Ec2xeisLmmAiGZOejlgcsf3RszqBNu9GXr3DlUlSsuXZ6p2yt7GxRAB8K++gKQAkhhBIYJKQENaF9XhloGtEufwM3yp5Dli06IJ1XV6UegopJSkpCC6kekBmj3SQlJVU7fqpsNi8dz/jd54+c5iGkJicDkJycjOpy0d1mRFcaWGc5nZrQ5I/AjRw1RWFXlELnSBuG3NLAc6qCcsy8H2fTEJIyUyDr/Hiyrbg8dAuzYigIDDz3h/jQawrH9n8548JISj8EGadX/uTy+m0ManN4sTgzEnCIelXTMKbQ0FAyMzNrNd/gYP+TqZrGvoeEhFBYWP3JZF1KS0sDoE2bNtXOVRyrSHMyqampzJ07lw0bNlBcHPiE79hVj85EQUEBRUVFDBw48JTf07Jly4DX+fn5FBYWsmHDhuMGUVUDl7S0NDp37oxOF9gYjYqKqvxZnm+OnYMSEuKfxFnTZy44OPisP+P1rWvXrqfVw9E03sfhnBIOZ/ob8PExKn+9NYp/vlaEPc+fT6mqMPJqK/c01fPc+26sbg8hmkauqgucomHUgVkPxU4MDidum7lyuEu/tBwGZubySsfW1QuhKpQadP7lcH2+ymFWKAoOvY5P4mK4fdcBVA1MDhfFXiNuo0qh1UREYRkxuUXosOFDh47yMitWirVwFOAgnejMVozla11906obTbN0KJqPiIJS2rn9gcKAtM2kBcdSaA4DwKXX4SpfNKBpOwtX3NoGk01HmxYelh3cQ9Yef8MyuImRK+7vQGjsWfRRdOqE56t96D9ef+Z51LHyUDLwmEmP4vSHir6OzQj+5y20CjGSnJxMq1atsFgseF6+BfX+t1DcXjRFwfPwWIIfGoNvy2OoydmVebv/Mpp2Q/vje1VFm/46Spl/wQD33SPQ4iMxPPkxilZxXQPagnvp1KXzubn5U+SbMx3tjvkozvKy338Vra69FDQNz88p6D/4AQDNZkJ77Q46dT718jscjoB6FaI2SMAh6lVNT8fB/0QcTtxgPlFj59iG6smOa5pW4/Hznd1u54477sDhcDBlyhQSEhKw2WwoisK7777L5s2b66VcZnPgBNyK+u3Xrx/Tpk2rjyLVueN9lk/2GW+oTKbTa/RarfDW0zZ+3+tC06B7eyM6VeHtl4L5fquDXZlerhxgoVWU/8/S2ItszFtrJ2Wvg47FLn4qVrG7INblJsVko0hVCDKouNw+YvNKyLGaMLu9XJaWjQq0KHVw2HZMY8mgw2m1EORwUKKv6FktH56lwf6YcNQkDw6Tmbi9Obx3xUV8Oqwzbr2O2Jxi/r7wf8Rl5HOYHtjIYW+TGPaFtSU4z0PT7BLshLCVoUSShd2io7Q0lpbGbGK7Gmn97xGs/e0yEjZtolMrE1PjIknNM1LSsQWHIoKJzCqlabhK8y7Blb/3rFaY9mZv0rYX4XH6aN4zFJ2+5s/TafloJvz1Gnjza9iTDmaDfwUpgwr7s6DMCaVOKHP7V31yusCrHd08sWm4f6LyvozyvUzwB24D2vnnWuxO9/c2WY3+wE5V/elCLdAs3D8vIi3fvypVXATYTP705atUKXPvhINH4KMf/fMTbh6KMrQLfL8TAPWSzlh1OigfWmaxWPxDZu68CsYNgI17ULq0wNC2KQaAg/Nh6Ub47SDKzcMwtm3qH3Y0ZShc0QvWJ0H7Zhg6lj80uPlSWL0Ngi0oY/tiDjoPG903XwZX94GfdkHHeAztm1E5VmDhg/DIeDiYhXJJZ8yhZ7a6WWW9imOcHz1dDY0EHOK8VvGUuKioqNq5U+0BqE/HC5gqnoYfOHCAfv36BZw7ePAgENhLc7x8Nm3aRHZ2dsBchwrz5s0743JXFRYWRkhICHv37j3jPMLDwwkODqa0tJT+/fufNH1cXBwpKSl4vd6AIDEnJ6daL87x1EbvTn1rDPdQlaoq9OgQGKgoisLQPlaGHpM2zKry2NggIIjjs/LpuiKe+8iB6lRpWWynojl+TeoRVsdGcyDIiqKAx6gHVcWuB3yGwGw0wKTHrPl4s19H9sbEEDHATl6otbLnJCMqmNfG92Xc3A2YKMGJjSxdHA69BUcMFIaYsTpcBHmK6PbKGIIm96SL283HH3+MtXMHgmPMXDsxFCZeU3nZFuX/7QzQIazGO1QUhfjudbCRYM+2MK9t7edbW1rGwLBugccu7VZz2qqahPmXvj3Wtf39/44VGVw9fYtouOvK6mnPN1EhNd8rQNeW/n9CnCdq4VGJEHXHZrMRGRnJ5s2bA54Ip6amsm7duvor2CmyWCw1Bkv9+/fHYrGQmJhIaenRMcSlpaUkJiZitVoZMGDASfOpaIwf+7R8w4YNtTJ/A/xP6K+88koOHDjA559/Xu38qTypV1WVq666ij/++IO1a9fWmCYv7+gkyKFDh5Kbm8sXX3wRkOa999475XIfr84aksZwD3XtumEh/Do/jls6apUP0gGCPF6uS8nkwd0H0Rl1RyeX17h+LrTPL8aoU9kbEwNAXrDVn1mVz/efN3xONAcIIYtoDjIi/TusTieKpvmHXkUEcfH3EwmafPYLNQghRGMiPRzivDdp0iTmzZvHjBkzGDp0KDk5OXz22We0bduWnTt31nfxTqhbt24sW7aMefPm0bp1axRF4ZJLLiE4OJgZM2bw/PPPc8stt1TuO7Jy5UpSUlJ4/PHHAyaTHy+fHj16EBkZyezZs8nIyCAmJoY9e/awatUqEhIS2Ldv3/GKdlruueceNm/ezLPPPsvGjRu56KKLANi9ezcej6failQ1ue+++/jtt9947LHH+N///ke3bt0wGAxkZGSwfv16OnXqVLlK1dSpU1mzZg3PPfccSUlJtG3blq1bt/L7778TFhZ2SmU+Xp01pDHJjeEezpVn74nmUEoZd//NRff8EvSahltRWN2iKU6dzh84eHw1bgSoaBqxbhd7LDXsb1E+oaBVXhZTt34fcCqIfHqn7GFPfBzdnx1A28my14EQjZ0si3tmJOAQ571p06ZRUlLCqlWr2Lp1K61bt+bJJ58kKSnpvA847r33XgoLC/n0008pLi5G0zSWL1+OxWLhuuuuIyoqioULF7JgwQIA2rdvz4svvlhtI8Lj5dOsWTNee+015syZQ2JiIl6vl44dO/LKK6+wbNmyWgs4QkJCeOedd3j77bf59ttv+fbbb7HZbLRu3Zrrr7/+lPIICgri7bff5oMPPuDrr7/m+++/R6fTERMTQ48ePbjmmmsCrvd///d/vPzyy6xatQrwb/w3f/587rnnnlO63onqvqFoDPdwLrVsbubt5+KY/kw6doyEeLwcCC0P3KsGG4pytOdCr6AFmfkuxubfXfvYYWzlL+MK8lBr6B3RU8agz0bR5KKoOrorIYRo+BStoc9cFEIIIY6RnuNhyuNp2BU9JSYDeyxmfMcGE2YdBFXp1fBpR8dkASjQ5VA2ozfupffeNK4pXo6Bo8uuejCQf/N1RL8/scYyuMvncHTu3JmEhARCQ+tgLsYFzG63k5SURKdOnWRycy2Sej0xp3JqD71MWu3Mo2wspIdDCCFEo9MsSs8915iZ9qMVze1Dp2nVA46K/Ts0zd+7Uebxr9Rk0KFqGorm499fvU+3g9mEagUYcGHXGTF73ZQSQtlVg44bbAghhDhKAg4hqsjPzz/p3gIejwe9/sRfHbPZHDAH43xUVlZGSUnJSdNFRTWeoSIlJSWUlVXf8boqg8EgT6Ibickjm3DHunzsRgMmpwe3UmXIlE7x7+UB/mMGBbwqFLnApEO16Vk5dwGXp26t3HPj/Z5D+OfQcbyRuIhu395P9HFWlhJCCBFIAg4hqpg6dSoZGRknTNOrVy+2bdt2wjSjR4+unAB9vvr666955plnTppuy5Yt56A058aLL77IypUrT5imV69evPnmm+eoRKKurbvOw5WJPmw+H0VehYIgi3/jQJOu+nwNk84/Z8PpxaNXKdAHk0FX/j2yP2s7dGBvVCwvLv2Epm9NJVKCDSGEOGUScAhRxT/+8Q+cTucJ04SEhJx0qdLo6OjaLFadGDhwIHPnzq3vYpxTU6dO5eqrrz5hmoq9X0Tj0LtvNDl9ocNT+UzedoCPOrUizRwUMFWjRm4ffzSJISckiLmDLwNFoXVeNvdtuAmjTlaUF0KI0yEBhxBV9OjRo76LcM5ERUU1quFSp6JNmza0adOmvosh6sGuZ8Lo9vfWhB6x0yunkJQgCzviInDpj24sSZnXvwyuqoCq8Gmf7uyKb4LR7eGv1nSe/ncb9KosiSnEhUyWxT0z8phGCCFEo6coCjuejqCb1ctBs4mBRwqYsv0wCTlF4PVBqRtKXKBXwaqnVU4hiqYSXljK5RFO/jGzrQQbQghxhiTgEEIIccFY+HxzzCb4JiqMQp1K5/R8QgvKwKCDUDPYDMTlFTMwOY1oZykLptpYea8sIiCEEGdDhlQJIYS4YOh0Khtnx/HF+mLGfBGM6vVhLfMQ5CrFq1dRFMiJsnLFbT24pZv8iRRCHEt6Os+E/DYVQghxwRl1cTDf6Uq4bJVKsdmMgoamU5jczMNHt5/fS1oLIURDIwGHEEKIC9KQAUG4B9R3KYQQovGTgEMIIYQQQohTIKtUnRmZNC6EEEIIIYSoMxJwCCGEEEIIIeqMBBxCCCGEEEKIOiNzOIQQQlywXC4fOlVBp5dx2UKIUyG/K86EBBxCCCEuOPMe+IPNKQ6sKDQtKmT07z9idbhJje3IxT+NxxRjqe8iCiFEoyEBhxBCiAtGSb6L/1z/PZayPMLC2+PU6Ulq1oJfWt1CsMPO9J9XsanTWwzJvb++iyqEEI2GzOEQQghxQXCUunnnyq/wlpk41LQ7Lr2BAosZo89Fv0O/E+Is4rWh12LQ2clcsqe+iyuEOA9pKKf0TwSSHg4hhBAXhOev30JEmYHcVtGgKHj1ekJdbu7/4V06H9kHwOqOg9kbksCOx37jtmvboSjScBBCiLMlPRxCCCEaNa9X4/F7dnNEF8T2Tq2gShChqSrLu15BsSmI1R0vJzsoHswapZqebU/9VH+FFkKIRkR6OIQQQjRKDoeXVZevRJ+VSw+zhz/i2pBibYHepwWkOxwex7+G3w/oK5/CheUVcmReEr6n+qMa5E+lEEKcDenhEEII0Sh9cvEXxB36g1EHVpNQkMrQA7/SPW1XtXSaqpIfFEqh1UJFKFIQHkJ2cAS/Xv7JuS20EEI0QvLYRgghRKOz6bXtGAvKuCjzd1689C4ORTbHWlJG+91pxLuzSW0eFTC0CsCnqrh1OoxeLygKmgLpSU561ZD/ez87+NOSMkoVFVWBLjaN96ZZ6dbSeG5uUAghGhAJOIQQQjQ65r8uwZPQmV+ad+NQZHOaH86m+6/JqJpG89Rcf8BRA608CLEVlWLweCk02sh863ea3tYdgNW7XIz5PzteqxGCzVRsAvaL28tlr5YSWlaAplPQNTGx+BbTOblXIYQ430nAIYQQolE5lOqgwBhOakQsZrWIVoey6Ld5Lw6DoTKN4vaiGY/5E6hpWOwOgkvsRB3Jw63TkRYbzif/TqbL0E6UOLxcs8gFwSbQqRg8PmKL7VjdXux6lYwgMzlWI2gQVORlxEt2/trFRudzfP9CiLojS96eGQk4hBBCNHjJKw+z/J+7SY6OptWRFIytEwgpLeVQaCuaHc7Dpx5tJLhVhTybGYvXh8nnO9p8UBQKgm1EZ+dSFmTiQNMYUppEcfX323jmHym4vS4u1hTSY6KwuL2AfyKkU6eSEWrFrSufFunTKEHBrSi8fmggwy5KP6d1IYQQ5xuZNN4I9enTh1mzZtV3MWo0a9Ys+vTpU9/FOCPnc72ej+68807GjBlz0mNna/78+fTp04f09POvUbdlyxb69OnDihUr6rsojdrmR39m6T/3sisunkKzke86X8TXPbvyXef2FJqCyGgRzu89mwMaeq8XDCpuo4EisxHtmLzcBgMFocEANM/OxatTGZO5go8+fp2Fny7mse/Xc9/3G+mQlYOq+d99JMh0NNgAUECnaei8Pg57g/k2zXpuKkIIIc5TEnA0UPPnz2fdunX1XQxRy7Zs2cL8+fMpLi6u76KcMytWrGDRokX1XQzRAO3aWsgzV2zk4x0WUqKicCsaBbagyvNenY7kqAg0wG0xkhsbgsXjIbzYTnCJA6DGwRE+1f+n0eD10iljPwaXFdxB/NSqI4fDY7C6fYzYdYABB1Orv1nTCM+306GglDZ2J+0L7Dz3YywvbNSd1r0VO32sOeBl1T4Pnd/yYHjRg/5FD1d96kHTjg2ThBDnjnKK/0RVMqSqgVqwYAGjR49m2LBh9V2U0/LEE0/w2GOP1Xcxzltbt25lwYIFjBkzhuDg4PouTq2bO3dutcbSihUryMjI4IYbbjijPG+77TZuueUWjMbzb3WgXr16sX79evR6+VVbG7ylLvYecOHSYNWMX8gMDcMZHlZ5vqyG/TLsJiPFNhsGjwelfPS1osHV321n9dBulBl0WDzeyvQaUGgxEQ149AoPrP8QJzGkh4ZTajIH5N0t/QibW8Zh8Poqj+nKPMS6PJVP81Sgmd3Fu195efEnFZdex5WtNZZOMmExVG+UXPOZh2X7fUTYy3DqdJRaAieef3kI1JeOljfGAh+Ngoua6Ii0SCNHCHF+kr+C4pzS6/XS+DpPeTwevF4vJlPdraxjqDJpt7acz58pVVVPqT41TcPhcGC1XlhDb37fWsLahal4Uwox+Ty4VB1h4TpMrcLJTCrAnVFMdnAkZUaDf/UoRcHo8WByu/GFhuA0GfyBhAY+BQyKAsbAz1iQ0wWKgttgIDc8mFbJuQDEZRUwffEPHIkI4evhF4Gi4FUUSo16sm0xmMrcPPjTO0Q7C8hUo/m+dWusnsDy63w+nDoFvc8/fMqrU7G4vdWGDqiA6oNmhXYOh9n4ar+C9fky/xkVVAV8OvXoQ1FVJS+oymdB06ot4VshywHDP9UAD5Xjw6rMVwkyaMQbfbSywMhOOu7qo8Ooqzkvr0/j+x0u0vO8XNzJSKsmtfO9cns1lu/XSC+B0W0UWodJYCTEhea0h1StWLGCPn36sHnzZhYuXMi4ceMYOHAg48ePZ+XKlZXp0tPT6dOnD/Pnz6+WR01jrivG9hcUFDBr1iyGDx/OJZdcwsyZM8nJyQFgyZIlTJw4kUGDBjFhwoQ6HVI0ZswY7rzzTvbs2cO9997LkCFDGDFiBP/973/xeDw4nU5mz57N1VdfzaBBg7jjjjs4ePBgtXwKCgp4/vnnGTVqFAMGDGDUqFE8//zzFBQUBKQ73XoFWLlyJX369Kn8d6zff/+dO++8k8GDBzN8+HD+8Y9/YLfbA9JkZmbyzDPPMHr0aAYOHMiIESOYPn16wDVP1cqVK5k6dSrDhg1j8ODBjBs3jieeeIL8/PzKNDXN4ag4VlJSwr/+9S9GjBjBoEGDmD59Ojt27Kh2HU3TWLp0KdOmTWPIkCEMGTKE66+/njfeeCMgncvl4u2332bSpEkMGjSIYcOG8eCDD7JrV/WNv87UV199xYMPPsioUaMYOHAgw4cPZ+bMmezdu7da2t9++40ZM2Zw5ZVXMmjQIK6++mpmzJjB9u3bK+thwYIFAIwdO7by51rTd+hE3G437733HjfccAMXX3wxQ4cO5eabbyYxMbEyTcV3cP/+/bz88suMHDmSQYMGVZbldOquqKiIZ599luHDhzN48GDuvPNOkpKSaizbsXM4xowZw7Zt28jIyAj4LG/ZsuWU77em3ycVx5KTk5k7dy4jR45k4MCBTJkyhR9//LFaHk6nk1deeYWrrrqKiy++mKlTp7Jhw4YaP68VvxuOVdN8jZMd++STT7juuusYNGgQCxcu5C9/+QsXX3wxJSUl1fL/448/6NOnT+VnpKH74I1M5v/rEKXb89BynJTlefHluMjb62DPDznkFajkhERSZjaBqqIoCqpPI7jMicnlJuZIHhf9sZ82hzIItTsILXPS68BBBibtRfH5exwiiksYu3Fr5TUzYyPIaBKGV1XYelFrlo3qx/qLO+HU68i1mimwmHDr/EOfcsJCKXFG8UvTtlw/7U5eumQgZbrAYVHbm0axq0koR0IseHUqOq8Ps9dbbV6ID1A1jZRwGz6diqYqVAQbGHX4DDp/kFAeVFVznGAj4Lyi+PNQA9OWuBV2lepYk6Njxg/Q7103vhqGZLm9GrfPKWDGm4X8e3EJ457NY+XmshNf9xQ43BqDP/IycbmPGd/4aP+2l6V7fSd/oxDnKa28r/Rk/0SgM358MXfuXJxOJ+PHj8doNLJ48WJmzZpFfHw8PXr0OOMCzZgxg5iYGO6++25SUlJITEzk4Ycf5tJLL2Xp0qWMGzcOo9FIYmIijzzyCEuWLCEuLu6Mr3ciWVlZ3HfffYwYMYLLLruMjRs38uGHH6LT6Thw4ABOp5Np06ZRWFjIwoULmTlzJosXL0YtH/9bUlLC9OnTSUlJYezYsXTs2JHdu3ezePFiNm/ezHvvvYfNZgu45snqNTw8nL///e889dRT9OzZk2uvvbbGsu/Zs4cHH3yQMWPGcOWVV7J161aWLVuGqqr87W9/A/xPtO+77z6ys7OZOHEiLVq0oKSkhH379vHLL78wevToU66rL774glmzZtGzZ0/uvvtuTCYTR44cYf369eTl5REeHn7SPO6//37Cw8O5/fbbKSws5MMPP+RPf/oTy5cvD6inp556itWrV9O1a1emT59OcHAwycnJ/O9//+Puu++uvLcHHniA33//nZEjRzJp0iRKSkpYunQpt912GwsWLKBz57NfrPKTTz4hNDSUa6+9lqioKFJTUyuv8cEHH9CiRQsAkpOTue+++4iMjGTy5MlERESQl5fHr7/+yp49e+jWrRvjx4+ntLSUb7/9lr/85S+EhYUB0K5du1Muj9vt5v7772fr1q0MGDCAq6++GqPRyL59+/j222+5/vrrA9I/+eSTmEwmbrzxRhRFISoq6rTqzuPxcP/997Nz505GjhxJt27dKoP00NDQk5Z35syZvPbaaxQUFPCXv/yl8njr1q1P+Z5PZNasWej1em666SbcbjcfffQRDz30EEuWLKFZs2aV6f72t7+xbt06hgwZwsCBA0lNTeXhhx8OSFPbPvroIwoLC7nmmmuIjIykSZMmdO7cme+//54vv/ySCRMmBKSv+P6OHTu2zsp0OpxOJ16v9+QJa1CQ52H9t4WEOpyYPJ5q581ON3a9Eccxw+Q0VcGt09HmQDrBpf45GMHFdtzAbx1bsycqnBZZOdz87XqC7aUcbNaMH7p3waXXY3F70Pm8fDukKzZ7GSVBlsp8LR4vZXofnioTvw1uD/8ePIZgvY8xv+8jNSSIjy9qz4DDmUTZHeyMieTzzm3B5QODAqqKV6cS6vGRaTbQpMyNij/YyLSZMGo+vOoxz/h8nLD3oi78lq+yYncZI1oEBh3/+93Dln3uo0XT4OWlxQzr5EVVz7x87yWpbMo82tTw+ODhdV6ujKuFYMbhCPivqB2NsV4vtN7j89EZBxwul4v333+/cojE8OHDGTduHJ988slZBRxdunThkUceCTi2aNEisrKySExMJCjIPyGwb9++TJkyhaVLl3L//fef8fVOJDU1lX//+99cfvnlAEycOJGbbrqJhQsXMmTIEF5//XWU8j8UoaGhvPjii2zcuJGBAwcC8N5773H48GEeeeQRrrvuusp827dvzwsvvMD777/PPffcE3DNk9WrxWJh5MiRPPXUU8TFxTFy5Mgay753717eeecdunbtCsCECRMoLS1l+fLlPPjgg1itVg4ePMihQ4d44IEHmDZt2lnV1bp167DZbMybNy9geEtFAHAqOnbsyKOPPlr5uk2bNjz66KOsWbOmsvH19ddfs3r1aq6++mqeeeaZyuAOwOc7+tQsMTGRrVu38uqrr1b+PMD/M7z++uuZPXs2b7755hnda1WvvvoqFosl4NioUaO44YYbWLRoUeX9bNiwgbKyMp577rnKn8mxunfvTkJCAt9++y3Dhg07o8buokWL2Lp1K7feeiv33XdfwLmq9VMhKCiI119/PeBn9uGHH55y3S1fvpydO3dyxx13cNddd1Wmbd26NS+//DKxsbEnLO+wYcNYtGgRTqfzuJ/lsxEWFsZ///vfyu9pnz59mDZtGkuWLKn8vbFhwwbWrVvH6NGjA1Yh69WrFw899FCtl6lCZmYmixcvJiIiovKY1+ulSZMmLFu2LCDgKCsr48svv2TAgAE0adKkzsp0OmrqfTxVmakqYMVYQ7AB4DKWrx5VQ0Nc9Xgrgw0Ar6qwrWNrXEYDJreH4pAQ1gzog8nlwlklYLGbjHTK2EduUDhZwZEBeSqA2eOhROdPr/g0MnQ6uvjKiMstBaBDTj5Ni0v52xWDKieVV3JroPNhAMxeH16bkT02I2aPD4dRR0yJ87x63rl5TybxpXkBx7YlBQNhAceyizR+37Ebk+HMJ6lvOdAUCPzMHizkuL2gZyI5ObnW8hJHNaZ67d27d30X4YJ3xgHHddddFzAeOyYmhhYtWpCSknJWBZoyZUrA6549e7Jo0SJGjRpVGWyA/6mvzWbj8OHDZ3W9E4mJiakMNir06NGDXbt2cf3111c2YiqOAxw+fLiykbZu3TrCw8Or9UKMHz+eBQsW8O2331YLOGqrXrt161atYdu3b1/Wr19Peno6CQkJlfW5detWxowZE9DwOV1BQUGUlZXx448/MnTo0IC6OVXHThquGMpS9d5Xr14NwJ///OeAYAMIeL169WpatWpFp06dqg1f69+/P1988QVlZWWYzYGTQE9XRbChaRqlpaV4PB7Cw8Np2bJlQIOsoq6/++472rVrV2fzJNasWUNISAi33357tXPH1hf46/zY+Q+nU3fr1q1Dp9Nx4403BqSbOHHiaQ8FqwuTJ08O+Cx26dIFq9Ua8HujYmjmzTffHPDeYcOG0bJlSw4dOlQnZRs1alS175xOp2Ps2LEsWLCAffv2kZCQAMDatWspLS1l3LhxdVKWM9G1a9cz7uFISND4cnEqDqORoLLqT7oNbhcuoxG914un6jAmTcNc5gxImxMWgstowOz2EOxyVzbsnTUsImDweXlqzRvMvOYhnIbA76DJ48WnuNF7PNy4+nu+7N6SdrnF2I1H04U6XVhcHkp1On8XgKKAQfX/16uhqT5yLQZceh0eg56S8l/lxWYDzQvsHAnyz/OopDvOEKo6pKJxy4AmNAsKDALUEC+LtwY+0b6olUqP7h3P6nqTQxXePuYrdHkLjU6dOp1VvuB/Ap+cnEyrVq2qPfgRZ07qVdSFMw44ahrGFBoaSmZm5lkV6Nh8K1bqqelpb0hICIWFhWd1vROp6ZoV5Tm2nCEhIQAB5UlPT6dTp07VGnR6vZ4WLVrUOB6+tur1ePlULWNsbCzTp0/n3Xff5aqrrqJ9+/b07duXyy+/nC5dupzW9W699Va2bdvGQw89RGhoKL169eLiiy9mxIgR1YaNnWqZK4YUVa3TlJQUoqKiiIwMfEJ5rIMHD+J0OqsFjFUVFBTQtGnTUyrb8ezatYs33niDrVu3Vut+rno/V1xxBatWreKdd95h0aJFdOvWjQEDBnDllVeetBfgdBw+fJgOHTqcckBTMeSrqtOpu7S0NKKiogIeBgAYjUbi4uLqfXnf+Pj4asdCQ0MDPlNpaWmoqkrLli2rpW3dunWdBRw11T3AuHHjePvtt1m2bBkzZ84E/D1JERERDB06tE7KcibONmi+79F43vjnYYpdZoLKyioDheLyVaXMTgdGt4qGf3lbNA2fAkVBFhxmI5Yyl78cLjdoGkFVgo3jCSkrRdU0Ru34niU9RwSc0wFhZU5u+fZr1raO56NOnSjqYWJQajpXH0zHWN5D6PZqoFX0Fmrg84FJT5DHS/OSMpx6HcEuBy6dyqFwKygKJUY9qgat8ko5EGmrnACPph0dUlVbQ6tqyqf8mA0vcy/XkRBTfXhJz3bwxPU6Xl1RSqFdo3srPf+6JRSr9fSW8z3WiAR4caiPf2zwUeiES+LhrasNWK21t6qcxWKRITN1QOq1ZjI/48ycccBR09NSoHLJyxM94T7RUzGdruZfbsc7XpfrkR/vHk907mzLU1v5Hq++js3r3nvvZezYsfz444/8+uuvLFu2jIULFzJ16lRmzJhxytdr0aIFn376KZs2bWLz5s1s27aNZ599lvnz57NgwYIaG36nWuYzrdOEhAQefPDB454/lXklJ5KZmcmdd96JzWbjtttuo1WrVpjNZhRF4aWXXgoIQIxGI6+//jo7duxgw4YNbNu2rbJunn32WS699NKzKsuZOl4PT13X3blSF9/T4/1uO92n/cer+6ZNmzJw4EBWrVrFjBkzyMjIYNu2bdx8883n7WpcZ6JDFxsvf9CRogI39nwXer2KZtIRHKLD64aiXBdb1uaSesRLSbGH7B1FGL1uIkvy2d4+nhZpuTTNLiAqt4S4zDzcIdUbRhpHF36yuMq4bM9GAC7fu5GN8RdRZAtC0TScegNOk5Guh5PZGxzEo1WC7S/btsatNzJx9wG2NonCVfGZMqhHJ2lrEFXmYn9UEC69//dYSJmbYKeHYrMBvc//ecsONoOiEFLmptSgo0uQD7dVz6ESFb3XR5Ee/KEPZxR8dAzX+M9QhR058O1hmNABekRBmEklygQRwScOEq8fYmX8IAv2Mo1QW+1t0zWzr8r9PRXsHgg3S2NNiAtRnf31qnjiX1RUVO1cWlpaXV32vBIXF8ehQ4fweDwBDQWPx8Phw4frbLL76YqPj2fy5MlMnjwZp9PJAw88wPvvv89NN910WsOsjEYjgwcPZvDgwQD8+OOP/PnPf+bDDz+sNi/nTLVo0YLvvvuO3NzcE/ZyNG/enPz8fPr27XvCwPFsfPvtt9jtdl5++eVqKxkVFhbWuC9E165dK4e6ZWZmcuONNzJv3rzKgONMhqJV1bJlS5KTk3G5XGe8L8Xp1F1cXBwbN26kpKQkoJfD5XKRlpZW+XvgRM72ns9WXFwcPp+PQ4cO0bZt24BzNa08FxISUue/16699lp+/PFH1q1bx+7duwHOq+FUtUVRFELDjYSGH/NZtUBQiJ6xdxz/6Wp+nov/3LOLoMP5hLlcZGuWao10DxDuKOHSfVvon/w7EQ7/zy09OJIr93xHbHEmmUEx/NhqALvjWhBeUsKK9u2rXWtTbDQ+BX6Mq9Ij6i7v5VAVMOvICTJXBhsARWYDQWX+3heLy02azYgxVMcN7RT+M9xGbPDxv1tbMzxcvRiynTWfV4HJHQAFHu6n0CMm8GHN6AR4dMBxsz8hg04h1Fb730mTXsHUeOJlIcRpqrOdxm02G5GRkWzevDngaWJqauoFs0P20KFDyc/P5/PPPw84/vnnn5Ofn39WT7WtVutZDycrKSnBc8ykTZPJRKtWrYCag8XjOXasP/gngQO1Ouzt6quvBmDOnDnVJkFX/ZyNGjWK3NxcPvzwwxrzyc3NPeuyVDTGj31avnTp0mr511Q/TZo0ITw8PKB+KrqvT6fuq7rqqqsoKirirbfeqnbuVJ/qn07dDR06FK/XWy3t4sWLKS0tPaXrWa1WioqK6m335IphSgsXLgw4vm7duhqHU7Vo0YLk5GSysrIqj7lcLj799NNaK9PgwYOJjo5myZIlrFy5kosuuqjyeyn8wiOM/DOxO5Pf7U2ZWSXUUYZa/jtBAwxOF3pNo8QSRIElGKPXvwJTvikEvc9B37RtxBel0yf9V274bTEGt5v9TWOJruFz69DrWdciLnA+SQWfBh6NUkP1P6dlev8xp17H4qkK2Q9b+OAa0wmDDYDesXqyHtCjPeT/V3C/SsZdsO0mhdz7dHgf0vPhGD0fjtZXCzaEEOJ8VKfPGyZNmsS8efOYMWMGQ4cOJScnh88++4y2bduyc+fOurz0eWHatGn873//44UXXmD37t106NCB3bt3s2zZMlq2bMnUqVPPOO+uXbuyadMm3n33XZo2bYqiKFx55ZWnlceWLVt47rnnuOyyy2jZsiVWq5WkpCSWLVtG165dT6uBc9999xEcHEzPnj1p0qQJxcXFrFixAkVRanX1ocsvv5wRI0bwxRdfkJKSwiWXXEJwcDCHDx/m559/5pNPPgH8iw9s3LiRV155hc2bN9O3b19sNhuZmZls3rwZo9F41pOaL774Yl599VWeeuopJk2aRHBwML/99hs//fQT8fHxAUNs3nrrLTZs2MDgwYOJi4tD0zR++OEHkpOTAz4HFb0fc+bMqVzStm3btpWTh09mypQp/PDDD7z11lvs3LmT/v37YzKZOHDgAIcOHeL1118/pTxOte7Gjh3L0qVLWbBgAWlpaXTv3p3du3ezdu3aanVwPF27duWHH37ghRdeoHv37qiqSt++fc9qEYPTMXDgQIYMGcLKlSspLCxk0KBBpKamsmTJEtq2bcv+/fsD0k+aNImvvvqKe++9lwkTJuB2u1m1atVZL0BQVcXk8YrA8dgVx8RRbToEMXRKHOsSjxBmd+Arnw+hc7nJNYWh83opMYTwVduhHIqKoc++7VySEbgXS0xpDi3y0ygzWbl9y4982K0bWeU9dqpP4+LsXDZERaDpVGqcKaKB0aPhPKbt71EVcHtYN3oPnePbVn/fKQo1q4SaVZoGn3EWQghRr+o04Jg2bRolJSWsWrWKrVu30rp1a5588kmSkpIuiIAjKCiIt956i/nz5/P999+zfPlyIiMjmTBhAnfdddcpT6auyaOPPsrzzz/PO++8U/kk+XQDjnbt2nHppZeydetW1qxZg9frpWnTptx6663cdNNNp5XXxIkT+frrr1myZAmFhYWEhobSoUMH/vrXv9a4KeHZeO655+jZsyfLli1jwYIF6HQ6mjVrFjDJWa/XM3v2bBYvXsyqVasqG8jR0dF06dLltPYYOZ74+HjmzJnD3Llzeeedd1BVlYsuuoj58+fzwgsvkJGRUZm2IuBeu3YteXl5mEwmmjdvzhNPPBEwVKZHjx488MADLFmyhGeffRav18sdd9xxygGHwWDgtdde44MPPuDLL7/k9ddfx2g00qJFi4AN907kdOrOYDAwd+5cXnnlFb777ju++eYbOnfuzNy5c5k9e3ZAHRzPjTfeSFpaGv/73//47LPP8Pl8vPHGG+cs4AD417/+xbx581izZg2bNm2ibdu2/Oc//2HNmjXVAo4ePXowa9Ys3n77bV555RViYmKYMGECnTt3rrbq3Nm45ppreOedd7BYLCecwC9g1E3NuGJSU5b+Xyq/fZuPr9SFw2hA9fm4a91KWuYe7Y1y69w15jHmj3W0yctA7/PxzuKlLGnXk2KTkWjFg7nUQZaqsj8mHFCotrOfCs2LHCRH2I7u5aH5Z5BMDt+OUVd7E6SFEKIhUrT6GscghBDnuVmzZrFy5crT2vm8tuTk5DBq1CjGjh1buVmnODV7vjvCx0/vpam9gMmb11UezwiJJMscSZ+sH7FwdDd3DRUwAwouxcCa0EtRXAp6r4bDppLcMpgHN4/Gq4H5yWJ8Ho7OSNep4PES73QTqmlkWwxkBZlBgXiDl3/GL6Vz584kJCSc0maY4tTZ7XaSkpLo1KmTrKZUi6ReT6xImXlK6UK0l+q4JA1Lnc3hEEIIceYWL16M1+tl/Pjx9V2UBidhSAxui5nYgqOb22WERrChZRfisvI5QgcKiMVBMEeUePKVWDwYKVaCWdFmGOnNw8lp5h9SFWUvYua2sag6FYNexfuvUO7vb0Cvh+YuN21Ky0hwunGb9ewLttCtg5Ep7SHlT0YOPFQ3++0IIURD02jWjMjPzz/pePFjV4uqidlsrranwIUsJyfnpGmCgoJqdfz6udQQ7s/tdp/SxPvw8PATLofckNjtdux2+wnT6HS6BrM87+n48ssvyczMZOHChQwcOLBWNki70Kiqwr2vdybxxlyG7FVQ0filZTvis/wLHvjQk4d/HxQnenbFNOGiI2kkR0WRbfUP5XMZ/d8lm776EKxXJ1qZM0HjnS1Olmz3YDEqjOxgYFofA2qVlbLcbl+19wohxIWo0QQcU6dOPel48V69erFt27YTphk9ejSzZs2qxZI1bFddddVJ0zz99NOnPD/gfNMQ7u+3337j7rvvPmm65cuX17hZZUO0cOFCFixYcMI0sbGxrFix4hyV6Nz529/+hslkokePHjz55JP1XZwGK7a1lXtnd2HLmCRaFWTjVXWUWqo/OMgMsrFkSG+Cv3eyNz4WQ5kLt06P2e4m2OOg+f8NrzF/RVGY3tfM9L51fSdCCNHwNZo5HL/++itO53EWLS93vPXzq4qOjqZNmza1WbQGbePGjSdN07ZtW6Kios5BaWpfQ7i/oqIikpKSTpquR48eZ7378/kiNTX1pPtaVDTKhTgRX1EZO175nbe3mGhSVMKYnzdhdfp3KfcoKrdfcxWlMVG0tzt4dPnnhJeUsCmuLRa7F1+wgTEHppzxtd1uNx9//LHM4agjMtegbki9nliR8tAppQvRXqzjkjQsjaaHQxoedaN///71XYQ61RDuLyQkpEGUszbFx8ef0u70QpyMGmKm+5P9uP/XAt59eBevXjGMXsmpFCkK/+3fg4yQYPqU2HHp9ZhdLkxeDya9h/euGszzJ+8AFUIIcQoaTcAhhBBCHE9CjzAGNSviKaUDb7Y/2ott9fpo5nKj+nykREUTlnKImMJCLt3+B60Xjq3HEgshROMhq1QJIYS4IIx87wr+2qqIG5KSiHO6aOdwMrSoBL2mEeTyEGr372mUb7Zx589X13NphRDnIw3llP6JQBJwCCGEuGBMmtWNNwbk8Mq6FQzOLyS6zEmkw0m3lEPE5+Wyu0kzbFM6o7MY6ruoQgjRaMiQKiGEEBeU4CdHM677rzT/25f8GNGOzPBo8qNDWTZpJGOe7khcJ5ncLYQQtUkCDiGEEBcc/bge9BvXg371XRAhRIMiw6XOjAypEkIIIYQQQtQZCTiEEEIIIYQQdUYCDiGEEEIIIUSdkTkcQgghhBBCnBKZw3EmJOAQQgjR6Hk9PvZ9l4XeoNJmSDSKIo0GIYQ4VyTgEEII0WiV5jl5d04y6V9m0LTEQUhxKV9aTVw5pzdtL29W38UTQogLggQcQgghGqV3HtxB9g/ZFIYGkduyGbmA6vXRIjWD1X/5hbs3N0Fv0tV3MYUQDYhW3wVooGTSuBBCiEbnrWt/wrBmH23ys4l0lIDmbyb4dCo5kRE49Qb2/pRTz6UUQogLg/RwCCGEaFR2vbePhO//oFleAQAXHTjE1qxWbOzUAY9BR5lRT0RhCStn7uCrqH3c9n4Pgpraaswrt1TjhQ8zyP/uAM5O8Tx7TzzNI+VPpxBCnA75rSmEEKJR+WreARIsZpYM7ofR7aHTgcNs6tgBh83s7+nQfHzXvxuaohBWXMq80Rt4aNOlKGpgp39Rmcaz137B82uXotd8eBWFf625muf7D6dPTjp/+uEHWhQUkh4dypUfXoWpT+t6umMhhDi/yZAqIYQQjcb3//qDYoOZveGxdPo1jeZ789ge3wp7kBnwj78uMJspMxhw6vUcCQ/lcFw0L1/+A/YiT0Ber3+WzXPfLEOv+QAfm5rGs65pCzweL3GZuYSXOjhiDaLf/hRWjf+CiXOy2ZPtPfc3LYQ4ZzSUU/onAkkPhxBCiAbPVejkxfFbSY8Io2u2nY6708vPlHGoZXRlOqdOhWOWxC0ItrEXjWVvpnLVnfGMey6fzFKNnmkHMHvdKDjJsJkZPvF+nHoDAB916c5HXboDEFrm4I3VKyjZlMI9mw/wyBM9uKKD6ZzctxBCNATSwyGEEKLBe3fEd6RHhoOi0OJw4GTwuPQ8APReL6pWfY0ZH+A0m/j++yKufPgIjgI3VqeTx35YDngBPf/teXVlsHGsQrOFqddOJDM0lp+btOWhf+9lwluFlLpkPRshhADp4RBCCNHAeV1e9kVHVb726AOXum1ypIDWhzNxBRmxGwwcjIwI6OWIzS+kWX4BTp2OndEh5JsttMzNJlhzowApIeH8FNfmhGVwo/JbTBjYXWyPbs6wj77m7bRWRMu0DiEaGRkudSYk4BBCCNGgvTL6Z6yajot/20PckTyKjBY8OpX8MCtOm5Eysx6Xzd87YXW7aZWbR06QjTK9gRbZOUSVlAJg8noZvj+FH9q1oSwkmnvG/4mIwnQ+S+gIDv/8DkXT0GrapVyngqoCCvh8vNp7BNdt2sBEdzF0Plc1IYQQ5ycZUiWEqBcrVqygT58+bNmy5bTel56eTp8+fZg/f34dlUw0BMWppSwZ+y0LOn9BkaZn0C+7CSkuxW42kNEsjC+v7sG+bs1IbRNFTrMw9B5v5V4cNreblvkFRBcXVwYbFYJcbqxuN3rNh4JCWng8RrsbT3mQoSlKeT5VhkspgLV8uJVZR1iwwuCiTH7o3pN2b+/luz9to8nrRmau9fDVQS+d3vLQ530PP6T6zkFNCSFE/ZMeDiGEEA1KQaqd1278hYLwSOgQhcHpYl98DL7yZW0dJiMhpfaAgQ8KoPo0fDr/0TK9nozwMMwuN9FFxZXpyvQ6PHpd+Xs1vCjYj1kuF0Xhr5tX0Ts3hW5ZqXh1KnP7XcHXrbvTPyuN2UuWYHW70dAwkU/P9NVctfs3hk19gtkWG5elJzEgdT/zvmjCTS070LSsiCOtmnNRcxM3dlMYl6DDpJdhG0Kcj2QFqjMjAYcQokGJjY1l/fr16HS6kycWjY69yM28GzdTEB5eOQ8jrLC4MtgAsDhdKAp4TMZj3u3vlXCrKumhIQCkRoRXBhweRSEptknlkCm3oqAHDD4NtxrYyIh2ljIpaVPl6+e//ojMidH81iyOe6+bxAPffceGZs05EhTMxN3fMTB9Lw/9vJJHrriJfSFNKYvRUabqicnLY1P7DuBROHQQlh/QQHNj8Lrx6Y3c0wteHVHzZHUhhGgoJOAQQjQoiqJgMsmSoxeSgj0F/PzUdn7LVSlT9ehUQ8Ckb6PbXe09NSxGRamiJyMynDKDoTKo8CkKh5tEUerxkRodiUenokdDAyp25WjtcrPHXCV40an0yzwYkPcNN8zgi869AfgjNpalrTvhLX8S+t+BQ3jzi7f5sPdQMOlIbhJLckxT/7wQlxfsHjAbqozSUnDrjMQVZPPBT1be2GbihUsNPNhP/mQLIRom+e0lGpwVK1bwzDPPMG/ePHbt2sXixYvJysoiNjaW6dOnM3r0aMA/1n/s2LHccccd3HXXXQF5zJ8/nwULFrB8+XKaNWsGwKxZs1i5ciVr165l9uzZ/PDDD7jdbvr27ctjjz1GVFQUS5YsYdGiRaSnpxMbG8sDDzzAsGHD6uQ+N2zYwLJly9i5cyc5OTkYDAa6dOnC9OnT6d27d0DaO++8k4yMDN5++23++9//8vPPP+NyuejZsycPP/wwLVu2PO36O5M6TE5O5uOPP2bbtm1kZmbi9Xpp3bo1EydO5JprrqmVeqmpTFWPde7cmQULFrBv3z6Cg4MZOXIk9913H3p94K+7lJQU3n77bTZu3EheXh5hYWF07tyZO+64g06dOlWmW7duHe+//z579uxBURTatWvH1KlTq/3cx4wZQ2xsLA899BCzZ89m+/btmM1mRo4cyQMPPIDX62XevHl8+eWXFBYW0qVLFx5//HFatw5cxsjlcvHBBx+wZs0aUlNTMRqN9OzZk7vuuouOHTvWSh2ej1LXpLL977/hy7YTVObEne8lO8TG3s7xFAXbsDlMaHr/hlqOICOKT0Mr73UoM5kweOwB+RUE2VB1KlanE01R8Oh0xGQVcKBZTECw0iwrn+LIEHrsSsZYppHUpgk7Q21kWE2gQYjDid3lAYsRvD5QFdCpFJkslXlkBodWBhsAuHyVwQaApqg8dtkkcqqspIWigFnvDzjKvGA6phdDg7SIaH86TeMv38Ouf6/kvysWYvJ68Kgqh5vG8dLEm3D3a487zIYCTOuqcFmL05ue+U2yj7+u9ZBWpNEvxsfro03EhZ1aHq4VSTgTt6NGWDBP6oTuiw1w8Ahc3QumDqu254kQ4sIkAYdosObOnYvT6WT8+PEYjUYWL17MrFmziI+Pp0ePHmec74wZM4iJieHuu+8mJSWFxMREHn74YS699FKWLl3KuHHjMBqNJCYm8sgjj7BkyRLi4uJq78bKrVixgsLCQkaOHEmTJk3Iyspi2bJl3Hvvvbzxxhv07NkzIL3D4eCOO+6gW7du3HfffaSlpfHxxx8zc+ZMEhMTqw1Bqov627JlC9u2bWPw4ME0a9aMsrIy1q5dy7PPPkt+fj633nrrmVbHKVm/fj2LFy9mwoQJjB07lu+++46FCxcSHBzM9OnTK9Pt3LmTe+65B4/Hw7hx42jbti1FRUVs27aN3377rTLg+PTTT3n++edp1aoVt99+OwArV67koYce4vHHH2f8+PEB18/KyuK+++5jxIgRXHbZZWzcuJEPP/wQnU7HgQMHcDqdTJs2jcLCQhYuXMjMmTNZvHgxavlwII/HwwMPPMDvv//OyJEjmTRpEiUlJSxdupTbbruNBQsW0Llz41vyKGtDFhum/oDe66N5fgEFihWXTs8fF7WmrLxnQVMUvKqKV+efX9Ftz0F2tG+FT1UpDAkiMr8Il1GPhn9PDc2gp9RkxK1TMXm9oChY3F6u/PkXTAYPBTYbWcYQIgrt7IwMYVeneFolpbG0R2sybP5dyVGg0GZGp5UBGhiOfode7n81Vx7YjsHnRe/1ofp8R4d1+ap3r+RagqvfuHqKjXFFIaqkkP+u/ACrx9+bo/N6aZd2mOff+C/RTd/Cne6/5sKdGp+OhQntTy1g+CHFx+UfecrHpSssT1H5YU4p+x+yEW49cR5l8zdSeveyytfO19YTqh1EhxsS18MfKfDC1FO7RyEaCJnDcWYk4BANlsvl4v3338dg8D8ZHD58OOPGjeOTTz45q4CjS5cuPPLIIwHHFi1aRFZWFomJiQQFBQHQt29fpkyZwtKlS7n//vvP+HrH88QTT2CxWAKOTZgwgUmTJvHOO+9UCzgKCgq4+eabmTZtWuWx8PBw5syZw6ZNmxg4cGBA+rqov1GjRjFx4sSAYzfccAN333037777LjfffHO1nobadODAAT755JPKHpcJEyZw/fXXk5iYWBlwaJrGrFmzcLvdvPfee7Rr167y/bfeeis+n3/loKKiIubMmUN8fDzvvvtu5c994sSJ3HjjjcyePZsRI0YQHHy0IZmamsq///1vLr/88sq0N910EwsXLmTIkCG8/vrrKOVPfENDQ3nxxRfZuHFj5c8mMTGRrVu38uqrrwb8vCZOnMj111/P7NmzefPNN+uq+k6L0+nE6/XWSl6//+s3FA1sTic+TcGt6igOM1cGGxUqGvQxeQUM3LWHiw4mkxsSTGRBMS63kW8HdaE0yIxPp0MHWJ0uiixmjHb/BPIQxcFl+36rbC6U6kws7jOIQVt34dbr2BcURIbl2HkfoBl06B0uPBZj+dK3Gt+27sLAW55i+q/f41VUQopLKCifF4JBBWdg3Wg61T/Oq+oTf0/5KlUWPRjKV7/y4f8HAWnb5WRidbuqlS3U6eDa7Rv5pOfF/usAL27ycHW8p1ramszdrKIR+DAiHwMLN5Zwe/8Tf1fLXvwh8B41FSehWPFvvKi9tgrHY+Oq997UEofDEfBfUTsaY71ardb6LsIFTwIO0WBdd911lY1lgJiYGFq0aEFKSspZ5TtlypSA1z179mTRokWMGjWqstEJ0K5dO2w2G4cPHz6r6x1P1WDDbrfjcrnQ6XR07dqVHTt2VEuvqiqTJ08OONa3b18ADh8+XC3gqIv6q1pmp9NZ+QdrwIABbNu2jeTkZBISEs44/5MZNmxYZbAB/vkeffr04ZNPPsFut2O1Wtm9ezcHDhxgwoQJAcFGhYreho0bN+JwOJg8eXLAzz0oKIjJkyfz0ksvsXHjxsrgAvx1WPU1QI8ePdi1axfXX399ZbBRcRwCfzarV6+mVatWdOrUiYKCgoB8+vfvzxdffEFZWRlms/nMKqgW1fQZPFOlRf6laas+N7Q43NUa6Dq3F49RT7Mc/87hVqcLa3YuTvSUmE04rCZ8VXryFECn+fCoKgafjz4H9wZcw+Z1cvWWbeSZwtid0AxdqInLD2fyfYsmuKr2CPo0VJ+GodSJT6fitZoAja1NWrN1RKvyMqrcvf5LUsKj2BsVy56wJlDm8QcPBhVMqn/olFEHikJ4STG9D+zmp4TO2Mv3CEFRQC0POo756xxRWszx6LXA5XXzSpwkJe05UZVXKihsDoRXO34w9QhJIUUnfG98ob1aI0Krutq+y83uP3biqyGIq03Jycl1mv+FqjHV67HDkMW5JwGHaLBqGsYUGhpKZmZmreZb8QS7akO2QkhICIWFhWd1veNJTU1l7ty5bNiwgeLiwMaGUsO46Ojo6GqTqUNDQwFqLGNd1J/dbufNN9/k66+/5siRI9XOFxWduAFzto53T+CvA6vVWhlQdejQ4YR5paWlAdCmTfUdpiuOVaSpUNNnpOLzc2zZQkJCKstV4eDBgzidzmpBS1UFBQU0bdr0hGU/F7p27VprPRzhfwln2/SNlJhMhNsd6DUvOCDhUDr7WvnrTVMUwnOKKYgIpiDIVvleJ3oy1TA8bh191+0hrXUUh9tGV573oeBRVVx6PTZnWbVr50UE8WPXjjhNRux6PT4gIaeQndHh/iFPXh+q099bYHM7sbrclKihuHQqZZqCommEejwYnDBk3y5u2LGee6+9jT2DmvmDiwpuH3g1cHhoVZTDwTl/Ji0knPgnj9lPRlFAVyXQKp/9/lPL9qQHh9OsOD8geanByOLuAwKO3XaRIWAe0on8JUThq8VawDARo8vF3cOb0TzsxENF3dMy8Lz4U5UjGiaOfse91/SjQ6+LTqkcZ8LhcJCcnEyrVq2q9QaLMyf1KuqCBByiwVKPXRu/nFb+B7qmRnmFEzWUjrfc6vGOazUth3OW7HY7d9xxBw6HgylTppCQkIDNZkNRFN599102b95c7T3Hq4/jlfFk9QenX4d/+9vf+PHHH7n22mvp1asXoaGhqKrK+vXrWbRoUeVwpbpyunVwLq9/KvUNkJCQwIMPPnjcfMLDqz+Nrg+1uVJYmzFt8LzgYeeLf5Bq1mFzutCVeInfnUVEXjEOmwmnQU9OaAj9v9tFTkwwBSYrYU472WoIHsX/3VQ1jeYHsvFYFDKbRaIBqs9HVkgwKAo745rTPeVQwLV3xcfjNBnJNxp4q30r7IbyP4suD/g0pv76PX/+5TtSgsMZlroXg9fL1ZP/wvfxbVF1Cu2Ky7CUz9n4z5CbiCsuYsaPq1nUczCFlvLASNMChli9+sUiAKJLi4gqKSInKCSwQqrO7fBqoEG+JYhB9/6dDz5+lYsP7cEHHIxown3X300nsxtvqAFVhWldVB7sbTrhd7eqK9rB8ole/rTGQ2aJRlurl3nXW+jQ7OTDoLR/XY0jxIrr499RIq1Yrk1A/9V6OHAEru6J/tkb0FvrvsFqsVhkyEwdkHoVtUkCDtFoVTxBrump+rFPps83mzZtIjs7m6eeeoqxY8cGnJs3b945K8fp1GFxcTE//vgjI0eO5PHHHw84t2nTJs4XLVq0AGDPnhMPOYmPjwf880L69esXcO7gQf+SqLW9WEDz5s3Jz8+nb9++JwxeGqP2t7an/a3tqx0vyHWRvq2A3+btJDMH0puH0/xgLrlaEL90b0OLP7KrvWfQ9iR+VdtyoGksQU4nhVYLPkVhSb8BqJqPzmmplJjMfNOtOy6Pv2G9OTr8aLABoCgYfV7e/N8iTF6NntmplaeiXCVoViORdmdlsFHxnscuv5U3P3+JDa88znWT/8SOyDj/SlSKis3tZPZXixm5fxdeVIxeLy+veI9bJ92Lt+KBhqaVTzpXAM0ffGgwui38e2gTuvz73/g0Da8PEnQKX9ZC3Y9up2N0u9Pf10bR67A+eRnWJy87evDBy47/BiHEBUsCDtFo2Ww2IiMj2bx5M5qmVT7xS01NZd26dfVbuJOo6E059un3hg0banXs/MmcTh1WNJCPLXNOTg6ff/75uSjuKWnfvj1t2rRh+fLlXHfddbRt2zbgfMV99u/fH4vFQmJiImPGjMFm8z+tLi0tJTExEavVyoABA2q6xBkbNWoUr7zyCh9++CE333xztfO5ublERkbW6jXPd2GRRsJGxNB5RAwA6TuL+PTGjXQ4mEGx1UqZxYDZEbgPh0VzMmBPEgeaxqICBq8Xp6piN5t5f+hlGDye8gnoGr2378did1JSw2IGRs2LyeuB8knVBSYLU0bfzraWbXl76RscjGjJio6DAt5TYrLS7ZZnuezQTrqkHGKHrSm3/foTt27fwICMQyhAriWIe8bexotrPuTmbT8QUVrC3eNvJy0kHE2rmFyugapySTysmqjHZjzaY6EqSsBoLSHEuSOrVJ0ZCThEozZp0iTmzZvHjBkzGDp0KDk5OXz22We0bduWnTt31nfxjqtHjx5ERkYye/ZsMjIyiImJYc+ePaxatYqEhAT27dt3zspyqnVos9kYMGAAq1evxmQy0aVLFzIyMiqXDa6ruS6nS1EUnn76ae69916mTZtWuSxucXEx27ZtY+DAgUyePJng4GBmzJjB888/zy233FK5P8nKlStJSUnh8ccfD5hMXhumTJnCxo0beeWVV9i8eTN9+/bFZrORmZnJ5s2bMRqNzJ8//+QZNWLNOodw+YL+HByzgl479vNbu1Z0/D2FiinnoVoJodjxlvkDYB8ETgDXNNw61Z9aUdnZrjnXfrmJH1s0ZUdEaMC12hfkUabTYS4fDTVz6ATWtO3KT4ueY2DqPjbGdagWcBSXB97ftOxMxuszaFOQzRdtutOhoBXxJXlsbtGGx6+awN6IaD7v2guLz0OI08GVB3diCjcTO7gN/fpHc1UbiSiEEI2HBByiUZs2bRolJSWsWrWKrVu30rp1a5588kmSkpLO64AjODiY1157jTlz5pCYmIjX66Vjx4688sorLFu27JwGHKdTh//4xz949dVX+eGHH/jiiy9o3rw59957L3q9nmeeeeaclflkunTpwnvvvcdbb73F2rVr+eyzzwgLC6NLly4BSwJfd911REVFsXDhQhYsWAD4e0hefPHFOtnwUa/XM3v2bBYvXsyqVasqg4vo6Gi6dOkSsCnjhaxLnxC0paPY+NAWmh/IwhrioElBEUbNjQ0nAIeiY1C8PlSPD608CFB8PgZv3UNKbASZMWHEZ+VidTrZmxDLJQdSKDIa2BwZhltV0Xm87AiKIOrul0n8aiGXJP/BijbdScjOYGCq//vXP203D/70Gf/X62qKTRaKdCqpRv+f1Sh7EQbNyx2/rSPVGMSBsBC+7t+dHaOGM2DDfu7bvZ57p7XCMHEAEArU/0IAQghRVxTtXMykFEIIIeqApmm8020FPfcdIMZZiB4vecYgfmnaitS4JpSGWnGrKh5Fof8v+2mRnovToOP7AR0oDLZhdroIsTtQNI2U8FBywsNYFh1Ose7oRPQ2zjK+e/ufjBl/D5k2CykLHkbl6J9Ot6py1Q2P8E2To8ssv/BdIt2zU/jq1n5MvLoZnTskVK6YJmqH3W4nKSmJTp06yeTmWiT1emLZyhOnlC5ae7aOS9KwXFizEoUQQjQqiqJw86aRrO7ZgxUJfdgU1I6dIc2xmy2UhvobSwafD7PXy4HW/nkgv3dqSXrTaEptVnIjwjjcNBpNUTA7XWwIDa4MNtA0BpY66FTmIdsaz2PrvyfHEszHHQIXEdjepAXfJnThypQ9TNzzCxh1zB54NV/27kePNgUy30KIRkRDOaV/IpAMqRKiluTn5590XwKPx3PSnbbNZnOtzw0433m9XvLz80+aLjQ0NGCzQiEADFY9BjNs653AdqeLTrsPg/Ho58RhMOAwGskLsrH2YpX0phEB7/fq9ZRYTJQqKvoqPRfRXh8RHv932m40M/zQQTa8t4CFXbvzY7vOXJKcxPYmLXit/xVoqsqEPTsYcmAnd//6FT906s7fVk7g448/PjeVIIQQ5zEJOISoJVOnTiUjI+OEaXr16sW2bdtOmGb06NHMmjWrFkt2/jty5Ei15X9r8sYbb9CnT59zUCLR0NydOIC54zdwJCKM/R1bEp7vX6TArdNhNx/dMyStWVSN7y+wWPnTX5rwcOcorvl7FocNJmxV9o35ulN3Omam06qwgCfWf0+/GTOY129E5Xm910t8fh53jZzIu3N7MzxCj9vtrulSQghxwZGAQ4ha8o9//AOn03nCNCEhISfdbTs6OvqE5xujyMhI5s6de9J07dtX36dBCIDgGDOPfD+UJX/fyS8bHJQG2YjOyiWzafUAo2KXiwo+QDVD28v8O8Vvfb0ZBcVebn6vBM8vZeiBLa0TeEk/ml4HdlOmKljtZeDzgapicblolZ7BwgensOKuWELMMpxCiMZLvt9nQgIOIWpJ1dWNxOkxmUz079+/voshGjhFVZgwqwu++EWUBZtQSr14VB2pzZsEpFPdHkosJiweLy6dihOF2e93D0gTFqxj0e3BdHnMTWu7iyCPl/+1SeDztm3x+nzcsvEnXjaYyLLa0PvcvPHXFlzSwXwub1cIIRoMCTiEEEI0Lu2jiP8tGVXz4srSkxMVRpnFP6xK8Wkk/D979x0fVZU2cPx3p2Zm0hsJhN4hVCkiiii4KE1UREAQwQVR7Ipld1XU3XfV3RUUabKCoEZCXYqiggIiSEdACIROIAmQkD6Zft8/hgwZElpICJDn+/kMkHvPPfecO0PmPve0wydwqbC7YS0CVQd/fas2hoCSI7uDAjQc/k8EI7/OZ9luF+i1PNFOxz+6atCEDOC1a10vIYS4QUnAIYQQ4qbS46vbWdHhNLef2EeQw0ZMQTanIsMoMBv4vl499jWrQ4cTp3mln5FmQ+pfNC+tRmHG0KBrVHIhxPVO1pIoG5kWVwghxE0luLqZB48N5sDt8VSz5tDu5BFqnkllQuc2LGtVn+PVwogqLLxksCGEEKJ8SAuHEEKIm46iUei8dgC2M4V81ucX/levLlqPhl47D3LnvqMM+aJ9ZRdRCCGqDAk4hBBC3LQCwk08t64HwzLtrJqbTmj7QO74ugdajcw0I4QQ14oEHEIIIW56IRFG+j1Vu7KLIYS4wckq4mUjYziEEEIIIYQQFUYCDiGEEEIIIUSFkS5VQgghhBBCXAbpUlU20sIhhBBCCCGEqDDSwiGEEKLKev9/uSSssxJqc3CXxkpQfARPDAwjLEi+HoUQorzIb1QhhBBVTm6Bm7teO41dq8XkhFxdAP81BPLQggO8ufAwhZ2q8/lbcZVdTCHEdUe6VJWFdKkSQghRpSxelsmYkQfIVjRkulVMDidNMnO578Bx9lvMrAsPJWT1IV7svIrkpLzKLq4QQtzwJOAQQghRpcxJyOSIycwZjYb2BYVEuj24DXpyQkNo6HJzd3oGO81hNMjKZ+X9y0jdeaayiyyEEDc0CTiEEEJUGaczHOQYDewIC0KvUVgWFcqSyFB2WQJQgfTQYGoUWEmuG8V7fW9nbbMmrOv2La+/tRePqlZ28YUQ4oYkAYcQQogqoTDdytd3/I9fgi0EOF2cNuhRFQW3RmGfxUSKUQ+Kt392NdXJA7sO0OJUFhtb1eXB8T/ySfxCJj61icJjuZVcEyFEZVFRLusl/EnAIYQQokr4st/PTLitDZ+umI1dW/LrL92gJyonF7dGw4Ob99E8LZPI7Hxa70/jQGg1bj98nL5TV3Cy9kS+aPA13Z/YR97pwkqoiRBC3Fgk4BBCCHHTmzn3DLtDApiw5hv6H9yGxe0pkSbCZiMmK5sMs4kgpwvF7cGcb0dRoUZeNtGF+Sh4vzjvPniEW/YdpeaHhbw9NQ2nW7pbCSHEhUjAIYQQ4qZmc6js+CaFnnt/pc+hHQQ6CnnwcBI6z7mgI9DlJhgt22rFsbdaJAA6l9vXMSLUZi2Rb6cjx/HotKxbkcHE5kv5YMQ2nIWua1ElIUQlUS/zJfzJOhxCCCFuams+2kFyUBATju0kX28k0Gnn/378jJp5/fmubgu0qopBa8Ct0eLQ69HqdOQYAghzuX15FOr1hNn98z0YGU5egJEOJ1J5695O1MzOJ6XfJj5ZfisajTzPE0KIIvIbUQghxE1r2r/28O16B/uCg/g9ug4BLgcAwfYCXls9i7lz/4EFDW6NFkVVvV+KikK+RU/0mVxcZ8d6pASFY9Oee0Z3KtDCxC4dATgeHsIDu5PZGx3O/Hp1CHo7jwKHPOMUQogi0sIhhLikUaNGkZaWxtKlSyu7KJeltPKOGzeOZcuWsWXLlkosmbiWXv9XOq6VJzkdEkuky81rPZ9hyO8rGPL7D6goJFWrzz/vGo7F7kLnVnHp9b4uVC6NFr3dzYHIYL5q24jYfCvHgu+gzZnTaDQKKxvVx2o0oHV7yNfpaJiZA6rKySALd+w7wWN9D/DQ6FqVWn8hRPmTGajKRgIOIcR1Y9q0aTRu3JiuXbtWdlGuC6tXr2bfvn08+eSTlV2UG8rR7RmMHp9Nnt5A/1N2DlQzEub2gM7I1+16s6LJ7QS6PLgUPU3TM9CfHctxMiiQ/dGRtDx+lFFrVhLotBPrSeXHprH8VisOgBOhQWj1WiIL7QRbHaQHm/mpYV22RUV7O24H6FjbvC4AP6+zEa67gzlNMyvrUgghxHVBAg4hxCVNmjQJ9RosejZ9+nR69+4tAcdZq1evZtmyZRJwXIZCh8r63YWs/2wX31AbTCbu3n0QvduDzWjwS3syMBSn3UHtjAyORYSRYzDgcblokpnFn7bt5JGd69Ce/bw3OHOGuQvm8UGnO3AZrGyrVpPk0Bo8tjGJf/XtCIpCriGQ3GAz2N2g0XgDD0UhO9CE1a7Qf5aGh3ooPN3JTYMIbSVcHSGEqFwScAghLkmv11d2EUQV5PGoeDyg053rwpBf4CJjWyam6hb+OOpk4io76cftNCiwEmWzE5zvpqshhZU1Y7jn4DGORUeUmneqQY8uNJRApwuTClazmf+FhfDa4cO+YKNIkNPOiKQVjOw5lN+r1abx6VOkRpjptfMgK5rVwaHVgkv1BhtF0+PqAEXBYTByLDSS8ZsUxm+0E1JgQ6f30DpWx5t3aOnUwoJWp3D4jAeNBuqFXzogyS50czRXoUEY6DWwOsWDWQctoxR+Oa7yx2mIskBKDuQ6wOaGmkFQOwTWpMDRbMh1QUQAPNBIId+ucjgH2lSDYCPUCoKMQlh2EDamQlwQDG8BvRvosLlUDFrIsoFFDwE66V4ihLg0CTiEuApLly7lnXfeYcqUKezdu5f58+dz6tQpYmNjGTFiBL179wYgNTWVvn37MnLkyBJPq6dNm8b06dNZsmQJ1atXB86NN1i5ciUTJkxg7dq1OJ1O2rdvzxtvvEFkZCQLFy4kISGB1NRUYmNjefbZZyusZaC0MRFF22bMmMH48eP57bffcDgctGnThrFjx1K7dm1fWrvdzhdffMEPP/zAyZMn0ev1VKtWjdtuu43nn3/ed30Ali1bxrJly3zHFo25+PHHH1m+fDnJycmcOXMGs9lM69atGT16NA0bNixTvcrzOv/4448kJiayf/9+3G43DRo0YOjQoXTv3t0vXbt27ejduzcPPvggn376KXv27MFoNNK1a1defvllzGaz7/pu27bNd0yRt99+mz59+pSpvterM9luZs7N5o9kO7Vq6Bn6QDB/+fg04cdzCXB7yNPryDYaCHbYyTOZCSy0EZF7jPWx1Wh8JIOYEANxmVkYnU5sRgPVbTa6ZeXi0etocyiFFW2a4dCf+7pzKdAoO5eYgnOL9mWZAnC4zTQ6klWifCoqg/uMZH9kNQD2RUVz2hLE8G17WB5fD1wecHt8q5SjKN7AQ694/60Abu/2nMAAMGlZZYPkhBxytC7aHUmj5+5DhNjsfBQbxaz2zSmIsqDRaVCB0AIbMQVW9sRE+pWqpCtvhVx2+NLHbDoFCw8CqvNcHS/AqIWGYd6ARa8BpweaRyj88w4N7WNv7ODkVIHKK2s8/HxMpdnZOt0Sc2PXSZSFvOdlIQGHEOVg0qRJ2O12HnzwQQwGA/Pnz2fcuHHExcXRunXrMuf73HPPER0dzejRo0lJSSExMZGxY8dy1113sWjRIu6//34MBgOJiYm89tprLFy4kBo1apRfxS6hsLCQkSNH0qJFC8aMGcOJEyeYM2cOL7/8MomJiWi13qe1H3zwAUuWLKFXr148+uijuN1uUlJS2Lx5MwBhYWG8++67vPXWW7Rp04YHHnigxLnmzp1LSEgIDzzwAJGRkRw/fpxFixbxxBNP8NVXX1GrVtkH6F7tdZ48eTIzZszgtttuY/To0Wg0GlatWsXrr7/Oq6++yoABA/zOl5yczIsvvkifPn3o0aMHW7duZfHixWg0Gv76178CMGLECFRVZfv27bz77ru+Y1u2bFnmel6v/m9SBsmHvLNHZWa52b2rgNpZeefWwHA4yTcayDVbAMgzm7Dp9bTdexxHdAiNUtN8Uy7qC23kG/TYjAY2N6vPIyt/Y8y3q/m0553YDHrcioLe5fQLNgBCC20UoqCqOnIIJIR8377UUJ0v2Chyxmzi25YN8aiA8+z0uUUtIxoNoHD3ziMM/mUPgTYHq5rXYsZdLXHqtFDgwhOoJyUmjACrgyFb9qA5e2j7E6dIiQ5lSY1mFE3KeybIzJlA09Vf6Kt1iWADvL3K/sjw35ZeoHLPfDeH/qwl3HTj3qz1X+pm7XHvv0/kq2xOd3NklJYQ441bJyGuFQk4hCgHDoeD2bNn+7oedevWjfvvv5+5c+deVcDRvHlzXnvtNb9tCQkJnDp1isTERAIDAwFo3749gwYNYtGiRTzzzDNlPt+Vys7OZujQoQwbNsy3LSwsjE8++YRNmzbRqVMnwDsW4bbbbuOdd94pNR+TyUTPnj156623qFGjBj179iyRZuLEiZhM/jddvXr1YvDgwSQkJPD666+XuR5Xc5337t3LjBkzGD58OGPGjPEdP3DgQF5++WUmTZpEr169sFgsvn379+9n5syZxMfHA/DQQw9RUFDAkiVLePHFFzGbzdx66618//33bN++vdTrUdnsdjtut/vSCS8h/bTbF2wUCSx0+D1DtGu1uHT+X1dOvY6auYWkRphLzO9udjhxaBTSIsOYfV8X6h9NpfapTHbU9rYgBpayyrgC1EzzDu4+TST5WDDgIDMgENV0vGR6VUVVOBdsFKeqhNgc/GXBBt+mhzcko6Lw2T2tvYFJvhNCDNhNepTzGhlORQaVzPMybvavZzl2WJBk59EmJa99WRUWFvr9XZGO58Ha4/5jgbLtsDDJziONyq9O14NreV2vlaKWY1F5JOAQohw8/PDDfuMcoqOjqVWrFikpKVeV76BBg/x+btOmDQkJCfTq1ct3EwzQsGFDLBYLx44du6rzXSmNRsPAgQP9trVv3x6AY8eO+QKOwMBADh06xIEDB2jQoEGZzlUUbKiqSkFBAS6Xi7CwMGrXrs0ff/xxFbW4uuu8fPlyFEWhV69eZGdn++XTpUsX1qxZw65du7j11lt921u0aOELNoq0b9+edevWkZqaWuZrdC1d7TUvkm9VUJRIVPXcDbX7vEXztGrpN3R6hwt3Kd0bHDotKQEB1Cu0cSYkkIO3NMet0RDg9mDXKOQZ9DgVBX2xsRqFWg2hWXnnfsaEFhtmssl0VqNf8jb+16itb/+dh4/TOCuHfR3838ci92/ZX2LbXbuP8Vm3lmeXIlbBo6JqFO8Y8+L1dd1cN7BFck+lkKTmlnu+R44cKfc8z5ft1KJVmuNW/T9vuSePkeTOu8BRN7ZrcV2vlVtuuaXc8pJpcctGAg4hykFp3ZhCQkJIT08v13yDgrxPPovGehQXHBxMTk7OVZ3vSkVFRWE0Gv22hYSEAPiV5aWXXuLtt99m4MCB1KhRg3bt2nHHHXfQpUuXy16Ree/evUydOpWtW7eWePJ2td3IruY6Hz58GFVV6d+//wXzz8z0nxb1Qp8X4Jq/h2UVHx9fLi0cAHfuzWf1hnPLeDsijBTa7JjOTler96iYbTasAQG+NBE5ufzcuAadTmRitegxO5y+fb/HRJNlNLBbp+XW1CN0PnmQpc3uwOgBt6JB73LR+mASB6rXxh5gIkuv549AM654IymRIQzesItW7q2EqplgA7dNR+3/5XJ3o3TW1KlDtsVClyMnSAkLRqequM6/AfF40JRyT5IXYMDbBwtvhKFRUBxu0oIt1MgtOFd/h8ebrngmqnpDt3I0D/fw58410GvLr8tnYWEhR44coU6dOiVaPyvC8CwP/919blB/y0gPwzvHobvJllC+1tdVVA0ScAhRDi5001w0laxykRuFi920FY2BuNzt12Lq2uIuFiwUL0vXrl1ZsmQJ69atY9u2bWzatInFixfTpk0bJk+efMlZsNLT0xk1ahQWi4UnnniCOnXqEBAQgKIo/Oc//7nqpv+rvc6KovDJJ59c8HrUr1//svItLe/r1fmB5tV4/gkTLZsWsGufndo19Nx7ZyBTlgezYUkmRocLu0aDS6cjLDeHEJsVk8OJW9GTFRrCb7WiaHkiE1WrJyMwgD0xkaSGeANGm1bL7QfW88iedbRJ+YPne47BptOBTsd/7urGfXt282tcPf4IMJCh874n62rGEqJL587V54JELS6qK8lEOmJpnJnF/7Vpwk/x9UCrAVRwuM8FEjoNuDx827IOvbccJLTAG0h5gITbmhS7gFqiT+USnlXAisZ1aJqaQbbZyLY6MWTUiaBdtEpGjpu8XBc1CqzE1DSy1m6h8HIbP0r7HPn9HlL9x5grvj9Ky8y7T1VLTWdQwKT3Vr1VFHSJUziYrfqeBDePVBjdSktIgKFEzuXBZDJdky4z0+5VuaOWys/HVJqGK4xurSXYWDF1uh5cq+sqqgYJOIS4BoKDgwHIzS3ZneDEiRPXujiVIiQkhJ49e9KzZ09UVWXixInMnj2bNWvWlJjJ6XyrVq3CarXy0Ucf+c3YBN4WAYOh8r70a9asyfr164mJiaFu3brlmvfFAtWbiVaj0P32QLrffq772ssPhsCDIZc8NjvfxWuvHUX7RxqBuYXYapwN5lSVM3odQ+4dzvi2d5NrjsCsPfc5URWFnxs3oX52DqvjYvzyrJ5z0u/nE0FhdBv6F/ZFnh0DYreRX9T6oCgQoPcGHMrZnxU3pwMCGP3YXfT8/QjVc/L5tUkNfm0UR9NoGNVWxwtdAgATUPq0vf4CL53kClidKgE678RaHhWMl5jaVlVVrE6wGKrG5/FCNIrCY80VHmte2SURlenGeCR0/bnJGgKFuD5ZLBYiIiLYvHmz3xPs48ePs3r16sor2DXgdrvJy/Pv46woCo0bNwb8uxCZzeZSuxQVtRyc//R/0aJFJborXWtFA7onTZpUamvV1ZSvqDvDjdLNqjKEBuqYNqk+f19zO+9t7Mo3f43krkMpdD2cQt2CQlqeOkNYrkK71JLvg1OjwVFKq9Sv1f2nWX7vjgd8wQZAvjGg5F2HRvFvQVA9ZJqNJNUKp1otlWUzqqP+M4g9LwSeDTYqj1mvoFEU9FrlksEGeP+/VvVgQwhxdaSFQ4hrZMCAAUyZMoXnnnuOO++8k4yMDBYsWED9+vXZs2dPZRevwlitVu699166dOlC48aNCQsLIzU1lfnz5xMcHEyXLl18aePj49m0aRNffPEFMTExKIpCjx496Ny5MxMnTuStt95iwIABBAUFsWPHDtavX09cXFy5jSUoi+bNmzNq1Cg+++wzBg8eTPfu3YmKiiIjI4OkpCTWrVvHhg0bLp1RKVq0aMHcuXN5//33uf3229HpdMTHx1/TqY9vNNUbBvKvBS2Z8tg2auxO5oHftqIB/vrI/SXGQYQ6nMTaHQQ7XeQWW6fjh7qtWV2/I3cc2oxW9fBb3EUG8atn/yjKV1Wpn36KzodPUiczh5DGVjq/Ke+XEKJqk4BDiGtk2LBh5Ofn891337F161bq1q3Lm2++SVJS0k0dcAQEBDBo0CA2bdrEpk2bsFqtREZG0qVLF4YPH05UVJQv7euvv84HH3zAzJkzKSjwDqLt0aMHcXFxfPLJJ0yaNImZM2ei0Who1aoV06ZN48MPPyQtLa2yqgd4F+lr1qwZc+bM4ZtvvqGwsJDw8HDq16/PK6+8UuZ8e/Towb59+/jxxx/56aef8Hg8vP322xJwXILRpOeFeR05uS+XY233EOB0khtkIdDtwar1LqZnUFWibHY0wEMnM1gVHkK60UiU08ltOfkk3tKPf3XoTbbWyc7oi6/xUuN0LiaHiwMxobzz/Q/si6rGL+0a8vZLwaz7fi4ao3QmEEJUbYp6o4xQFEIIIa7QgZdX4pywg38+2JNTocF+++rk5BLodAHgBKxGI7qzX4l2BX4KC+FYxNk1MYp1map36gyd96fwv9aNqX06l0KthoMxYXTdf4znN6xl/7v3M3Z4DE6nkzlz5tCsWTMaNGjgm4lMlA+r1UpSUhJNmzaVwc3lSK7rxR1V/u+y0tVW/1LBJbmxSAuHEEKIm1aD/3Tnh3wjxhQrSnAg6tkxG7XTMojLyCInKoRTQYHY9HrsGg2K6qFmVibP/LqMO2JqMPL+R7zrgnhUigZujF2+EZdRR3aYk63V6mOxuXl+1RYibIWsfudhJgwPr8QaCyHE9UcCDiFuQllZWZcc1+ByudDpLv4rICAgwG/hOyFuRHf/tRVfjzlItlZDQ2shNU9k0PaPw9gMOlbUrU6Iw4FVr8Om0wJakqNjmHp7Tz5cMpN1sQ35/NZzM6Pdt+sgkVYbq2rXpvVRK3ce/oOd1cI53iiKR5+Ko/2t0oohhBDnk4BDiJvQY489dslxDW3btmXbtm0XTdO7d2/GjRtXjiUT4trT1wpmVAs33/52hMyYKKqfzgYgN8hMuN2OU6Mh77yplfdHV2dPTE0m/pRIl8OHWVejIbWyctB4PGyrE8PKxjXJCTCihupJeycMTRWZwliIqk5WGi8bCTiEuAm999572O32i6YJDg4udV2Q4ooP6BbiRnb7/7Vj0tijBKbYKAgMgJPgMupQAJeicEqnxapRiHS5CTy7iJ9Dq0cBssO0fNa1LRqPh5C8AlwaDQFmLe89EMiT7S6+aKUQQggJOIS4KbVu3bqyiyDEdWf6uzV56ondnKoWRlhWPka7Ew+wLDqc4ybv2hj7VJX4QgetzmQSn3aELTXq8o877yW0wEp86klm/LMmDesEVW5FhBDiBiNz9QkhhKgSAk0avkxogQsP+5rV5mjtGA4HGH3BBgCKwkGDjju2JfOvHo/x6V39GLR+FwfuyWftN80k2BCiylMu8yWKkxYOIYQQVUpUbh4FAQrVCnM4bLUD0X77C3VanFodNqdK+30HeX75negiTJVTWCGEuAlIC4cQQogq5dV5bTHY4VhQLNbAat4VyItpeDqb49ERNKvu4OVN90qwIYQQV0laOIQQQlQp+shAxv18B/vWprHs1T3kaDVsCA/BptVSPd9Ka6uVW99rQb8uMiW0EEKUBwk4hBBCVEmN74il8W+x3Lklm6z0PJrdWo3o0BD0uuqVXTQhxHVKvXQSUQoJOIQQQlRp7dqFAqGVXAohhLh5yRgOIYQQQgghRIWRFg4hhBBCCCEug6w0XjbSwiGEEEIIIYSoMBJwCCGEEEIIISqMdKkSQgghgGWfp7Dox3wcGi3VDE4+mNkMrUa6TwghzpEuVWUjAYcQQogqb8GEQ3z9uwZHUBAAWcBzfbZz+74kgowuui/uS0CDsMotpBBC3KCkS5UQQogqbcX/TrH0NzsOnf8zuJToSPIxk11oYNojvzD/tllY8xyVVEohhLhxScAhhBCiyso4lMP6D3cTezKzxD5Vo6D1qNg1RrY1aMycRnfy4pDdWFOyKqGkQghx45KAQwghRJU1t/dPxJ7JoXpGNkabHcXj8e2rlpXLlpaN2NyyMYFOFwEeldzQUBYMXFmJJRZCVCYV5bJewp+M4RBCCFElfXz7SgI8GjRulRrpeTyRvB6nXsuuxjVIjwkmOzIU23nHaFX4PbgmgwsdaE2GSim3EELcaKSFQwghRJWTtj2T4BPZOLUaLHluTFYXCmBwurnlj2NUP5lb4pjQ3Fwe3zifHvs3sTf836iqeu0LLoQQNyAJOIQQQlQpzgInnz6zi9zwIE7GRhGYay+RJjrjXMARlp1P7ROn0dncGI8ZaHjwJFpjDnc8cwKb01PiWCHEzUu9zJfwJ12qhBBCVCnTB29Er9eSrzWhVVWcBi06l3/gYCp0UPt0KvF70qh7zDugPN+o5+UBvXFotby7cBWtjhyk3hsmTvwrHEWRPttCCHEh0sIhhBCiSsnOdJNjMePSaQm0OzgVF4KnWLyg1Tm40/obr3w/zxdsAATanYz9diM7akXzj75duHfjUR45kMLHty6qhFoIIcSNQwIOIYQQVcbbf0nCpdeRERSIxeEEID/UxIEWsaTXDCFEW0CM5QTVCzIpwFLi+Jpn8jA7XGyuF0tIto0ck5kIm52sLu9e66oIISqFcpkvUZwEHEJUIaNGjaJPnz6VXQwhKsX+745j+vEYVrMRrariPtsNygM4A3Rkxgaj1bmonXMSgEDySuRxLDIYq1FPzexstKqHJTUi2BxVl7T1Hn4f/M21rI4QQtwwJOAQQly2ffv2MW3aNFJTUyu7KFdk9erVTJs2rbKLAUBCQgJLly6t7GJUKS6bmxn917L8jT9QA8zkmi2YUDgaE83hmGgO14glJSoSxe7hy9tuZ2+1OACCySOGVIqGgBaYDGxs14TG2fn834pvyQuzk2028VOdWDZENePk0gL+02opPw1cijujoBJrLIQQ1xcZNC5EFTJp0qSrmsozOTmZ6dOnc8stt1C9evVyLFnFWr16NcuWLePJJ5+s7KLwzTffEBsbKy1N18i2pSn8/uJmGh3OQKPCvN6dUDXeZ22qRuObTcZh0HOsRjRBDhu/NOhI3ayTRBbkUJNjWM0GDgTX4XhMBFmhFu4+nk6fvX/g1mfx6M5WzG/UhmndWjHux194cOdu2An/PqhjcrcOvNSg/NfqyCjw8NYaF0sPwvHzG2EUQKP49+jwqH5T55h08G4XGNpCR6RJwa1Ctk3FpIMgozyHFEKUPwk4hKhC9Hp9ZRfhglRVpbCwELPZXNlFuSwulwu3243RaKzsolQZBZl2dAFa7PkuCh0e8rPdnAkxcfqUnYgDpzi44DjZe/MIzLZyKDqcrJgw9Kh0znOgUSE3yITtIov1OYx6AlwODkVV581eT1I38wSt9p6g/okMmljTaZKeTtOUVL7u1omZnbrzRat2/B4aBMCmWjV46LH+LP5iLg0ys+i3bRf/ub0dLxzrDUfw3vQr+X5dvKuZId8OBS7QKtAiKwu7TkdhuJnTWj1GLeSpGtwqeBSNN5AAUFVvP7DSnh1ozr6UYml1mnP/VqHQozJ2FYxd4/LmU1zxPBVQNBBjhrrB0DbGuy3QANFmDd3rKrSI0pLnUMl3QGxg+fRbT89XMesh2Cj94MX1R1YRLxsJOIQAli5dyjvvvMOUKVPYu3cv8+fP59SpU8TGxjJixAh69+4NQGpqKn379mXkyJElnpZPmzaN6dOns2TJEt/T/3HjxrFs2TJWrlzJhAkTWLt2LU6nk/bt2/PGG28QGRnJwoULSUhIIDU1ldjYWJ599lm6du1aIfUcNWoUaWlpfl16irbNmDGD8ePH89tvv+FwOGjTpg1jx46ldu3afvUDGD16tO/43r17M27cOAAcDgdfffUV33//PcePH8dgMNCmTRuefPJJmjRp4jtmy5YtjB49mrfffpvCwkLmzZvH8ePHefzxx33X9ccffyQxMZH9+/fjdrtp0KABQ4cOpXv37n51+vXXX5k9ezYHDx7EZrMRGhpKs2bNeOaZZ6hduzajRo1i27ZtALRr18533Ntvv33ZrQxFdU9MTGTx4sWsXLmSjIwMJk+eTLt27fjxxx9Zvnw5ycnJnDlzBrPZTOvWrRk9ejQNGzb05VN0/rS0NL+yFP/M7NmzhxkzZrB9+3asViuxsbH06tWLYcOGodNVzV/ZBWccLH1rN8d/z0FVYH90JGmhIaiKwml9AbsDAxixN4uaZ1wY9RryQgMIxYkpNxePVsvuFrXZ53LTevthNG4PHu0FnuKrKk6tFhQFDQqnzFHUPbHDL0mNzCyiz+SwomkbDgb6B8eFBj3ftG7Omz/9isHjITonj9PBFtAp3pv986bOPVmogtsDGgW3B34PCjubERCgUKDReoMHqwsUDwTovEGHRgMaFZylRBxuQCl2ruLnVJRzLSCq6g2CdEqJcuFRwaWCRkHVQJoN0vJV1p/rXeYtj07BpFNxq+DwQJc4SOyjJcZSthuyUwUqjyxysfqYikELz9yi4d/dtDLlsBA3gar57SXEBUyaNAm73c6DDz6IwWBg/vz5jBs3jri4OFq3bl3mfJ977jmio6MZPXo0KSkpJCYmMnbsWO666y4WLVrE/fffj8FgIDExkddee42FCxdSo0aN8qvYJRQWFjJy5EhatGjBmDFjOHHiBHPmzOHll18mMTERrVbL3XffTUZGBosWLWL48OHUrVsXgLg4b393l8vFs88+y86dO+nZsycDBgwgPz+fRYsW8cQTTzB9+nSaNWvmd95vvvmGnJwc+vXrR0REBNWqVQNg8uTJzJgxg9tuu43Ro0ej0WhYtWoVr7/+Oq+++ioDBgwAYOvWrbz00kvUr1+f4cOHExgYSEZGBps2bSIlJYXatWszYsQIVFVl+/btvPvuuZmEWrZsecXX6c0338RoNPLoo4+iKAqRkZEAzJ07l5CQEB544AEiIyM5fvy4r95fffUVtWrVAuDdd9/lo48+IjQ0lBEjRvjyDQvz3mj++uuvjB07lpo1azJkyBCCg4PZtWsX06ZNIzk5mQ8++OCKy3wz+Pnj/Rz/PQeA9OAgUsNCffuinC5uy8whLjsPc14BeocLt1aD02T0Pux3u9G53dgNevY3qk5kdg6nwkNL3mQDwQVWFFWlMAB0LhcD1v1a6kBHB7Av2EShXuvdUOy+367zbksPtLAvKgJcHtBrSz0figJajfcG33Ne8GB3g1kPeQ7vz4FX0DXLBehLBjglz32B/RrFe3egKbZfh3+AowIulcJi5/jlODz/s4fEPtrLL2sxL650s/qY9xwON3y0yUOH6gqPNCtbfkKI64cEHEIU43A4mD17tq/rUbdu3bj//vuZO3fuVQUczZs357XXXvPblpCQwKlTp0hMTCQwMBCA9u3bM2jQIBYtWsQzzzxT5vNdqezsbIYOHcqwYcN828LCwvjkk0/YtGkTnTp1omHDhrRs2ZJFixbRsWNHvyf0AImJiWzdupWJEyfSqVMn3/b+/fvzyCOPMGHCBD777DO/Y9LT05k/fz7h4eG+bXv37mXGjBkMHz6cMWPG+LYPHDiQl19+mUmTJtGrVy8sFgtr1qzB4/EwadIkvzz+/Oc/+/5966238v3337N9+3Z69ux5VdcpMDCQyZMnl2hpmDhxIiaTyW9br169GDx4MAkJCbz++usA9OzZkylTphAeHl6iLHa7nffee4/4+HimTJniO8dDDz1Ew4YNGT9+PFu2bClx3SuL3W7H7XZfk3Md3Zzl+3dWKV3uAl1utC4XeocLAEeAscTNtt7pIifETP39KdTSp5EVGogzwIAjwIhdr8PocKL3eHBrNdgcDloePULtMxkUYMLDuRvevAAj/23bhBzT2a50qoq3z5OKxuPhwT/2cTA8lFd7dcOl1V56yWFFAU8pq5WrZ/N2esBSSldIRfG2clTUQufnxyKlna+Uuq084sFqLbly++VYcVhb4sTfH3DSp07p+RUWFvr9LcrHzXhdy7OrrnSpKhsJOIQo5uGHH/Yb5xAdHU2tWrVISUm5qnwHDRrk93ObNm1ISEigV69evmADoGHDhlgsFo4dO3ZV57tSGo2GgQMH+m1r3749AMeOHfMLIC5k+fLl1KlTh6ZNm5Kdne23r2PHjnz77bfYbDYCAgJ823v16uUXKBTloygKvXr1KpFPly5dWLNmDbt27eLWW2/1Xbuff/6Zfv36VXiXo8GDB5d6jqJgQ1VVCgoKcLlchIWFUbt2bf7444/Lynvjxo1kZmYyZswY8vPz/fZ17tyZ8ePHs3Hjxusm4LjcepUHQ4QHW67332aHo8T+fL3O/973AvcDBpsDBTA4XVQ7nY09QE9WVCgmG9jMATjP/t8PKyz03VSYsGHHgFPR4dBpeaN/j3PBBni7Xykq4XYn2ToNo/r34owpoFiXpktUrqir1YUmc1DgglGLVinZMlLEfba7VEUqJfs4o5WkpANlyq6GoR6nrf5rn4Q4TpKUlHHR444cOVKm84mLu5mu6y233FLZRajyJOAQopjSujGFhISQnp5ervkGBXkHmpY201NwcDA5OTlXdb4rFRUVVWLwc0hICMBll+Xw4cPY7fYSYyyKy87OJiYmxvdzUVej8/NRVZX+/ftfMJ/MTO/qzwMGDGDNmjW8//77TJw4kVatWnHbbbfRo0cPXzel8lRaecHbKjN16lS2bt1a4qng5XaNO3z4MIBft6/zFdX7ehAfH3/NWjhCn81j6V/24bJ5qJGVw6ngIKxGbxcjpwKbQ4OpEx1O6zwrOpcbvc1RopXDpdUQk3baL1+DzYkCuHRa38xVRVIjIvCcPf6b2zuwoUEDVI0Gt1aD0e1GRaFaoQOXRiFHr6XH6WyytBqWx4T7t65oFN84jRJdnIpaR0oLHIxab2tCgA4K3WAopVuWonDBYMSDt3WkqFtU8YHkReeG0rtdFXXx0p2X/vzWFC1nz+9NZ9Gp/PtuA02rNy29TJfwnxC4f5FKvtObX3ykymvdoggxRpWavrCwkCNHjlCnTp0SLYyi7OS6ioogAYcQxWg0pQ8mLZpK9mKDFy9286XVlt4H+ULbr2bq2rK4UL3hysrSoEEDXnzxxQvuPz8IKN7aUZyiKHzyyScXLFf9+vUBCA0NZfbs2Wzfvp2NGzeyfft2PvroI6ZNm8bHH39cpnEaF1NaedPT0xk1ahQWi4UnnniCOnXqEBAQgKIo/Oc//7nsbglF1/n555+nUaNGpaaJiir9xqsyXMvZuep3MDNqXhgH12ficat0d6nsS1XJ02rZFBhI25MunBl6zgQYCVA9GB0uFIcLZ4ABvctNoU6HywWGs12ubCYjBcEmPEXT45ZyTo9Gg1VR+L1+fX4r9n6Y3B5anckn1OFGf/Y9s2sUcjUK+y0mGuRZiXQ42RUWRL5R5w0oVLw38EWDtVXOBQsq3jRGDbhVFI+KxePEoSg47C5icvOoll3AEXcoOeGB/q0hqur9Fndd4MIV5Q3nzq0UK4NabK7c0rpmOT1nx3mo3sHoeGetUhQIMkLPBhr+fafClpMKmYXQu75ClLnsM+Hd3QAOj1FZut9DWIBCrwYK+guNMynGZDLdMLPb3UjkuoryJAGHEFcgODgYgNzc3BL7Tpw4ca2Lc81dLOCqWbMmWVlZtG/f/qIBzKXUrFmT9evXExMT4xuYfjFarZZ27dr5uhrt37+fIUOG8Pnnn/Pxxx9fstxXa9WqVVitVj766KMS3Z1ycnIwGPwH+16oLEWtJyaTiY4dO1ZMYW9g5jADLXrF+n5uf/bvp3xbwoB4v2PWfZfOz/9KJuLkGX6vV4t4m5PcEDM5kcF+6bQeD87zZpEKycrHpcLh6oGcL8Lu8htMbvSoBLo9dDvtHWtSoNWQHGQk0Gaja/5e+j8YSP3mDWhdJ6QMNT/3hDktz8XHW2FjKqxNVXG78bae6DRng5qLZONSvQ0RZ4MNrQYGN1eIj4Ivd6tkFEDdMHioMcRHarinrhbNZf6/6RtUhmpdQKRZYXgrGSQurl/X9nHgzUMCDiGugMViISIigs2bN6Oqqu/m8fjx46xevbpyC3cNFDWvlxZw9erVi48//pivv/6aoUOHltifmZlJRETEJc/Rs2dPEhMTmTRpEh988EGJVqDi+WRnZxMaGuq3v6iFoXgZi8qdk5Pj6ypWXoqCq/NbghYtWkRmZiaxsbF+200mU6nXr1OnToSHh/PFF19wzz33lCinzWbD7XZjsVhKHCtK17lnDJ17xmDNsDGhz3qOx0QRbLeVSKecfdqveLw35QFWGwH5+fxWrxkF+pIBR2m34XqXk0JUUi0mTgbq+KiPiUGdjcyZc4w6Mc2oWw49/GKDdLzf1X/boWw3Hg80CNfidKss3e+meRQ0jrj8r/dXb736sgkhxMVIwCHEFRowYABTpkzhueee48477yQjI4MFCxZQv3599uzZU9nFq1DNmzdHo9EwY8YMcnNzMZlM1KhRg/j4eAYNGsTGjRv5+OOP2bx5M+3bt8disZCens7mzZsxGAxMmzbtss4xatQoPvvsMwYPHkz37t2JiooiIyODpKQk1q1bx4YNGwD4+9//zqlTp+jYsSOxsbHY7XZWrFhBQUEBvXr18uXZokUL5s6dy/vvv8/tt9+OTqcjPj6+XKYe7ty5MxMnTuStt95iwIABBAUFsWPHDtavX09cXFyJrnYtWrRg8eLFTJkyhbp166IoCl26dMFkMvHOO+/wyiuv8NBDD9G3b19q1qxJXl4eR44cYdWqVfzrX/+6bgaN30jMkQG88WtX3hi8HefR7BJT3aoKoNEQkFtA6Jkccs0mkmrVQFFVInNzMbjdOM4GvhqPSoDDgTXAv0tZtZNpRAY6+SLxHt82p9NZwTWDeqHnAnK9VuHBJvK1LoS4/shvJiGu0LBhw8jPz+e7775j69at1K1blzfffJOkpKSbPuCIiYnhrbfeYtasWbz//vu4XC569+5NfHw8Op2OCRMmMH/+fL777jtfcBEVFUXz5s19iydejlGjRtGsWTPmzJnDN998Q2FhIeHh4dSvX59XXnnFl65nz54sXbqUb7/9lqysLCwWC/Xq1eODDz6gW7duvnQ9evRg3759/Pjjj/z00094PB7efvvtcgk44uLi+OSTT5g0aRIzZ85Eo9HQqlUrpk2bxocffkhaWppf+qeffpqcnBzmzZtHXl4eqqqyZMkSTCYTnTp1YtasWcyaNYvly5eTlZVFcHAwcXFxPProo36LCIoro2g1vJ94C6Mf2UaD49newQhnFc1OpXO5yAwO5Lf4Rr7xHYfd0UTYHKgKeBSFAJebNgcOcigygqS46mjdHm7btwfVBK8uua8yqiaEuIZkWtyyUdRrPTpVCCGEqCRuj8qndyzHaggGVcWl0+E5u1ifJSePpNhqnAwP9TvG7HASYfdOx2spLOSxlasJKrSxr0Y4YSftnIkp5L5jY0vM+OR0OpkzZw7NmjWjQYMG5d6dr6qzWq0kJSXRtGlTGdxcjuS6XlySMv6y0jVVLzyBSlVU9pGdQgghxA1Gq1FIqxWHw6DHEWD0BRt4PHT/Y0epAzSiMnOwKgrNDh1mxA8/EVRoI8dkhgAPbvMZum0adfFVvYUQooqTLlVCXMeysrIuudaBy+W65IJ3AQEBfgsMCu8g7PMX2CtNZGTkNSiNuJZe/KQpc/quIM8YgjNAj0OnIzIri2r5edy5by9zb/UfRd0q+ThpkSE0yDhBhCMbFfi0ezee+Pk7oo++gSHCUPqJhBA3HelSVTYScAhxHXvsscdKjAE4X9u2bdm2bdtF0/Tu3Ztx48aVY8lufCtWrOCdd965ZLotW7Zcg9KIa6lalJ4+/2qD808fM6HPcAD0LhepQSG47ND46AmOVYskwOWkddIxWh44QXaYhTbHjqDFw/HwMGIyTlIr/W8EBsoUrkIIcSkScAhxHXvvvfew2+0XTRMcHFzqNKvFXU8Lxl0vOnXqxKRJkyq7GKKS1Ls9lrWWUFS3G0Wr5YwlkG2xtVEVhcYpaTROSUPj8WApcJBnNtL56B50Hg9nLIF83qkr4z+phU6CDSGEuCwScAhxHWvdunVlF+GmFRkZKd2lqrg2O59j793fsqlVK4IKC1HPG4fh0WjIMxuZcXdH6uXl0PrkSXZVr06E1oauXnQllVoIIW48MmhcCCFElRQYaya2QxyNDh8hM9Bc6grC+6tHkRdkxuHRsaFuPRw6LS9ObXPNyyqEuD6ol/kS/iTgEEIIUWX1ntWJzg9E8uy386iXe9hvX47JyMZGdTA6XNgDFGqdPsErf6lOeLh0DhBCiCshvzWFEEJUabe93papu22M+Opj2mfu4LgplqOh0WxvfCshLjdtjx/jL1+2whjTvLKLKoQQNyQJOIQQQlR5T87qxH/zrdy1cjkxhac5HRRK2zg9kz+MQbHUruziCSGuEzItbtlIwCGEEKLKUzQKIxd1x+PphscDd+kU7qrsQgkhxE1CAg4hhBDiLI1GQSOjG4UQolxJwCGEEEIIIcRlkC5VZSPPcYQQQgghhBAVRgIOIYQQQgghRIWRgEMIIYQQQghRYWQMhxBCiCot1+4hfqaHlPyS+wI0cPRJDdEWeT4nhJBVxMtKfoMKIYSo0kImlh5sANg8UG2Kh5VH3Ne2UEIIcRORgEMIIUSVNXev67LS9ZgvzzWFEKKspEuVEEKIKuvVNZeXzgPYXCoBOpkSU4iqTKbFLRtp4RBCCFElbUp1cTTv8tP3X+zG7ZGWDiGEuFIScAghhKhyxqx00THhyo759jDc+c01GstRYINB/wZdfwgYAF/+fG3OK4QQFUC6VAkhhKhScu0qk38v27Hr0sq1KCWpKhgeBpfn3Da3Bx77FP77E6z5RwUXQAhxMdKlqmykhUMIIUSV8tXuq2ul8KgV2K3q/GCjuF+SwO6suHMLIUQFkYBDCCFElbH6mJsxV9k7qdl/K6hb1buJFw42ikQ9VjHnFkKICiQBhxBCiCrB5VG5a+7Vt07syymHwpzv7jfh7cRLp8uzQ90nK6AAQghRcSTgEEIIcdMb+b0L/Ufl1zLx6LLLW7/jkg6kgeZBWLX78o85ctp7jOcSrSFCiHKnXuZL+JOAQ1x32rVrx7hx4yq7GOI8o0aNok+fPpVdDCGuWNwkF//9o3zzTNgLDnc53Fa0eK5sdycqoO0PudarL4MQQlQwmaVKXLZ9+/axevVq+vTpQ/Xq1Su7OKICLF26lLy8PAYPHlzh50pNTaVv3748/PDDvPbaaxV+vgux2+189913rF27lv3793PmzBkiIyNp3rw5I0eOpG7duiWOcTgczJgxg++++47Tp08THR1Nnz59ePzxx9Hp/H+tLliwgO3bt5OUlERKSgoej4ctW7ZcsDw7d+5k5syZ7Nu3j5ycHCIjI2nfvj2PP/44cXFx5V7/m9WZQpUJW9y8t7HizlHtUzdZz1/F16jVBrarbHUJGeJ9dLh/MtSLubq8hBCigkjAIS5bcnIy06dP55ZbbqnQgGPdunVotdoKy19c2NKlS0lLS7smAcf1Ii0tjX/84x+0bt2a+++/n8jISE6cOMGCBQtYtWoVEydOpF27dn7HvPHGG6xZs4a+ffvSsmVLdu7cydSpUzl+/HiJ1rkvvviCnJwcGjdujM1m4+TJkxcsy/r163nhhReIi4tjwIABhIaGcvDgQRYtWsTPP//MnDlziI6OrojLcNNweVTG/uxmwu8Vf65sJ8zc6WJ4yyv4KlVVuOMvsG5f+RXEA9R/Gt54EP5vSPnlK4QoQabFLRsJOMR1x2g0VnYRRBUSGhrK119/TePGjf2233fffTz66KN8/PHHfPnll77tv/76K2vWrOHRRx/lxRdfBKBfv34EBQXx9ddf88ADD9CqVStf+mnTphETE4NGo+GFF164aMCRkJCARqNhxowZhIaG+rbXr1+fv//976xcubLKBIOHs1U2pqu0iVZoHO79gj+e5+Fvv6qcLFDRaWBjGuTawenx3nNXhhE/wogfveM57q0Dd9VUiNC5iVyyjs6zFvDgmdOoUb9gSskGpxsVKu525Z8Lva/itArERcKATpCZD/e2gYc7n9vvcsNPO8HhArMBVu+BxtXhoVvBZIQtB+DQSejaHKJDK6rkQoibnAQcNzGn00lCQgI//PADR48eRafTUatWLXr37s0jjzwCwOnTp/nqq6/YvHkzaWlp2O12atSoQa9evRg6dKivpWHatGlMnz4dgNGjR/vO0bt373Ifb9GuXbsS+f7666/Mnj2bgwcPYrPZCA0NpVmzZjzzzDPUrl0bgPT0dKZNm8bmzZvJzMwkMDCQmjVr8uCDD9K7d2/A+wT/nXfeYerUqSWeWo8aNYq0tDSWLl3qt33Pnj3MmDGD7du3Y7VaiY2NpVevXgwbNqxE95lLGTduHMuWLWPlypVMmDCBtWvX4nQ6ad++PW+88QaRkZEsXLiQhIQEUlNTiY2N5dlnn6Vr165++bhcLr766iu+/fZbTpw4gclkok2bNowePZoGDRr40hV1Wxo5ciTNmjVj+vTpHDhwgKCgIHr27MmYMWN8dejTpw9paWm+96DI+dfq9OnTjB8/nt9++w2Hw0GbNm0YO3as7324Wj/++CPLly8nOTmZM2fOYDabad26NaNHj6Zhw4Z+afv06UNsbCx/+ctfGD9+PNu3b0dRFDp27Mirr75KZGTkJc8XGhrqd3NfpF69etSvX5+DBw/6bf/hhx8AGDRokN/2QYMG8fXXX7N8+XK/gONKWgMLCgowGo0EBQX5bS+qh8lkuuy8bmQfb/Xw0moPnrNjG/52q0LdEHjih+t7KOb3R+D7Iyqv/byE4csTzg2SzMv0pbnmz0bdKhw9Df9a4v15xs/QYTFs/BAycqHrm7A7peRxz38OnRrDt1u9Pxv18M2L8MCt167sQoibhgQcNymn08kzzzzD1q1bufXWW7nvvvswGAwcOHCAVatW+QKO/fv3s2rVKrp27UpcXBwul4vffvuNTz/9lBMnTvDXv/4VgLvvvpuMjAwWLVrE8OHDff3ar0Wf8q1bt/LSSy9Rv359hg8fTmBgIBkZGWzatImUlBRq166Ny+VizJgxnD59mv79+1OrVi3y8/M5cOAA27dv9wUcV+rXX39l7Nix1KxZkyFDhhAcHMyuXbuYNm0aycnJfPDBB2XK97nnniM6OprRo0eTkpJCYmIiY8eO5a677mLRokXcf//9GAwGEhMTee2111i4cCE1atTwHf/mm2+yYsUKOnbsyEMPPURmZibz5s1j+PDhTJ8+nSZNmvidb926dcyfP5+HHnqIvn37smbNGr788kuCgoIYMWIEAC+//DKffvop2dnZvPTSS75ji49hKCwsZOTIkbRo0YIxY8Zw4sQJ5syZw8svv0xiYmK5dIWbO3cuISEhPPDAA0RGRnL8+HEWLVrEE088wVdffUWtWrX80p8+fZonn3ySrl278txzz7F//34WLlxIQUEBkyZNKnM5PB4PGRkZhIeH+23fvXs30dHRxMT495ePiYkhKiqKPXv2lPmct956K7t27WLcuHEMHTrU16VqwoQJ1K1blz/96U9lzru82e123O7yX48i0wav/aLHo567Nf/HBg8BOqiE2/Urpnc5+UfxYON6tOkAtuVb0P60C31pwQbAmfxzwQaA3YnnmenYuseD9vqoXWFhod/fonzcjNfVbDaXY27X/++h65EEHDephIQEtm7dyvDhwxkzZozfPk+xqRTbtm3L4sWLUZRz/4EGDx7Mm2++yeLFi3nyySeJjIykYcOGtGzZkkWLFtGxY8cSrQMVac2aNXg8HiZNmuR38/fnP//Z9+/Dhw9z9OhRnn32WYYNG1Yu57Xb7bz33nvEx8czZcoUX0vAQw89RMOGDRk/fjxbtmwp07Vo3rx5iYHSCQkJnDp1isTERAIDAwFo3749gwYNYtGiRTzzzDMAbNiwgRUrVnDPPffwf//3f7737p577mHo0KH8+9//5r///a9f3ocOHWLu3Lm+p+0PPfQQjzzyCImJib6Ao2vXriQkJGC32+nZs2ep5c7Ozmbo0KF+1zgsLIxPPvmETZs20alTpyu+FuebOHFiiSf5vXr1YvDgwSQkJPD666/77UtJSeGf//wn99xzj2+bRqNh3rx5HDlyhDp16pSpHAsWLCAjI8PvcwaQkZFR6kBygKioKE6dOlWm8wEMHz6crKwslixZwvLly33bO3fuzD/+8Q8sFkuZ8y5vf/xRztM+nbUrx4zd7d+SpaJQWE6z0FY0j6LhRhiBlrFsPYG7jhN6BcdoUrNI3rQdd2h53rxdvSNHjlR2EW5KN9N1veWWWyq7CFWeBBw3qe+//57g4OASN0vgvRkrEhAQ4Pu30+nEarWiqiqdOnVi+fLl7Nmzhy5dulyTMl9I0c33zz//TL9+/UrtxlSUZuvWrfTp06fEU+my2LhxI5mZmYwZM4b8/Hy/fZ07d2b8+PFs3LixTAHH+d1x2rRpQ0JCAr169fLVBaBhw4ZYLBaOHTvm27Z69WoARowY4RcoNmrUiDvuuIPVq1eTlZVFWFiYb1/Xrl39uvYoikK7du2YO3cuVqv1sp/+aDQaBg4c6Letffv2ABw7dqxcAo6iYENVVQoKCnC5XISFhVG7du1Sb3KjoqL8gg3wdgmbN28eKSkpZQo4duzYwfjx42nUqBHDhw/322ez2TAYDKUeZzQasdlsV3y+IhqNhqioKDp06EDXrl0JCQlhx44dJCYm8pe//IWPPvroirvxVZT4+PgKaeGo7YTQ3SrZ9nOfbYNGJdAAZ2zX/5NFrceDG67roEMFIsb0RbtwE6w9cNnHeZpUp1Gn6+fGrbCw0PdQoap0N7wW5LqKinB9fHOJcnfs2DEaN258yQHYLpeLL774gu+++46UlBRU1b+PdG5ubkUW87IMGDCANWvW8P777zNx4kRatWrFbbfdRo8ePXw31bGxsYwYMYIvvviCe++9l0aNGtG+fXu6d+9O8+bNy3Tew4cPA/Duu+9eME1mZuYF911M8e5RgK/Pfmn9/YODg8nJObe0cWpqKhqNptSn7PXq1WP16tWcOHHCL+A4/3wAISEhAOTk5Fx2wBEVFVXiM1U8n/Kwd+9epk6dytatW0s06ZdWj0vVDbzB9PnlM5vNpdY7KSmJF154gaioKCZMmFCivgEBATgcjlLLbrfb/YL4K/XOO++wc+dOEhMTffncddddxMXF8f7777Ns2TL69etX5vzLU0VN7mAGZvf0MOJ7DxmFEGKET7tpqRMM3eZ6cFzna9059HrG9h7Kh99+hU69DsecKArKq/0wNasD9arD9iOwcEPJdK3rwH1tYfwysDmgdhSaL18o564p5cNkMl2X5brRyXUV5UkCjipu/PjxJCYmcs899zBixAjCwsLQ6XTs3buXiRMnlghAKkNoaCizZ89m+/btbNy4ke3bt/PRRx8xbdo0Pv74Y1q2bAnA008/Td++ffn111/5/fffWbx4MV9++SWPPfYYzz33HIBfi8D5zn9aW1T3559/nkaNGpV6TFRUVJnqdKGxDhfafrXvQ/FWravJu7zyuZD09HRGjRqFxWLhiSeeoE6dOgQEBKAoCv/5z39K7VN8OWXasWOH32QHACNHjuTJJ5/027Z3717GjBlDYGAgU6dOLXUK2sjISE6fPl3q+YrW5CiL9PR0li9fzoABA0oELd27d+f9999n69at103AUZH61Ndw/EmF5CyoFwIWg/f/re1Fhe8Pe1BVFasT1qdCWgGEGuF/yZBe9salq3Z3HLxwC1QP0hD8xP3s39+RevN/ZG9SEkHx9Yj7+QCGPSmQX3qwWq4CDeBRoFVtiI+Dri29s0wlnYBbG4Ll7FPrAAMseBVSMsDpgmAz7DoKcRHQ8OzDj7H94EQmNI0Dma5cCJkWt4wk4LhJ1a5dmyNHjuBwOC7Y/QPgu+++o23btvzzn//0256SUnIg4cVu1iuaVqulXbt2vu5L+/fvZ8iQIXz++ed8/PHHvnRxcXEMHDiQgQMHYrfbefbZZ5k9ezZDhgwhPDyc4OBgoPSWm9TUVL/uKkWDk00mEx07dqzI6l2RGjVq4PF4OHz4cIlZm4paZUp76n85KvM9Bli1ahVWq5WPPvqoRFe1nJyci36WL6ZRo0YlBpCff4327t3L008/jdlsZurUqcTGxpaaV/PmzVm+fDnp6el+A8fT09M5ffp0mbsgFo39KD7GqkhRMFwRXZiuV0adQovz4nlFUbiv3rmb3v7F5kaYUmw8/akCD9WmXJumkH0joFF4KV+lHarjbPMoO+fMoVmzZkS8PhDD2ZY3thzwzg5VYC+/gihA4RwwXuT/SPWI0rfXLDab210t/PeFBXpfQghxFa6PqSZEubv33nvJzc3l888/L7Gv+JNojUZT4sl0YWEhCQkJJY4r6st5rbtZZWdnl9hW9OS7qCz5+fm4XP6jSo1Go6//flG6oiBi06ZNfmm///77Ek+tO3XqRHh4uG/htvPZbDYKCgrKVKerceeddwIwc+ZMv/fuwIED/PLLL7Ru3dqvO9WVMJvN5ObmVlrLVlFrxfnnX7RoUZm7r4G3W1rHjh39XsVnWCtq2TCZTEydOvWiAVuPHj0A+Oabb/y2F/183333lamMtWvXRqvVsnr1avLy8vz2FU3V3KxZszLlXdVEWzSor+jYNBj0FRhDRxgvEGxcSrsGkP8NqAsvnfZy1I6CnK8vHmwIIUQlkhaOm9SgQYNYu3Ytn3/+OXv27KFjx44YjUYOHTrE0aNHmTx5MgDdunVj4cKFvPHGG3To0IHMzEyWLl3q6wNfXPPmzX2LkuXm5mIymahRowbx8fEVWpe///3vnDp1io4dOxIbG4vdbmfFihUUFBTQq1cvALZs2cI//vEP7r77bmrXro3ZbCYpKYnFixcTHx/vCzzq1KlDhw4dWLhwIaqq0qhRI5KTk1m9ejU1a9b0C1pMJhPvvPMOr7zyim862Zo1a5KXl8eRI0dYtWoV//rXv67pjF3gnTr1nnvu4ccffyQvL4/bb7/dNy2uwWDglVdeKXPe8fHxrF27lg8//JCWLVui0Who3759uQzCvxydO3dm4sSJvPXWWwwYMICgoCB27NjB+vXriYuLq5An/GlpaYwZM4bc3FweeeQRdu7cyc6dO/3S3HXXXb6A+/bbb+eOO+7g66+/Jj8/nxYtWrBr1y4WL17MfffdR+vWrf2O/eWXX0hOTgbOtRwWzSIWFBTkm6I6JCSEQYMG8dVXX/Hoo4/Sr18/goOD2bFjB99//z1xcXFVojtVeWpfXYfjZbj1Kxcb08s//1PPlEMXo5hQSM8u27FBAZBb8uGQEKLiVH5H8xuTBBw3Kb1ez6effspXX33FDz/8wOTJkzEYDNSqVYs+ffr40r300ktYLBZWrFjBmjVrqFatGg888ADNmjXj6aef9sszJiaGt956i1mzZvH+++/jcrno3bt3hQccPXv2ZOnSpXz77bdkZWVhsVioV68eH3zwAd26dQO8szndddddbN26le+//x63201MTAzDhw9nyJAhfvm9++67/Otf/+L777/nu+++o02bNkydOpV//vOfvoXvinTq1IlZs2Yxa9Ysli9fTlZWFsHBwcTFxfHoo4+W6NJ0rbz33ns0btyYZcuWMWHCBEwmE23btuWpp57yW/jvSj366KOcOHGCn376iQULFuDxeJg6dWqFBBxFrRjFx63ExcXxySefMGnSJGbOnIlGo6FVq1ZMmzaNDz/8sMT7Ux5OnDjha8H67LPPSk2zZMkSv9la3n//fT7//HOWL1/Od99951tT5fHHHy9x7M8//8yyZcv8tk2dOhXwTnZQFHCAd7xQ7dq1+d///sfMmTNxOBxER0fTv39/Ro0a5TeDmbh8G4boWJTs4sEl5Zfn5iEKmvLogpj6OTR5BpKv8LMdGQinZ1/9+YUQ4hpQ1OthVLAQospJTk5m8ODBpQ7eFqIiqKqK5j/l00qmvnLp53VOp5M5Z8dwNGjQoNSWY5+Rk+G/Ky/v5EPvhNnPX2ZJb15Wq5WkpCSaNm0qsymVI7muF7dFmXJZ6dqpT1VwSW4sMoZDCFEpdu3aBUD9+vUruSSiqlAUhaThV/+191LbcijM+aY/Dev+cel0daMl2BCiEqkol/US/qRLlbgqbrebrKysS6ZzuVyXXLAsMDDwqtYwqCxWqxWr1XrRNFqttswDuW8kl/N52LRpE4cPH2bOnDlER0dz++23X6PSCQFNIjScelolenLZG/f/c3cFfXXe1hRCzZB9kd8nyZMuvE8IIa5TEnCIq3Ly5En69u1bLnm9/fbbfuNLbhRffvkl06dPv2ia2NhY30xDN7PL/TxYLBbatGnDiy++eEMGmeLGFmXWAq5LpqsUWV9B42cgObXkvv8+BTpZC0MIceORgENclYiIiBLrG5QmLy/Pt5r2hdyoXWt69epVYmai81XUqszXm8v9PDRt2tS3JooQleG3QQqdvrnyVo7E3tegq8S+T71/b9gHr8yCuHD44lkIqBq/R4QQNx8JOMRVMRqN19WieJUhLi7Ob02Hqkw+D+JGcWsNLc6XVPQfXf4g8h/7wz11rmELw62N4df/u3bnE0Jcksy0VDYyaFwIIUSVpNMoDGh0+em7xEl3JiGEKAsJOIQQQlRZr3a4vCBCr4BRJzPPCCFEWUiXKiGEEFXWLTEKgXrId1483fHR8nxOCAEemfK2TOQ3qBBCiCrt5NNa+tYDnQImLRiK7etbz7vIX7RFvi6FEKKspIVDCCFElWbWKyx+UL4OhRCioshvWCGEEEIIIS6DrCJeNtJGLIQQQgghhKgwEnAIIYQQQgghKowEHEIIIYQQQogKIwGHEEKIKi8rxcqB746TeyivsosihLiOqZf5Ev5k0LgQQogqx2bz8HH/9SgpOSTHxdDi2EnC8qwAeKJNDF75JwzB+koupRBC3BykhUMIIUSV03vwbjLzVHbVrsHq6tX4pEM8y+LrkRNixpXl4LVeG8jJucRqgEIIIS6LBBxCCCGqlI8TMqlvs7O7dhwJ9WtxMMjC0SALZqsdo9WOCjQ+mUmf545WdlGFENcZFeWyXsKfBBxCCCGqlMTVeShGI8mBZjyK98bgnoMpdEw7TWZMBKm1Y8mJCqP+yUxWPPZrJZdWCCFufDKGQwghRJVx6nghLc9YMTtd3HYiHXdUOIeDA4k/fYb0mjG4DN5xG44AI3WthWSsTMXlcKMzaCu55EIIceOSFg4hhBBVxszndlH35GnizmTR6nQmo5IOEGe1kRdo9gUbRexmE6pGQ8a2zEoqrRDieiNdqspGAg4hhBBVRn6eB73b7ft5fqN6HDcZORpooV7ycZrsOkyNY6fQuD0AOLUa9n6yp7KKK4QQNwUJOIQQQlQZJpvD928PsD/ITGihncGb9xKSU4DJ5iD6ZBa1jqSjqB52Nq5F+s9p7H50ZeUVWgghbnAScAghhKgSEoeuJ+7kaaKyclE8HkwFdqrlFHDrsZOYXG6/tGFncjHZCjkUE86n99/J58eNnJiws5JKLoQQNzYZNC6EEOKmd+L3LBolbsXs9LZwODRaUizhPOZQWFM/tkR6jeohtCCL0+b6qIrCwerVeHfBSaa9UDLvlD0ZfPOv3XT8eTP6UBMtljxKUO3Qiq2QEKJSyCriZSMtHEJch5YuXUq7du3YsmVLpRx/PZs2bRrt2rUjNTW1sosibgAel4cvRm5m9z2LMTsdKKhoUDF6XMTln6FF+hke2HUYq85/FqrYwkyabTnJ61+upHXycSLyrTRLzeCJbr8w48lfeCIhjxZPJPF4j9W8/M4hZoTX588PD+SQqkOp81dWvS3T6QohRBFp4RBCkJCQQFBQEH369CnT8UuXLiUvL4/BgweXc8mEKDt7gZN3+mzCZjTSIjffb+4YBQhQXWhUDzVyraiABSsmjw2z00aaOxpFp6BxQ891SWRHGFA9btKNwfzVUA39L2focuIUCzrdQn6gyXfOoY8MY5J2Hj3+/S2Tlx7GUUehUX03HlWeiwohqi4JOIQQfPPNN8TGxl5VwJGWlnZNAo4nnniCxx9/HIPBUOHnEjeur+afZHXCSWK1WsIddpx6LYrDP40CmFxOCvRGAALsDmIKszmqj0XRKH7pFAeMGXQPOSbj2a0W5nYIx1ks2ADAoOXlhx5gzc5GTJq3CNsfOlb9YWdY5zDq2vfyf4+E0KRdLEatBk1McIXVXwhRMWTK27KRgEMIcUPR6XTodPKrS/jLz3by6yfJbPwuA7fWg8XhoVvaadBqyA62cCIomPCCAr9bBRU4UisSg9WJWwupplpkpAdjynWXyD/TFFAs2PByukopiKLw+0d/p3Z2Fi6MGJ0qA/av5+H9m0izBPFOZnde3/wptbPP4NAY+CO6DrVyT5ATE0lyi+aYAxS6bduE/pY6WB+4A33nhuirh3J063EKU3Np3K0eijmgXK+dEEJUNPnWFtelpUuX8s477zBlyhT27t3L/PnzOXXqFLGxsYwYMYLevXsDkJqaSt++fRk5ciRPPvmkXx7Tpk1j+vTpLFmyhOrVqwMwbtw4li1bxsqVK5kwYQJr167F6XTSvn173njjDSIjI1m4cCEJCQmkpqYSGxvLs88+S9euXSusrosWLeKrr74iNTWVatWqMWDAAAIDA0tNm5+fz4wZM/j55585efIkFouFDh068PTTTxMXF1civdvtZtq0aSxdupTMzExq167N8OHD6dGjhy9Nu3btAEhLS/P9G/C7bhfTp08f0tLS/PICmDp1qu/nbdu28d///pfdu3fjcrmoU6cODz/8MP369bv0BTpPae9r0bb58+fz7bff8u2335KVlUWdOnUYM2YMt99+e4l8fvrpJxITE0lOTsbpdFKtWjU6derECy+8gF7vXQCusLCQzz//nBUrVnDq1CmCg4Pp2LEjTz31FLGx5wYab9myhdGjR/P2229js9n45ptvSE9Pp2bNmjzzzDPccccdHDhwgI8//pidO3ei0+m49957efHFF0sET8eOHWP69Ols2rSJnJwcoqKi6N69O6NGjcJkOu9p+k3MmuVgz8Q9nEo8RK5NQ0FMEAV1Qsg77cCq0eFUtESdziYyJ5e4zDNQoEFxaWkaamZHh7pkGvWcrhGFVael1tF0QrV6TpkDqWbN951jX81YssODIEzFnJOPzuXidGQgZoOT8DM2X7pTwWa+6tysZCHPdpNSPB6q554hwxxMjexc8vUWtGomR0NNBNucTG/1J2a07IDe4+apbetonJXmbV1x27ktbRdOAsiwKrRbtYnY3Ax0qh2Sj2P65ldAhwrUwg2oeAANGsCAx9v2gveZq+1clzEFMOrBo+L2gEsbiLNGLOZIBU2OldxmtZkw6FEcgRaGtTPQMOrc+JXVxzwsPqgSF6gwPF4h3HRtnuaqW4+gztkEISaUEbejVA+7JucVQlwbEnCI69qkSZOw2+08+OCDGAwG5s+fz7hx44iLi6N169Zlzve5554jOjqa0aNHk5KSQmJiImPHjuWuu+5i0aJF3H///RgMBhITE3nttddYuHAhNWrUKL+KnZWQkMBHH31Eo0aNGDNmDDabja+++oqwsJJftvn5+YwYMYL09HT69u1LvXr1yMjIYP78+Tz++ON8+eWXfjfBABMnTqSwsJD+/fsD3kDur3/9Kw6Hw9d96t133+Wjjz4iNDSUESNG+I4trQylefnll/n000/Jzs7mpZde8m2vW7cuAL/88gtjx44lIiKCIUOGYDab+fHHH/n73//OiRMnGDNmzJVdtIsYN24cOp2OIUOG4HQ6+eabb3jllVdYuHChX/A0adIkZs6cSb169Rg8eDCRkZEcP36cn3/+mdGjR6PX63G5XDzzzDPs2LGDbt26MWTIEI4dO8aCBQvYuHEjs2fPplq1an7nnzdvHrm5ufTr18/3+XnllVf44IMP+Pvf/06PHj2488472bhxI4mJiYSFhfHnP//Zd3xSUhKjR48mKCiIBx98kOjoaJKTk5kzZw47duzgs88+qxKtO9YsB8t6rsSSlEFIjoNgICPXRhYaVKOBghATjQ+moGjh1kOHyfMEkKUJRAV2tq+L0+gNGDWAxeXmUP04wnMKSCGc04FBWJx2UmKjyAoL8p5QUXAGGNDlF3IiyILLpKN1QTpGp5uZXVvyv46NQFG8AYbdBWcXBcSoo8PRZNxo2FqzPgCHIiNY2Kw9qSHBHDNF8p/Ot3M4MNRXt5e79+Peo5todsYbpCuAARuN0rIIwIYGHeAGXCi+gMLpO14LqGhR0Z/9CRRsaIrPnaMCNqcvvdaVjeFQAcoh72cneN9JxizfSeTT/2b8L3bWjgmkbZyOyds9jPnJ48tkyg7YNlRLsLFigw71f9vwPDQJPN46qJ+sRLP1bZSa4RV6XiHKQrpUlc3N/80lbmgOh4PZs2f7njh369aN+++/n7lz515VwNG8eXNee+01v20JCQmcOnWKxMREXwtD+/btGTRoEIsWLeKZZ54p8/lKk5eXx+TJk6lbty4zZswgIMDbTaJPnz6+AKG4qVOncuLECWbOnEmjRo182/v06cPAgQOZNm0a48aN8zsmOzubOXPm+OrTv39/Bg4cyPjx47nnnnsICAigZ8+eTJkyhfDwcHr27HnF9ejatSsJCQnY7fYSx7vdbj788ENMJhOzZs0iKioKgAEDBvDkk08ya9Ys+vTpQ61ata74vKUJDQ1l/PjxKIr3C6Fdu3YMGzaMhQsX+t6/P/74g5kzZ9KuXTs+/vhjjMZz3WSeffZZ37+XLl3Kjh07GDp0KM8//7xve8eOHXnhhRf49NNPee+99/zOf/r0aebNm1fi8zN27Fg++OAD7r77bsD7PgwZMoR58+b5BRzvvvsukZGRzJ49G4vF4tveoUMHxo4dy/Lly8s8zqa82e123O6SXY/Kw44FxwlLSkd3rjGC1BphoCjkWcxEn87GZjFS/8RJtB6VHKMZnGC1GHEE6P3yUgC928OBOrG02XMYm16PTa8nz+LfWnQsMpxvW8XQa8dhuiUdBeBIVDD/u7VxscwUMOrA6kCPB6dZx/DvV/PUgyP98pp4V2eaJx7kmX4PYsy30ijzNIdDw3BqvV+5QQ4b59NRiI0gzOTg/Wp24Q0XSrvGTlTMxepov8QVVVDO+7qPsNkY9sd6ZrXozPs/WfniYR3vrNefvWJeB7Nh5g47I+M9VCT9e0vQeIoFTKfzcHzyI+53+l70uMLCQr+/Rfm4Ga+r2Wy+dCJRoSTgENe1hx9+2BdsAERHR1OrVi1SUlKuKt9Bgwb5/dymTRsSEhLo1auXX3emhg0bYrFYOHbs2FWdrzQbNmzAZrPx8MMP+4INgGrVqnHvvfeyaNEi3zZVVVm+fDlt2rQhOjqa7Oxs3z6TyUR8fDwbNmwocY7+/fv71ScwMJCHHnqISZMmsXXrVjp37lzu9SouKSmJ9PR0Bg8e7As2APR6PY899hivvPIKa9asYejQoeVyvoEDB/qCDfAGlmaz2e/9+/777wF45pln/IINwO/YVatWodFoGD58uF+a22+/nUaNGvHLL7/g8XjQaM7NLt67d+9SPz8Wi8UXbBRp3bo1c+bMwWq1YjabOXDgAPv37+fJJ5/E6XT6vcetW7fGZDKxYcOG6ybg+OOPPyos7xOHCgjzeNAXu9d267zXWUVB5/Hu8Jx9v7JDzQSftmG0Ob2tEIr/E0iPRkHjcqPivZ3OswTgMpz7+vMo8EtcLPuCLbx74Nx0y/tjSnnCrihojFr0Tjem07nsqFYyWM4LCGBu87Y8uHsHE75dRIStkFNmC0/d+wDf129MjtFEzfws/zKiw43+vJyKWjhKFOK8n8s2A1ZcfjYAR0/ls3tPGpm2liXSJB3LIEl7skz5X65maVmcPwVEzoHjpCQlXdbxR44cKfcyiZvrut5yyy2VXYQqTwIOcV0rrRtTSEgI6enp5ZpvUJC3a0VpYxaCg4PJycm5qvOV5sSJEwDUqVOnxL569er5/ZyVlUVOTg4bNmyge/fupeZX/Ma3SGl5F3V1Kjp/RSpaK+P8+gDUr1+/3MtR2jiWkJAQv/fv2LFjKIpCw4YNL5pXamoqUVFRBAeXnEmofv36JCcnk52dTXj4uZvS0j6vwcHBJbpewbnPXE5ODmazmcOHDwPe8SjTpk0rtUxnzpy5aJmvpfj4+Apr4Yh4IJfNCemYcBCa520NiDidR2rNcCyFNjLCQ4jNyCQ1MowGqSfRGT1khgcScSYfU4GdwsBzAbxDoyEiK4cwm5W0mhFYdXp21o7jzi37OB0XSqFeT1poCCcCvMGnTa8j8Gx3pMapmaWUTsWDglVvBD1MbdUVnduNS3tuHESnIwepkZvDOz+twOzy5hVtLeDzb+fT6onnMLr9R5t70GAnFAOFeIMHZ7G9Gkq2cuhR8KCe7VKlYkDhvCm4ziszZ0d/FHEDE1vfBcDQjkE0bxbKA8c9zD9wrh5aReWJjhE0jajYrk2aAR3g45/8tgUNv4umTZte9LjCwkKOHDlCnTp1qtT4poom11VUBAk4xHWttJto8D7xB/8n0ue72M2QVqu9ou1qJc+hX3T+Dh06MGzYsEoty/XsUp+XIoqiXPSzU1YX+vxcqFxwrmxFfw8ZMoROnTqVmra04KeynN86VJ7qtDdjm3IHB176lZOndYTlFBKQ58Clgslmw63VUBAQAKqNdfGNqJl2mnUdGqF1ABrvzXShXodDqyHqTC7h+QWAt6UjL8iMSXWzr2EsblUlLToSgCCPhzRgQev6PLnW23pTKzOPgb/uZl6npri1GjQeFU+J1gQNnQ4kkR4ezsGIGLod2MuUBV9jUwy+YKNIqN3G2i8n0DDv1NmwwoQTCw6C0WFH0eSBx3Y2mABvtyo9Cno4O2hc9QUPrrPpNKjoUX0/n2v/UIv9bceIUeNE8YAt0MTz9w0isJqFV28zMuYOI4qiMP1eFfPP3kHjNQLhvc5a2tc8v9Wl/KkfDEBVFdSvfoNQM8pr92Hqe/lPpE0mk3SZqQByXUsnK+qUjQQc4oZWdAOWm5tbYt+1eIJ/NYqehh85coQOHTr47Tt06JDfz2FhYQQFBVFQUEDHjh0v+xylNYkXPUkv/jT+am++L3R80TnOr0/xbRUxGP9iateuzfr160lOTiY+Pv6C6WrUqMFvv/1GXl6erzWiyKFDh7BYLISGhpZbuYrGsWg0mit6j29WTe6Lpcl9D19wv73Qjer2cOyPPP44bMeUcJxcVcVk9xDotqMEmjG5FCJz8+DsmhqHq0WRFeTt8pYZHoLB4STA7sBmNFDf7iBVr2NJizqkBZnoejCVbJMRxeFm5pRvSQ0L5N8PdyJDX3L9l80RtXlgz2b6F25j0M6t1MrOQkXBScB5nZ9U6pwNNtxo0OJEQz4BWNHgAI8LD+DGgAcDquJCrReO6XCGN8YwG2DUPahdm6OcyIaoYJRaERAdipJfCIFGlLoxvrMVX+Sw+HNqE/BZKdc0NEBhVs/Sg+aKpBj1KB8Pho9l4VAhblYScIgbmsViISIigs2bN6Oqqu/G9/jx46xevbpyC3cJHTt2xGg0Mm/ePPr27esbx3Hy5El++OEHv7QajYZ7772XefPmsXLlylK7VZ05c8avew/A/Pnz/cZx5Ofns2DBAoKCgvz6tJpMplKDtstlNpvJzc31ew8AmjRpQkxMDEuXLuWxxx4jMtL7NNnlcvHll1+iKAp33nlnmc9bFj169OCbb75h8uTJfPzxx35jhABfHbp27cq6dev44osv/AaTr1u3jn379nHfffddtOXiSjVu3Jj69euzYMECHnzwwRLdw1wuFwUFBYSEhJTbOW9kRpMW0NLo1nAa3QoPDvLO0JZV4GHD7kKOP7ua44GheLQatKqKQ6slK9Dil4fDoMdsd+Bxawm12ngh+TCntBoOxkbxr+5tQVEwOZx02X+c+OMZdNp7gqW31IfCYq2nqopN0fHNLXdAkIFAmw6tfQsnwyzo3Cq3+B58qEAhVoOW/zXvzhdtbiMsOoAJT1SjWp1g0Hlv9LUUzT11YTJHjhDiRiMBh7jhDRgwgClTpvDcc89x5513kpGRwYIFC6hfvz579uyp7OJdUHBwME899RQTJkxgxIgR9OzZE5vNxsKFC6lZsyb79u3zSz9mzBh27NjBG2+8wU8//USLFi3Q6/WkpaWxbt06mjZtWmKWqtDQUIYNG+YbaLx06VLS09P529/+5jdQvUWLFixevJgpU6ZQt25dFEWhS5cul91/Nz4+nrVr1/Lhhx/SsmVLNBoN7du3Jzw8nFdffZWxY8cybNgwHnjgAcxmMytWrGDXrl0MHz683Gaoulzx8fEMGzaMWbNm8eijj/KnP/2JiIgIUlNT+emnn5g1axZBQUH06dOHZcuWMWvWLFJTU2nbti0pKSnMnz+fiIiIcp3OF7ytRO+++y5PPfUUgwYN8k19bLPZfFP2PvPMM9fNoPHrVZhFw30dLLCxF8tnHmPp/7KpfzoDVVFKDCYHiMzLx6PR4DHoqJWeSWh4EHXST+PUavklJopCg56XHupCv92HGP79djbXjyE91AIOj3caV7cKgQYwaOj7+x4GbdyJ0a3l5d73ElJYQNfQfcRzkhq316DOlEcxKwoDgYHX/tIIIcqBTItbNhJwiBvesGHDyM/P57vvvmPr1q3UrVuXN998k6SkpOs64ABvf32TycTXX3/NpEmTqFatGkOGDCEwMJB3333XL21gYCAzZszgq6++YsWKFfzyyy9otVqio6Np3bp1qYvoPfvss/z+++/MmzePM2fOUKtWLf7+979z7733+qV7+umnycnJYd68eeTl5aGqKkuWLLnsgOPRRx/lxIkT/PTTTyxYsACPx8PUqVMJDw+nS5cuTJ48mc8//5wvv/wSp9NJnTp1+Nvf/lamhf/Kw7PPPkvDhg2ZO3cus2fPxuPxUK1aNTp37uwLxHQ6HZ9++qlv4b9Vq1YRFBREt27dePrpp4mJibnEWa5c48aN+frrr5k5cya//PILCxYswGKxEBsbS58+fWjfvn25n/Nmdt/wWuSn2fl9ZQBGBQILCskvNh2uzuUmpLCQQqORbIsZp86I260hLSSY2zOzyAgwsic0GKdZzy1HTjLz7qbcsXc/nY4dYVdcOIvib8Gj1TB11nyansokOs87ViQt0EJaqIXXXqhJyr4DmJp1J7xBgwoZNySEEDcCRa3s0bBCCCFEBfI4Pbz88C5QFFwoFAQYMTqdxOTkYXS5SIkIo1BvICg7j2Ox56ZvDsvPJyNAz60HU7l3/e9srxXLfV/eQfUu3mDT7VFZ81Masz47Qoe9KTQ7mcGhyDDWPXIr/32rLm6Xizlz5tCsWTMaNGgg3eHKmdVqJSkpiaZNm8rg5nIk1/XiVikzLyvdXerwSyeqQqSFQwghxE1No9fw/pwWvPbILqrl5vp98blRuG3DPgKtNta0a+J3XLbFQvNTGdRLOY1qVNk2qC1PdDnXsqXVKNx9T3XuvufcdNp3AU/48hZC3GzkKX3ZSMAhxBXIysq65NoDLpcLne7i/7UCAgL8Foi7HuXn52OzlVwRuTi9Xl8uT22tVitWq/WiabRaLWFhYVd9LlE1GQM0vDe1Ef3HppBaLYSOR05S80w+d65NQqOqnIwM4e4NSazqqHKoZjQAqqKgyyqgyYmjfN2xBe+8XruSayGEEDcmCTiEuAKPPfYYaWlpF03Ttm1btm3bdtE0vXv3LjHA+3rz73//m2XLll00Tdu2bfnss9Im2LwyX375JdOnT79omtjYWJYuXXrV5xJVV1CsiWORIeyNjaBWnp27Nx0kO8TCsrtbYzV71xWpnnbGt1p5gN3BpOb16LJnL+//2h2tRsZgCCFEWUjAIcQVeO+997Db7RdNExwcfMkpZqOioi66/3rw2GOPcd999100TXktRNerVy9at2590TQVudCcqDqiCmzsBbSqdwrkXzo09gUbAKmx4WhQ0bnc9Fi3izn9umAdFC/BhhBCXAUJOIS4Ape6Kb6Z1KtXj3r16l2Tc8XFxZVYd0KIivBSFy1799vYF2phVusGRIVZSqSJzLfSZcs+MjwqUQWFtBl6bf4fCCGufzItbtmU36pVQgghxHWu39A4HthxADWnkDUNauBwlxwCeuuW/VjO5DPttniG7T5ArS7VKqGkQghx85CAQwghRJXSvnEAmrNxxi/hwRQWWx9D43ZzJsTEgagQXl63g3ELZO0TIYS4WhJwCCGEqFJ6DKtBLbsDgGydlu9DAlkTaCbZoCfI7iSioIDMYBODtvbCEivrEAghzlFRLusl/MkYDiGEEFVKzeZBDKmXTu7RfP4IMuMBamXl0u/wCfRWG+/c3pYf3q+OOVq+IoUQojzIb1MhhBBVzmPjGvKI1UXakUIyUgv5bLqd7xrXIqx2AGuer0atcG1lF1EIIW4aEnAIIYSokoxmHXWaBVGnWRDtukdXdnGEEOKmJQGHEEIIIYQQl8FT2QW4QcmgcSGEEEIIIUSFkYBDCCGEEEIIUWGkS5UQQgghhBCXQdXIlLdlIQGHEEIIAZz54Qg/fnKAbS4TJ9vV5KWh0bRqElDZxRJCiBueBBxCCCGqNLvVxfd3LCHLoSfGdYxn0vdSsN3Eh9vvo27/1rw1IqyyiyiEEDc0GcMhhBCiypo2/Th/vWctuafdWE7n4cow4bAH0PT0AT77fhJ7F+3E5VIru5hCiOuEqlzeS/iTgEMIIUSVdPKkg6SZBwgodBPgcAMaCrVmdgW2pFAJQKd6+GrZJ2yt8ya43JVdXCGEuGFJwCGEEKJK+vt7h0gLDaZGToHf9uYFuzGpNhRAg0rHE3tIvP3zyimkEELcBGQMhxBCiCpnxOA/OKMxk1/NxJYAI3VTM2h4NA2NCtUcJ0ukj0xLqYRSCiHEzUFaOIQQQlQphTl2nB49ARoIA3JDAtnRtA6/dGhGWkw4JwMiShzjNATzVpfvr31hhRDXFVWjXNZL+JOAQwghRJWy5uNkwux23Fqtb5viUTkVGkRyw5p83ONx0i3hvn2FmgCMWYE0Pp3HuC7fVkaRhRDihiZdqoQQQlQp3+/MJ00fCoDR6aJafgFGtxuHRgMeFUeAkaceeI341N2oqkK1FBttD+bQOu8Isanmyi28EELcgCTgEEIIUWU8+fRWNkTWpkNGFtluD9Xz8tB5vNPeGjweUFUcqoqq0bG4ya3kaBXCqheQFJVK9dw8IuxWlvbfyvj5t1RyTYQQlUGVvkFlIpdNCCFElXDqcD6zDHVomWclzWLBrtH4gg0fRUHr8bArNJA9oUEY7S7q5mej1dv5tlUdZnRoRUa2i6FPJ1dOJYQQ4gYkAYcQgnHjxtGuXbtyzbNdu3aMGzfumhy/dOlS2rVrx5YtW8p8vvOlpqbSrl07pk2bVuZyXYmKqIM458u3djNl5BaGpGegAvkBRgp1Wkpb0u+PEAtHA810yMzlg5Wrmbj4O1775TfmJSzgTwf3Y7a7Sc0sIPf7Q37HvfqTA+UfdpT37Cj/sGP4t8pjxx/hs/2R16SOQghxvZKAQ4hKkJCQwNKlS6+bfG5G+/btY9q0aaSmplZ2UcrsZqhDZXM53Tz9wn62b87lVEQMRlVFczbMcGq1pJn9x2Q4NRosLg9NzuTRLjWN9ocO++3/8+bt5AXo6HFkN2uf+h8PjNyG9h0ryv/Z+NdWBQwa0CvgKTpC4bPDsYRONjF9u+Ma1FgIUZFUrXJZL+FPAg4hKsE333xTLoFCeeVzo+vZsyfr1q2jbdu2vm3JyclMnz693G/W161bx9/+9rdyzROubR2qikPpDsZ1/Y5TRwrZFxWNpcCK3ukk0OUmqNAGQIFRj02nw6HVYtPpcGq1mN0eGhVYqZ2VUyJPvceDS6/wRbMuBGQbqbGnAJPDBXotaBRQFNBpvIGHjwIojFqhEPWJg0yrp0S+QghxM5NB40KIG55Wq0VbbIrTimQ0Gisk32tZh5vRmQwbeoPCrqNuJn14mL2GIE4aDNg63EbrzGxuP5ZGWG4+epeLggAjzaxOssKCKbAEYDcZcZ299ipw2GKiVZ6VY1FR2HU6jC6X7zwngoNwauCf89diJZRWxzLxmHTeQKM4Dd5WDuVspgBuDxlWhchPXOBRGdwExnXR0DBSfw2ukBBCVB4JOMR1ZenSpbzzzjtMmTKFvXv3Mn/+fE6dOkVsbCwjRoygd+/egLd/fd++fRk5ciRPPvmkXx7Tpk1j+vTpLFmyhOrVqwPeMQrLli1j5cqVTJgwgbVr1+J0Omnfvj1vvPEGkZGRLFy4kISEBFJTU4mNjeXZZ5+la9eu5V7HorESaWlpfuMmipd39erVzJ49m+TkZBRFoWHDhjz22GN+5blUPhs2bGDx4sXs2bOHjIwM9Ho9zZs3Z8SIEdxyS/nNsHPw4EEmTJjA9u3bMRgM3Hbbbbz00ksXTP/jjz+SmJjI/v37cbvdNGjQgKFDh9K9e/dS02/cuJEpU6awf/9+AgMDueeee3j66acxF+sKU/S5mTp1qm/cxfTp0wEYPXq0L13v3r2vevxFu3btSuRTtK1Xr15MnjyZ5ORkQkJCGDBgAI8//ji5ubm+z53VaqV9+/b89a9/JSoqqlLqcL1zuTzM/vAYR7bnoHW6MVitFGg0aGNNRO7+g+6HNhHgcvLfDn2Y1aQd6Vo9bo9K/6SDdD92gjuBiOgQPm/flv6pJwlye8iIjiAzMoxax9PxaDXkR5nQA6F2G06Xg8zgYFAU8nQ63xej3WBgfufO3Lt1KxH5+RyMCOfZh3ry4PpkNHi7X419ojuFAYYLV0ZV8UYdgKp4gw8F0GhISFZJSFbBY/du94Di9qB1u4kyK7x1l57Rt1447w3HPby/zkVKmoM6R0+TZbeTYQ6gpz2FtwdWw9SuLvxzAXy3DUwGSMuC45lgd4JHBa0GtAq4VXB7vEGTVgGTEWwOcLovXC+txtuic7E05ws0QqHDe77zKYBRD01qQHq291VEo4BJD5Eh3vPqNNCqLvoaoTT5fhsBVpe3fne1gLcHwOJN8OUaCAyAl/rCn1pffhmvZz/+Dh8tgXwbDL0TnuxR2SUS4rJJwCGuS5MmTcJut/Pggw9iMBiYP38+48aNIy4ujtatW5c53+eee47o6GhGjx5NSkoKiYmJjB07lrvuuotFixZx//33YzAYSExM5LXXXmPhwoXUqFGj/CoGvPvuu3z00UeEhoYyYsQI3/awsDAA5s2bxwcffECdOnX485//DMCyZct45ZVX+Mtf/sKDDz54WfksXbqUnJwcevbsSbVq1Th16hSLFy/m6aefZurUqbRp0+aq63LixAlGjhyJw+FgwIABVKtWjbVr1/Lss8+Wmn7y5MnMmDGD2267jdGjR6PRaFi1ahWvv/46r776KgMGDPBLv3fvXn766Sf69etHr1692LJlC3PmzOHgwYNMmjQJjab0XqF33303GRkZLFq0iOHDh1O3bl0A4uLirrrOF7Jv3z7Wrl3LAw88QK9evVixYgWffvopRqORZcuWUb16dUaNGuX73L399ttMnjz5gvlVRh2uF5+9c4TUbdmAt5HAqTMRnpFFpvUkz25eBEBSRG3G33IHhQ43uFVuO5HOPcdSKbq5jz+Vy6D9hwkICPDlq2o0HI+JpFpOnl9/Yr3bQ6DVRoEpgEC7nTO6EDx4Gyn2x8aw8aF+qC4P+jMF7I+IYGrXVry1ZCMnwwPJCTyXP6p6buxGUaChnM3IrfrfaGvx3khz9m9VBacb1ebCBaTlqDz1PzsmvcKwW0q2gOzP9HDXbAc2F4CObVHVfa0su6jOiU/X8aVjESzceOEL7fZA8XhBVcGlQl7hhY+50LGXI99+4X0qYHPC70dK7vOoUOCAgtPntiWnoQf8rsy+VFiyCVKzzm1buRPW/gM6Nb7Cwl5nftsHPf/uve4A6/aC3QXP9arcclVBHllFvEwk4BDXJYfDwezZs9HrvV8n3bp14/7772fu3LlXFXA0b96c1157zW9bQkICp06dIjExkcDAQADat2/PoEGDWLRoEc8880yZz1eanj17MmXKFMLDw+nZs6ffvtzcXD755BPi4uL44osvfOXp378/jz76KBMmTOCee+4hKCjoovkA/O1vf8NkMvlte+ihhxgwYAAzZ84sl4Bj8uTJ5Obm+p7KAwwYMICxY8eyb98+v7R79+5lxowZDB8+nDFjxvi2Dxw4kJdffplJkybRq1cvLBaLb9+BAwf497//7WvZefjhh/n3v//NnDlzWLFiBT16lP6Er2HDhrRs2ZJFixbRsWPHcp+BqzQHDhxg5syZxMfHA3D//ffTu3dvPvroI981KS4hIYEjR45Qp06dUvOrjDpcCbvdjtt9pXeclydlZy5+ncsUBavFRLsT526eZ7W6h0KdFgq93Z26HT1aIp/qBTbOFAs4AFw6XamDF1tuP8qZSAvpNUJJtRhZowulQ2YOqWYTDt3Z0gSaePbnnZhyrLzbrxPReYVgd4Ne4w0sig/NUIq1ZnD2Rr44t3p2aIdyLr275NiOietsPNzUWWL7zG0KNldRwEKJLl1zmndk2nvTqHLLFBYPNgDcHlzTf8TRqmbllKecGD77Ad15nw/PtB+w/fmucj9XYWGh3983A7O5yv1PuO5IwCGuSw8//LAv2ACIjo6mVq1apKSkXFW+gwYN8vu5TZs2JCQk0KtXL9/NPXhv9iwWC8eOHbuq812pjRs3UlhYyMCBA/3KExgYyMCBA/nPf/7Dxo0bL9j9qLjiwYbVasXhcKDVaomPj+ePP/646rJ6PB7Wrl1Ls2bN/G6GFUXhscceY/Xq1X7ply9fjqIo9OrVi+zsbL99Xbp0Yc2aNezatYtbb73Vt7127dolurU9/vjjzJkzh9WrV18w4KgMLVq08AUbgK8L2y+//MLAgQP90hZ97lJSUi4YcFzvyuMzdGGljGVRVfIN58bPmJ1nb8LPjpHQe+z4ui6dFZhv5UxEqN+2rIAATGYTkdZzN1Nap5tCo4aCQB3JIYFkGw1kGw3k6XXUt/o/lf+9SRxBaWc4GRrIydBAcHjA6QFTKV+nSlH5ig/kKF6n84pcShKnvZCkpJK/97Iyo4DoCx6nd7uR57BeZ/JySElKquxiXJVaeblEnbfN5nKSVIH1OnLkSIXlfa2VZzdiUTYScIjrUmndmEJCQkhPTy/XfIOCggB8YyeKCw4OJien5Cw1FenEiRMA1KtXr8S+om1FaS7l+PHjTJo0iQ0bNpCXl+e3Tzl/gGsZnDlzBqvVSu3atS9Y1uIOHz6Mqqr079//gnlmZmb6/VzUjai4yMhIgoKCLvs6XCulfWaDg4OBkp+vos/dtf58laf4+PgKa+Go1yGdo+uzz21QVQLzC1kTdwv9923G5HLy+I4fmXrLbaQFBEChk4UN6/Pilt3YdN5AWwUCC21EZmRxJiwEj0bBUlDIvphA0qrH0PF4GsF2O1qHC7fTyfq2DSg0GrAroPWouDUKDc5kowb4txLa9ToKwgL9tnnHX6jnukidv+9C/92U8xLqNODyf4r96l0WmjZtWuLQ56tDwhGVXMfZlhRV9WvlePL31ege7gBf/3qBk9+cPHWj0Rw+5ftZNegIeukhmjYt+XvqRqK8ZEJduhPFcW4CA+0LfUr9bFytwsJCX+vr+a3kQlYaLysJOMR16UJ981XV+yjvYjfMF7sJutAsQBfaXnS+G43VamXkyJEUFhYyaNAgGjRogMViQVEUvvjiCzZv3lwp5VIUhU8++eSC72/9+vWvcYnKz8VmmLrZPl9QcbN1Afz5L3VZ+N809vyahc7pQnHYSWsQTt0oA3/RPknbozsJdDl4Mn0fPxmrk2YyohpMBNrTaZGVhUZV+T22EfmaSKLO5BB5Jge3orAvLgaDAlaNAjotTpcWj17l5yb12R5iIVunJdTlpkmuld0RgbQ9eojfGzTBXez9a3kojV9rlrKQn8MDRk3J2arg7FS5in+3Kr9uUCo4PWgNGjRaHUbVQ50Qhfe66+nXvPQZrJqYYf0IDx9tcJOS5qDRoXTyz+SQGaCjj+40f36vFZqmPaFjY/h2K1gC4FQ2JKdCthXcbtDrwGzwjp2wO0HReAdnB5shuwCsjrNjUc6jUbyDtLUaKLCX2hWs1GMigiC/EApLdhFDp4EgM3RoAKdyYMcRbxCnAAEGb5lqhAGKdwritvWw1wzHtmQDQXkuNMFmuLsFmlfuh5U7YPZqCDKhPN8bU4eGly7f9e62ZvDL3+GTb71jbB7rirH/bRV6SpPJJF2RRLmRgEPckIqeHOfm5pbYd709+S7NhQKmogHBhw4dokOHDn77Dh/2LkBW/En6hfLZtGkTp0+f5q233qJv375++6ZMmVLmchcXFhaG2WzmaCl95w8dOlRiW82aNVm/fj0xMTGltlyUpqjOxWVkZJCXl3fJwfzl0YpT2W6GOpSFoig8NLI6D40s2fLodTcADwBvn93iDd5e4OhxGwnD1qG6DFi1WsLzCvBoNGQFGeh29DeabTjEwL5Po/F4MLhcpAUHsj48CPvZIPjh3Wt5busKdKqHLdHx3Lb/FEvatSLbYqLpsZP02/A785+6QCudSz37rVrsfSv6p04DGs+5qXIVeP1WlX/eeZEZri6hebSGz/tq8A6dvkCw/mwv7+sm5LZaOfCnejRt2tT/xrj/bd7XzaZjI/i6UWWXQogykYYhcUOyWCxERESwefNmv6fEx48fLzF24HpkMplKDZY6duyIyWQiMTGRgoIC3/aCggISExMxm81+YxwulE/RE/Xzn6Bv2LCh3Prea7Vabr/9dvbs2cOWLVt821VVZfbs2SXSFw1snzRpUqmtUOd3pwI4evRoifdz1qxZANx5550XLV9RV4DSrs+N4maow7WiKAqKolCnpom//Nydv/7ShW59wllVP46fa8bywM4VPLRrBadM1bg/5STmQhsKkBYQ4As2+u/dxGc/fEF8xgmaZKbxaNJPtMncx7jFi/lnwv8Y9esafmpbA52iEGh3Emy1oXO5vd+kOsU3ta1v7Mb5g7k9YNGrHBmtQX3NcFXBhhCicqga5bJewp+0cIgb1oABA5gyZQrPPfccd955JxkZGSxYsID69euzZ8+eyi7eRbVo0YLFixczZcoU6tati6IodOnShaCgIJ577jk++OADHn/8cd+6I8uWLSMlJYW//OUvfoPJL5RP69atiYiIYMKECaSlpREdHU1ycjLfffcdDRo04MCBA+VSj6effpr169fzwgsv8MgjjxAdHc3atWvJysoqkbZ58+aMGjWKzz77jMGDB9O9e3eioqLIyMggKSmJdevWsWHDBr9jGjRowJtvvkm/fv2oVasWW7Zs4aeffqJt27b86U9/umjZmjdvjkajYcaMGeTm5mIymahRo4bfwO7r3c1Qh8p090tNuRtY/XMWU633UHf1UfbGNsboUdG4PaBAjbx8X/ohu9f7Ha/gITPcxrzGtxGXk8PW6rVZW7c+GhVCCh18N+M/pEwfzUOHo71Bi8vjnX1Kp5wXaKjg8rB4gELfhhXXFU0IIa5XEnCIG9awYcPIz8/nu+++Y+vWrdStW5c333yTpKSk6z7gePrpp8nJyWHevHnk5eWhqipLlizBZDLx8MMPExkZyZdffulb+K1Ro0Z+08NeKp/q1avz6aef8sknn5CYmIjb7aZJkyZ8/PHHLF68uNwCjri4OP773/8yfvx4EhMTfQv/vfvuu6UGBKNGjaJZs2bMmTOHb775hsLCQsLDw6lfvz6vvPJKifRNmjThxRdfZPLkySxcuBCLxcKAAQMYM2bMBceBFImJieGtt95i1qxZvP/++7hcLnr37n1D3azfDHW4HnS9O4w9x+ozVn2Sek6PdxFwvQ6Ny02MtZBWGVnsiAwj11hygGyWycRHne8gOreAANUbRHjHiLuonfIOLUMN2ACbS+WtX5x8slnF7vJ4xyxovK0eD9Y4xYw+gYSEhFzTegshxJU4efIkmzdvJjMzkx49ehATE4Pb7SYvL4+goKCLjlW8FEW9kUctCiGEEJdp7x+5vPv2MZrnnJu17aRRj9ENu6MjCCtIZ+b/xmPweGcCcika7hs0lpX1WqDxePhw0c9srVODue2a8MD+g8z7puVFz+d0OpkzZw7NmjWjQYMGEnCUM6vVSlJSUskxHOKqyHW9uMVhCZeV7v6swRVckvKjqirvv/8+X3/9NS6XC0VRmDFjBp06dSIvL48777yT5557jscff7zM55AxHEIIIaqEJvHB/8/efcdHUeYPHP/M9s2mV1JI6EiVKkXp7egWEFSQA48iTc+uZwHxbGfDAxERUOTHSe9NREAUpKMikNBSSCG9b5/5/bFkyZIAgVDD8369omT2med5ZnY2O995GsU6DYleRqwqia9rR/PfBnXYHx5CjNWGryaQD7uPZ+09DzCrRTfa//0tfqzVBABZpSK4yEyOt57ecWdY+NU9t/hoBEG4FRSpYj93kq+//poFCxYwatQo5s+f7zH+08fHh549e/LDDz9UqgzRpUoQKiAnJ+eKaw44HA40mst/pAwGg8cYjNuRxWKhsLDwiumCg8uZGvQ253Q6yx1fcjE/Pz+PhSeFquN/8+rx4IR4dqiDiPdxrWp/2NdEDYsVg6yQ7B9Ook9v5tevjqNUtz2jzY5Vr2N/TCQZ/wm4VdUXBEG47pYuXcqDDz7Ic889V+53ZP369fn5558rVYYIOAShAp588klSU1Mvm6ZFixYcPHjwsmn69evHlClTrmPNrr8tW7YwderUK6YrPTPVneLcuXNlpgkuz5dffumxerpQdRiNKjp7mZnnMLi35Wo1LK4WxL05BdTNyGVd3SgUp4KkyO7ZZhpn5jP3/ubUtuUCIuAQBKHqSE1NpXnz5pd83Wg0VuhB5OWIgEMQKmDatGlYrdbLpvH19b3i9KUhISHXs1o3RLt27Zg5c+atrsYNERQUVKFjq1dPzHVflQ18KJiN3xVz0vtC/3SzWk28ycDBsJrYSg+MlBUMskz1rBxSA438/EmdW1BjQRBuF1VxytugoKDLPlT966+/CA8Pr1QZIuAQhApo1qzZra7CTRMcHHxHdpeqCL1eT5s2bW51NYRbrGG3MDpMP0C8l5EkL1dLR7DZQuPCIn70u6jLo6JwT4GZ9tuhaUUAAQAASURBVMlpjF3cHY266t1sCIJwd+vRowfff/89Dz/8sLvbd8nCs7/88gsrV67kqaeeqlQZIuAQBEEQ7jrTVrbA8fhBYjONxBsNNCiyEBvkWzahJOFjs1OkkvHWi3lWBEGoeiZPnsyePXsYOHAgrVq1QpIk5syZw/Tp0zl8+DANGjRg3LhxlSpD/PUUBEEQ7joqtcSrc5pwwt+PP/39SDB54SXL5SSUqBWfxCOre9z8SgqCcNuRpYr93El8fHxYsmQJ//jHPzh37hx6vZ59+/ZRUFDAhAkTWLRoEUZj2XWKroZo4RAEQRDuSv6+Oh66T8uK3bnINgd1zA5+DQ92rQyuKKCSCC40M/+Htu7uBYIgCFWRwWBg/PjxjB8//obkL1o4BEEQhLvWO/8I5ejc2ozzKSJHraFdShpaFNCo8bHa+WigQQQbgiAIlSRaOARBEIS73t+/asETNpkvv07HcKaAyCg98yb4o9WI53KCIFRtr7766hXTSJLEu+++e81liIBDEARBEACtTsWk8dWYdKsrIgjCbasqTou7Z8+eMttkWSYjIwOn00lgYKAYwyEIgiAIgiAIwrX56aefyt1ut9tZvHgx3377LfPmzatUGaKtWBAEQRAEQRAED1qtlmHDhnH//fczbdq0SuUlAg5BEARBEARBqABFqthPVXLPPfewb9++SuUhAg5BEARBEARBEMq1a9cuMYZDEARBEG6GZ7bY+fI3OxqbE5tGRYBJIvU5I+oqOIhUEIS7x4wZM8rdXlBQwL59+zh69ChjxoypVBki4BAEQRCEK6g7y0JCloJTo8Zm0IIkkWd14P12MQuG6hl8j/g6FYS7gVIF1+W5VMDh5+dH9erVmTp1Ko8++milyhB/IQVBEAThMsx2hcw0C+j0yBoVnL/hsOk16IAhq2WGb7CTN1mDXlP1bkYEQajajh8/fsPLEGM4BEEQBOEytibK5OsNqGXcwUYJm16D3mrHKks8utZ5ayooCIJwmxMtHIIgCIJwGcmJBRhtGoq99GVflGV8iuxYvPRsPO5AfK0KgnC7S0lJuab9IiIirrlM8ZdREARBEC7DqtKjkh3lT3WpUpEV6IVkdyA7lZteN0EQbi65CvSa7Nq1K9I1jEU5duzYNZcpAg5BEARBuIycAhmLXlOmO1UJWa0ClYRTLQIOQRBuf+++++41BRyVIQIOQRAEQbiMlCIACRTlkkGHIAjCneLhhx++6WWKgEMQBEEQLmHggmLWn1WjVGiKFRGMCEJVp4h1d66JCDgEQRAE4bwfvzrKuKMBFOoMIEGRQYPTS43GCfLlWjckCRT55lVUEAThOjtw4ABHjx6loKAAWfb8eyZJEhMmTLjmvEXAIQiCINx19n98iCOLkqielEpMVjIKDpa0aMEbgwfgFeCgyKSjbkYKXU+e5mC1GsSGRyHJiufTTUUBWQG1yt3dSv+RDcvz2pveP1oQBOFa5ebmMnbsWP744w8URUGSJBTFNSat5N8i4BAEQRCEq7Cw1xYKcpz0+/0Qdqeds9oIMn2DCDR7M23hzxyP9OfxY7/SLuEUego4GhHI3Pu6srze/eT6GbDpNKgdMkE5xRisTpIifF0zWEkSNgUemG/j11HlTKErCMIdr9zZ6u5wH374IbGxsXz88cc0bdqU7t27M3fuXKKiovjmm284fPgwc+bMqVQZYuE/QRCu2v79+5k9ezYFBQW3uip3rLVr17Jo0aJbXY27wvGvjvJdk8XMq7+GeQ3WEHjsLNWzMjgSEs1JVT1sdhO+WRbkHDtxof78a/sWWiZkYsebAiJZVa87s+7vRaFJR1RqAWHphfgWWFHLCvkmnWdhksTujCp4RyIIQpX1888/M2TIEPr06YPJZAJApVIRExPDW2+9RWRkJO+++26lyhAtHIIgXLUDBw4wZ84c+vfvj4+Pz62uzh1p7dq1pKam8vjjj9/qqlQ56acLWf76MZSjGdTOSsO3uIj78rJID/Xht9otSI0Ids82ZYyx0mh/IqfCApj+4H30OX6MMEsmPqSgwYoDHRN2WvmgV1eKvXQkRKpxatQXCitn5iq1Q6w4LgjCnSM/P586deoAuAOOoqIi9+v3338/n376aaXKEAGHIAgVVlRU5P5jJAjXiyIrKAqo1J437rJTwWmXKciy4LApIEF2kpmU2EKObMvEnONAsdlwoEU6P22tIklEx52j7tls5CiJ+Grh6B12miY4ORsQiUOr8wgQzN56Mqv58nOjaAwOO4N+P4wfiahwBQ0abGi06Xzx+TqOR4XyTa97yfLzulDJi8dqKAqSrBD2Rg6rRpf9rCiKglNxxSnaUsfrlBUkQHV+jIjdqbhflx0yklq6OeNCbHbQaUtXGJwylA6yLqXIAjoNnM0AuwPUWogIcO2bkQcGLZjtcDQJNCoIMEFUMDhksNjAZIBiK2QUgFYFdaq59rU7Ia8IrHZX/obzrUoaCd2xVCSbEaJCXNuCfFxjapwy6LWu90elgpwC8DO5jketdtVPqyn/mEu/VpU4nK5zI8YXCRcJDQ0lMzMTAJ1OR1BQEMePH6d79+4AnDt3rtJ/f6rgJ0qoqLVr1zJ16lRmzZrF8ePHWbZsGenp6YSHhzNq1Cj69esHQEpKCgMGDGD06NGMHTvWI4/Zs2czZ84c1qxZ417yfsqUKaxbt44ff/yRzz77jJ07d2K322ndujWvvvoqwcHBrFixgkWLFpGSkkJ4eDiTJk2ic+fON+Q4+/fvT3h4OK+99hqffvophw4dQpIk2rRpw0svvURwcLA7bUnd9+/fXyafVq1a0a9fP6ZMmVLmvNSqVYv58+eTkJBASEgIo0aNYsCAAaSlpfHJJ5+wf/9+HA4HnTp14pVXXrnqm/aS87xkyRJWrlzJDz/8QGFhIU2bNuXll1+mRo0a/PTTT8ydO5f4+HgCAwMZOXJkuXNtr1q1iqVLlxIfH49Go6Fx48aMHj2aZs2alXu8ffr0Yfbs2cTFxdGgQQMiIiJYt24dAAMGDHCnL+/6uJSioiK+/fZb9uzZw9mzZykuLiYsLIxu3boxevRoDAaDR3pFUVi1ahWrVq3i9OnTAERERNClSxfGjRvnTme321m0aBGbN28mISEBjUZDdHQ0/fr1Y8iQIe50KSkpzJo1iz179lBQUEBoaCg9e/bkqaee8ij7Wq+Hhg0bMmfOHE6ePImPjw99+vRhwoQJaDSuP7n9+/cnNTXVnU+JL7/8klatWnHq1Cm++uor/vjjD3Jzc/H19aVGjRoMHz6cBx54oELn+E6gKAo75iexb1UaTrtMk+4h9JpUE7VW4scv49m7PK28nS66YdJdmIxWkgjIKCQiKY+EJqGkhQa5UyUHBNEk8QyyqmxPYqtBi1Ot4tmdO2mUdSHYyNV78VzPp/g5pjFah5OORxJ59+ufGPtcX486GItt2LVqHFo1aqdMdHoRdo2Kbt84sGsG86yUzOt1YNZhmTd+kcmyuPaL8YGPO6s4fKiI5XssaNUSA1vrOJMuszvOTrifxGOnEqi35C90IXpqTWlG1Lj6lTzrl3DwFIyeBQdPQ6Pq8MUY2B0LH6yCAjMMbg+zx4GPsey+u2PhsU8gIePG1O0SjECTKyXSqkDGFYAAqCUI8oX0PIgKct2AJ2RAs5rwwgD4bB3sPwUNomDGaOh6xRJufwVmGPclLNnlev9efhBevvnrMFQVShUM2Fq3bs2uXbt4+umnAejduzdz585FrVYjyzLffvstHTp0qFQZIuAQmDlzJlarlYcffhidTseyZcuYMmUKUVFRZW5Cr8bkyZMJDQ1l3LhxJCUlsXjxYl588UW6dOnCypUrGThwIDqdjsWLF/Pyyy+zYsUKIiMjr9+BlZKRkcHYsWPp3LkzkydP5sSJE6xYsYKioiJmzpxZqbx/+eUXVqxYwaBBg/D19WX16tW8/fbbaLVaZs6cSevWrRk/fjxHjx5lzZo16HQ63njjjWsqa8qUKRiNRkaOHElubi4LFy5k0qRJjBs3js8//9yjDu+++y61atXyeA8///xzFixYQKNGjRg/fjzFxcWsXLmSsWPH8vHHH5e5mT169Cg//fQTDz74oDsArV27NkVFRWzbto3nnnsOf39/AOrWrVvh48jIyGD16tV07dqVv/3tb6jVag4ePMiCBQuIjY1lxowZHunffPNNNm7cSOPGjRk1ahQ+Pj7Ex8ezdetWd8Bht9uZOHEiBw4coG3btvTu3RudTsfJkyfZtm2bO+BITU1lxIgRFBYWMmjQIKKjozlw4ADz58/n999/54svvnAHBtfi119/ZdmyZTzyyCMMGDCAHTt28N133+Hj48OoUaMAeP7555kxYwa5ubk899xz7n1r1qxJbm6u+4/+I488QrVq1cjNzeXYsWMcOXKkSgUcf2zO4NdFye7fD29MxxSgxT9cX36wAVd8OuuXXUxmmI9HsAGgqFQci6he7j6FkkTMuRw6nD6NEy0KrhU13r//EX6OaQyAXaNma7OaRKfncU9SFsejXQ8qJKeMl8WOMddMrq+BQm89Ob56AvKtBOVaSK7mw0fJ1Wl03ML4nZ7TTCYUwJB1TqKSrGicYHMoLPrF6n49NU/hs8AopnqfJuRcMcef/g3vpgH4tw+97Dm4anYHDHgPkrNdv/+VBH3egaILdeF/O8HPC2Zd9FDBaocB70LmzR/PVaHbPvtFUxU7FVewAXA268L2w2dgxH8vBCbHzsKD70PSV66WkTvZywtg0U7Xv3MK4ZWFroBqwH23tl7CbePvf/87u3btwmazodPpmDRpEidPnmT69OmAKyB5/fXXK1WGCDgEbDYbCxYsQKt1NSl369aNgQMHsmTJkkoFHI0aNeLll1/22LZo0SLS09NZvHgx3t7egOtCfuyxx1i5ciUTJ0685vIuJykpiffee48ePXq4t6lUKveT/ho1alxz3mfOnGHp0qWEh4cD0LNnT/r27cubb77JM888w7Bhw9xpCwoKWL9+Pc8//zxeXl6XyvKSgoKC+OSTT9xNm/7+/nz00Ud8+OGHLF68mGrVqnnUofR7GB8fz3fffce9997Ll19+6X6/H3zwQQYPHswHH3xAu3btUKsvdJ04ffo0M2fOpE2bNh71qFOnDtu2baNz587ulq2rERkZyfr16z1u7B999FFmzZrF3LlzOXLkCI0bu270tmzZwsaNG+nduzdTp05FVeoJdel5whctWsSBAwcYOXJkman7SqebOXMmOTk5fPbZZ+6b98GDBzN9+nS+++471q1bx4MPPnjVx1Ti9OnTLFmyxH1eHnnkEYYMGcLixYvdAUfnzp1ZtGgRVquVPn36eOy/Y8cOsrOzy1yvtxur1YrTWbmxCsd/ySyzLfbXLPzCdeWkrhiLlxazV/k3iHZd2Xy9zcUk+ZjY2qwWQ07vw89qoUAKxlfJ5NfqDcukPxYdjOP8509yyihqFVmBrvICcs2gKOT7GAjIt6K3O5EUBZUCs38vf44WpyJRbNDiW2Qr93VZpeJIjRC6/JEAQOqKM+iaeV/5RFwF1f5TGEqCjRKlg42SuqzZi+Xj4Z77/nocwy0INm4I50XBSYEZ6+aDOPu1vDX1uU4Mq/eWmSHIsWI3tu6Ny01vNps9/l8VXMv37d2kfv361K9/ofXUz8+Pb775hvz8fFQqlft+rTJEwCEwePBg980nuPryRUdHk5SUVKl8H3vsMY/fmzdvzqJFi+jbt6/HxVu3bl1MJhOJiYmVKu9yQkJCyty8tWrViqVLl5KUlFSpgKNz587uYAMgICCAmJgYTp8+zaOPPuqRtlmzZmzbto2UlBT3AK2rMWTIEI9+lCXBRMeOHd3BRuk6lH4Pd+zYgaIoPPnkkx7vd0hICP379+d///sfsbGxNGx44SarXr16ZYKN66F0+Q6Hg+LiYmRZ5r777isTcGzcuBGAZ5991iPYADx+37RpE76+vvzjH/8oU15JOlmW+fnnn6lfv36ZloK///3v/N///R/bt2+vVMBxcRAmSRKtWrViyZIlFBcXX/GLr+SzsWvXLtq1a3dd/tDfCEeOHKl0Hna1o8w2yWjFoS7/5rsiMiL88M8prFhiRaHRkQQa5atpfuocX7dty9s/bOS/7fuzpUk0DnvZZ+gqp4wsqwjKKiIryDOwyfU1oJIVlPO/O9QSskpCkmXC1AWAoUx+AJqLb3QvElBgcf87W5NH8bFjFTu+CtIW5tNEJSHJymXTFQd5EXtR2TpLLo0lkC6/6x2hpGWrtFP2PMzX+XzfbPWDvPBOyfHYlq6XSb3CccXHx9/AWt1cLVtev6BRrno9qjh58mS59yS+vr7XrQwRcAjldmPy8/MjLe0SXRquMd+S2YzKeyLu6+tLXl5epcq7mrqA6xiBSpdbXt4+Pj4EBweju+iJasmH91rLjIqKKje/8s6pj4+Px3uYkpICuLpEXaxkW3JyskfAER0dfU31rIilS5eyfPlyTp8+XWZF09LT7SYlJREcHExQUNDFWXhITEykfv366PWXXv8gJyeH4uJiatWqVeY1Pz8/goODSU5OLmfPirvStXalgKNly5b07duXtWvXsnHjRho2bEibNm3o0aNHufW+VRo3blzpFo6IACvfHzlJcZ4r8NDoVXQbVQtToJbvDsdiK7q6lbslWUFWq7gnPZHdgY0uLNJXzkxSABq7E/JdLXqh+WbqnM1EY0ijQ/Iefq0fxm+16hNxrgDV+Ztpk8XG/roRrsHdStk7bEUlobY58Sm2owCZgV6onTKB2Hi/u5rYHxWO53jWo3Wwk+JUByUhhVEH5lLxVq3UbJrEp7teu8eHpv9sg9pHy3XVABxjY9HO+sG9yTGwFapD8agSXa1Qik6D5p1hNGjQoOy+Y+LQzt5yfet0C8iNq6M+cuEhjeOh+6jxUNdbWKPrQ/XOMJTBHyPZXJ8zuXoQ/q8MxT/Mr9z0ZrPZ3fJvNJYzZkeocvr160fdunXp27cvvXv3JiYm5rqXIQIOocxT4xKlV5m8lMvdcJTumlOR7Uo5X+DXy6WO8eJyL3WsDkfZJ7FXyruiZV6Nqy2rsuf04sHb18vChQv57LPPaNu2LUOHDiU4OBitVktGRgZTpkwpE4DcKtfzeoCKvx9Tp05l+PDh7Nq1i0OHDrFw4ULmzZvHc8895zH4/Va6XGBXUV61vBg7rxl//ZSJwybTsHMwfmGufCcubMmv/3eWwxvTsRY5AQVJllFKPYa2q9Q4JDCcn+FJkUBtc5KvMdH34G/Enh+zYVepiK9WNigPT7jw1NdAMc8e3YAWOx3js+k47yijB41hXquumIrtyCoJg0WHf6ErGig0lnPTryiYimxYtWoSq3mjUuA+TQKfdCwg3L8O+4dr+D5W4bcUBa0KOlWXeLiumoz8QNYfsKLVQP+WBpKynOw8ZiM6WE0HnZrcZveiD/ei2tAaqE3XOdgo8cU4eLCtawB485po+rVyDTZetBNyi5AGtcNQ9xLdJ798Gh5uB/N+gr1xrkHYJa0l/iYI9oHUnAvbSkdUGgnUGtcMSorsGuANrve4mr9r5qrsAiiwuGazUgFeeogIwJldiJRRgHQ+OXqN68fLANWDoXUdqB0GGfnw63Hw9YIODSEmBGKToWF116xXh89Am7qoezWHrX+40t5bA82A1mgu83m+YwxoA39Nh6W7wN+E6vEOeFVgXIrRaBRdke4SU6ZMYePGjXz++edMnz6dBg0a0KdPH3r37n3dxtaKgEO4opKn6Pn5+WVeq+zT4NtN6RaIkqfSUDWOs+SPxqlTp8q0lJTM/FTRPyyVnR5vw4YNRERE8Pnnn3vcoO/atatM2ujoaHbs2EFWVtZlWzliYmKIj493D3orT0BAACaTyX28peXn55OZmUm9evXc227k9XClc1inTh3q1KnDk08+SUFBASNGjGDGjBk8+uijN2d61JvEy09L64fCy2w3+mjoPq4G3cfVqFA+2SnF2HKKWbyjmCPLZM7l+VE/JRmNxcEpnzAIu9DKobPZCI3PJSIp171/JAlosXvk+cKOtXzdpjsFPq4gyGR2vV6kV2M2lR9wSQoUm3QoL2qx2+18//0etGpXq6FJJ/FUE4mnLpr4KCJAzejuF27sgnxUNKtRElgYCLw3sELnoNJ6NnP9lPAzwdN/u7Z9bwJrcTHHjh2jQYMGlbsxHlhq8HT3e10/VU2dcHj1kVtdiyqhKs5SNXToUIYOHUpmZiabNm1i48aNfPzxx+6Vx/v06cPf/vY3wsLCrrmMKhC6CzeayWQiKCiIffv2eTyhPXv2LNu3b791FbsBSroQ7d2712P7woULb0V1rquOHTsiSRLfffedxxP6zMxM1q5dS3h4uMegscsp+XIvLwitCLVajSRJHteTw+Hgm2++KZO2d+/egGuGrYtbPkrv/7e//Y38/Hzmzp1bJo+SdCqVig4dOhAbG1smuPnmm2+QZdljeuYbeT14eXmRn59fptUjLy+vzHH6+PgQGRmJxWLBai07mFeAwAgvqjUK5pnx0fzrp46MOdCXDkn/oPDx5iRGBpNvU9wP2KvlZpMf7HmDqqZsq5W39cLYCa3NianYhkWnJi3s/GKXF713eqsTnVj0TxCEO1RwcDDDhg1zj2d8+eWXkSSJDz74gK5dK9e9ULRwCBVSMoPQ5MmT6dSpE5mZmSxfvpzatWtz9OjRW12966ZXr1588cUX/Pvf/yY+Ph5fX192795Nbm7ura5apZWs47BgwQJGjx5Njx493NPiFhcXM23atEt2d7tYyYDuzz//3D39bO3atSs8EL5bt27MmDGDyZMn06VLF4qKiti8eXO509F2796dHj16sH79epKSkujYsSM+Pj4kJiaye/dulixZArgmKdi5cydz587l6NGjtGnTBr1ez+nTp0lISOCLL74AYMKECezZs4cXXniBQYMGUb16dQ4ePMiWLVto0aKFe/pfuLHXQ+PGjdm5cycffvghTZs2RaVS0bp1azZt2sSiRYvo0qULUVFRaDQaDh48yO7du+nRo8cN6+ZWFak0KnrNbEdPWeGXr+NJWnOS0NOnSdUEEFWUSVqUP9XO5gKQShRRnEHFhSBixT3t8Mu34FdcTIFej1mvuRBsgKvF5HzQoXbK+BRYMFpFwCEIwp0vJCSEunXrEhcXR1xcXKVnLRMBh1AhJesWbNiwgQMHDlCzZk3eeOMNjh07VqUCDm9vb6ZPn84nn3zC/PnzMRqNdO3alWnTptGlS5dbXb1Kmzx5MtWrV2fp0qXMmDEDrVZLo0aNeOedd2jevHmF82nWrBmTJk1ixYoVvPPOOzidTkaPHl3hgGP48OEoisLq1av5+OOPCQoKokePHgwYMIDBgweXSf/vf/+b5s2bs3r1aubMmYNarSYiIsK9Ciq4Zr6aMWMGCxcuZPPmzXzxxRfodDqio6Pp37+/O114eDjffPMNX375JRs3bqSgoICwsDBGjhzJU0895RH03Mjr4YknniA5OZmtW7eyfPlyZFnmyy+/pGXLlsTGxrJz504yMzPdx/rss8+WmfVMqBhJJdFhTE0YUxOAjJP5bO+9AZNXMaebhGAqtFBEIYu876fH6T8ILXK13N2XFMeqBR+SrzYx8Onn0VvKGbtzvnuFU6NGIysUmHR0ia56XS4EQaj6FEVhz549bNiwgR9//JGcnBx8fX3p27dvmSncr5ak3MiRuoIgCIJwm5IdMkXpVnShBvQaCUe+jSNR06hlPYWvzdWdyoaWd1sMY+rQv11x0UH/zEJ6tTfx/QDX+AvXGI7vadiwIXXq1PEYByRUXvH1GsMheBDn9fK+rbmsQulGnBl0g2ty/ezfv5+NGzeyefNmsrKy8Pb2pnv37vTu3Zv27dtXajHcEqKFQxAEQbgrqTQqfCIuTPup8dXRLH8aOdsS2TduBcacQp7qO5RMbz98Cm3uwePlUhS8rHZ3sCEIgnCnGDZsGF5eXnTp0oU+ffrQoUOHS06+cq1EwCHcdnJycq44v7/D4bhixG0wGG7bRdMALBYLhYVXXqAsODj4JtSm8ux2e4XWFwkICKjwWBFBuBUCukTTOvZZAB7ba+eD7/PQF9so8NZdtpWjWCeCDUEQ7jzTp0+nc+fO12W680sRAYdw23nyySdJTU29bJoWLVpw8ODBy6bp168fU6ZMuY41u762bNnC1KlTr5hu//79N6E2lff7778zbty4K6Zbs2ZNuQsVCsLt6Nn7tBT/aeX1rFDPYOPixQQlidwAMaBfEKo6uQpOi9urV68bXoYIOITbzrRp06449aevr+8Vp2QNCQm5ntW67tq1a8fMmTNvdTWum3r16lXoeK60Yrgg3G7MWs2FVctLSBKSU0ZRl5pdXrTcCYIglEsEHMJtp1mzZre6CjdFcHDwHdNdqiJ8fX1p06bNra6GIFx3IdW8kdKUMkGH0eKg2FSqn7OYg0UQBKFcYuE/QRAEQbiMuEwHgTlmj4DCt8AKFzV6eIlvVEGo8hSpYj+CJ9HCIQiCIAiXYbar8S80Y7LYMes16OyuSS2Sq5VaBFCW2TZcdKkSBEEoj3geIwiCIAiX8dkQI1atCpVDRpEg31tPSpgPSBIqhxOtxc72wRL3hYuAQxAEoTwi4BAEQRCEy/AxqOnfTEexUYPB4kAlyxgtdkyFFka3UGN73UinmmJKXEEQ7lyFhYV89dVXPPXUUzz44IP88ccfAOTm5jJ//nwSEhIqlb/oUiUIgiAIV/DFkz7MkGV+Pm7H309FNV8VYd4qpCo4RaYgCJemVMHPfFpaGsOGDSMtLY2YmBhOnz5NUVERAP7+/nz//fckJyfz+uuvX3MZIuAQBEEQhApQqVR0bnjjFsYSBEG4FT788EOKiopYtWoVgYGBtG/f3uP17t27s3379kqVIbpUCYIgCIIgCMJd6tdff2X48OHUqVOn3Fbb6tWrX3FB5isRLRyCIAiCIAiCUAFVsUuVxWIhMDDwkq+XdK+qDNHCIQiCIAiCIAh3qdq1a7Nv375Lvv7jjz/SsGHDSpUhAg5BEARBEARBuEuNGDGCDRs28NVXX1FYWAiAoigkJCTw4osvcvjwYf7+979XqgzRpUoQBEG468XNOMDmb9Mo1nph1utI8zERc68f/3wtGoNRrK8hCIJLVVxFfODAgaSkpDB9+nQ+++wzAP7xj3+gKAoqlYp//vOfdO/evVJliIBDEARBuGspGXnsbP01x71qE12YR7FKh5QvcV+xnfQ9Jkb8Zea9z2pRK9pwq6sqCIJwwzz99NMMHDiQH374gYSEBGRZJjo6mp49e1K9evVK5y8CDkEQBOHuU2Dm2OP/w7w1FVnjh0bvJMU/CADJJKM/W8jpyEBydDqGT8sk3Gzh7X+G0rCl7y2uuCAIwvVjNpt54oknGDx4MI899lilu05digg4BEEQhLuC0+og7Zd0Nn8Uh9/BZFpkHqe+fBoVMq0sJrZGdsKrSMJod3AiOpzsWqFESTInkYj192Xaf1JZuNAHtebq+1Q4ZXhofj7rEyUckopaFHPkRX8MRrFCuSAIt47RaOTs2bM3fBFTEXAIgiAIVd7RGrMpSrDhQyFt0aLFQk1Oul/3thfRK2E7KUoTACJyc6mWm8ea9vdRv6CQfVp/0kxGxgw5ij3Gh4cHBfFge1OFy+/1vYr9SRLYnKBWOCUZME4tJuU1b8J9xRgRQbhTKKqqN4ijQ4cO/PLLLwwdOvSGlSFmqRIEQRCqtNQhyzAn2InhLCasONCh1padV96gFKPC4f69ZmoapmIzGgXq5eRSOycPp06DLcPB0hkpPPJ2RoXK/+JIEPvjZVczh0YFsgISoFYT8X4xb281X69DFQRBuGrjx48nPj6eF198kf3793Pu3Dlyc3PL/FSGaOEQBEEQqrT0JfFo1eDnzCeeIIr0Wk5G1qbW6ViPdHZ0yKjJNXmxsXULzoYGI8kyKAr1svI4HlHN/XTTrtXgOF1EQnoAMaGX/ip9I74LCdYAwAlOBVAuvGh1glrirZ+c7Eq1smmY/gYcvSAIwuX17dsXgJMnT7Ju3bpLpjt27Ng1lyECDkEQBKFKUuwO/qozhwKNDzq9BblIIk9n4ExwEN55Vv5StcEgWwkgDW91FsmqumCXWNemFanBrlV3FZUKRVHI8faiSKfhSJgvWUYt/hY7Tc7lM2dJJm+PD0NVTjeLZzfYSbAFopJkorPTSTH5Y9OcH7OhnA88FJAUhc2xdv73BzzWVAQdgnA7q4orjU+YMEGM4RAEQRCEq6UoCvt8v8BgAV8Ah0Q8NTkdFIqXzUZMVg5OjBRhpAh/LP4W9tRvSFRqgTvYKCFJEopKxa7oQPINroDBrNOQZ9Ai70+l+5hkXhvmQ/fOfh77fXlAoVNyLAuWzyI6P4tMgzeTuj7B9w3aulo7jBpAcrd5DFtqJ9hLRY86YiC5IAg3z6RJk254GWIMhyAIglDlHGo+D53Vc1uRZMSpURNQZObiZ3kBWXbG7/oGu8p6ofWhlGKNxh1suLfpNByuFoBNreI/3+Uz7rWzADhlhY9X5eFtKeZ/y2YQnZ8FQLClkG83fU1oUR5oVXBRq4iMikcW2yp34IIgCLch0cIhCIIgVBkOq5OVHdZgy/Gmhs6Ot9XmDi40iozKKZe7UnCmny876z/GOe9QgnMLyPL3cXedkGQZm778VodMgx7FFyKKreRkO+n14lmizpxlwNGfuLd9b8ILcj3S62QnHZLjWB7UFuSy+RWZFbp/a+aH4YZyu2kJgiBcbzNmzLhiGkmSmDBhwjWXIQIOQbiE/v37Ex4ezldffXXV+8bGxvLZZ59x/PhxCgoKGD16NGPHjr0BtRQEAcByKpez3f+P09l6bH6+gIr40CC0Dge10rPROZ2cDfHHoVKR7WX0aOWwajVsua8FNq3OnZ9/UTEWjQaLTouiUuFnc1DvXA5xYQHuNEH5FmKyz892pShY1GqkrBzG7llBk7QExvQaSbFGh5fDs9XieGC4a+y4WnLNWFWKLKnYEWtH+5aTFUP0DGwsulcJwu2kKk6Le7mAQ5IkFEURAYcgVMbs2bOpX78+nTt3vm55OhwOXnrpJRwOB+PGjcPHx4e6detet/xLS0lJYe3atXTu3Jn69evfkDKE6y82Npbt27fTv39/IiIibnV17mhnNyVxfMIW/NOz8bbbsQRGQKkOU3aNhnN+3oQVFJAUFgCShC5DJlNvItSWg4+ST3Kor0ewAa7+xl4OByqgyKBHkmV6x6UgOxVOBvtRrcBCu3N57pJk4LTJwC+1a9Gh5lQaJZ0m3duP1zs8wifb/ufOd+a9XfkrOArUKjxmrCrFodYgaeDBpQ6+KpIZ3UYMJBcE4cY5fvx4mW2yLJOcnMyiRYvYt28fc+bMqVQZIuAQ7mpz5syhX79+5QYcy5cvv6ZZG5KTk0lOTubZZ59lyJAh16GWl5aSksKcOXOIiIgQAccdJC4ujjlz5tCyZUsRcFwD87F0Dv79B7KTrJhVRuwqH875e2PTa5BkBa3Ds6+STavGYZBAkjCY7USkFSABefjjRS4GueyaHCXU56fFTfQyUN2qpmt8OukaHXXziz3GgaiAqGIrdU5ZsElwyisEZJlPW/ZkQ0wTGmUns6NmQ7K8fFxjREwaV9DhVKDYXqZ7laJSgU7in2uK+HpHHnP/HkiDYDW5FgjyqnpPWAVBuL2oVCqqV6/Oyy+/zPPPP88777zDxx9/fM35iYBDEC5Bp9NdOVE5srJcA0T9/PyukPL2pigKZrMZLy+vW10VoYqRk7JIX34cTUwAQYESNIhACfAm9aO9yD+cwJzrpNDqwKaBoFMpGIqL8VHlY9YYOOVbnXRTMFkmX7R6A1qnggLY9BoUlQqV7CxTXrHRAOZ8UBR8860egUIqNcguMiA5nSjqsit+29VqkCS0gFWlotDXm+4ZeeVOjalTFPb7e5PkpUdxyq51NoDYoHBiI6tD6X1sMhhVrm5VJi0U2EtlpAK9GlQSRQG+7AWefuMvev11iKX1W1MrJ51qBdk8cuIAG5u1xVQ/nJGOM9T8YRdEBMCnoyAzH2JCICaUtHyZXzYlcE9RJo2HNQetBv6Ih3k/Qc0QaFAdGkZBVPA1vZ/XnaLAvpNg0ELTGre6NoLgqQpOi3slrVu35qOPPqpUHpKilDMdx11k7dq1TJ06lVmzZnH8+HGWLVtGeno64eHhjBo1in79+gGuJ8kDBgwoty/+7NmzmTNnDmvWrHE/rZwyZQrr1q3jxx9/5LPPPmPnzp3Y7XZat27Nq6++SnBwMCtWrGDRokWkpKQQHh7OpEmTrmvXntJKxiO89tprfPrppxw6dAhJkmjTpg0vvfQSwcEXvmhK6r5///4y+bRq1Yp+/foxZcqUMuelVq1azJ8/n4SEBEJCQhg1ahQDBgwgLS2NTz75hP379+NwOOjUqROvvPIKJpPpqo4hLy+Pr7/+mp9//pmMjAyMRiPh4eH07NmTJ5980p1u6dKlbN++ndOnT5OTk4Ofnx/33XcfTz/9tPv9Kal3eUqOu7wxHL///jtz584lNjaWgoIC/Pz8qFu3LqNHj6ZJkyaMGTOGgwcPlslzzZo1VKtWjfnz5/Pbb7+RmJhIXl4eQUFBPPDAAzz99NP4+/uX2W/r1q0sXryYuLg47HY7YWFhtGvXjmeffZZNmzYxderUMvu0aNHCXWez2czcuXPZsmUL6enp+Pr60qZNG55++mnCw8M9jnncuHG89dZbmM1mli5dytmzZ/n73/9ObGwse/bsYfPmzXh7e3uU9ddffzFixAjGjh3L6NGjL/XWlfHDDz+wceNG4uLiyM7OxsvLi2bNmjFu3Lhyu58dP36c+fPnc+jQIQoKCggMDOTee+9l/PjxREVFeRzHd999x5EjRzCbzYSEhNCyZUsmT57sPr8Oh4OFCxeyfv16kpOTMRqNNG/enHHjxlGnTp1yz0n//v096lPeZ2TMmDGkpqYyb948Pv30U3bv3o3NZqN58+a8+OKLxMTEABf+Xlys5HNltVr55ptv2Lx5M+fOnUOr1RIWFkb79u155plnKnyOb1cFH//I+tnnyDG4AvLIgjRanIvjsHdD6mRnonM6+aVBXXzUKQw8sg0VCk7UWPHDiGvaqUPB9fnLVB9VqVYBWYJCHwOgYCqwolY8XwuUraR7GTEWOYlKzveo0/4WNYmrHYneZsfgcKBSFFAUdFY7aUH+KJJEtlaDU6tx32yk6bVUs9o98ilQq9gSFuBaUVyvdvXsMjsACbRlgxl8tRduXgrtrtYO/flgo5ybmlan4rCgpkVaPC/9ug5/SzGLG7fl5e5DcVhknj74E19s/e7CDioV/xv/FCO092FXuZ4vPv3HNr6wH4Dtf3lmrpLgpYfgvWHlv3E3S3IW/G0aHEl0/d6tKax+BUyGMkmLi4s5duwYDRo0EA9GriNxXi/vy8ZrK5Ru3JH+V050h3j99df54Ycf2Lt37zXnIVo4zps5cyZWq5WHH34YnU7HsmXLmDJlClFRUTRr1uya8508eTKhoaGMGzeOpKQkFi9ezIsvvkiXLl1YuXIlAwcORKfTsXjxYl5++WVWrFhBZGTk9TuwUjIyMhg7diydO3dm8uTJnDhxghUrVlBUVMTMmTMrlfcvv/zCihUrGDRoEL6+vqxevZq3334brVbLzJkzad26NePHj+fo0aOsWbMGnU7HG2+8cVVlvPLKKxw8eJBHHnmEunXrYrVaOXPmDAcOHPAIOBYuXEjjxo0ZMmQIfn5+nDp1ilWrVrFv3z6+//57/P39CQgI4O233+bNN9+kefPmPPTQQ1csPz4+ngkTJhAUFMTQoUMJDAwkOzubw4cPExcXR5MmTRg1ahT33nsv8+fP56GHHqJ58+YABAQEYLfb+e677+jatSudOnXCYDBw9OhRVq9ezeHDh1m4cCFa7YUBojNnzmT+/PnUqlWLxx9/nODgYM6ePctPP/3EuHHjaN68OSNHjixTVmCgaw0Bh8PBxIkT+f333+nWrRvDhg0jMTGR5cuXs2fPHhYsWEBYWJjHMf7vf/8jLy+PBx98kKCgIMLCwmjYsCE///wzmzdv5pFHHvFIv3r1alQq1SWDt0tZsmQJfn5+PPTQQ+7jWrlyJU899RQLFy4kOjranXbnzp289NJLGI1GBg4cSPXq1cnKymL37t2cPHnSHXAsX76c999/n9DQUB555BHCw8NJS0tj586dnDt3zh1wvPHGG2zZsoU2bdrwyCOPkJWVxdKlSxk5ciRz5szhnnvuuapjKc1sNruDzwkTJpCcnMz333/P888/z+LFi1Gr1XTt2pXMzExWrlzJyJEjqVmzJoD7OD744APWrFlD3759eeKJJ3A6nSQlJbFv375rrtftQskrZu+MOHJ8Lry/yT7VcJgNVCvMR+d0kuHrw5HaoXy4bqE7jRonOgopJgCdZCZVHYbGISOrLszsrlJAa3di12vKzPeuUiDZz5/QvFzO+fkSnFmMwepwv+4wKiiShEWvw6LXISkKhmILef5GkCQkwKAoFJ0PAhRgb4A3NYqt1Ci24pTgpLeRRmlnwOkHAYYLU956aSHvElPd2mVXZiouDB5XSZd8grq/em06n/yLb1fNdm97bvdGrBotr3UbwqxmXRl2bDftU04CYJVUTJKbuIMNgNmNO/HZZwsp034rK/D+CnikLbSqc/GrN8+/Fl0INgC2/gH/3QCvPHzr6iQIVdyqVavK3Z6fn8/+/fv54YcfGDx4cKXKEAHHeTabjQULFrhv+Lp168bAgQNZsmRJpQKORo0a8fLLL3tsW7RoEenp6SxevNj9xLh169Y89thjrFy5kokTJ15zeZeTlJTEe++9R48ePdzbVCoVS5cuJT4+nho1alxz3mfOnGHp0qXup+Y9e/akb9++vPnmmzzzzDMMG3bhqVlBQQHr16/n+eefr/DTk8LCQvbt28egQYN46aWXLpv2+++/x2g0emzr2LEj48ePZ/Xq1YwYMQKj0UifPn148803iYyMpE+fPlesw2+//YbFYuHf//43jRs3LjdN27Zt0Wg0zJ8/n6ZNm3rkqygKmzZtwmDwfFLXtGlT3nnnHbZv3+5+b44cOcL8+fNp1aoV06dPR6+/MGi0ZIEeHx8f2rRpU25Z4Gq9+/333xk+fLjHk/E2bdrw7LPPMmPGDKZNm+axT1paGsuWLXMHLQBOp5OwsDBWr17tEXBYLBY2b95M27ZtywQuV/Lf//63zHvUt29fHn/8cRYtWsQrr7ziLmPq1Kl4e3vzf//3f4SGhrrTjx49Gll2PeI+d+4cH330ETVq1GDevHn4+Pi40z399NPudL/99htbtmyhR48evPvuu+4xOj169GD48OF89NFHfP3111d1LKXl5uYyfPhwRowY4d4WEBDA559/zt69e2nXrh1169aladOmrFy5kjZt2tCqVSuPPLZv30779u3Lbb26nVitVpzOst2XLkf6K5FzOn/PjYqCWafH6HC1FpwJC6FOVmKZfTXYyMabZK8gcnX+SErZOWWl8w32CpRZZ6PIaCTRS4+xsJhTtQPxz7WgtTuxGjTkG1ytrT7FxfgUm1EkCatKjdl04RrVlppNSgGcKhUnfbw46XPhb9j4+D/4qU49bKVnsZEk13iN4ovOlVq6MG7DCWhUrgDkUhQFZBj65+4yLz325y5e6/kYSLC3Wk13wJFi8ifL6OORtlpRHrpyzl0J2y9/4Wh468YVGfbElgkYHbuOYSsuLpPWbDZ7/F+4Pqrieb2eLTVVcZaqku/c8gQEBDBmzJhKzVAFIuBwGzx4sMfT5dDQUKKjo0lKSqpUvo899pjH782bN2fRokX07dvXo3tK3bp1MZlMJCaW/aK9XkJCQjyCDXB1kVq6dClJSUmVCjg6d+7s0UUnICCAmJgYTp8+zaOPPuqRtlmzZmzbto2UlBSPLiyXo9fr0el0HDlyhJSUlMsOtC25kZVlmeLiYhwOB/Xq1cPb25sjR45cw9G5lLxfO3bsoG7duh5BQEVIkuQONpxOJ8XFxTidTlq3bg24goyS92fTpk0ATJw4sUw5FR3Ivm3bNlQqFSNHjvTY/sADD1CvXj1+/vlnZFlGVeopcd++fT2CDQC1Ws2AAQOYM2cOJ0+edL9nP/74I0VFRQwcOLCip8Ct5D1SFIWioiIcDof7min9Hu3evZvc3FwmTpzoEWyUKKn7jz/+iN1uZ/To0R7BxsXptm/fDsCoUaM8zmO9evXo0KED27dvJycnh4CAgDJ5VIRKpWLo0KEe20re38TERNq1a3fFPLy9vTl9+rTHub4dXctnScJBsCOPPL1vqY0SWoedXIMXflYLYbl5HKxftpXJgRYFNQVa1+fQz2om13ChW6YC2M93W3LoVOisF26qZQmKvQy03ncKg8VGvo8eJIm0MG/ia7vGVgTl5eFrtrj3MWDHYtFTfP4zq5NlVLKrVUUFVDdbSfS68PDAaLfy+JFd/Kdzf7LxDKbLtFiocAUcHtskV5Rkk12LApbeR1HcwUiOsWxX1Gyjt6uFQoGW5+Ld26MLsojOzyTR90KX2TSTHw5JQnOJ3tSnAiSKjx0r97WboUatIIKOp3hsOxflTdpl6hQfH3+Da3V3qkrntWXLlre6Cre1rVu3ltkmSRK+vr5lulJfKxFwnFdeNyY/Pz/S0tKua74lN0Pl3TD7+vqSl5dXqfKupi5wYWBzZcstL28fHx+Cg4PLDL729fW96jK1Wi3PPfccH3/8MQMGDKBWrVq0atWKzp07c99993mkLZm+7a+//sJq9VxquKCgoMJlXqxnz55s2LCB+fPns2jRIpo0aULbtm3p1auXR7B1OVu2bGHhwoXExsbicDg8XsvPv9CvPDExEUmSKjWdbkpKCiEhIe7zXVrt2rWJi4sjNzfXI8Ao3ZWptIEDBzJv3jxWr17N888/D7jGpQQGBtKpU6errtvx48f58ssvOXDgQJmnaKWvpZIA/ErdnEoeDFxppq6UlBRUKpW7G1NptWrVYvv27SQnJ19zwBESElImQLzaz9hzzz3HW2+9xdChQ4mMjKRVq1Z06NCBjh07egSHt1rjxo2vuoUDoPhfTjI/SiRP4/oSCy9Mp2H+GQ76N8LLbiM6I4vAHDs7Y1rzQMI+JFwrcGdQE5OUj1p27Ves0RJemE2e3oQiQbGXDp1iJyw/i0TfCBxqByoZHFoNRb5GfIuKUTkdJMQEUOitQ2uXMRQ73Df2OoeDbG9vFEnC22JGb3fgbTbjPD+Q3KbRUCBJ6HF9cTbLLUTvlEk16IgoyGXempmEFeWjdpRz0Be3XGhVlG2DOc+pQLEDdOfHgJTse76eX7buzuj9PxFkLgRARuL9DgOg2MbTh3+iQ/IJd1YqRWGevI9hxe1J8/LDaLcy7dcVcH89lF9iPWqgAI5JvYkZ3L1C7+ONIn0SgnzyA1RxqQA4769PwFvDCPAxlklrNpvdrfMXt5gK106c17uPJEkEBgaW6YFRwmKxkJ2dXalZFUXAcd6lvshLxtRf7qny5b501eXMenK57TdyDP/lblZKl3upY734BrkieVe0zIoYNGgQnTt35pdffuHAgQNs3bqVJUuW0KNHD9577z3ANYh54sSJREVFMXHiRCIiItDr9UiSxGuvvebuWnMtdDodX3zxBUeOHOG3337j4MGD7gHA77zzDl26dLns/j/99BOvvvoqjRo14oUXXiAsLAydTocsy0yaNKnM+ZAk6Zqm5a2MS/2xqVatGu3atWPDhg1MnjyZ1NRUDh48yPDhw9Foru7PSFpaGmPGjMFkMvHUU09Ro0YNDAYDkiTx8ccf3zbN+Nfymb8e13vnzp1Zs2YNv/76KwcPHmTv3r2sXr2a5s2b88UXX3i0xN5KV9vCV8JrVHuGDGnJuXWn0AR5EaIUQKOHqK42cOaDfdgOnKG97RwJ1ZpR5BVJnYQzFFm8ydF6Y8BMqi4IAJtaS6opAD9rEV5FdmJSM7mXw6Sagkn0jaDI1wub8cL17NDBqXuCkRXXe2TXqbFrVajtDmw6LWkBge6uEvleRsJyc9E4ZbytrvEXTpudAj9vtE7X3xCNAk3yi2mSX4zWYadV6hnmth2EVWfAO7+QYEcx+Xoj2Wqjq9VCLYFR7ZoKV1Fcg8lLXy+y4vqRJHAo4HC40kkSWocDu0GLCoVEn0B6Pfo8Tx7dTevUM6x/oCPNu8fw3rlfqa3+A4bcD0MfAJMeqWYY3eqEk+hU+GvuXmLOJhGw4mGIDoXUbJjzIwT7QHQIUqPqaGuGccuvrvrRcOy/sCsWjDrULWtzpc4wRqNRDG6+AcR5vXt069aNDz/8sMwEKSV++uknnn/+eY5VovVTBBwVVPKUuPRT6BLJyck3uzo3VOkWiNJTu94OxxkcHMyDDz7Igw8+iNPp5M0332Tz5s0MGzaMRo0asWnTJpxOJ59//rnHk3Kz2Vyp1o3SGjdu7B7DkZaWxhNPPMGsWbOuGHBs2LABvV7P7NmzPW7sy2u2jomJYdeuXcTFxV1yvAhc/qY4MjKS3bt3U1BQUKab0enTpzGZTOXOjHUpDz30EL/88gvbt28nNjYW4Jq6U23bto3i4mI++eSTMuMX8vLyPFrESmZ2io2NpW3btpfMs6RlJi4uzr1PeSIjI5FlmTNnzpRpPTpz5ow7DVy+ZaKyn4UrBZJ+fn706dOHPn36oCgK//3vf1mwYAE7duyge/db+wT6elCb9EQMaeixzQg0/KQj0JEGl9lXkRWSNyaS8J8DOA+noDKrwKbFjDd5BBBkzgZwzSh1EV8K6HryZ36I7kKhzhtZpcKhVmHWaj37ZUsSuSYTfqW6WKllmSZZuSQH+JZZr8+u1vDkkKk49Ca8HU5M5lzipj9H7ac+IDs03BVkeGkuDCSXJDBoIN/mGrvhlMHiAI0aNBfqoXE6CMrNoWV1LbMGexFdN4Cz+TIhXk3Ra+4F4EInvZrwWu9yz5lWLdFsTBugzYWN4YHw5qPlpr/lVCp44HJXgSDcOuVNiX2nu9IDMbvdXukW9tunff42ZzKZCAoKYt++fR5vzNmzZ939wquKkpu3i6c/W7hwYXnJbwqLxYLFYvHYplar3TeNJYFgScvRxR+eefPmldu64eXlVeGuLrm5uWW2hYWFERAQUKE8Sj6speuhKApz584tk7ZXr14AfPHFF9jt9jKvlxxfydOn8srv3LkzsizzzTffeGz/9ddfiY2NveouOg888AAhISGsWLGCdevWce+9917TuJ+SMi9+j1auXOlew6RE27Zt8ff35//+7//IzMwsk1dJHt26dUOr1TJnzhwKCwsvma6k+9f8+fM9yj958iQ///wzzZo1c3enioiIQK1Wl/kc/P777/z5559XdcwXK+mmcPEDDKfTWSYwliTJ3VXsRna5vFNIKomovjHcv/1hOmSNp9myHgR38UJvKCCDMJyyCY3TgeQs+3kPNOcSZs6gU/KvABSbvJBUqnJvIBxqNU61GkmW8TKb8S0qIiY7B6+isoOXkSTsehOxgV7UzSkiPiCM/eE1OB0Q4go2NNKFYKOESgKD2hVwqFXn/39+HIdRTRDFrP3fR2x+PYL1r0USXdd1XUb5qtBrqt4NjyAIN1dhYSEpKSmkpLjGTOXm5rp/L/1z/PhxNmzYQEhISKXKEy0cV+HRRx9l1qxZTJ48mU6dOpGZmcny5cupXbs2R48evdXVu2569erFF198wb///W/i4+Px9fV1D969VRISEhgzZgxdunShdu3a+Pj4EB8fz7Jly4iMjHRPCdu5c2cWLVrEM888w0MPPYRWq2XPnj2cPHmy3Kf5jRs3Zu/evXzzzTdUq1YNSZLcN/sXmzt3Lr/99hsPPPAAkZGRKIrCzp07iY+P95iW91K6devmntK2b9++OBwOduzYUSaQKqnXiBEj+Pbbb3niiSfo2bMnQUFBpKSksHXrVr799lt8fHyoWbMmJpOJZcuWYTAY8PHxITAwkNatW9O/f3/WrVvHt99+S0pKCi1atCApKYlly5YRFBR01TNOlAweLwmQrnXGivvvv5///ve/vPnmmzz66KP4+Pjw+++/s2vXLqKiojy6KxkMBt544w1efvllhgwZ4p4WNycnh99++43HH3+czp07ExYWxvPPP88HH3zA0KFD6du3L+Hh4aSnp7Njxw7efPNN6tevT9u2benRowc//PADBQUFPPDAA+5pcXU6HS+88IK7bC8vL/r378+qVat47bXXaNmyJUlJSaxdu5a6desSFxd3TccPrtnrVCoV8+bNIz8/H6PRSGRkJDExMfztb3+jY8eO1K9fn4CAAFJSUli2bBm+vr507NjxmsusiiS1Cu/+dbmnv+vBQ/Kg77Atz6J+xln+1NbEqXUtBghgshbSJsk1tbBvURHVEvKJr+sagK13OLBd1CKidThBUfAym1GfD07VikLz1HQ2NqyFn11BpSgUadX42pzIwCkfI3HBPugdMg8Ne4FaeZmcNkRcmPK2NKXUXFoaFai04JDBS0OwpZDnDmyh4+HX8PK/tgVIBUEQLuebb75xL4kgSRLvvvsu7777brlpFUXh2WefrVR5IuC4CiNGjKCwsJANGzZw4MABatasyRtvvMGxY8eqVMDh7e3N9OnT+eSTT5g/fz5Go5GuXbsybdq0K3YbulHCwsIYMGAABw4cYPv27djtdkJCQnjooYcYMWKEu4tSs2bN+PDDD/n666/58ssv0ev13HfffXz11VflLkz3yiuv8MEHHzB//nyKiooALhlwlASZP/74I9nZ2ej1eqpXr87rr79eoa5FvXr1ori4mEWLFjF9+nR8fHzo2LEjEydOpFu3bmXST5o0ibp167JkyRIWLFiALMuEhYVx//33u4/XYDDw73//m1mzZvHJJ59gs9lo0aIFrVu3RqPRMGPGDPfCf9u2bcPHx4du3boxfvx4qlWrVuHzX+LBBx90XxPX2rUnKiqKzz//3L3OiEql4t5772X27Nl8+OGHpKameqTv1KkTX3/9NfPnz2f16tUUFxcTGBhI8+bNPWZxGjRoEFFRUSxYsIDvv//efY20bt3aY9readOmUb9+fdatW8dnn32G0WikRYsWPP3002VmhXruuedQFIXt27ezY8cOGjRowCeffMLKlSsrFXBUq1aNN998k2+//Zb3338fh8NBv379+Ne//sVjjz3G3r172bt3L8XFxQQHB9OxY0dGjhxZ6SdMVV3ksuEABPf4H3UPp6DSW1C0EGY+R/3ME+iddhTggLYteqsTnzwzWWF+6JxOTBYrFp0WBfDPKyQ0I4sCf293sFFCBYTmF7Hy3po4VSoapufjm1mITSWhkyTqZWRTWMef4+og8AsAs+yaJsvmdA0EL+G8KAhRSWBQ0fLsKd58QM2AKZ4zHAqCcHtQpKrROej+++/Hy8sLRVH4z3/+Q9++fWnUqJFHGkmSMBqNNGrUiCZNmlSqvLt+pXFBECouMzOTvn37MmDAAP71r3/d6uoIwiWlvvoTXu8vx5c8wIJ0ftGLPMmPX3VdAZBVEsebVCc3+PwYJ0XBO78In/wiHJKExd+r3LmkDoeHcSosgN/D/eh8OgMvh4wVcHipWfdZOFqNihVrExl0tJpruIfZ6Wrl0KlcQYdC2ZmrFAXUEu1CZXb9Q8wMVBFiRewbQ5zXy5vZbGOF0k04XP6YqtvRjBkz6NmzJ/Xq1bthZVSNME0QhJti2bJlOJ1OHn5YrPor3N7C3+vKmboNcaICDCjocaInRalzvjsTqGSFhr8n0uy3k/hl5dLgyAmqJ57DJ7+IhJjwcoONfJ0Ws9FAZIGFUb8dxeCQcTqsdOnpx8qPI9BqXF+rD/ePpmdhkqvlwlsLvjrXQHGV5PrmvThz2TVz1fonyp8pThAE4UaZOHHiDQ02QHSpum3l5ORccY57h8NxxSlJDQbDdVu05UawWCzlDvK9WHBw8BXTCDfO5s2bSUtL47vvvqNdu3Y0aFB2BpmKXLNeXl7iiZlw09x7fDwn32yM7t3NOHQSyUoERoeCj9NCgeZCK4JesdIs/ijpOlfXuzxfb8xGA9aiIvSlpgN3ShIFRi/UuPo0BxTbiOyt519jy1+/5tvRgfSfdpx9de8pO2hcpzo/FS5gc1ItK5MFz4QTYBQDwgXhdlYVVxovceDAAY4ePUpBQUGZiXYkSarUauMi4LhNPfnkk2X6sl+sRYsWHDx48LJp+vXrx5QpU65jza6vLVu2MHXq1Cum279//02ojXAp//rXv9Dr9TRr1ow33nij3DQVuWZHjx7N2LFjb0QVBaEMSSVR951OrP1vEkU+GrxyHWiUXFrafydP9iZP5YO3UkxwTho/Vmvv3k9ndwUZ5wL8CSwowGCzY9NoyPbxRoWrR1RgYRFpPt588Nilx9UEhnszWvmFJ5b/wrN9RriCC7vz/HgNDcgKk3ZtpGZ+BvOHjaBHbfGVLAjCzZebm8vYsWP5448/UBQFSZI81qEr2SYCjipo2rRpZVbJvpivr2+564KUdrsPMm3Xrp17lgTh9lWRgK8i12x5K9ILwo1W59/NiJt6EIuXFp3VjhqZQCWfQKfr76cKmTDLOfK0rrVXvIvMBGbnkx3oS8bFs9s5nfgVFqO2OWg4IoZg38v3TDZ08qajOo/A/AKy9SbXIoAAFie1CjLYXfsekjr05o+hoiuVIAi3xocffkhsbCwff/wxTZs2pXv37sydO5eoqCi++eYbDh8+zJw5cypVhgg4blPNmjW71VW4KYKDg0V3qSribrlmhTtPg4mNKTyWQ8LKRPL0JpwOFWoudBcw40P9guMUaHxINYYjKTLtjx1gXbvOyCrJNdhRUfAxmzEVFZHaPIrx42rRtG7FVlsvHNOObrth6TkJTFrXDFVqOG0KZ+5Qic4NxCBxQRBunZ9//pkhQ4bQp08fcnJyANeaWTExMbz11ltMnDiRd999l08++eSayxABhyAIglDltZ7ZgZbTZQoSCjkzxpfwbXsxKhas6Ngecy+Ha1fnicMbqJ99jFMhMay5tyeoJBRZ5oxOx/2pqTgUiRdW3Icp4OrXxpgz3IC8TmJ5HKBV4a128utYL5qGiLlbBOFOUhVXGs/Pz3dPC28yudYnKlkqAFxT6H766aeVKkMEHIIgCMJdQaVR4VfbF7+tQ4GhHJ9zjAMf/84DCbvIxZv/NXqY1HDPbqgGq517c/Mx2yWeeLPuNQUbJZY9ISZMEATh9hMaGkpmZiYAOp2OoKAgjh8/7l5v69y5c0iVDLREwCEIgiDcle4Z3YD6/7iHjTUtRBeloJyVyAgOwHF+1XFJVggsKmLCfxsQ0tD/1lZWEAThBmndujW7du3i6aefBqB3797MnTsXtVqNLMt8++23dOjQoVJliIBDEARBuGtJkkSf+L9z9ouDVJuygpY797KjzgOkBEbg0zKYMS+0ueLAcEEQ7iJVr0cVf//739m1axc2mw2dTsekSZM4efIk06dPB1wByeuvv16pMkTAIQiCINz1osa3gPEtAGh6i+siCIJwM9WvX5/69eu7f/fz8+Obb74hPz8flUp1XdZzEwGHIAiCIAiCIAgefH19r1teop1YEARBEARBECpAkaQK/dxpUlJSePPNN+nVqxf33Xcf+/btAyA7O5t33nmHo0ePVip/EXAIgiAIgiAIwl3q5MmTPPTQQ2zcuJGoqCgKCgpwOBwABAYGcuDAARYuXFipMkSXKkEQBEEQBEG4S/3nP//Bx8eHJUuWANC+fXuP1zt16sTGjRsrVYZo4RAEQRCEq6AoCkqe+VZXQxAE4brYt28fjz32GIGBgeWutxEREcG5c+cqVYZo4RAEQRCECrJ88xvOf3yJ0VmAHR3myHB8D7yBFBZwq6smCMJNoKjuvPEZV6IoCgaD4ZKvZ2dno9Nd+6KnIFo4BEEQBKFC0pbHoYyagcmZjwoFHVZ8kxM41uxTj3SvrirA571ifD5y8vWB1jicyi2qsSAIwpU1bNiQHTt2lPuaw+Fg/fr13HvvvZUqQwQcgiAIgnAZcq6Z7+5ZwJSvz1Ks0eE8v/KXgoSCnqj0NIqM43Au3s0ba/J5/6SRQq0Oq1bDz1F1eGJZND/8ab/FRyEIglC+MWPGsHPnTt566y1OnDgBQFZWFrt27WLUqFGcPn2aMWPGVKoM0aVKEARBqNIUReHc6I0ULzmOQ6XBWi+c0En3Eja83hX3lR1Ofqv1EccbtWHirh8JsueWelWNghGT7MRpcfLXiC3899UmYPLM41RIAD9N28vRnkW89XzMdT02QRBurjtxytsr6dSpE++99x7vvvuue+D4iy++iKIoeHt788EHH9C6detKlSECDkEQBKHKyvliP9kTfkCPEx24fvYVkPHkac4+qSdm8yME96wOgFxo44++q8g4mkOQOQ+z1oDOacFXlmmYlEnD/FiPvCUcgAPQoAbq2BMwOMzk4dkXWqUonIyKpmhXPl/s2Mr4Nd1uxqELgiBU2IMPPkjPnj359ddfSUhIQJZloqOjeeCBB8RK44IgCIJQHvOBNLJafYmMGu1Fr2mQseJFASaO91pPUAM9dX4ewpomy8g1eRONwqGw2mgcMpIMTjX4ygWoKG8shgy4ulepZQ3x70zhaFg1/vngIH6uUxeAsHwrdo2GxOBAyILBJ/MJqXP9VvAVBEG4Wp988gl9+vThnnvucW/z8vKiR48eN6Q8MYZDEARBqHJSW83Fhp40AimvA4QOB3ocyGhIP+Zge/3v8JezeeLUCrK8TKgdMhKgqEBWq8jV+pGr9QwSFCRAiwJkGnz5on1n3u3eC4Bl38whsLCI6Kwi6mUUuvfJ8PFh7lvHb9hxC4JwY1WVlca/+uor93gNgJycHBo0aMDu3btvSHmihUMQBEGoUqwJ+UhAMgGoASs6DNjcryuAHS0aZDQ4MVGMV04OMcpRFCQsGNDbZXegonXImA0afqzWifbpe8Gu54SuPmbJSKgznWBNMh2feZmEwCAA3u3ei1lL/kf/P48SH1wDAP+iYvQOBzaNBltGMccOFdCguc/NPC2CIAiXpSg3bkY9EXAIgiAIVUrisPXk4IcOBQmJAgxIKGhx4ERFPt44S339ZWm9SfL1RioqJMaSiJfdhlzqdZUCGodMvtaXYkKI09dBkVwdBFI1EXx9f1t3sAHgVKsZ8+gTSGY74UVWup1KpFp+IdHZBThUEvFhQXz5YQLT/9f45p0UQRCEW0gEHIIgCEKVcPie/2GLzUNCQYeCFgtm9KgAM0bMuFo3ZI/exBJ+DjNai4aTuoYUyYGonTKFJtfXo8Yho3XISApEFGficOpR1J69kZN8g8tWRgWKSkVKiDffhTUCoFZ6LuO2H6LdiZOgc7J4XThD+gWV3VcQBKGKEQGHIAiCcEfLWXWSxIfWYsd4vhuUhB0tamQMWFBQu9NKgAYnDtSAhBYb3koRWouabF0EZzS1MBSC7LSS72/ArlNj16qQZAWKJQ5Uq41drUFrl/HJt6OWFdofT2Rd24um2HUooFVBqVWJT4f6s+OeaPr9buXxP7bxyYdBKH17Id0B/b0FQXC5E8ZnVFRycjJ//fUXAAUFBQAkJCTg61v+pBaNGjW65rLEoHFBuE20atWKKVOmeGzr379/pRfbuZtMmTKFVq1aXXFbZa1du5ZWrVqxf/9+97b9+/fTqlUr1q5de13LEi5NkRUO1vyGlIdWI6FGjw0NDjg/m5T1fNDhWqDvAgkFbwoJJYtgcvCimERdNRQk/B2FRNiy8JJtpXaQUNQqkn2DsOh1ODUqLEYNOYF6FGBg7O+8u2Yt/sVm1LJMTFYeAWYLaMremPweFUJIUS5mjY4mKWcZMPgIZrt8Q8+TIAhCeaZPn86gQYMYNGgQI0eOBGDq1KnubSU/jzzyCIMGDapUWaKFQxDucrGxsWzfvp3+/fsTERFxq6tzU2zfvp3Y2FjGjh17q6siXANrUiEpn/7Buc9/x+S0IqN2T1kr4UQBnGhwoCITP5yoUSFjwoIeByaK0eI8n5sKG0YcqImxpePvLAYgUe9ftmCVZwDh0Ko4dF8MhdlaGqYW8tqWg/wVFsC3bRtesu7J/t6cCA4h1bcTiiJTPbeQfzx4kOdeqkHLTuV0zRIEQbgB3nvvvZtangg4BOE2tnz58hve3SIuLo45c+bQsmXLKhlwvP7667z66qse27Zv3866deuua8DRokULfv31VzQa8Wf1elKcMvn7M3DE51F0IBNbShEZ/xeHEx16ZHcXKgAnKmxo4HyLhhXt+alrXeM2CjCiIR+NO9hwUaEQas9xBxsAOqejQvVL9/cnqVoY/gWFhOQV8FvNapdMK8kKikrFkchqtE48h0Ojo3axGXVeIXPfcbDnTBGDe4YSEmG8ijMkCMLNVFW6VD300EM3tTzxzSgIt5DFYkGj0VzyJlWn093kGt1cTqcTu92OwWC4cuJrdLnzez2pVCr0ev0NL6eqyP8pmcLfzqGWnOR+cwRHYgGKQ0GDE7VJg6VIQXaAEwkZDQqgxYkaJ0HYUTBjRYOr+5R0fqpbV7ABICO5g40LJNTI5a7LEeLIoXQv4xp5WZzz9sWpco3/UMtOfG0F5Bj83Wmy/X2wGlzvea63iaD8QrQOGR+zlQLjhWshMjuf4b8dJbjQzMbGNQkuNGPT6+D8jYusVlM3r4BZ2wI4uXA7J0KiOG3yoabdTnC0iYerWTD8mUbmsXxktYo6j8Rw39i6qDSX7hXtsDr548O/yNuTQWjbEOo/2wCN98VLIHo6czifs8cLCa9pIDo1GeVYOuouddC0jbnsfpfitMmc+imNgjQLMfeHEFxXTAMsCHcrEXAIFbZ27VqmTp3KrFmzOH78OMuWLSM9PZ3w8HBGjRpFv379AEhJSWHAgAGMHj26zBPk2bNnM2fOHNasWeN+mj5lyhTWrVvHjz/+yGeffcbOnTux2+20bt2aV199leDgYFasWMGiRYtISUkhPDycSZMm0blz5xt2rEePHmXevHkcOnSI4uJiwsPD6du3LyNGjPC4eT1y5AjLli3jjz/+4Ny5c6jVaurUqcPw4cPp0qWLR54lx7llyxY+//xzfv31V3Jycli9evUlWxb69+9PeHg4X3311TXV79SpU3z11Vf88ccf5Obm4uvrS40aNRg+fDgPPPCA+/0AGDdunHu/fv36lRlPcjmKorBq1SpWrVrF6dOnAYiIiKBLly7ufEuun5kzZ/Lnn3+ydu1a0tLSeP311+nfvz+KorB8+XJWrVrFmTNnUKlUNGzYkNGjR5cZg2G1Wvnyyy/ZuHEjBQUF1K5dm/Hjx5dbt5LzXjLeYsyYMRw8eBDAI9+33nqL/v37V/iYL7Z//37GjRvnkU/pbYqisHDhQpKSkggKCmLw4MGMGDGiTD4VfW/vZAmTfyH9v0fcv6twokXBC6srGMhzYAQK0OOkJOhWsKPFjhYLenwoRo8DO2rUuFojLOhQISMjnQ8dXMFIaVrkMltdwYwDBa37FR+blfuTTpHi44M3udQpTMDLYeaYX22OB9bkz5h6pIcEuPOQFAX/zDxeWfozalnhSPUQ/tv3PvK9DPT98zQRuYU4NWr6HE1AkVyFqmWZYoOePD9fnGoVzcwyByPq0SU9i3tzLSyvEcnBPB26vWdpmpF/viSZ/XPPcHLLOZ5Y0QFJVTZ8spudrLt/I7rYXADit6WS+v0Zuh7qj0pbfpCy6csE9qw6B8CAgzuxZKS4X9P/uzf617qXu9+lOG0yK/6xh3NH8gDY/d84ur3VmAYDoq4qH0EQqoaq8e0l3FQzZ87EarXy8MMPo9PpWLZsGVOmTCEqKopmzZpdc76TJ08mNDSUcePGkZSUxOLFi3nxxRfp0qULK1euZODAgeh0OhYvXszLL7/MihUriIyMvH4Hdt4vv/zCiy++SPXq1Rk2bBi+vr78+eefzJ49m7i4OD744AN32u3btxMfH0/37t0JDw8nLy+PdevW8eKLL/LOO+/wt7/9rUz+EyZMICgoiKeeegqz2YyXl9cNqV9ubi5PP/00AI888gjVqlUjNzeXY8eOceTIER544AG6du1KZmYmK1euZOTIkdSsWROAqKiruyl488032bhxI40bN2bUqFH4+PgQHx/P1q1bPQIZcA1SczgcPPTQQ5hMJmJiYtx5bN68mW7dutG/f3/sdjsbN25kwoQJfPjhh3Tq1Mmdx7/+9S+2b99Ohw4daNeuHWfPnuXFF1+sUJewUaNGoSgKhw4d4u2333Zvb9q06VUd89VYvnw52dnZDBgwAB8fHzZu3Mh///tfwsLCPK6Rq7n2bjWr1YrT6bxywovYkgpJn/mXxzYZ1YVg4zzXv1VltpT823w+6LCjQYcTByoM2JAAGVfwoUbGWWqGKm+K8cLizqlkILkaBxoUZOw4uNCqaHTYqZ2TTTAJGHB1t2qaG8fByGBOh4dgki8M9q534ix+uRe6ZDVOymDsloPk+ptokpyJTa/FWSpglJwyitNJdqC/u6UjzO5Ad37hLa2i0DU1g/XhoTTOyC5zHnMTiojblkz1doFlXju+/CyauFyPbdYzBSStOkVI37J/M/Mzbexd4wo2IrPTqV0q2ACwvvMjjlEtkHwr3hJ56od0d7ABgAK7/htHdLeA69ZN1Gw2e/xfuD6q4nm92u/Zy6kqXapuNhFwCFfNZrOxYMECtFpX83y3bt0YOHAgS5YsqVTA0ahRI15++WWPbYsWLSI9PZ3Fixfj7e0NQOvWrXnsscdYuXIlEydOvObyymO1Wpk2bRqNGzdm1qxZ7ifKjzzyCHXr1uXTTz91z0YE8NRTT5Wpw9ChQ3n88ceZO3duuQFH7dq1mTZt2g2v3++//052djbvvfcePXr0KDe/unXr0rRpU1auXEmbNm2uaTanLVu2sHHjRnr37s3UqVNRqS7cJMpy2dl3LBYLixYt8uhGtW3bNjZu3Mhrr73Gww8/7N4+dOhQRo4cyccff0zHjh2RJInffvuN7du3l2mFadGiBS+88MIV69u2bVs2bdrEoUOH6NOnz1Uf77VIS0tj2bJl7mt44MCB9OvXj8WLF7uvkau99m61I0eOXDlROZTDBSB7rmarukQ3JxWXXvW2pMvU+cYC7GjOBxsSeRjdgYYaJyYsGLBjwlwmqFHhRI3jslM2ZhNKoRec9KpBgcaHZMWPwIRUQiQJs1FPUHY+EWlZZfZrlJhBhs2OIrkWA/Q4D2oVxVqjO9goEeBwIuMKtYItVgwO5yXrduLgGQr9z5XZnvh7PrpyTl3Cn/Fk1sovsz07QUE5/1H1tRSXeR2znVO//YG9esW7RCX/WTYfc5aNo38eQ6W9vjds8fHx1zU/waUqndeWLVve6irc9UTAIVy1wYMHu4MNgNDQUKKjo0lKSqpUvo899pjH782bN2fRokX07dvXfaMGrptkk8lEYmJipcorz549e8jKymLChAkUFhZ6vHb//ffz6aefsmfPHvdNn9F4YXCnxWLBYrEArqBo+fLlFBYWetQdYNiwYTelfiXl7tq1i3bt2pWpx/WyceNGAJ599lmPYAMo8zvAoEGDyozZ2LBhAyaTic6dO5Obm+vxWocOHfjqq69ITEwkJiaG7du3AzB8+HCPdJ07dyYmJoaEhIRKHtH1179/f4/zbzAYaNKkCX/88Yd729Vee7da48aNr6mFQ67lJO7VeJwZlgvbkHAioS4VYCiAARu2Ut2cStOe706lwXE+XHGlKUbn0arhRI2MCj2OiybHdVFjxZtzFBN+PsAp2w3LKpk46FvbHRzUzimgdk7BFY+10MtAXGQoDRJTywQW4OpSVYZyYeRJvLcXeQY92QYdgRabRzJJUmj9UEN8yhlgHjQkn/3fpGM02y9kq5Zo8vcW6KuVTe+sI3N4SRzFuQ4SgsJwSCo0Sqm61Q+mdo/WV9UyEaopJHHVQUqf8vCW/jRqeukZvK6W2WwmPj6eGjVqePwtFipHnFfhRhABh3DVyuvG5OfnR1pa2nXN18fH9TStvG4yvr6+5OXlldleWWfOnAHw6GpzsaysC08ys7OzmTVrFjt27CA7u2y3h/ICjpIuRDe6fi1btqRv376sXbuWjRs30rBhQ9q0aUOPHj2oVavWNdfhYklJSQQHBxMUVLEVk6Ojo8tsi4+Pp6ioiJ49e15yv+zsbGJiYkhOTkalUpV7HmvWrHlbBhyX+syUvoav9tq71a55gLwX1Fv9N+JH78D8Vw4qlYJadlCMHi+sqFFQAAsatDjxoxALOhyo3YGEBqd7rIaOkhtjV6BQXluFBdcDEtf0uJ6zT0nYseGFAxU+ZCJjwoafRxqtIuNnM5Ond3XL+LV+FL/Vj8LbYqP3wZNEZ5ZtNbCrVWxpXpftdSMJrBXG2D3HPGumKHgVW9B7W90DzwE0DgcSkGbQsys4EI2isLd6OH87Ee++eTeoZNq9cS9hdcr/zNVo7UXuey059fZhdLlWJH8d935+HwG1Lv0Zfeyteqydfob0eNjZrRMdjx5CnZKLqnV1jPOHojaZLrlvebzu9aLrG435bWYcxVk2oloH0m1qE7y8rv8NrNFovK5dZgQXcV6F60kEHMJVK++pNbgGDgOXfQp2uSei6ou6HFxpe0l511NJns888wz16tUrN01ISIg77cSJEzlz5gxDhw6lYcOGeHt7o1KpWLt2LZs2bSq3S1FlZmS6mvqBawGf4cOHs2vXLg4dOsTChQuZN28ezz33HEOGDLnmelRGecevKAoBAQG88847l9yvdu3aN7JaN9SlruHSrva9vZN5t6tG4yNDcBbZUZu0OM4VYY3PQxNsRC60oQoyYv41leyfUilaHocmqwANdmRUKMhY0WNDhwkHdjTnF/vDPZOV46KvNgca8jFiwoKMhBoHyvnOVFZCUeEkkLN4cw4N3mReFHAA6J2u1oINzevwXZd73dt/qxfFewu3UjcjlXAlgWf6PonOoXCgZjh5JgORGbnUzLPxS61o6mZkE1RsRlapwOlErVFjKiiiyKDHoVYRlJlNprcJay0fRvYL4LWaXvhWM2IyBqHS3gMWO06VGo1BdcXWhmaj63DvU7Vx2mQ0hitff1ENvHn6yybYLE50BjWK0heKbUima595reGDUTQYEInDKqM1XrkOgnAnEGM4ro0IOITrztfXF4D8/LJP/ZKTk292da5KydN3o9FImzZtLpv2xIkTxMXFlTsb16pVq255/UrUqVOHOnXq8OSTT1JQUMCIESOYMWMGjz76KJIkVXoAZ3R0NDt27CArK6vCrRwXq169OomJiTRp0uSKT9QiIyORZZmEhIQyQUhJK8GV3Oi1Ta7Ftby3dzq1ydXyoAkzoQnzfIKuH+KL/5D6MLtzmf2Sn91O9vTDSCjn2zrUgIQFLWqU860frgcjEjIKKvLwIg8TF/r4uEZ/+FKMH8WYyATAQCEazLjmyXJxShIFWj3VzYnI3tXQOB041K6vT4tOy7bGMZhTLTzWbTAWSYfO5sSuUYOiYPXW0+fnw+yrHcPB6hGoZCeNsnOxarUe3aycVitvbG6PRn+ZG3OT7rJjTS4mqaQKBRul6c6nlyQJKhFslK6DCDYEQbiav12CUCEmk4mgoCD27dvn0Qpx9uxZd//721W7du0IDAzkm2++KbfLlsVioaioCLjQ0nNxS8vJkydv2HFeTf3y8vLKtLD4+PgQGRmJxWLBarUCF8ahlBcgVkTv3r0B+Pzzz8uUV9FWqL59+yLLMjNmzCj39dJdiUpmq/ruu+880mzfvr3C3alKjvlGdMu7Vlfz3t7tIj/rTBPlWYI+6Xh+Sl0nCgqq8/814sCIDSO2UuNCpFL/v/Dv/PPdqVyhiZoigvAiGy2uc23VqFjXqjHdcn/g/tzdLFwyncOfvkRg0YUxHBta1OO1gQ+CXU1wVjG+BVaCsovxyzWTqdXzfct7CLTbqV5UhF0t8UN0GBlGHXYJzBoV2Vp4/z81Lx9sCIIg3MFEC4dwQzz66KPMmjWLyZMn06lTJzIzM1m+fDm1a9fm6NGjt7p6l2Q0Gpk6dSovvPACjzzyCAMGDKB69eoUFBQQHx/Ptm3b+M9//kOrVq2oWbMmtWrVYsGCBVgsFmJiYkhMTGTFihXUqVOHY8eO3dL6rV+/nkWLFtGlSxeioqLQaDQcPHiQ3bt306NHD3fXpkaNGqFSqZg3bx75+fkYjUYiIyNp3LhxherUvXt3evTowfr160lKSqJjx474+PiQmJjI7t27WbJkSYXy6N+/P0uWLOH48eN06NABf39/0tPT+eOPPzh79iyrV68GXDfmHTp0YN26deTl5dG+fXvOnj3LihUrqF27NqdOnbpieU2aNGHJkiW8//77PPDAA2g0Gho3bnxDplmuqKt5bwWXav9sSXGmk6R3D+OLBQ0O7KhQzq/D4bjEmA5PEmqcFBFMEUHI578W1dgI4wSpxmCap/+Ot+PCFKGNzp3lmV828FavIWicDu4/nkSxXk2q1htnqS6nXhYHRXon+yNCyTWZ6JKaRYO8InzsDvzMxWyqFUE7qYhFUyMxaG6/VjdBEMpSyln7RrgyEXAIN8SIESMoLCxkw4YNHDhwgJo1a/LGG29w7Nix2zrgANcN7bfffsu3337Lxo0bycnJwdfXl6ioKJ544gnq1q0LuPrlT58+nc8++4x169ZhNpupXbs2U6ZMIS4u7oYEHFdTv5YtWxIbG8vOnTvJzMxErVYTERHBs88+y6OPPurOr1q1arz55pt8++23vP/++zgcDvr161fhgAPg3//+N82bN2f16tXMmTPHXVb37hVfLOytt96iVatWrFy5km+++Qa73U5QUBD33HMPEyZM8Ej73nvvMWvWLDZt2sTevXupXbs2//nPf9i0aVOFAo5evXoRGxvLDz/8wNatW5FlmbfeeuuWBhxQ8fdWuKDWv++j5jutiX/lN1I//B0ddkCNDjtO9KjPDy5XLhl4KBixUETw+a5ZLk50nPSqwx8BtSjUeJEZWY0O6b8RYHe1PrU/E0fP2L94cdsmVHaJXKOWiT2Hl8ldLSs4gFO+XjyQlo1WUYgotlI9q4CHVUnMXHTj1n8RBEG4XUjKjRh5KwiCIAg3mS3bzF9tlyGfyMUXMwoSeXijw0YuplJBh2tGKwmFAPIIIe/8gPQLQYlNpWZHTD3XAO/z/Gz5PJK0BgnI1/jj7XCS7B3I3PaDeXXrp7R78l3SvC+sPi4D6QEmFJWEWlZ48sRZNIqCJMuEZmTz1OL7qBEinvtdq+LiYo4dO0aDBg3EbErXkTivl/dhp58rlO6lHR1vcE3uLGIMhyAIglAl6AKNNI8bTtO4x5EBBQk9dryxEk4OgRQQRD7hZBNCLl6YycGXBELhojU6srxMHsEGQJ7Ol1ytLye8a7Isug8nfCI5Vq0WakVCQsXcDbOon1UyMYZCjo/B3f2ifl4hmvPP90xmC0XNQ0SwIQh3IEWSKvQjeBJ/7YQ7Xk5OzhUXIHM4HO6Vmy/FYDDcsMXx7jROp5OcnJwrpvPz8/NYBPJOZrFYyiy4V57g4OCbUBuhMrR1g3AgoceJFtd0tmoUTLgmSnAiUYwRkDDiOD+M3IKEwT27lZfDUjZjRWF9RE9S/APY0Lwu//FvzwNJJwgt1rAnphUdTu9m26KpFGgNpHv58eSjL5Ph5UXt3ALuyS1EUakoVqtAlpn2etn1aARBEKoqEXAId7wnn3yS1NTUy6Zp0aIFBw8evGyafv36MWXKlOtYszvXuXPnGDBgwBXTffnll1VmEPOWLVuYOnXqFdPt37//JtRGqKzwr//GuX9sRE3ZhxHO89PolnStMmLGz2jGapZxnl8kMMxipVpRLmkm/ws7ShKFei8+HHg/WT6uribHI0O5Jz0bndyJdFMwdTNPk+AXhMqm4p+7fuOvqAuLbMqAGmj/UCimgMpPOSsIgnCnEAGHcMebNm2ae4rXS/H19b3itK9VZVG16yEoKIiZM2deMd2lFqi7E7Vr165CxyzcGQKfaoK+eQgnWy7Ey91u4brpt6CG8yt4+FJALfvLKKezcdz3MZq8fOxokTFyX9opUk3+FOq0BNmT+TW0DX9Wr+YONkrEhgTwZ0A2SaaWHA6Iwphn4d3HunJfYgL3puUhSTrydDr+Cven95EzPDwm5mafDkEQhFtKBBzCHa9Zs2a3ugpVjl6vv2sWnysRHBwsuktVMaYW1WgqP89fbZbi3JeKhEIxOpyoCSAPU9twYnZPciWuF4Jf7vsoRTYUWcH+/X6KVsTh/0cWvtlnqWE5jQYnR6P7lltWnz1HqF6Uwdcd7qeWVyERmUVsC41mW5iEzikT6bDQ+2g8o6c3vi0XnhQEoWLE+IxrIwIOQRAEocqSJInGe13TQBfvTUMT6Y0u8tJjtSSTDgnQj74f/ej7CXA4iW06i9NJTmoWxvPOL1+xrF0DEgJD3fv0/usotWwp/NogmvZxJzh0TyPuLTBTu9iKWYJ7U9MJKCzmhaWt8Q4x3OhDFgRBuO2IgEMQBEG4K3jdV+2q95E0au45OhE2HuSHp7YSZMnlh9nv8mGX/hyMqINvATQ+nY/XsNb86/12FJ8pYHW7TRSYjDjVKowFxbw/5H5+fsML7yARbAiCcHcSAYcgCIIgXEnvFvRIbo691ivsL6xJu8NFtDv8OwB+Kjv3vT8EAK+aPgw+8SBHlySw+bRMbEgC/9chBb2mzq2svSAI14noUnVtRMAhCIIgCBUgSRLaP6fS9oMNhH8fR4oUQODw5tR/xXOmNo2PlqZP1aGB3c733+8BAm9NhQVBEG4TIuAQBEEQhAqSvA1I0x6m5jSoeasrIwiCcIcQAYcgCIIgCIIgVIDoUnVtVFdOIgiCIAiCIAiCcG1EwCEIgiAIgiAIwg0jAg5BEARBEARBEG4YMYZDEARBEErJMyssXJfNiRMWWpps9OvgQ0ALsQq9IAhiDMe1EgGHIAiCIJz32fw0fl6fhcbkhUFW2Aws+LOYIX9uZtTuHqhUomOAIAjC1RJ/OQVBEAQByEo2s/znAry1WgyyAri+JMOtDvIKJN5utY2jO9JvbSUFQRDuQCLgEARBEO56axck8eyzp+iWkA6aso3/3z/QiGJZ4qVP01i+4OwtqKEgCLcDRZIq9CN4EgGHIAiCcFebNuwQX/zsJNjq4Ky/N7n6sgHHOYOOOR0a0/ZkIhu+TUS2Om5BTQVBEO5MIuAQBEEQ7lon/8on3qwlwCajs9tZ1qwOe6ICsaovPKF0SHBfXiGNLDZqFxTw0k+/sSX6O2RZvoU1FwRBuHOIgEMQBEG4K5nNMp++kYBKrcauVvFHeBB5XnrSvQ0saxjJ4VBfADQKeMkKtSw2trZrwZ81oohJz2FfzHwUu/MWH4UgCDeTIlXsR/AkAg5BEAThrvTKmOMU6fV4FxZQI+0cOocDjdMVQMgqFWpFKbNPQEERTeLPIgG+Z/P48N51DO67j8OJtptce0EQhDuHCDgEQRCEu86h3/M5I2kJzcomMr8YP7ONHxpE41Cr3WmOh/jgvOhJpclidf9bBUQWFLG8UT2e+WcspzLsN6n2giAIdxaxDocgCIJwV3nkqTjqncmgoUoiyOJqmYgP9MGi9fxKtGg15Oi1BFtcgYTKKdP2yAlkLjytCygyM+jACSRZYdKwP/kzOpg2OgtzPqyNt+4mHpQgCMJtTAQcgiAIwl3jnTY/cK9ODxoVOtuFQd+BxdYyaSVFweiUaRZ3hiKDnoikbCwWDXE+YWT4GvilbjW+bd0Yzk+BWSs9l35/xbK6WV1iXsnk9L/9btpxCYJwc4gpb6+N6FIlCOetXbuWVq1asX///ltdlStq1aoVU6ZMuSn730nnRRAuJS/dysxm6zkaFYXVoMXL7kQjK5TcOoQUmrkvPs1jn3syCjDZnWT5+WCVtIRmFKCWFVQKhOVZ0Fold7ABcDrUnwAlj9i5rzFn83z6TjiG1iy6WQmCIIiAQ7ir7N+/n9mzZ1NQUHCrq3JHi42NZfbs2aSkpNzqqtyxtm/fzuzZs291Ne4KyQnFTB15hL2N6mPUqvG1lA0CMvx8uS85h75Hk2iRkkPPk+dolZoLikJSWDDh53LK7NMqseyq4z9FNyTi6U8Z+tB4CnUGtn8fxM9JxhtxWIIgCHcM0aVKuKscOHCAOXPm0L9/f3x8fDxe69OnDz179kSr1d6i2t2eyjsvcXFxzJkzh5YtWxIREXELa3fn2r59O+vWrWPs2LG3uipVUkKGne8nH8SRUEyRlx4fGZqe+YvTQX78XCucezLyCM8vdqd3qFXkeZuILLBQrciGTadF5XSidTqx6nQcuSeaoOxCTKW6Xp3zKRtINEzOY8L2Izw9tCtHgyNRy07iFuXyzSonDpWapv4O5kSdQ71oF8XYKErIwIgGU1g42ld7o+nfEPnH41A9CKlpBCq9+HskCLcT0aXq2oiAQxDOU6vVqEvNUCO4iPMi3O4URaGw0MHns9L44ZSTHL2WJ/fFolarKDZoCcrMpd/vxwFoB3T6y5dBk4cw8K8zNE7NxqZRk+NtAiDHZOT+3/+iyORNbEwUVp3r2i/yNvBrm/r03PYHABaNmu9b1/eoR62MXPociUfvlHliXyxfdWjCo8f2MqtFN1J8AgA4CjT5PpbJB3LRI+OFikwvibzEVGoM+xgHEjISEjJarJRMzKsAitGI+eleJETWoGaH6hjsdpQCM1KrWqiCSj1AsTtAo/bo7iUIgnAr3XUBx9q1a5k6dSqzZs3i+PHjLFu2jPT0dMLDwxk1ahT9+vUDICUlhQEDBjB69OgyTyBnz57NnDlzWLNmjfvp7pQpU1i3bh0//vgjn332GTt37sRut9O6dWteffVVgoODWbFiBYsWLSIlJYXw8HAmTZpE586db8hx9u/fn/DwcF577TU+/fRTDh06hCRJtGnThpdeeong4GB32pK6l9dHv1WrVvTr18/d37/0ealVqxbz588nISGBkJAQRo0axYABA0hLS+OTTz5h//79OBwOOnXqxCuvvILJZLqqY8jIyGDhwoXs27eP1NRUrFYrkZGR9O3bl+HDh5e5Cbbb7SxatIjNmzeTkJCARqMhOjqafv36MWTIEPdxAgwYMMC9X8l7XHJtfPnll7Rq1Ypff/2VZ555hhdeeIGhQ4eWqd/IkSNJSkpi06ZNaDSuj1JiYiJz5sxh79695OXlERISQvfu3RkzZgxG49V3qzh16hSfffYZhw4dQqfT0b59e5577rlLpv/hhx9YvHgxJ06cwOl0UqdOHYYPH0737t3LTb9nzx5mzZrFiRMn8Pb2pkePHowfPx4vLy93movPS8n1DzBu3Dh3utLXyZXIssz8+fP57bffSExMJC8vj6CgIB544AGefvpp/P39y+yzdetWFi9eTFxcHHa7nbCwMNq1a8ezzz7rbn1RFIVVq1axatUqTp8+DUBERARdunTxqGtubi6zZ8/m559/Jisri6CgIDp27MjYsWM9yi7vs16i5DP21VdfubeVfF4efvhhZsyYwdGjR9Hr9XTu3Jnnn3/efV7HjBnDwYMH3fuUeOutt+jfvz9paWnMnj2bffv2kZWVhbe3N9WrV+fhhx92/426WxT9mMDpT37n9EkzsaEhWDVaEgO82XxPDLlGHSN//h2DVk+utxfB+YV0sdsxWW2gVmFXSaQZjTx09KBHnlE5+fQ/HMv6ZvUJsshk+5iwq9XsD/fjRLAPSxtH8be/EghxeN6w5/mZ2N2qLgarnQ31IolQq0mw2bGpVNRLy+aFrYfRO10D0RsnZ3H/2Tjq5KXz4t4NFOgMzLm3M0m+QXzS5gEm7/sVUOHESEAxqCjESiAKWiSc6MhD4kJrigRgNqP/ZAM18UdCQSYXNXYcGLBixKoykmoKpFCnpmlWLBqcyGhQACd6VMhoJDOSGlCBZHeAXgtP/w3p/SdAdwe2psgKyty9mLecRFUjEO3znVHFBN7qWgmCcJG7LuAoMXPmTKxWKw8//DA6nY5ly5YxZcoUoqKiaNas2TXnO3nyZEJDQxk3bhxJSUksXryYF198kS5durBy5UoGDhyITqdj8eLFvPzyy6xYsYLIyMjrd2ClZGRkMHbsWDp37szkyZM5ceIEK1asoKioiJkzZ1Yq719++YUVK1YwaNAgfH19Wb16NW+//TZarZaZM2fSunVrxo8fz9GjR1mzZg06nY433njjqso4ceIE27Zto3PnzkRFReFwONi9ezczZswgOTmZf/3rX+60drudiRMncuDAAdq2bUvv3r3R6XScPHmSbdu2MWTIEB5++GGKiorYtm0bzz33nPvGsm7duuWW37ZtW4KCgli/fn2ZgCMxMZE///yToUOHuoONY8eOMW7cOHx8fHj44YcJDQ0lLi6O77//nt9//52vvvrKnbYikpOTGT16NDabjUcffZSwsDB27tzJpEmTyk3/xRdfMG/ePNq3b8+4ceNQqVRs27aNV155hZdeeolHH33UI/3x48fZunUrDz74IH379mX//v18//33nDp1ipkzZ6JSlT/Eq2vXrmRmZrJy5UpGjhxJzZo1AYiKiqrwsdntdr777ju6du1Kp06dMBgMHD16lNWrV3P48GEWLlzo0YVr5syZzJ8/n1q1avH4448THBzM2bNn+emnnxg3bpw77ZtvvsnGjRtp3Lgxo0aNwsfHh/j4eLZu3eoOOAoLCxk1ahRJSUkMGDCAe+65h9jYWJYtW8a+ffv49ttvrzo4Li0uLo5//vOf9O/fn169enHgwAFWr16NSqVyX7OjRo1CURQOHTrE22+/7d63adOmOBwOJkyYQEZGBoMGDSI6OprCwkJOnjzJoUOH7qqAo3Ddac4OWIVOgeQWDVGhwuhwUj8jj5rZR/ika3MWt29CnxNpFEgOFnZqglWjpkVSBr3/OsPWWpE8u3wPerujTN7+RRaKdFoy/F2riR8P9iE29MKsUn9UD6XbmYwy+52NCkZnsZHq40WyUY/zfCvC0agQ3u3dmk+W7QQgMdCHkX/+zKPH96E6304x/tBPtHpyCgl+QeAeru5EgxkABxpAQkGDlUBU2FDjuaCgGidWDICEjA4vUlDjwIoPGlmiunt8mgH1+YDEhjcG8pGQXU0l50+HAkgWG3y6BjLzYMEzV/sW3XIhnx1AWXQcJ+AEHEsP43XsVSR/MW5GuDFk0XJ4Te7agMNms7FgwQL3jUq3bt0YOHAgS5YsqVTA0ahRI15++WWPbYsWLSI9PZ3Fixfj7e0NQOvWrXnsscdYuXIlEydOvObyLicpKYn33nuPHj16uLepVCqWLl1KfHw8NWrUuOa8z5w5w9KlSwkPDwegZ8+e9O3blzfffJNnnnmGYcOGudMWFBSwfv16jye8FdGiRQtWr16NVOrD/fjjj/PGG2+wevVqxo4d626pWbRoEQcOHGDkyJFMmDDBIx9Zdj1xbNq0KXXq1HEHMVcae6BWq+nTpw/fffcdp0+fplatWu7X1q9fD+Bx8/f2228THBzMggULPG5Y77vvPl588UU2btxI//79K3z8X3zxBfn5+e6WBYBHH32UF198kdjYWI+0x48fZ968eWWOf+jQoTz//PPMnDmTvn37etTr5MmTfPTRR+5WtsGDB/PRRx/x/fffs2XLFnr16lVuverWrUvTpk1ZuXIlbdq08XhCX1E6nY5NmzZhMBg8tjdt2pR33nmH7du3u6/bI0eOMH/+fFq1asX06dPR6/Xu9KWDry1btrBx40Z69+7N1KlTPQKmkmsA4NtvvyUxMZGXX36ZwYMHu7fXq1ePDz/8kAULFvD0009f9TGVOHHiBPPnz6dx48YAPPLIIxQVFbFmzRr++c9/4uXlRdu2bdm0aROHDh2iT58+ZfZPSEhg0qRJjBgx4prrcaNZrVac51flvlEyP92PpEChUU+x0fNa0TllWiam80ODGJK9tPxwT013F6J9MWGkexsZ8tMR9A4nuQYvAi0Xxms4JYmtjWpRK/vC5BFp3nqP/FN8DGRq1QTbLxxjsUrCbnPSZ08scx/rciGxQQMaFScNQST5m9A7ZL7o1IRti9e5gw2AIEsRTx/+iZc7D+GcyZvwomyMnHMFAoBMHmaqoaAGJBwYywQcKpyoseDEiIIGOyaU84FKaQ680ZMLgBo7ChIX3yaV/l1ZuAPzh0+Ab8X/Rt9q5rxC/Jef8NimpBVQ/H97kUa2vkW1uvOZzWaP/1cFV3PvIdwYd23AMXjwYI8nqKGhoURHR5OUlFSpfB977DGP35s3b86iRYvo27evO9gA102byWQiMTGxUuVdTkhIiEewAa7uG0uXLiUpKalSAUfnzp3dwQZAQEAAMTExnD59usyT9GbNmrFt2zZSUlKoU6dOhcsofTNqt9spLi5GURTatWvHxo0bOXr0KB07dgRg06ZN+Pr68o9//KNMPpd6Ul8Rffv25bvvvmP9+vXum1tFUdi4cSO1a9fmnnvuAVw37ydOnGDs2LHY7XZyc3Pdefw/e/cdHkW1N3D8O1uz2Wx6ISQkEEIPSC+KdBCpSlMsIChSBK7Xcr16Ldiu5fUioICISBGRUA0goEiXTkAQCaFDKiG9bLbP+0fIkiUJBAiEcj7PE0hmzp45Z7bNb05r2rQpOp2O3bt3VzjgcDgcbN++nYYNG7pc0EuSxLBhw9iyZYtL+nXr1iFJEr1793Y5NkCHDh3YunUrf/31F23btnVuDw8PL9Wl77nnnmPx4sVs2bKl3ICjMkiS5Hx+7XY7RqMRu91Oq1ZFFwlHjhxxvnbXr18PwPjx412CjeJ8iq1btw6Al19+udRzXvLvLVu24OPjw+OPP+6SZsCAAcyePZvNmzffVMDRuHFjZ7BRrFWrVuzYsaNC74Hiz4nY2Fj69u2Lr++d2T3kyJEjt/wYuuw8lFz7juJZX0Op8QoJPh54FFpICvfllEc1Gp1JpFpmNiaVip9bN2JY7HGUMliUSk5XC8DLbCOpZAaSxH4PLR6yAk+bnRSthjStGskhY25c63I6Ly1oirt3qpg4qgeazEIKtWr8TPmlyupXmI/aZsXdakZDtjPYAFBgQ00uFnwu/V12QCe7hApKKBHUlExVMn3pcKN08vj44ziuCLzuZJLZTh2bo9T21LOJZMd5lPEI4XqcPXu2qotQaVq0aFHVRbjv3bcBR1ndmLy8vEhNTS0j9Y3nWzwTUll30z09PcnJybmp411PWaCojsBNH7esvA0GA/7+/mg0rsvrenp63tAxbTYb8+bNY+3atSQkJCDLrl+qubm5zt/Pnz9PvXr1Sl2Q3qzIyEjq16/P+vXreemll1AoFBw4cIDk5GQmTpzoTHfmzBmgqM9/eVOdZmZmVvi4mZmZGI1GwsPDS+0r2dJS8viyLDNo0KBy88zIyHD5u7grVEn+/v4YDAaSkpJK7atsGzZsYOHChcTHx2OzuXZ5ufK5lSSp3K5vxRISEvD398fPz++q6ZKTk2nQoEGp7m3FY36OHTt2nTVxdbPvu+LxZPPmzaNnz57UrVuXVq1a0a1bNxo1anRTZatMUVFRt7yFo2CMgswXN+NpNKE1WzBrL3+2WJQKYmsEUiMzF+/C0ov2uVls/F07gJYXi6azPVQnnEOEg0PGp0SxNXY7Dc8kYZYUpOo1ZOqLAmG/PCOdTpxnaZP6LvnKColfo2oW/aGSSgQbRezuGgpNdrDLLK7fhv/sXuOyP7p+GyYe+B1fSxqKK1ovoCjoKPrfgoqCUvvtaHFQ/Dkno8IIOLDiScmZ7tXkuTxGSelzJHO5lcM2qA31WjUtleZOVlhYSG7PWnj9cvryRk8t1cZ2I7iaofwHCldVWFjo7AVxI2MPBaEs923AUd5d7+KLWukqd9Su9iVb3mw+5W2/8iK6Ml3tzn7J45ZX1ysvAiuSd0WPWRFffvkl0dHRdO/enZEjR+Lj44NKpeLYsWN89dVXt/TcldS7d2/+97//sW/fPtq0acMvv/zi7G5VrLgszzzzDO3atSszn+LA61aRJIlp06aV+xzUrl37lh7/emzatIk333yTRo0a8dprrxEUFIRGo8HhcDBhwoRSz60kSVd9T94qN/I5cLUZvSr6mh03bhz9+vXjjz/+4M8//yQmJoYffviBYcOGuQS6Vamyg/uyuI9qhkal5vj/DhGRnMqxsBDMahVJXh780qgmBWoVg3ccRqNSc6CGP7YS515rs2Nz15TOVHH5ObUqFCR6e+CXb6TfhoOc02uIC/GhfvJFIjLy+S2ydFBe02iiXoEJhUOmwGplm0/N0seQJEBm0kOPoXbYeebvXZhVKraF1uOFw1t57MQBFKjLacFwoCUdFUakK1ouHIAJ76JDYEVDFhJW7KhRk8F5j1BO+FQjX6Ok19n9KOxu2NEgYcdIAGoKUGFCki7lKzuQFQrkJ9qjnvsS6rtwCt5zb7XBq2ENFL+dQKrpi+bdHigjgqq6WPcEnU4nuiKVQb5Wa6FQpvs24LiW4ovDkndai92Ou7+3U8kWiOI7sVD19Vy7di3Nmzfnk08+cdleVre38PBwzp49i8ViKdXCUtKNXLT27NmTqVOn8ssvv/DAAw+wceNG2rRp4zLTV1hYGFAUcLVp0+a6j3ElHx8f3N3dOXfuXKl9xbMvlVSjRg127txJtWrVymy5KEtxq0xJ6enp5OXlXXMig5u9+F+7di1arZZZs2a5dJ0rqwk/PDycnTt3cvz48VJdlUoKCwtj69atzlmnyhMSEsK5c+ew2WwurRw2m43z58+71L3k50DJVkqz2Ux6evp1DZS/0rXOYWhoKE8++SRPPvkkZrOZCRMmsGDBAp555pk7tpvVreA1IopWI4qe994ltk8HZIeM/fV2qDQKnt6fy1PrHFyQ1PgUmKmTloVZoXC5iw9gVCtxt9o5HuDNwlb1KNSoUTgcDPaIo/3hs2xvEMofnnqq/Z1IY6OJizqNc2B4hNHEQ9lF3aTa7DtO9ZRMjkYEku5V4qLM5ij6AWxKFW90eoL3Hnqc2AWTGP73DgAS3KtxMDCILJ077ZNPUCunqPXRgRor7iiwYrn0vwIrEjIySswYwN0NRfe6qGoaUBjU0DoSpd2KqnNjIg06IvMKkQrNEPgSKBTOL/nyPxWvHP1xd5G1ShSTeuD++WNVXRRBEK5CrDReDr1ej5+fH/v27XO5K5mYmFiq//zdrvhiee/evS7bFy5cWBXFcVIoFKXuCBcWFrJo0aJSaXv27Elubi5z5swpta9kHsV3a8oKJMvj4+PDgw8+yObNm1m/fj0FBQX07t3bJU29evWoXbs2y5cvJzExsVQeNpvturqUKZVK2rdvz9GjR12mK5ZlmQULFpRKX9zaMn369DLvvF/ZnQrg3LlzpV7L8+fPB6Bjx45XLV9xM/v1nMeSilthSg7mlmW5zOeveCzJjBkzsFpLrxBd/Pw++uijAEybNs0l35JpoKhuWVlZ/Pzzzy5pfv75Z7Kysujc+fJg4OIubXv27HFJu2jRolLHuF7F5/DK10V+fn6p1kWtVuscc3Wj5/xeJCkkVJqi11Lrlp6cfMebvLf1nP/El+kTAijwc+dgdT/nKIk0Dx3zW9bHIkksaVaHwkvTwDoUCqI7NMJWonVQkqGaxUq/tCxa5BRQu8BEhNEEgCGvkLCkDFQOmfcXbqHFiWS88wtxyzdBzhVdl2SZbqcOkaHx5Kh3GBf8alCtvh/t9VZa5aVzNCCMP0LrkhIciv39wbgl/R/K2S+gnPwUqjNfoZSXo5BXoJSX4i5/j77gS9x/HotmyjMoP3wCqW8LpMfagpceSaFA8tJDNV+4ibFrgiAIlU20cFzFkCFDmDlzJhMnTqRjx46kp6ezfPlyateuzdGjR6u6eJXmkUceYcaMGXz88cecPXsWT09Pdu3aVWrw8e3WtWtXVqxYwZtvvknr1q3JyMhg9erVLq0wxYYOHcr27duZM2cOR48epU2bNmi1Wk6fPs25c+eYMWMGgPMO+bRp05xT59auXfuaA3n79OnDtm3b+PLLL/Hw8Cg12FqSJD744APGjh3L0KFD6devHxEREZhMJuf0rePHj7+uWarGjRvHzp07efnll3niiScIDAxk+/btZGVllUrbqFEjXnzxRb799lueeuopunXrRkBAAOnp6cTFxbFjxw52797t8pjIyEjeeecdHnvsMcLCwti/fz8bN26kefPm9OjR46pla9SoEQqFgu+//57c3Fx0Oh0hISFXbYEoqWvXrs4pbXv37o3NZmPr1q2YTKZSaaOiohg+fDjz58/n6aefpkePHvj5+ZGcnMzGjRuZP38+BoOBbt260b17d3755RcSEhLo0KEDBoOB8+fPs2vXLpYsWQLA8OHD2bhxI59//jnx8fHUq1eP+Ph4YmJiCA8PZ9iwYc5jt27dmvDwcGbNmkVOTg7Vq1fn0KFD/PXXX2WuFXI9GjduzJIlS/j0009p3749KpWKqKgoTpw4wccff0yXLl0IDw/H3d2duLg4YmJiiIqKuqnJHu4n9Zt6smmKJ1CDuANZfPvWcdZG1STRz8Dn3VuQ4166S9j2qDDn7yf8PGiclouH3YGP1Uasl55Qc9GYC635cuBbIz2P9xYVTYU7o0MUFwzu2JUKTvt7oZAdhGWn8kgNM00WvOHy2eV76adhGWVXvfBwZZwCQRBuAbHS+I0RAcdVDB8+nPz8fNauXUtsbCy1atXinXfeIS4u7p4KODw8PJg6dSqTJ09m7ty56HQ6unTpwocffuhyt/d2e+WVV9Dr9WzYsIGtW7cSFBTE448/TsOGDRk3bpxLWrVazddff83ChQv59ddfmTFjBhqNhrCwMJeL/KZNmzJhwgRWrFjBRx99hN1uZ9SoUdcMOB5++GG8vLzIycnhscceK7P/er169fjxxx+ZO3cu27ZtY/ny5ej1eoKDg+nbt69zBqaKCg0N5bvvvnOOZSle+O+DDz4oMyB48cUXadiwIYsXL+ann36isLAQX19fateuzWuvvVYqff369fnnP//JjBkzWLFiBXq9niFDhjgHx19NtWrVePfdd5k/fz6ffvopNpuNPn36VDjgeOSRRzAajSxatIipU6diMBjo0KED48ePp2vXrqXST5gwgTp16rBkyRIWLFiAw+EgKCiIhx56yKVL1scff0yzZs2IiYlh9uzZKJVKqlev7rLwoYeHB3PmzHEu/Ldq1Sr8/PwYOHAgo0ePdpk6WKlUMnnyZL744guio6NRq9W0bduWb7/9lueff75Cdb3aOYiPj+e3335j48aNOBwO3nvvPZo3b07nzp2JjY1l/fr12O12qlWrxogRI1ymmxYqrkFzHx77JIot32fjV2Dmqf3H+KZ9EwquGLPg0GkJyi4gzdOdP6t5YVYpCMsu5K9LA8nj9W6Emiw41BJK2Y5dujxmpECjZODRY7Q4n0aClwGD1cxbfR5iXI+DNGxSsfeFIAjCvUqSb9fIW0EQBEGoQonZdhp+baZQraRuWg7HA72xKRUgy2jzzby55SB6u50sNzWfd2+FTanAzWqnSWImfoVW0jUqkjVqXtu6jy5HTpKmNWBSqHFzWDG5wV/BfnQ4m4hJqeLjLm1ZPL8uv65aSsOGDYmMjCyzdVa4cUajkbi4OBo0aCAGN1cicV6v7p1esRVK9+FaMRVvSaKFQxAEQbgvhHorSf6Xjp6vJhDv68PjR5PI0GvZ764jwGzijzohhGfmUTcti0bn0jhUM5Bup9PwMRV1ofK32giw2MhUSmgdNmoUXu7emOPQ4J/pxco69dnVthYbpoWjlm7ttMGCIAh3CxFw3CGysrKuOaf9lbPqlMXNzc1lgcE7jclkIj+/9GJYVyo5A9S95F6uv91uL3N8yZW8vLxcFt0UhNvJQyPxx1dhLJyZyIY4EyEOmRO+ek5VC+DUpTT6QgsFkoKAtHxnsFHMz2ojwce31OxXx3x96fRjO0Z1vrwgqtUqAg5BuNeIMRw3RgQcd4hhw4aRkpJy1TTNmzfnwIEDV03Tp08fJk2aVIklq1wbNmzg/fffv2a6kjMz3Uvu5fpfuHCBfv36XTPdN99847J6uiBUhWfGhjLv4kVS020keOtd9nna7DyYmoneXPZaRCalmi01wohKv4jWZicuyJem7zWnTolgQxAEQbhMBBx3iA8//BCzufRKsCV5enpec0rMgICAyixWpWvXrh3Tp0+v6mJUmXu5/n5+fhWqW926dW9DaQTh2pb9y4+2b6W7bPMptNDl9MVy54w3KiR2RAazrV4wL8f+zes7u3F900EIgiDcf0TAcYdo2rRpVRfhtvD3978ruwtVlnu5/lqttlIWPRSE28XbTcHYcDP/KrRjURXNOFUrq6BUsGEFMtUqgqw2MlVK9EqZcVsO8MpffW57mQVBqFqiS9WNESsDCYIgCPetl8aH0O+vM/jlFwJgU5S+mDApFez3KhobF5iZQ0hyDs/t7XVbyykIgnA3EwGHIAiCcN9SKRV8900dxptSeXZ3PF6FVqxXBB1xejca5RuplnyRApOdi/UD8NWJr09BEISKEp+YgiAIwn3Ny6Bk0qd1ONEwiOr5JtQOGYsEmSol8e5awk0WaposZClk6gwM5ejH92a3SEEQrk2WKvYjuBJjOARBEAQB+OVVb1r8V0Hr5Gy0Mvhabfja7OBwcNFHw9oFbZBE/21BEITrJgIOQRAEQQB8vVSc+sSHbzZo2XvKgtZk5YJSw4geevo3EGvHCIIg3CgRcAiCIAjCJQqFxLhH9IxDf+3EgiAIQoWIgEMQBEEQBEEQKsAhulXeEDFoXBAEQRAEQRCEW0YEHIIgCIIgCIIg3DKiS5UgCIIgCIIgVIBYafzGiIBDEARBEEqQZZntwzaw55yaHHd3/Dzg6c8bEhhhqOqiCYIg3JVEwCEIgiAIl6wdto1Th3I4FhmBI7joKzJDlpn6/FEeejqYXi+EVXEJBUEQ7j4i4BAEQRAEYN1T21hs9aeWXoFDqSQ4K5MWp06gkGUOhdVkya+FJHpn8eIgn6ouqiAIVUR0qboxIuAQBEEQBGBrtjt1My6gt1locCCRDqfjUMoyJqWSvT7eJBm8+WFFJo90NhDuJ74+BUEQKkp8YgqCIAj3vYMbLuCTnomnyQJA7fQLKGUZgBf69uX32rWdaVu/l8GFr4OqpJyCIAh3IzEtriAIgnDf2/jWn85gA0DlsANwwtfXJdgASHNzo3ff/Sw7aLqtZRQEQbhbiYBDEARBuK+tW56C3mx12Xbexx8Ao6rsjgBq3Ih9YSd7zlrL3C8Iwr3JIUkV+hFciYBDEARBuG/NemYvi78+h4zssj3Zy5d4fXVqX8whIjPLZZ+73YE9wJMUgwc/jIq9ncUVBEG4K4mAQxAEQbgv/bUuGcvuFPr+eYZqljQKDO4YPdyxqlVojVbybHoO+NRhwLk06uYXYLDZCTFbaJ9XgAbY2bQWW2sH8/2kI1VdFUEQhDuaGDQuCIIg3HeS4vP43/RU/uraijZJqdTMy3fus6mUyFYlF5r6Y/RxR6uQGJyQWvQ4LwO5blpkwNtuZ19kCM87HLAoiWefqI5aKbpSCMK9TBZv8RsiAg5BEAThvrL4/06wcXMOR8Kq0/PUOVQ2BxqTBatGhaxQkObjjTHYDb3ZjJvV5vJY34JCct206EwW6pgs5Om0uBXmsWZPOm/vUZLi48kjIQ7Wj/KootoJgiDceUTAIQjCHS85OZl+/foxatQoRo8eXe62ytC3b1+Cg4P59ttvndtefPFFUlJSWL16daUdR7j9Mi+aiav/LQ/mpKOIrIsBmYjUFDS5DhSyjEOCjABvjNXcih4gl85DZbPTe/Mh6py9gCzBibBA/q4fTIGbhm6nE9kRGsSvDl+kN3OZ3FuN/+2toiAIwh1JjOEQBOGekZeXx6xZs9i/f39VF0W4w2QfvMC5mtOIDw7hu259OFy7Ae6SkqSg6jgURV+FChn8LmajutSqYVapkCmKO4wqJTJQ42waEefT2B4Vxqo29fDNyicorYB0Xx8KAgIpDPAGtRLcNbyy1s6iC42qqsqCINwCMlKFfgRXooVDEIS7UnBwMDt27ECpVDq35eXlMXv2bABatmxZaceaPn06slzG7W7hjic7ZHYMXIbflqP8Ubc1uV4GHCVeMxathotBPgQnpwNFQYe+oJAcbwM2lZKjQT5srhVEjk6DZ6GZ0en5fD2+N/E1itouvurXmn+u2uvMr1a2kZQwP0g3gkbF+ux6nJpxjnqGc7zZ15sHu4bd3hMgCIJwBxABhyAIt0RBQQF6vf6W5S9JElqt9pblX5Jarb4txxGuj6PQSnZCIefSLJhSCqnTzJOEJSc5tjaZJIuWQpUW37x8ovJSmNe2NzWTUrGW8VyadBrsSgmlXSbbXUucr4EcnRtaWWZrnWDM6qIAJVenZcpjrbGqL391WtRKfugURdsLeUCJXlhqJZhs4KHmREQEJ8wOfl1vRheTQlRmEp9uXUJU6jkUYf541fKDWkGgUIAEdGgAgb5QLxg8dEXbvMWYEEEQ7l4i4BBuqdWrV/P+++8zc+ZMjh07xrJly0hLSyM4OJiRI0fSp08f4Or98WfNmsXs2bNZtWoV1atXB2DSpEmsWbOG33//nSlTprB9+3asViutWrXizTffxN/fnxUrVrBo0SKSk5MJDg5mwoQJdOrU6ZbVdcuWLSxYsIDjx48jSRJ16tRh2LBhpY5ZPEbglVdeYcqUKfz999+o1Woefvhh/vGPf+Dr6+uS3mKxsHDhQtavX09iYiIajYZmzZoxevRo6tev70y3f/9+xowZw3vvvYcsyyxcuJCEhAT8/PwYPHgww4cPv6F6JSQk8P3337Nnzx4yMzPx9vamYcOGjBo1igYNGpSq09dff81ff/2Fl5cXq1atAuD8+fPMnj2bvXv3kpOTQ0BAAN26dePFF19Ep9O5HO/PP/9k2rRpxMfHo9fr6dq1KwMHDixVritfM8X1B5g9e7azpSM4OPimx16UNYajeNv333/Pl19+ya5du7BYLDRr1ozXX3+d8PBwlzwq+jw6HA4WL17MqlWrSE5ORpIk/Pz8aNq0KW+99Raqchaiu9fY7TK/L0jmz82ZaN0UVFNZsGw6h8Joxa5WkO+hJdfTE43FisJux2jQcyr1AlqTGbVDJirvIvl6PdtaNeKIOhKAMwoF3nn52DSuQUetxDSqZeaQ4unBD+2bU+CmASDPTe0MNoqVDDaKXfDWw4U8TBIc8dBBoRVkGXzcioIFpYTGYsUiK9Ag8fSRXSR4+NNYmYTX6RQ4nQIUTa0rA8zagMTl4KXk7zJX9IVWSOC4tFelhDB/8DPAqVSICoMvhkOrOtc833+lOXhlg40DqTLtQhRM6aEi0vcWdAv56xy8MhcOnIa2dWHKSKhTvfKPIwjCHeX++OYSqtz06dMxm80MGDAAjUbDsmXLmDRpEqGhoTRt2vSG8504cSKBgYGMGTOGhIQEoqOjef311+ncuTMrV66kf//+aDQaoqOjeeONN1ixYgUhISGVV7FLli5dymeffUbNmjV54YUXAFizZg2vvfYab731FgMGDHBJn5aWxtixY+nSpQtdu3bl2LFjrFq1iri4OBYsWICbW9GgVZvNxoQJEzh8+DC9evViyJAh5Ofns3LlSp5//nlmz55Nw4YNXfJevnw5mZmZ9OvXD4PBwLp16/jqq68ICgqiZ8+e11Wvo0ePMnbsWGw2G/3796d27drk5uZy4MABDh065Aw4AC5cuMDYsWPp1q0bXbp0wWg0AhAXF8eYMWMwGAwMGDCAwMBAjh8/zuLFizl06BDffvut8yL6yJEjjBs3Dnd3d4YNG4bBYOC3337jvffeu2ZZa9WqxSuvvMLkyZPp3LkznTt3BsDd3f266nw9CgsLGTVqFI0bN+all14iKSmJxYsX8+qrrxIdHe3s7nU9z+P333/PN998w8MPP8zAgQNRKBQkJyezbds2LBbLfRNwbPkphe3LLgBQYHfge+wcSocDAKXVQYa/PxqLBY3FQlq1ACISk8lTqckzFAULF708ORcSTM2sBB4/vJ6A/HTiguqwo1ob7CoVZp0byDJhKRdpeCoRpSzjYTY6gw0Ajc1eqlwKh8M55qOYwWzj9xBfcjWqoojAbAc3NdgvBQI2GYveDWxmdEYrL3UbBoC3qYANy76g5YWzzrxKXuKX9XupEMBRoqufzQ6nLxT9AGw7Cj0+gHOzwLP894HZJvPIIispl2YG/uWkg+OZVo6NVaOozBWTzVZ45ANIubSQ4toDEJ8Mx78uat0RhLuAWEX8xtwf31xClbNYLCxYsMDZNaVr167079+fJUuW3FTA0ahRI9544w2XbYsWLSItLY3o6Gg8PIq6IbRq1YqhQ4eycuVKxo8ff8PHK0tubi7Tpk0jNDSUefPmOY85aNAgnn76aaZMmUL37t0xGAzOxyQmJvLKK6/w1FNPObdFRETw5ZdfsnjxYp577jkAoqOjiY2N5auvvqJdu3bOtIMGDeKJJ55gypQpLrMpAaSmprJs2TJnOfr370+fPn2Ijo6+roBDlmUmTZqE1Wpl/vz51Klz+S7piBEjcFy6+CuWlJTE22+/zWOPPeay/YMPPsDf358FCxa4dLFq3bo1r7/+OuvWraNv374ATJ48GYfDwZw5c5wtBIMHD+b555+/Znn9/Pzo1KkTkydPJjIykl69elW4rjcqOzubZ5991qX1yMfHh2nTprF3717nc3Y9z+PmzZupVasWX375pcuxJkyYcMvrcz3MZjN2e+kL8spyaEum83d9vtEZbACYdFosbhq8cnIw6nXICgVuRhMmvetFtax2MH7DPDR2KwCtEg7jU5DD2pqP4JmYSsNzF9BarNhRYEPCzewg/GIW5wJ8ANDaHPjmmsj0dHPmOWTbUfRmC4s6NaZQq0ZptZOtUhUN/oCiiMCtjK9WmwODw0y6u6dzU7abnn8/PIjfl31xs6erfNkFmFfvxf5463KTbDwHKfmuLTknMmV2nimkebXKK4pi0xHcUlxXbedUKqY//sbRsvZ151dYWOjyv1A57sXzeitvPAkVIwIO4bYYPHiwSz/4wMBAwsLCSEhIuKl8hw4d6vJ3s2bNWLRoEb1793ZecAPUqVMHvV7P+fPnb+p4ZdmzZw+FhYU8+eSTLsf08PDgySef5H//+x979uyhW7duzn16vZ7Bgwe75DN48GC+/fZbNm/e7Aw41q1bR82aNWnQoAHZ2dku6du0acMvv/yCyWRytohAUfemkuVwc3OjcePGHD58+LrqFR8fz+nTpxk4cKBLsFFMccUdSS8vL2fgUOzkyZOcOHGC0aNHY7VaXerQtGlTdDodu3fvpm/fvmRmZnL48GG6du3q0h1JrVbz1FNP8fbbb19X+W8HhULBk08+6bKtVatWQFE3suLg4nqeRw8PD44dO8aff/55U8H4rXbkyC1eXVsFxffzbVe06kiXBvDbSwz+lsu469j69CFnsFEsMvMcUj0Jq1WDxmKlADcclzopKXAQkJXHuQAfJFmmzZlEGv+RyrSHm4Gs4NU1e2h87iIAQ7f8Ta5Ow9P/GkBhRSZ8dEBQdhZ5+gCXzXG+t7470dn8DPLj4srdn5PjBkSW2p6RdJK4LGvpB9wgXU4aDcvYfjIzFXOc5YbzPXv27A0/VijfvXReW7RoUdVFuO+JgEO4LcrqxuTl5UVqamql5lvcilA81qMkT09PcnJybup4ZUlKSgKKWiiuVLytOE2xkJCQUgORNRoNISEhLmnPnDmD2Wx2CVaulJ2dTbVql29Dlneur7fuxcFgvXr1KpQ+JCTEZcYoKCo/FI3DmTVrVpmPy8wsupNdXO+aNWuWSlPWub0TBAQElBq47uXlBeByvq/neXzppZd47bXXeOGFFwgICKBFixa0b9+erl273lGD16Oiom5pC4fqqTyWfZ6ELEOh3o18Dx0e+UV3XN1MFtyMheQZPPDOzCbf4EGhu65UHiHJWaW2WRUqbAoVWd5uZGv0qCyXuyQ5UPD05r/wMNlQKRzUvfTa7HvkNDNbN8Yz3+xMq7XZ2dIwjEI3dVEXqgpMYtb+TBwno1wDjs4Jxyp0Pm6UvV1dajzTE67SDaQBsCBZZsPZy2kG13PQvUXpIOSmNGiAvUcsyt8OOTfZBrUlosdDN5RdYWEhZ8+epWbNmqXGggk3TpzXqyvr5oZwbSLgEG6LK++GFyuealS6yhv4ahc1V17gXmv73Ti1aWRkJP/85z/L3e/j4+Pyd3l1v9VKtrIUKz7fzzzzjEtXopI8PT3L3H43KO91DaVfaxV9Hps0acLPP//Mrl272L9/P7Gxsaxfv545c+bw3XffOQOaqnarZwh7oIM7ftU8OLwlC41OQa16tYhfcJKCEzmovNQEZxZw0aQl30OPR3YeCYH+1LhwAY25aMB2tYwc9NkS+QoPPBz5znz3hzTDqiwK3C4YDIRk5Loc181m54ntRzgV4YtRXzSeo0FaBnMXrUUrWTkTZCDNw4Pf64Tya7NLgbBKAdYSXQztDlC6vjZUdhuheZkMjN9LTGQLbEol7ROP88XWxdiAQpUatd2OVnbtqli0DoiEVamkUKPBy2FFodcWDUrXu0GDEEjPA4sNqnnDQw0gyAv+Og+NaqAc2RV392s/V6uflJl/2MHBVAdtQxQ83ViBSnELLqxWvQULthQNGm9TB9UzHVGpbu4zS6fTiS4zt4A4r0JlEgGHcEcovujMzc0tte/K1oE7TWhoKACnT5+mdWvXftLFd/ivbHVISkrCarW63LG2WCwkJSW53OGvUaMGWVlZtGrV6qoXt7dCWFjRegHHjx+/6TwUCgVt2rS5atriVqmymvFPnz5doeNdLXCtStf7PLq7u9O1a1e6du0KXJ6UICYmhmHDht3q4t4xQuvqCa17edxP7datKvQ4h0PmwqYkDj69gQRHe5Q+ZrxNOewPrsN575rYFQqO+fnQMsdWKuAAGQUyWpsVI0UBR+MzyQQVZqPAgRkNp0z+zO/WnIfSc8kxmjhWzROrRgE2uSjYkOWinxKvx4dOx+FlMtIkPYEnkg9To2kQbVsZYNH3oFBgoGzFOSiB0iF95dGqJF5srrx0pFt5IDWM6n5rjyEIwh1HTAsh3BH0ej1+fn7s27fP5c5wYmIiW7ZsqbqCVUCbNm3Q6XRER0dTUFDg3F5QUEB0dDTu7u60bdvW5TEFBQUsXbrUZdvSpUspKChwmUa3d+/eZGRk8OOPP5Z57IyMjMqryBXq1q1LREQEq1at4tSpU6X2V6S1qF69etSuXZvly5eTmJhYar/NZnN2PfLz86Nx48Zs3bqVc+fOOdNYrVYWLVpUoTIXN/+XFbhWpet5Hq8c4wE4p8290+p1p1IoJIK7hdLrwgh6XXyRmoueZW29riTrq7G2Ti1+rR3OOW9PCn3t6Lnc+qEnH5OvhFJrx8toAsBgNBJeeAE9hegw400edXMu0PNMIoE2O3VyC3nobAZYHJdnjHIARiuS0UJkahI91In8PLUOr8WO5d1dIxkc+ypt5zwDr/YXszMJwl1GlqQK/QiuRAuHcMcYMmQIM2fOZOLEiXTs2JH09HSWL19O7dq1OXr0aFUXr1wGg4GJEyfy2Wef8dxzzznXFlmzZg0JCQm89dZbLoO4oahVZPbs2Zw6dYoGDRoQFxfHqlWrqFmzpssg5KFDh7Jnzx6mTp3Kvn37aNWqFXq9ntTUVPbt24dGoyl3bMTNkiSJ9957j3HjxjF8+HDntLh5eXkcOHCAdu3alRowXVYeH3zwAWPHjmXo0KH069ePiIgITCYTiYmJbNq0ifHjxzsHm//zn/9k9OjRPP/88wwePNg5LW5Fxwp4e3tTo0YNfvvtN0JDQ/H19UWn09GhQ4ebPh8343qex0GDBtG4cWMaNWpEQEAA6enprFy5ErVaTY8ePaq0Hnerei29eX9DO+Y/up7he3explFDkODF2I2EkoORokDVnULO2/yIadUVh0KJZHcQkXEBJa7dnNwpdJka09diw6fATFbxVLiOopYOWalgQOQxhjwchCT539Y6C4Ig3ElEwCHcMYYPH05+fj5r164lNjaWWrVq8c477xAXF3dHBxxQNMOUv78/P/zwg3PBubp16/LFF1+UudhgYGAgn376KVOmTOHXX39FrVbTs2dPXn75ZZdBeiqViilTprBs2TLWrl3rvCgNCAigUaNGzuDmVmnUqBHz589nzpw5/P777yxfvhxvb28aNWpU4RmU6tWrx48//sjcuXPZtm0by5cvR6/XExwcTN++fZ2zOkHR+IXp06fz9ddfM3/+fDw8PJwL/10ruCn24YcfMnnyZKZPn47JZCI4OLjKA47reR6feeYZduzYQXR0NPn5+fj6+hIVFcWIESOoW7duVVXhnjB8XU82T9xCozUb6Hx+Kwq7Hhkt7lye/jPD3YPO8X+yIzIKk1qLWnKUykcCzlZzHfztV2Am69L4KTebhWeiZCYPcuPn5RlA0K2sliAIwh1Pku/GUbSCcBcrXpX7yvUzBEG4PYzZZqIHrqV97G7Cci53U7NLEuP7DiMyPZ2nDuwBSUG8XyBRyRdRlVgHZFftSH7o0OlyhrLMTr0bWe4aepDPzx9VR6FRYrVaWbx4MQ0bNiQyMvKOGfB/rzAajcTFxdGgQQMxuLkSifN6dRMHlz/FdEnTlja4dqL7iGjhEARBEO4r7t5aRmx8nM/aaClUyQw9so/A3Dy+6NiTfTUi2FcjgsVNW6Fy2LEqVbQ9e5YRe/fiYyxgT81avNGjC9WsDnwtNvQ2G5FpqSweW5+Orb0An2seXxCEu5dDDM+4ISLgEO5LWVlZ1xwXYLPZUKmu/hYpXqjtbpKfn4/JZLpqGrVafU/dja3I8+3u7i7u5t1nXprTisnPHeLnug8xeP8BqqcXEpaZw3kfT5BltA4ZX1Mhdg9vfuh8eWalDhdzWRYeRFRGJi/+8QetNzxO7TCxXoEgCEJ5RMAh3JeGDRtGSkrKVdM0b96cAwcOXDVNnz59mDRpUiWW7Nb74osvWLNmzVXTNG/e/J7q8lWR53vUqFGMHj36NpVIuBN4RAUw/r+RLP7HAQo0GoJz8uhwKpHTNasjARaFgiN+Brb7eRCSb6JeVj4KwN9iJSIrn64nL5DUt5kINgRBEK5BBBzCfenDDz/EbDZfNY2np+c1pyENCAi46v6yrF69+rofU5mGDRvGo48+etU0d/NifGWpyPNd1grtwr3Pt0dNRsWGsLLeCgLzstD5WFDb7NQ6c4EvOzcmyadohYyT3h6kuWvpmFQ0hXH93AKMOJjwaf2qLL4gCLeZmPL2xoiAQ7gvVXSGpXtRREQEERERVV2M2+p+fr6Fa1O7q1E/Gs7RXZ4EpmdS61wGR6v5OoONYsd8PGiTmkUuEoMeVDBibOtychQEQRBKEisOCYIgCPe9x2a1IS4sCMlmxyfHiFFb+n6cLEmYbTbanjzLiLE1b38hBUEQ7lIi4BAEQRDue5IkoSuwsCeyFgDt4pPwNLp2w6t7IQuNp4bX93ariiIKgnAHcCBV6EdwJQIOQRAEQQAe7uLB6WAfTGoVHmYrX87bQNvjSQRmF1A3I4cHU5KY8r/wqi6mIAjCXUeM4RAEQRAEoO+7UdT4/QKfFzTh8f0nqHMhgxk/LUfllcVvdZoyetMgPHTiPp0gCML1EgGHIAiCIFzStFsQi7oFcSGzCY7UPJIPpaNpGsyrDe6u9XYEQRDuJCLgEARBEIQrBPmqwNeH4IZi5XBBEC4T0+LeGNE2LAiCIAiCIAjCLSMCDkEQBEEQBEEQbhnRpUoQBEEQBEEQKsAhelTdENHCIQiCIAiCIAjCLSNaOARBEAThKjYvTOSzzXb0Vht2CWwBapb+NwSdRlnVRRMEQbgriIBDEARBEMqxcuoZ5hxS0PRCBhqHAwBHGgwaY+KX7+tWcekEQbjdHGKWqhsiulQJgiAIQhn++juPuQegdkY2Slkmw92Ni3oddoWCqLR8rKl5VV1EQRCEu4IIOARBEAThCnaHzCf/TSUkNx+1w0FcoB/nfLxJ8Pbi7yB/LAolrT4poKDQUdVFFQRBuOOJLlWCIAiCAGw/Yea/i3KIUllwzzQRnJOP1uFAQqZ2eiYFKiXn/f1wKBQkexk4HujNI++n88engVVddEEQhDuaCDgEQRCE+5rZJlPrjSx8LFay1Gri7AqCCqETuahkGQCd3YFfgZEhO/fwbbfOFGi1dDxzkT+reVZx6QVBuJ3ESuM3RgQcgiAIwn3rxwP5jFhsx6pRk6LVOLfbFTJau2t3KaObG1q7g76xB1nXvClKWUZv1N7uIguCINx1xBgOQRAE4b71zFIZq0oJJe9a2h0M2HeizPQam5VaaRfJ02rwKygkIiOPuPiC21RaQRCEu5MIOARBEIT70raTprJ3KBUcjahWanPIxXT8c/NI9PXBqlKR5qGjSUIaH79xCmNmOXkJgnBPcUgV+xFciS5VgiAIwn3prXXWyy0bsuzy+1kPHe0uQuj5i6hUdqpnZVI3MYl0Dw9WN29KvdNJhCWl41FoorqHmu/anaLLm1FEPVen6iokCIJwhxIBhyAIgnDfmb/fxI5koPhOZMkuVZLESS8PFtaqTge9O5laDbVycthVJ4zaOad4cccy5rZqz8ujHkFrsTFk2990jDvDn/+JpeEztVGoROcBQRCEksSnonBXmzRpEi1btqzqYlRYWeWdNWsWLVu2JDk5uYpKJQj3n1FLr9EFSpIwumnRyjI2nRsXfDx4ac8PDIv9hYdS/uS7VV8zZudvmDUqfuj2AH+HBvLhwIdp+GoqE7tsY9XMsseACIJwd5ORKvQjuBIBh1ClFi1axOrVq6u6GNd0t5Tzdtm/fz+zZs0iL0+stHyjVq9ezaJFi6q6GPcds02m65vn0VnsV02ntNvpfOYCKZ4GFECXE3sJLMhySfP2xuXO388G+fCvX3bz8uo9xFfzJ/6bE0T/cx+XZtUVBEG4r4mAQ6hSP/30001dyL/99tvs2LGjEktUtpst570mNjaW2bNni4DjJqxevZqffvqpqotx3yi0yrw37RRDBu7B/6906l/IcO1GdYVRx85h9tLjUBZ9TRrMxlJp/Iz5l3/PM4IkoVbIPHLwNL4FJpJ25LD1pzr8+EoOc79PQxbRhyAI9ykxhuM+ZzKZUKlUqFSlXwo2mw273Y5We+fOM19e2QVBuE/kFEBaDgn+gXz9eyEnE02E+WuIjyvgkFFFBhBkLkCFkgw3T9S1vLAoFeSWWHPjSgaLFS+bjQSV0rltZ82mDDm0AaV8eW2O6AfaARCZnEGz8ynO7T6FJjY1CWdh+6agKApqJKuDf31iRuFwEJGaQXN1ITmykqwIf0ZHOniwrQFZUiJJ4KMFX51EaoGM1Q41PF0DI6vRRkFqIZ7hHiiUVwRN6blkrDyAtnlNPJrWgJOpoFaCWgV7T0LMHni0OdQKhKOJYLfBb3/C6bSishaYIM8EtQLAZINQP2gYCim5YLUWDa43WcBsBasdTl8APwM0qgH5Jhj0IHjqoJo3TF5VdJxWdSE7H86ngwxE1YCLOeCuhY5RcDoF1h6E3i2L8jqZAg/Wg7RciKwGeYWw5Qg83BB8Dc6qSgnpqDLyueNcyAaLDWr4V3VJBOGOcdddqa1evZr333+fmTNncuzYMZYtW0ZaWhrBwcGMHDmSPn36AJCcnEy/fv0YNWoUo0ePdslj1qxZzJ49m1WrVlG9enWgqG/9mjVr+P3335kyZQrbt2/HarXSqlUr3nzzTfz9/VmxYgWLFi0iOTmZ4OBgJkyYQKdOnW5ZXY8dO8bcuXM5ePAgeXl5+Pr68sADDzBu3DhCQ0NvuI4bNmxg2rRp7Nixg6ysLGJiYli9ejWzZ88mOjqamJgYfv/9d9LT05kxYwYtW7bEYrGwcOFC1q9fT2JiIhqNhmbNmjF69Gjq16/vPO7+/fsZM2YM7733HrIss3DhQhISEvDz82Pw4MEMHz7cmbZ4LENKSorLuIaSZb6W4jrt37+/1LYtW7bw1VdfsWnTJgoKCqhfvz6vvPIKUVFRzrQOh4PFixezatUqkpOTkSQJPz8/mjZtyltvvYVKpbpmOXfv3k1MTAxHjx4lPT0dtVpNo0aNGDlyJC1atKhQPa5U/PwtWbKElStX8ttvv5Gfn0+TJk144403qFmzJps2bWLOnDmcPXsWX19fRowYwYABA0rltWfPHhYsWMDff/+NxWIhLCyMQYMGMWjQIJd0ffv2JTg4mLfeeosvv/ySgwcPIkkSbdq04V//+hf+/v4u5xegX79+zseX9TosT0FBAfPnz2fPnj0kJiZiNBoJCgqia9eujBo1Cjc3N5f0sizz888/8/PPP3P69GkAqlevTufOnRkzZowzndVqZdGiRfz666+cO3cOlUpFWFgYffr04YknnnCmS05OZubMmezZs4e8vDwCAwPp0aMHzz//vMuxy3p9FWvZsiV9+vRh0qRJzjyL348NGzZk9uzZnDx5EoPBQK9evXjppZecwXHfvn1JSUlx5lPsm2++oWXLlpw6dYpvv/2Ww4cPk52djaenJzVr1uTZZ5+lffv2FTrH94WPliJ/vBzJZMHkE8TaxyeS6hlARpoCWfIE96Jk593dXWehuoaeCamoHQ50ZgsGixWl7CDFM5BPu4xg5K5VeFry2RLxAFPaPsZL6/bQ5HwayhKtF3aFxM8tGziDDQClQ+bZPw7T+mQSDklie4NwfmnXCNmiYMffMvwtg1TUzUulgEhviM8suj7vGiaxtJ8CHzeJY4vPsPfTv7Dm29AH6+j0ZSuqtfQHhwPj4/+HbtUe/Ch6nF2hQOlwXbwQgB+2XvskJGYW/b/vFKzce43EKbDreNGvi2+ixfnj5aW3eblDzqXWJQkY3wsmPQGD/g/d5iM0UUjYhzwIC/5RFFRVJYsVRnxddA4cDujaBJa+Bj4eVVsuoVI5xErjN+SuCziKTZ8+HbPZzIABA9BoNCxbtoxJkyYRGhpK06ZNbzjfiRMnEhgYyJgxY0hISCA6OprXX3+dzp07s3LlSvr3749GoyE6Opo33niDFStWEBISUnkVu2T79u3861//QqfT0b9/f2rUqEFGRga7du3i5MmThIaG3nDeL730En5+fjz//PMUFhbi7u7u3PfOO++g1Wp5+umnkSQJf39/bDYbEyZM4PDhw/Tq1YshQ4aQn5/PypUref7555k9ezYNGzZ0Ocby5cvJzMykX79+GAwG1q1bx1dffUVQUBA9e/YE4IMPPmDy5Ml4e3szcuRI52N9fHxuuG4ljR8/Hh8fH1544QVycnL48ccf+cc//sGqVavQ6/UAfP/993zzzTc8/PDDDBw4EIVCQXJyMtu2bcNisaBSqa5ZztWrV5OTk0OvXr0ICgoiLS2NmJgYxo0bxzfffEOzZs1uuA6TJk1Cp9MxYsQIsrOzWbhwIRMmTGDMmDFMmzaNQYMG4enpSUxMDP/973+JiIhwef2vWLGCTz75hMaNGzNy5Eh0Oh179uzh008/JSkpiX/84x8ux7t48SKjR4+mU6dOTJw4kRMnTrBixQoKCgqYPn06AAMGDKCgoIDNmzfzyiuv4O3tDUCdOhWfDvTixYvExMTQpUsXevbsiVKp5MCBAyxYsID4+Hi+/vprl/Tvvvsu69atIyoqipEjR2IwGDh79iwbN250BhxWq5Xx48cTGxtL27ZtefTRR9FoNJw8eZLNmzc7A46UlBSGDx9Ofn4+gwYNIiwsjNjYWObOncuhQ4eYMWPGTbWa7dixg2XLljFw4ED69evH1q1b+eGHHzAYDM7Xz6uvvsrXX39NdnY2r7zyivOxtWrVIjs7m7FjxwIwcOBAqlWrRnZ2NnFxcRw5ckQEHMV2HoN3fnIOzayTdYFn/t7Bv7s8WXb667hI2BPoS/3cAuqlZzr7HVfLK8CMP5v8H6XG+XQ8TkgMk//GoZFcgg0ASSHxxcINnAry4WhoIJuiatHj8CkePJ54KYVM1yNnSPX2YHNUrVJlszngWOblvzeel3l3h4P/1jOx892DFDeyFKQUsuWf+xiypSeKn7bjvmrP5TJA2cHG3SanRFc2GfhqLVzMhc1HAJAcMqrFO+DB+jChd9WUsdjMX2HR9st/bzwM7/wEX4+qujIJwh3irg04LBYLCxYsQK1WA9C1a1f69+/PkiVLbirgaNSoEW+88YbLtkWLFpGWlkZ0dDQeHkV3Klq1asXQoUNZuXIl48ePv+HjlcVkMvH+++/j4eHBjz/+SGBgoHPfqFGjcNzkl0jt2rX58MMPy9zn4eFR6oLrxx9/JDY2lq+++op27do5tw8aNIgnnniCKVOm8O2337rkk5qayrJly5znq3///vTp04fo6GhnwNGrVy9mzpyJr68vvXr1uqk6laV+/fr8+9//dv4dERHBv//9b9avX8/AgQMB2Lx5M7Vq1eLLL790eeyECROcv1+rnG+//TY6nc5l28CBAxkyZAhz5869qYDDz8+PyZMnI126IPH29uaLL77g888/Jzo6mmrVihYn69GjB71793Z5/aenp/PFF1/Qo0cPPv74Y2eegwcP5osvvuDHH39k4MCBLsFrQkICn3zyCd27d3duUygULF26lLNnz1KzZk2aNGlCZGQkmzdvplOnThVujSopJCSEX375xeV1NmTIEGbOnMmcOXM4cuSIsyVqw4YNrFu3jkcffZT3338fheLy0LOS74VFixYRGxvLiBEjeOmll1yOVzLd9OnTycrKYsqUKc6L98GDBzN16lR++OEH1qxZw2OPPXbddSp2+vRplixZ4jwvAwcO5IknniA6OtoZcHTq1IlFixZhNptLvaa2bt1KZmZmqefhTmM2m7Hbrz7w+lZSbTjIlZ2ijvnf+I2Yks57uLMzwJcHL16+6pcA1EriG4Zi1GupF5dEWGYOOZ5aLGolGR46PI0mLGoV3kYzKodMvZRM6qVk0uJ0Mg5F6YCnybkLRQFHBWw6Z+f8xWTkKz7+C1IKSYtPx//3P+/eL/TrJG84VGoOINvGw1ie71wl5Smm+f1QqefAsekvTMbS43/uZIWFhS7/3wtK3lgVqsZd+/k0ePBgZ7ABEBgYSFhYGAkJCTeV79ChQ13+btasGYsWLaJ3797Oi2coupur1+s5f/78TR2vLLt27SI7O5vx48e7BBvFSl5w3Yhnnnmm3H1PPfVUqbu769ato2bNmjRo0IDs7GyXfW3atOGXX37BZDK5dEXp27evy/lyc3OjcePGHD58+KbKfj2eeuopl7+Lu66UfI14eHhw7Ngx/vzzzxsOVEsGG0ajEYvFglKpJCoqiiNHjtxQnsWeeOIJZ7ABOMvYoUMHZ7ABRa0t4eHhLnX7/fffsVgs9O/fv9Tz9vDDD7N48WL27t3rEnAEBASUusht2bIlS5cuJSEhgZo1a95UfYqVfO/abDaMRiMOh4PWrVuXCjjWrVsHwMsvv1zqtV/y7/Xr1+Pp6ckLL7xQ6njF6RwOB9u2baNevXqlWgqee+45fvzxR7Zs2XJTAceVQZgkSbRs2ZIlS5ZgNBqv+cVX/L7ZuXMn7dq1c3kf3Ulu9rV9s7w8ZCKv2OZfkFM5mUsS7lcJppJC/fBPzSTbu+gzT213UC2ngMXtGvL7A5HUTU5n9IZYvArNAIRl5HI82LdUPhc9K34RFKLKJVN5sdR2hU4iIfssVh81NSqc290tP9wHQ4brhBUX/TUkx8VVUYmKVA/QEnzFtuwQA2equFw36uzZs1VdhEpzo92byyK6VN2YuzbgKKsbk5eXF6mpqZWar8FQNECtrLu4np6e5ORU0hdcCcVBTMmxEZUpPDy83H1hYWGltp05cwaz2Uy3bt3KfVx2drbLBXB5z8+tOF/lubIMxV1/SpbhpZde4rXXXuOFF14gICCAFi1a0L59e7p27epyUXw1iYmJTJ8+nd27d5eatUm6yQ+mK7vOeXp6AmW/Hg0Gg8vrv/jLYty4ceXmn5mZ6fJ3ec8bUOnP3dKlS1m+fDmnT58u1WpX8jwmJCTg7++Pn5/fVfM7f/489erVu+okB1lZWRiNRiIiIkrt8/Lywt/fn6SkpOusiatrncNrBRwtWrSgd+/erF69mnXr1tGwYUPatGlD9+7dyyx3VYmKiqrSFg7q1sO26RSqtQcBMCtVHA4IReVwYCvrpszVxnCUse+Yt4GmmWW/5mVJIt+99OdDk/Np/P5AJMer+7PkwUaM2njAue9geDVCMvLQW6wAZLtr+e2B2uVWT6eUKbQXlSnIXebzru7U9WmKPfYIp5YVdc2SFNDyXw2p3bQGRNamYNkh9ImXgxIZ7vrVAGQJkC/Xw/5gXVRfPY/c82Oki7kA2OoF4/XeM3j5GcrN57aYFIpj11kUx4rWVJKDvNB9PoIG9a6/FbgqFRYWOlu0r2y9F4QbddcGHOXd5S+edvBqF3pX+5JUKpXXtb0qpzm80TpeOSC3IvsiIyP55z//We7jrhx3Ud75up0q8pw1adKEn3/+mV27drF//35iY2NZv349c+bM4bvvvnNeKJbHaDQyatQoCgsLGTp0KJGRkej1eiRJYt68eezbt++m6lDe6/xar/+Sv7///vvOAd9XuvLi+GqtZ5X5Wl+4cCFTpkyhbdu2PPnkk/j7+6NWq7l48SKTJk266W6DlaW895jNZiv3MZVxDt9//32effZZdu7cycGDB1m4cCHff/89r7zyisvg96p0R8xe98s7sP0otjNpLPFvQPYfEGI0keqmwWCy4mGx4ma10OfIIb5v/SBWjYoup49gUUqsq/3A5SCjjOf5hJcH60MCeehCBh42u8uFe7WULLRWOxY316/QLP3li7OjIQHO3/PcNATkFJCnVXGsug92pYLYmsG4F5rxVCmoJVuo28CDh2orcVNJRHpLPBwCv50rmiiqV4SE+6UbIJ0+bU3UM3XIOpFHtdb+GEIuBbDu7nB2BtkLdpA7dxuFLeoQ+dLDKLfHQVYB+LjDV+vgeDLUrgYRgZCcDblGSMoompmqLG6qopmj0vPBbgeNsmh2KpujKKIpplKAUlk0u1WDGqBRwNo/QauGdnUhJQvOXwSVCupXByRwU8MzHYvGOuw4Bu3rQ7Av5BYW/V5oQeoUVVTmFbuhV3OUvVuiAzjzDeaVuzifcYGQEb1x96ziYAMgzB0OT4Ff/wSTBalXC3Tud8D75AbpdDrRFUmoNHdtwHEtxXeCc3NzS+272TuYt1pxC0R8fDxt27YtN93tqmONGjXIysqiVatWN92d60o32wJQGdzd3enatStdu3YFiu68f/bZZ8TExDBs2DCg/HLu3buXixcv8u6777rM2AQwc+bMW1vwa6hRo6iDhbe3N23atKnUvG/2eVu7di3Vq1dn2rRpLq+pnTt3lkobFhbG1q1bycjIuGorR3h4OGfPnsVisaDRlD3lqY+PD3q93jnTVUm5ubmkp6dTt25d57bi91hOTo5L8FkZ769rncPIyEgiIyMZNmwYeXl5DB8+nK+//pohQ4bcEe+bO8bDDVE93JBngWfLHQpWk+EpVtpOLyQmsgka2Q7Il5oAyj+X+wJ92RfoS0h+If0TL+Bms5Gr1eIwWNBn5OHg8mJWBRo165te7uRVPTMXo1qFVaVAwkKIoZBnl7UlrHbFJ8XoU04DiH+UD/5RZeSjVOI9ogPeIzpc3hZZ4u76iPJbqavU0Ievvr+Gf9GMTyXp3bAPaENuXBwhqqq/weWkVkGfltdOJ9y1HOLj94bcswv/6fV6/Pz82Ldvn8tdxcTERLZs2VJ1BauAtm3b4u3tzY8//kh6enqp/cX1uV117N27NxkZGfz4449l7s/IyLjhvHU6XZkB0+1y5dgGuNyVrWS5yitncSvKlXeud+/eXeV93Lt3745Go2HWrFmYTKXvXubn52OxWG4o7+K7Xjf63CmVSiRJcjlvNpuNefPmlUr76KOPAjBt2rRSLR8lH9+zZ09yc3OZM2dOqTyK0ykUCh5++GHi4+NLBTfz5s3D4XC4THVd3MVw717XaUEXLlxYgVpenbu7O7m5uaVeOzk5OaXqaTAYCAkJwWQyYTabb/rY96OoYDX5H3mS/4EBh6TEJ7+cO/pXcLPZaJeexTlfb+ID/UnxMvBngzCWPNqGxZ1asqZ5PY4H+DK3Y1NSLq0R4W6y8NBf51jWpSHer+fQYV44/5nZ+LqCDUEQhHvJPdvCAZdnvZk4cSIdO3YkPT2d5cuXU7t2bY4ePVrVxSuXm5sb77zzDm+88QZPPPGEc1rcrKwsdu/ezVNPPeW8KLoddRw6dCh79uxh6tSp7Nu3j1atWqHX60lNTWXfvn3Oi9ob0bhxY2JiYpg5cya1atVCkiQ6dOhw2/qNDho0iMaNG9OoUSMCAgJIT09n5cqVqNVqevTocc1yNm3aFD8/P6ZMmUJKSgqBgYEcP36ctWvXEhkZycmTJ29LPcoSFBTEv//9bz766CMGDx5Mr169CA4OJisri5MnT7JlyxaWLl16Q7NMFQ/onjZtmnP62dq1axMZeeUw3rJ17dqVr7/+mokTJ9K5c2cKCgr49ddfy5yOtlu3bnTv3p1ffvmFhIQEOnTogMFg4Pz58+zatYslS5YARa/T7du3M2fOHI4ePUqbNm3QarWcPn2ac+fOMWPGDKBo3M6ePXt47bXXGDRoEDVq1ODAgQNs2LCB5s2bO9fyAXjkkUeYMWMGH3/8MWfPnsXT09M5qcPNioqKYvv27Xz++ec0adIEhUJBq1atWL9+PYsWLaJz586EhoaiUqk4cOAAu3btonv37lftEilcm16rIOt9A4YPFK5dgsqhkKFBVi6nJAU5Otdzr0OB1dOLuAYGnt38NwO3x5Gv06AvNBPXNoTfp4SzePGuW1QTQRCEu8c9HXAUz7W/du1aYmNjqVWrFu+88w5xcXF3dMAB0LFjR7777jvmzp1LTEwMRqMRX19fmjVr5nJRdzvqqFKpmDJlCsuWLWPt2rXO4CIgIIBGjRq5XKBdr3HjxpGTk8PSpUvJy8tDlmVWrVp12wKOZ555hh07dhAdHU1+fj6+vr5ERUUxYsQIl6415ZWzevXqfP3110ybNo3o6Gjsdjv169dn6tSpxMTEVGnAAUUL84WFhbFw4UJWrFhBXl4e3t7ehIeHM3bs2GsOxC5P06ZNmTBhAitWrOCjjz7CbrczatSoCgcczz77LLIsExMTw//+9z/8/Pzo3r07/fr1Y/DgwaXSf/zxxzRr1oyYmBhmz56NUqmkevXqLhMZqNVqvv76axYuXMivv/7KjBkz0Gg0hIWF0bdvX2e64OBg5s2bxzfffMO6devIy8sjKCiIESNG8Pzzz7sEPR4eHkydOpXJkyczd+5cdDodXbp04cMPP6Rz55ubgvPpp58mKSmJjRs3snz5chwOB9988w0tWrQgPj6e7du3k56e7qzryy+/zJAhQ27qmEIRDzcFD4ZJ7DzPNRcEDDQVtSgVlrWonMMBCgUXPNx4a3hnPI0FvNTAwfAenoT5a7BarbeoBoIgCHcXSa7KUc+CIAiCUAXsDhm3t/OwyVK5AYe71cZTpxIILjRzJNAfyxVBh+Rw4FlQQHTz2tTMyKRXcx3Tnrw81sdqtbJ48WIaNmxIZGTkNSehEK6P0WgkLi6OBg0aiMHNlUic16t7cvjZCqVbPL/mLS3H3eaebuEQBEEQhLIoFRLrR+roNqf0WI6J2/cQlptPVrVg56QGOpu1VMAhSxL+Jgvvr91Alxdq0O7JJqXyEgRBEETAUWmysrKuOSe9zWYrs496SW5ubnfsQl+3S35+fpmDnEtSq9XibuEdxmq1VmitDh8fnzti2mRBeCBYhdpux6pSgSzjZ7bwUGoGw/88gl9+AXknTnEqOBi7JHGofdkzBhaolbQJzaHds71vc+kFQRDuHiLgqCTDhg0jJSXlqmmaN2/OgQMHrpqmT58+TJo0qRJLdvf54osvWLNmzVXTNG/enG+//fY2lUioiEOHDjFmzJhrpise+yIIVc1fL9GjoYpfjhWNxchw07KqZnX+emoA/12/iTYJyfhnZ7OsbSvsZQTJ7hYrWToN3X54qgpKLwhCVZDFtOQ3RAQcleTDDz+85nSVnp6e15xGNCAg4Kr77wfDhg1zToVanuL1EYQ7R926dZk+ffo1093oQHVBuBXWPOfBooOFjP8+iywPA0gSZ/x8GPrUAHSSgydOpeBhsRKVepFELwPZOjeQJPzyckjwVONrvGdnlxcEQag0IuCoJE2bNq3qItwzIiIiiIiIqOpiCNfJ09Oz0hcYFITb4almOvp/oeWJhUYO/FWIRamgVUo6Go2dXJ07NXLzAYjIysGak4csgb4wD5UcSEGgGFQrCIJwLSLgEARBEO57eq2CNc97AEVj6H6ab2Hr6kwu6O1FC5JfSqe+tChjpt6LLDcNC/8vuErKKwhC1RArjd8Y0RYsCIIgCFcYOjwYX28FNXIKyNVqSu1vd3YvP72hxV0nJkAQBEG4FhFwCIIgCEIZ/vtdAwJ8JHzz8vDLuwiyjOSw0+rcPlon/om2UWhVF1EQBOGuIAIOQRAEQSjHO3Mbowj3JE+rpd+RNbywex6+pgsE//1JVRdNEAThriHGcAiCIAjCVbzzfeNLv3UBoEbVFUUQhCrmENPi3hDRwiEIgiAIgiAIwi0jAg5BEARBEARBEG4Z0aVKEARBEARBECrAgehSdSNEC4cgCIIgCIIgCLeMCDgEQRAEQRAEQbhlRJcqQRAEQShhS7yZV6ZmYpEg0GgixGiiXVtPxr5cHUnMUCMI9zW7+Ai4ISLgEARBEAQgy+ig/+tJGExQXZJAlpGVKtI8DSw/JnPsscO8Obs+wYHaqi6qIAjCXUV0qRIEQRDuexuOWnh78AF8C+yXWzEkCQkJtQw6m8zOsAD+PfF01RZUEAThLiQCDkEQBOG+N/Xj0yx5oA4ZblrOuWnIVimxAelqFUqLBS+TiXoZ+chWO6fPF1Z1cQVBEO4qokuVIAiCcF/LMDrYXLsGRkniD1/D5R2yTIDZSvX8AgwWG1alkiwPD5746CL7vg2rugILglBlxErjN0YEHIIgCMJ9y2SVqft+Dka9G5jsLvvUsszjiRfQ2R0AuDkcqB0OTLKmKooqCIJw1xJdqgRBEIT7kizLNHwvnUydG9IVwQZAWIHJGWwUc7PbUQIjR/2J3SHfppIKgiDc3UTAIQiCINyXBi2x0OF0BkiAXBQ8GGQHnvai4MOsLP0VKQN2GfJTMvhiYdZtLK0gCHcCh1SxH8GV6FIlCIIg3Hdkh0z+ukR87DYiM3LJ0KjxcMh0SssmyV3LJn9vEnVaUtw0BJsszsel6rTULizkSFAd5uzK441hvlc/kA2Mn59h79ZDZKHFy5qHyl1NvZ4hBLzaCm19v1tcU0EQhKonAg5BEAThvmK3OZg48BBdcvLJV6vwDfLhpI8XLc+mk+aupXqhmRCzhSQ3DStrBBGVk4+fxUqSh45j3h60vJjNA+m5/F2rGoM+TmPZfwLLPI4pKZ+Hx8YRYk5DAjJVBo571ECVb6Fw4XHOzt+FOiKIiGPjbu8JEARBuM1EwCEIgiDcV178xykCjSYuqJR4G000OpfKn37ebKgZgM5mp2VqFl1SMsjWqPktxI+DQT4ujz/pqScyMw+N0Up8np1jsVnUb3E5TfreNA70XIOPOQMfSUmOSk+sb0MytQbcrGb0FhsOtQG1ZEI6n0eK8ms8G+hpfGTE7T4VgiAIt4UYwyEI94j9+/fTsmVLVq9eXdVFEYQ7lizLnDJKHAjx53h1PxxKBXqrDYtWjVKWGRyfSPO0bHzNFiLyCuiZmF4qD3ebHff8ApLUKuK0brz0vzRe77eNOQuSiOm5hosPzqJD9h6aGeMJsGSwNvQhEg0BGDVuZOq9yNNpCMzLwlLoQWGhHw6Hlot/29kfNLMKzoggCNfDgVShH8GVCDgE4Qbt37+fWbNmkZeXd0fkI9xdtmzZwqxZs6q6GPeNpX+aaTo+Cf83Mvmjfg1+i6rFL40j+LhHG7aFBYMsUyunAB+z1eVx4flG3Kw2598Kh0yHkwmsDakGkoRdIbEpOAjVeQtBL/9K0w2biLSfQyUXDTzP1XhgVbp2Jsh2M2DR2Ckagg4goQasaTLr/L4ldfKOW3gmBEEQbj8RcAjCDYqNjWX27Nk3HShUVj7C3WXLli3Mnj27qotxT7PYHbzwk5GoN9LY/8991M/MxUcBdoUCrA7INmPLMnHY4AlZhZgdpfOQgJZpOdTIK6RhVj5Dj5wi3sebDHc3l3Qb64XSNm8XNRxpLvc2lXIZmQLV5RQa8DdmhZIClRqjSk2h5EZgdi5/fXiYWMOHnHpkNtYssaq5IAh3PzGGQxAEQbjjOWSZTedlUvKhXmYOf27PYqvGQLDDgqdGQkopZKdDxzYvb4xqFW5mK2EFJkLzClHptEzu0Yp25y+QatCBTQaLHUyXWy6QIUFSUqhUuKy9ka7VkKdW0zElk2Q3DcvDQzApFCDLaBwOLIqi+3ZWhRI1ylIdKQLM2fiZsslw83Zuq268gLvVgQM7LR370DpyUGIjRQrliK4x+VotJoU7us1nKfR9iQKFkuPeYewOqUO97FzCAtwwGArxquOO4e1HILzsQevFMrPtHPzbhL+PksDUTHL2p6N3V+H/SAi6Ol7lPi4vsYCUPel41fIgqLmYTUsQAOxipfEbIgKOO8zq1at5//33mTlzJseOHWPZsmWkpaURHBzMyJEj6dOnDwDJycn069ePUaNGMXr0aJc8Zs2axezZs1m1ahXVq1cHYNKkSaxZs4bff/+dKVOmsH37dqxWK61ateLNN9/E39+fFStWsGjRIpKTkwkODmbChAl06tTpltSzb9++BAcH89Zbb/Hll19y8OBBJEmiTZs2/Otf/8Lf39+Ztrjs+/fvL5VPy5Yt6dOnD5MmTSp1XiIiIpg7dy7nzp0jICCAkSNH0q9fP1JTU5k8eTL79+/HZrPRsWNH/v3vf6PX6ytc/uIyAfTr18+5veTzkZyczMyZM9mzZw95eXkEBgbSo0cPnn/+edzc3CqUz8WLF1m4cCH79u0jJSUFs9lMSEgIvXv35tlnn0WpVFa4zNeyceNGoqOjOX78OFarlaCgINq1a8fLL7+MWq0Givq///zzz/z888+cPn0agOrVq9O5c2fGjBlT4WM5HA7mzp3L7t27OX/+PDk5Ofj5+dG+fXvGjh2Lt7f3LS1fdnY2s2bNYtu2bWRkZODn50eHDh0YPXq0y7HLei8VK34Nf/vtt85txa/HAQMG8PXXX3P06FG0Wi2dOnXi1Vdfxd3dHYAXX3yRAwcOOB9T7L333qNv376kpqYya9Ys9u3bR0ZGBh4eHtSoUYMBAwY4PwPuJ2abzCPL7GxNLPpbadPTLLGAx+OOobzUK+mMl4H17cPg0sVA29RsHriQDUCBWslpHz3ba196DmUZjGW0PMiwrH4oDyZn4GOykqFRk+Tmhl0hccrLnWMabVGwASBJWEpceLRNPM+poGrorBbqpiUjo8KMGzn40DglkfPeRswaJQGmLIJyCsjDGw+y8ebyGJFw+QwWq4ptHm3IR8tFryhkKYqgwot0SttJk8xEZDTYEhVYZRuGrSk45sQgrf0PUs9mZZ67PX8W8sk3GdguxVbV07Losj8OpV3G59U9NPiiNSETGpV63PHl5/jjzViKG2hq96tBp8mtKvBsCYIglCYCjjvU9OnTMZvNDBgwAI1Gw7Jly5g0aRKhoaE0bdr0hvOdOHEigYGBjBkzhoSEBKKjo3n99dfp3LkzK1eupH///mg0GqKjo3njjTdYsWIFISEhlVexEi5evMjo0aPp1KkTEydO5MSJE6xYsYKCggKmT59+U3n/8ccfrFixgkGDBuHp6UlMTAwffPABarWa6dOn06pVK8aNG8fRo0dZtWoVGo2Gd955p8L5DxgwgIKCAjZv3swrr7zivEitU6cOACkpKQwfPpz8/HwGDRpEWFgYsbGxzJ07l0OHDjFjxgxUKtU18zlx4gSbN2+mU6dOhIaGYrPZ2LVrF19//TVJSUn85z//uanzVGz69OnMnTuXiIgInnrqKfz9/UlMTGTTpk2MGTPGeUH/7rvvsm7dOqKiohg5ciQGg4GzZ8+ycePG6wo4rFYrP/zwA126dKFjx464ublx9OhRYmJi+PPPP1m4cKHzmJVdvvz8fEaOHElCQgL9+vWjfv36xMfHs2zZMvbt28f8+fOvK/i80vHjx/nnP/9J3759eeSRR4iNjSUmJgaFQuF8vkaOHIksyxw8eJAPPvjA+dgmTZpgs9l46aWXuHjxovO1k5+fz8mTJzl48OAdE3CYzWbs9tKrc98KC48p2Jp4+etKdjjoeSLBGWwA1MrJo3ZGLqf8vdBZbTS+FGwA7K/uS56b5nJiSQKtEoyu4zVQSChwsCfQF0+TFZ3lcv08TRaydLoyy9cgPZuMsHDerVcPgIapZ/j3r4u5aKtJoaTlgsITR54CN7sFyeFFGt4ARHAY7RV5hVoTgDZF9VQUBTQX3APZVK09vZK2YUWNSnaApEKWJRSyA8u477Ed+azMsn37U7Yz2ABIDvQhIciP8NQM8gxazr65D8/BoSg9L7/f7BYHe/57mJK9wU6tSqDWgOoENHedsasqFRYWuvwvVI578bwW3+wRqo4IOO5QFouFBQsWOC+kunbtSv/+/VmyZMlNBRyNGjXijTfecNm2aNEi0tLSiI6OxsPDA4BWrVoxdOhQVq5cyfjx42/4eFeTkJDAJ598Qvfu3Z3bFAoFS5cu5ezZs9SsWfOG8z5z5gxLly4lODgYgB49etC7d2/effdd/vGPf/DMM8840+bl5fHLL7+43IG+liZNmhAZGekMBq68+z19+nSysrKYMmUK7du3B2Dw4MFMnTqVH374gTVr1vDYY49dM5/mzZsTExODVOJO6lNPPcU777xDTEwMo0ePdmkNuhFHjhxh7ty5tGzZkqlTp6LVXr4EmjBhgvP3DRs2sG7dOh599FHef/99FIrLQ8AcjrL7qZdHo9Gwfv16Z0tPsSZNmvDRRx+xZcsW5+uisss3f/58zp8/zxtvvMHgwYOd2+vWrcvnn3/OggULGDt27HXVp6QTJ04wd+5coqKiABg4cCAFBQWsWrWKf/7zn7i7u9O2bVvWr1/PwYMH6dWrV6nHnzt3jgkTJjB8+PAbLsetduTIkdt2rJ2ngoHL3YbcLTY8SgzkLhaQX8gpfy80dofLAMUs9ysv6wGNEtxUl7tVSYCXFpvsoOmFTCQHZGjUOCSJWlk51M7NZ6/Bgyy169fmhNhdZAaHk633dG47Wq0WMQ060vavRC4qPXBIRV2wPByWSwcqUoj+UuhxmUnpVtQCc0W3jYtu/hhVWjQ2BzJKVLIDGRUSVqSkdOLi4kpV0WqDCxlBpbbneBQFTjalAkeBjWNb/0IReTmYsly0Ycmxlnpc/M4TpOsMpc9lFTt79mxVF+GedC+d1xYtWlRaXmIV8RsjAo471ODBg13u8AYGBhIWFkZCQsJN5Tt06FCXv5s1a8aiRYvo3bu3M9iAojvser2e8+fP39TxriYgIMAl2ICi7iVLly4lISHhpgKOTp06OYMNAB8fH8LDwzl9+jRDhgxxSdu0aVM2b95McnIykZGRN3zMYg6Hg23btlGvXj1nsFHsueee48cff2TLli089thj18yr5AW51WrFaDQiyzLt2rVj3bp1HD16lA4dOtxUedevXw/A+PHjXS7mAZdAZ926dQC8/PLLLhfzQKm/r0WSJGfd7HY7RqMRu91Oq1ZFXTaOHDnifG1Udvm2bNmCj48Pjz/+uEuaAQMGMHv2bDZv3nxTAUfjxo2dwUaxVq1asWPHjgq9xorfh7GxsfTt2xdf32usZF1FoqKiblsLx+N6ifklPorydVou6N0IKjC5pDvtV3TRn+OmcdkfmmPknLdrq1Xj1Cx8801sDQ8susDXqAjPNdLhbLozWAk2mbmgkmiZcoGEAH8ezM1no4+ns1vVkGN7+XLbd7w4+N1SZT7vF0hrkrBJRV+zOtmEEtklTTqh+ErJaGUzUDTdZpxHQ1Q2Bza1a3dJhWxH6ZCQL5XOJilwk4uCAtvDUTRo0KDMc1e3Zi7Hz7oGZ9UycgDQWOyoq+lo+GhTJNXl94hcX+Z8WBb5542XHyTBA30bYQi/8da/ylZYWOi8OaUrp/VJuH7ivAq3ggg47lBldWPy8vIiNTW1UvM1GIruVl15Zx3A09OTnJycmzre9ZQFiuoI3PRxy8rbYDDg7++PRqNx2e7p6VkpxyyWlZWF0WgkIiKi1D4vLy/8/f1JSkqqUF42m4158+axdu1aEhISkGXXC5bc3NybLu/58+eRJMnZjas8CQkJ+Pv74+dXOYNHN2zYwMKFC4mPj8dmc70gKlmvyi5fcnIyDRo0QKVy/fhTqVSEhYVx7Nix66yJq5t9XReP15o3bx49e/akbt26tGrVim7dutGoUem+9lXlyuDvVupfH/6TbueLfTJmOwQZC9kXGkTb86n4F5qxKBSc8zJgt8pgd4BCYk296nQ8m0ZIbiFBuUa0FhtmtRIkibCsfNL1Wv6q7uvSmtA0JcelZUQtQ66bG7Oa1Kd7eg7+NjuDLmZxQaNGZ3cwdfsylLJM/QtnuGBwfd01vHAOBTIq2Y5NUtLQcZR0QjFxuRXVipZN3l0JtSagdlhI0NUgS+WLm81CvsrNpZWjbu4ZbOjQACalCrW96D2SV6MWhuiJUE7r7D+fV/PJzAzOJ9tQSzJR8ecJzMpDbbET4KGgwY+d0Ht6lHpclymt2fyPveQlGFF7qGj1ryiCGgTc4DN4a+l0OtFl5hYQ51WoTCLguEOVd8e4+IJTusosCVe761jeIOPytl95gVuZrnZXvORxy6vrlRepFcm7ose8U3z55ZdER0fTvXt3Ro4ciY+PDyqVimPHjvHVV19VWpklSbrqa6qybdq0iTfffJNGjRrx2muvERQUhEajweFwMGHChFL1ut3lK3nc8pT3PrvaQP6KPl/jxo2jX79+/PHHH/z555/ExMTwww8/MGzYMCZOnFihPO41H7VX8normSwThLrpSTxiJ8MvCC+bDZNChcZk4/FMB9NP29iZKONeYOaor4G/fQ0kGXRYUYBVJjIjm3Rvd7J1lwImSXIGHe5ldNPysDsIsFzergSqW4paFuxS0efJiH2rOOcTzFm/omCz7ZmjdIuPJZEIfOz5pCsN+MvpeGDkDPUwo0OBHaXGSJ62JnHahs78g/OzcBgh36MAm1aJUrJTPUzGw9eLvwe2oK6/jKpzHfJy7KibemOIuHoLWI1gNTM+qEbqRRueBgUqcxDmi1GoALdaBiRl2Z+JAU18GbzxEfISCnAPdEOlE5cLgiDcOPEJcpcqvitf1h3uit49v1uUbIEovlMMVV/P8i5GfXx80Ov1zlmSSsrNzSU9PZ26deteMx+AtWvX0rx5cz755BOX7Tfbta6k8PBwdu7cyfHjx0t1BSopLCyMrVu3Omd1uhlr165Fq9Uya9Ysl25jZfUZruzyhYSEcO7cOWw2m0srh81m4/z58y4tFCXfZyVbAc1mM+np6YSGhlaovmW5VgAVGhrKk08+yZNPPonZbGbChAksWLCAZ5555o7tZnWreWklvLQASmq28KEmgHPYtZa6QG9nL8airj8Oh4Mt8RaemV9AmoeODHft5WCjmCSB1UGCl47aWUaXXYUKBXZJwqSQcHNcDhirZacQkpsGgL8xm2k/f87IIe/R79BOOsbHYceGFTUKWcLfVoAFLe4U0JADWHBDhYUDbo2LMpNlwnIz8LQU4GnPwahww1DTjYBfRqMKvTw2pG6Jcnlf57mrFnDpte6mQeOluXri4tOikPAML936IQj3M7tYRfyGiIX/7lJ6vR4/Pz/27dvnctc0MTGRLVu2VF3BboGwsDAA9u7d67J94cKFVVEcp+Km5iuDPoVCwcMPP0x8fDw7d+502Tdv3jwcDofLdMPl5VOc15V3xQsLC1m0aFFlVAGARx55BIAZM2ZgtZYeKFp8/EcffRSAadOmlRokfr0tLcUtTSXzkWWZOXPm3PLydezYkaysLH7++WeXND///DNZWVl07tzZuS08PByAPXv2uKRdtGjRdQ+Uv1Jx3+gru1nl5+eXar3TarXOMU2V0Y3ufqJQKOjSwI3kT334XzMLLRMvIpXzet0T7MMZb3fsEpgVCs7ptOSrVCBJHPdwJ0WrIV2t4qi7G19F1GVXtaJukwVqLSsbdKLRiVOochyoVJkYyKcBu6jGOcI4jYQNmaIh41pMZKs9Oa8rClj9zZnk62XOeXuSF1adsIRXCD70ukuwIQiCcDcTLRx3sSFDhjBz5kwmTpxIx44dSU9PZ/ny5dSuXZujR49WdfEqzSOPPMKMGTP4+OOPOXv2LJ6enuzatYvs7OwqLVfx3fZp06bx6KOPotFoqF27NpGRkbz00kvs2bOH1157jUGDBlGjRg0OHDjAhg0baN68ucvUplfLp2vXrqxYsYI333yT1q1bk5GRwerVq11aeiqjHsOHD2f+/Pk8/fTT9OjRAz8/P5KTk9m4cSPz58/HYDDQrVs3unfvzi+//EJCQgIdOnTAYDBw/vx5du3axZIlSyp8zK5duzqntO3duzc2m42tW7diMplKpa3s8g0fPpyNGzfy+eefEx8fT7169YiPjycmJobw8HCGDRvmPHbr1q0JDw9n1qxZ5OTkUL16dQ4dOsRff/1V5loh16Nx48YsWbKETz/9lPbt26NSqYiKiuLEiRN8/PHHdOnShfDwcNzd3YmLiyMmJoaoqKibmkzhfveP3p6Mf9RA4Ad5ZHqU6JsuyzS6mEOrlAwOV/NhZb1g/HLMeBa6BrgpbhqOuGmQL93hfHDof4jMTqNxRg6vfdqEfrU15CXks669JwYyaZ92DD9HGnY0mPFCTSoSMjYUHFXXRW1yoLdYSfL1Q6F00LmDleo/DEMQBOFeIwKOu1jxOg9r164lNjaWWrVq8c477xAXF3dPBRweHh5MnTqVyZMnM3fuXHQ6HV26dOHDDz90uRt9uzVt2pQJEyawYsUKPvroI+x2O6NGjSIyMpLg4GDmzZvHN998w7p168jLyyMoKIgRI0bw/PPPu3TluVo+r7zyCnq9ng0bNrB161aCgoJ4/PHHadiwIePGjau0ukyYMIE6deqwZMkSFixYgMPhICgoiIceesily9PHH39Ms2bNiImJYfbs2SiVSqpXr063bt2u63iPPPIIRqORRYsWMXXqVAwGAx06dGD8+PF07dr1lpbPw8ODOXPmOBf+W7VqFX5+fgwcOJDRo0e7rMGhVCqZPHkyX3zxBdHR0ajVatq2bcu3337L888/f111LuscxMfH89tvv7Fx40YcDgfvvfcezZs3p3PnzsTGxrJ+/XrsdjvVqlVjxIgRLtM5CzdGqZB4sbmSXWsS2Vq3qIWhWraRbueKukc9fD6dh8+nc9DPkwyNFkmWCbTa0F3qThVosbFH70a+smgAeoHKnQU/ReLhXjR2x7e2J4/9PZidz2xhfawPkdlJ1LScw4QKHQrMkpJkORyMerwxYfRQEG7KoMVnTdA/377sQguCcMewix5VN0SS78SRsoIgCIJwC/X6ZwK/BwViVSl56GwaLVOyXPaf9HRna7A/kTlGQsyuLR0XVUr26nX4FRay9FU/Otcue0yEKd9EbO2pWLMMaGwWlLIWFDIXPd1xSBI6i4mm3zxIwDONb1k972VGo5G4uDgaNGggZlOqROK8Xt3DY1IqlG77N8HXTnQfES0cgiAIwn1n7Zc1eOilJPaG+ZOvKf1VqLY7kGWZkLwCuGIqbS+7nQfTs7C7q8oNNgCUWiWnv6hOvbB6+J11I/fbOPIuFtLqxboEjWkOHm7lPlYQBOFeIgIOoUKysrKuucjXlbP+lMXNzc1lgcE7jclkIj8//5rpbnZ179shPT39mmk8PDxKrfZ9o+x2O1lZWddM5+Xl5bKopSBUlYWv+vDly0c5WN2PPLUSg7XoM84BxBn0TNh1CFmt4Ui469oqHhYrjdMyGDv3gQodR+mhxP+xcGoPb1LZVRAE4TZzVMEU7fcCEXAIFTJs2DBSUq7ejNi8eXMOHDhw1TR9+vRh0qRJlViyyrVhwwbef//9a6bbv3//bSjNzenZs+c107z33nv07du3Uo534cIF+vXrd81033zzDS1btqyUYwrCzagV4U5WQz8670tgWvOGhOWbcLfbOe6pJ8hopHqeEQdG0j09SPUpmqhBabdzUaXgP5MbEllNBM6CIAgVIQIOoUI+/PBDzGbzVdN4enpec8rOgIA7c6XaYu3atWP69OlVXYxKUZF61K5du9KO5+fnV6FjllyDRBCq2ryPQol8S4fG7uAvHwMoJNCpqG4uBIrmjn/gbCKRyWlYVUqsCol5zRvQooHo2y4IglBRIuAQKqRp06ZVXYTbwt/f/67oLlURbdq0ua3H02q1t/2YgnCzlCoFZz4PoOOo86T7a5DVRbNPxQf6kumuxddYdKNFb7GQpdTijoII3dW7lwqCIAiuxMJ/giAIwn3P325l+PFzuNuKggmbUsFXDzdjb1gQKQZ38t00aJUKLEoFmz8OrOLSCoJQVeySVKEfwZUIOARBEIT73mfvhZLqrqN90kXntgwPHQtbNeRQeDUUahXZblqy9DokcTEhCIJwXUSXKkEQBOG+FxmuZfXcCH7dm0fyogzO+XhQoFHjkCT2hgZywseTtolpzJxdr6qLKgiCcNcRAYcgCIIgACqVRO8HPWnVxIOnv0wnaP8pNLKDGh38mTjGH78aQVVdREEQqpitqgtwlxIBhyAIgiCUEOihYMM7gYAYqyEIglAZxBgOQRAEQRAEQRBuGdHCIQiCIAiCIAgVIGagujGihUMQBEEQBEEQhFtGBByCIAiCIAiCINwyokuVIAiCIFyFxeJg14Z09p+y0KyFgS4PeVV1kQRBEO4qIuAQBEEQhHI89U4ytvNmFIBRqeSHU2byl6bxx789qVbNraqLJwjCbWYTQzhuiOhSJQiCIAhlGDTwMMrTBchInNFq2OzpziGNliS1lib/Z+SHX/OquoiCIAh3BRFwCIIgCMIV5m/Kp3FKJklaDXv8DFx00xBolwly2LHIMhe1GkZtgQWbC6q6qIIgCHc8EXAIgiAIwhW2TY3n27aNiQ3yJjKngMY5eYQUmvAw2/CTHWC1Y0bijRgRcAjC/cSGVKEfwZUYwyEIgiAIV7jg7kGyuxuDz6cSaLIAUKPQjJ/FykEfT9AowWbHrFRivWhEHeBexSUWBEG4c4kWDkEQBEEoYdmWHNI1KoILzc5gAwBZpkFaBo8dPcljJ87ibTJjBVpPulhlZRUEQbgbiBYOQRAEQSgh9t97aeamJNPLw2V7QEY2emMhPpJEZEY2zS9k8N/OLTmuEtPkCsL9wip6S90QEXAIgiAIwiUz3j5K0wtpSJh4OPE8W1q0RSPLyLJMlkFPQrUAAHSFJiISU2mecpF4nTspOXqCvdRVXHpBEIQ7k+hSJQiCIAjA4X05LDmt4kJAAKkGA11PnaLZX8cx5OYjy5CvvzxOo1DnRmqAL8NijzD45GnavJONI9dUhaUXBEG4c4kWDkEQBOG+l5xs4pF5BQzMzedIzTBS1GF0PnUKvdFErVNJ/NUwAjRqlHY7Dx46Tp3zKZg1aoLzM+j912GC83J5+T9hTPuqUVVXRRAE4Y4jWjgE4Q4xadIkWrZsWdXFuCl9+/blxRdfvOa2O8X+/ftp2bIlq1evdm5LTk6mZcuWzJo1yyVty5YtmTRp0m0u4Z1z/Hvdsy+fpltaDha1ikYn45n0yzqOB1XjgpcbyX4BeOYUtV60PxhPs/izeBSa8cvJx2LXYEPJk7GHWKQOquJaCIJwq1klqUI/givRwiHctxYtWoTBYKBv3753RD73k9t5zuLj49myZQt9+/alevXqt/x4wt3n6fHxHPbzYZdGg95uZ0lYCL4tmjNu358E55gpKMjCppYxGjTUPZd8xaMl9lWvQZvks1jsDmRZRhIXG4IgCC5EwCHct3766SeCg4Nv+qK3svK5Vy1fvrzUBdjtPGfHjx9n9uzZtGjRolTA0bx5c3bs2IFKde2Pwh07dqBUKm9VMYUq8vG0BA7mqMjy0+Ahg/XSc5zp7s701i14Ze8hFIBbgYlmh09gUSrQXZHHhoaR/BXuh8lDR4PPczn2hpi1ShAEoSQRcAjCfcxkMqFSqSp0wX2jNBrNLcv7ZikUCrRabYXSVjSdcOf74rNzmH6IY23DcNLc3cnycCcoz0SBh5tLuiydllytBm+zBYtWjcZiY9GDUYzdGOtMk6HXsbJFffJVKpAlAo+fY1rbvViUCsySmhaJ58n2T8Go/JlTI/vS5NkHULqpISkTactfOA4lwOLtSMkZyGolimc6QMs60CQMWkSC2537/hGE+5G1qgtwlxIBh1DK6tWref/995k5cybHjh1j2bJlpKWlERwczMiRI+nTpw9Q1Ne9X79+jBo1itGjR7vkMWvWLGbPns2qVaucd5UnTZrEmjVr+P3335kyZQrbt2/HarXSqlUr3nzzTfz9/VmxYgWLFi0iOTmZ4OBgJkyYQKdOnSq9jsVjJVJSUlzGTZQs75YtW1iwYAHHjx9HkiTq1KnDsGHDXMpzrXx2795NTEwMR48eJT09HbVaTaNGjRg5ciQtWrSotPrk5+czf/58Nm/eTHJyMjqdjpo1azJkyBAeeeQR4PL537BhA9OmTWPHjh1kZWURExND9erVyc/P5/vvv2fTpk1cuHABvV5P69atGTduHKGhoS7HS01NZcqUKezatQsoail49dVXyyxb3759CQ4O5ttvv63QOauIK/Mstn//fsaMGcN7771H3759na9DgDFjxjjT9enTh0mTJpVKfzUtW7Z0Pg4un8/y7N+/3/l7eno6s2fP5o8//iAjIwNvb28efvhhxo4di6+vr8vjTp06xZQpUzh48CAajYYHH3yQV155pULn5X5ktMi8+ouJnw9ZMRgttJRNqAsseJxIwys7nz8a1cIsKWh1Ohlfo4kNdWrwV7A/7h2bcdHtchCp1Ml42B0ueXtYLBgsVsxqFTk+Xuh1hfzSoh6xNavT/a9TZOt1LGvVgHRPPVjsqGQHu8IjOevrT7WCbLqcPMLSpg+z7IEHqJV5gQXvfo1izKcAWCUlStmOAihu/5Msdvh+c9HPJXLxvjLqLgMWyYBN0qP0UqPtXxfJTw8FZhjYFro9UGnn+aosVpi3GfadhNZ1YHgn0IgpggVBuEwEHEK5pk+fjtlsZsCAAWg0GpYtW8akSZMIDQ2ladOmN5zvxIkTCQwMZMyYMSQkJBAdHc3rr79O586dWblyJf3790ej0RAdHc0bb7zBihUrCAkJqbyKAR988AGTJ0/G29ubkSNHOrf7+PgAsHTpUj777DNq1qzJCy+8AMCaNWt47bXXeOuttxgwYECF8lm9ejU5OTn06tWLoKAg0tLSiImJYdy4cXzzzTc0a9bspuuSl5fH888/z+nTp+natSuDBg3CbrcTHx/PH3/84Qw4ir300kv4+fnx/PPPU1hYiLu7O/n5+YwcOZLU1FT69etHREQE6enpLFu2jOeee44ffviB4OBg5/FefPFFLly4wIABA4iIiODAgQOMHj0as9l8zfJe65xVpi5dupCens7KlSsZMWIEtWrVAigVQN2IAQMG0Lp1a5dtOTk5TJ06FU9PT+e21NRURowYgdVqpX///oSGhpKQkMDy5cvZv38/P/zwAx4eRQvMJSUlMWrUKCwWC0OGDCEoKIjt27czYcKEmy7vvarl10biMmSQlKRqdCRZVHy2aRsKh8zbj3fELkl8smILXiYLs1s2ZHf1QAAKdNrLV/OA3VeH2WhFW1C0srjK4aDviXMUuLtxukZ1ZEXRJX+NAgs764ayL6LEZ5JD5oHEJEKMeaxt1IgEb38SvP3ZVyMSJAml3c6CpTNokpbofIhGtleoftcaDVIo++GQNZAF5nnH8SSh6DHf/ArfjIbRj1wjh0ow6P9g9aUA+7vf4ZdY+Pnft/64giDcNUTAIZTLYrGwYMEC1OqiO1Vdu3alf//+LFmy5KYCjkaNGvHGG2+4bFu0aBFpaWlER0c7L75atWrF0KFDWblyJePHj7/h45WlV69ezJw5E19fX3r16uWyLzc3l2nTphEaGsq8efOc5Rk0aBBPP/00U6ZMoXv37hgMhqvmA/D222+j07n2+B44cCBDhgxh7ty5lRJwTJ8+ndOnT7sEQsUcDkep9LVr1+bDDz902fbFF1+QlJTE3LlzqVu3rnN73759efLJJ5k1a5bzzv6CBQtITk7m3XffpV+/fgAMHjyY//3vf/z000/XLO+1zlllqlOnDk2aNGHlypW0adOmUmcBa9KkCU2aNHH+bbVaGTduHEqlkv/973/O7Z9//jk2m40ff/yRoKDLsxh169aNESNG8OOPPzpbCGfMmEFubi7ffPONs6xDhgzh9ddfJz4+vtLKXhnMZjN2e8Uumm+Vnedk4tJlUFy+LG+ecAHfAhM/PhhFlrsbvQ+fxMtkoUCtYnvNYNcMJIqCDnc1OMCkVWPRKFEooEnyRbQKSKgW4Aw2AOpnFpCtVnNMp0VCQkYmMreAzXOKWtI+6dKZ/+vSxSVS6HHiME0unK/0+kuAGxkYKaqXHR023FFjBMDx8TJMzz5c6cd1KcORBHSr97tujNlLYewJ5AaVe6PoSoWFhS7/C5XjXjyv7u7u105UQUYxKcQNEQGHUK7Bgwc7gw2AwMBAwsLCSEhIuKl8hw4d6vJ3s2bNWLRoEb1793Ze3EPRxaJer+f8+cr/or6aPXv2UFhYyJNPPulSHg8PD5588kn+97//sWfPHrp163bNvEoGG0ajEYvFglKpJCoqiiNHjtx0WR0OB7/99hu1atUqFWxA0RiFKz3zzDMuf8uyzLp162jWrBmBgYFkZ2e7lD8qKordu3c7t23ZsgU/Pz969+7tks/w4cMrFHDcqz744AP+/PNPPv74Yxo3bgwUdXX7448/6Nu3L1qt1uXcVq9endDQUPbs2cPo0aNxOBxs376dhg0bugRGkiQxbNgwtmzZcptrdHWV8fq9WX8l6QHX1iq9uaiHtVFbNPbBzWoDwKaQcJTxfkAhgVLhbO1wSAocComDtavzd3gQT/ydiNpxuSlEBlJtMirj5Z7cFxQqst10eJsKeWH3Hv6vS2dKRhwellu3IKAC15sKjhKz3cuZecTFxd2yYwN4HDpPvTK2n/vzbwrIvaXHLnb27Nnbcpz7zb10XiuzC7NwY0TAIZSrrG5MXl5epKamVmq+BoMBoMz++56enuTk5NzU8a5XUlISABEREaX2FW8rTnMtiYmJTJ8+nd27d5OXl+eyrzKmzszOziY3N5d27dpV+DHh4eEuf2dlZZGTk8Pu3bvLDaJKBi5JSUk0bNiw1IxN/v7+zufyfvPtt9+ybt06xowZQ48ePZzbz549i8PhICYmhpiYmDIfW/x+yMzMxGg0lnp+oOzXYlWLioqq8haO8Noyb+93YHRcfi/tD6vG0NhjPHQyga0RIeyKCKHv4ZN4ma00Tb7In9UDnGndzVa8bTaSPa6YEMAuIztkGh9LIsnDjZq5l+/0pqtV5F/x2i9UKPi+ZUte+WM7nqYSwYVc9M+6ek1Jd/fA35hfmdVHBgrxc/4tYUdzqXUDwPHEQzRo0KBSj1lKnbo4PlqH4nz65ePWDCBsSLeiQO4WKiws5OzZs9SsWbNUS7Jw48R5FW4FEXAI5Srr7jgU3RGHq18wX+1CpLypRcvbXny8u43RaGTUqFEUFhYydOhQIiMj0ev1SJLEvHnz2LdvX5WUy83NdSae4vPbunVrhg8fXhVFum7lvfaq4gJ43bp1fPvtt/Tq1cs53udKjz76qHOyhSvdrbNf3QnldneHP0bb6LfARHqOAw+bnQfyCjjQsBZhCRd4KvYY6xvUZHK3VvQ/eIInD5/ArlKSrtUScTGHp3Yf58/IasyudsX4IQnINrE/yB83mw3JIROWb8ImSRz1NbiM/Si2MbIuY/bsZnNEBHXTUzgecOkGigxGlYZ/9xjKZ+t/xMdkROJyb66S/18vGVBixYEKJRbcfQuRvAOg0AID26L+bBhq99vwPP36Lrw6zzloXPG/53A3eFzzYZVFp9NVapcZoYg4r0JlEgGHcMOKB8bm5pZuNq9oC0BVKu+itXhA8enTp0sNCj5z5gzg2kpTXj579+7l4sWLLmMdis2cOfOGy12St7c3np6enDhx4obz8PHxwWAwUFBQQJs2ba6ZPiQkhISEBOx2u0uQmJ6eXqoVpzw327rj6elZ4dfdrVyE7c8//+TDDz+kWbNmvPPOO6X2h4aGIkkSNpvtmufWx8cHd3d3zp07V2rf6dOnK63M95pmISoS3ix5cesDFLUSGS0OcowOfDUexO5x5/ckBd2zrZzbnkWjlExsBi0F7hrcbDZMxVNDyzKYbWAviipMBjd+99WjsDpwaFUgSWgy8sF2uStTu3NJTF+9ge9atSamcSPGbf8NL4uVNQ1a4FFoolp2Bg8nHEdl0pHlF4y7JR8kE6rcPCRkFLh22QKQ1QqkUD+Mag2mR1rgP6E76LRFx63uA2oVCkni9l3WX0X9UPjl7aouhSDcFoViCMcNEQGHcMP0ej1+fn7s27fPZXXdxMTEO66/eVl0Ol2ZF61t2rRBp9MRHR1N37590ev1ABQUFBAdHY27uztt27a9Zj7FF+NXttDs3r270vq/KxQKHnnkEZYuXcrPP//MY4895rK/IqseKxQKevbsydKlS/n999/L7FaVmZnpnL61Y8eOzJs3j19++cUlkJo/f36Fy13eOauosLAwNm3aRFpaGoGBRbMOWSwWli5dWuaxoOzA+GYkJiby6quvEhgYyP/93/+5jHcq5u3tzUMPPcSmTZv466+/nGM7ismyTHZ2Nj4+PiiVStq3b89vv/3G/v37neM4ZFlmwYIFlVr2+4W7RoG7pqil9sHOATxYvGN8NQDSEwrY9No5grPyaX4+DatSgW9WAUlh/oQVmsnQqFhXoxpmkwOHpxYuvZcsvnpU+WY6HT1Jy4RUhh6OQ2N3oLdqcKBm6KaRBHqqeNpqZfHixTRs2JDIyG54eV17QcAr360el34EQRDuZiLgEG7KkCFDmDlzJhMnTqRjx46kp6ezfPlyateuzdGjR6u6eFfVuHFjYmJimDlzJrVq1UKSJDp06IDBYGDixIl89tlnPPfcc86uMGvWrCEhIYG33nrLZTB5efk0bdoUPz8/pkyZQkpKCoGBgRw/fpy1a9cSGRnJyZMnK6UeY8eOZd++fXz00Ufs2bOHBx4omns/Pj4em81Wakaqsrz00kscOnSIN998k40bN9K4cWPUajUpKSns2LGDBg0aOGepGjZsGOvXr+fjjz8mLi6O2rVrExsby+HDh/H29q5Qmcs7ZxXtLzxkyBB+++03xo0bx8CBA7Faraxdu7ZUdzEomhVNoVDw/fffk5ubi06nIyQkhKioqAodqzz/+c9/yMnJYdCgQezcubPU/uIZuP7973/zwgsvMGrUKHr37k29evVwOBwkJSWxbds2evXq5Zylaty4cezcuZOXX36ZJ554gsDAQLZv305WVtZNlVUom38NPSsXN2BUv4No7A4ansog19eDwJyisRbpWg1mWSoaWF5ipiokCZvBjZ7nE3jsYJxzoLZRreLXz4IJ9BRfrYIgCCWJT0XhpgwfPpz8/HzWrl1LbGwstWrV4p133iEuLu6ODzjGjRtHTk4OS5cuJS8vD1mWWbVqFTqdjsGDB+Pv788PP/zgXDiubt26fPHFF6UWIiwvn+rVq/P1118zbdo0oqOjsdvt1K9fn6lTpxITE1NpAYenpydz587l+++/Z/PmzWzevBm9Xk+tWrV44oknKpSHh4cH33//PQsXLmTDhg1s27YNpVJJYGAgTZs2dWk58fT05LvvvmPy5MmsXbsWKFr4b9asWYwdO7ZCx7vaua+Ipk2bMmnSJL7//numTp1KYGAgAwcOpGHDhqXKUK1aNd59913mz5/Pp59+is1mo0+fPjcdcGRmZgIwZ86cMvcXBxzVqlVj4cKFzJ8/n61bt7Ju3To0Gg1BQUE8/PDDdO/e3fmY0NBQvvvuO7788kuio6OdC/998MEHLoPRhcojSRJTFzZm1LC/OBRVE98Sg75Ti1f5dshFXa0kqeh/ix1sDvYFBdLJ7RSeJhvZOjemP/QA7/qIBe8E4V5muaERV4Ik360jcgVBEAShkuw5XsCHH6fSKCvP2XXquIc7G6r58//t3Xd4FNXXwPHvlvRegEBCDwkl9ARUkF4FpAkCUgQsCNgoih2Vn1h4BQQERJoCIlIMoRfpSFcRCCAllNDT2ybZ3Xn/WLOybBKSkE2B83mefSB37sycmd1k58wtE5icht5Ow7ly7qRnGE0Jx78qJCYz8vBfzGndmHSNmluf+5qXZVp0qQrMU5cqkXepqalERkZSq1YtGdxciOS85k71Rmye6inTvG0cSekiLRxCCCEeeU2DXOgypBynpybh+O8NzOrJqfhfuob239tyDe/Es7SSH8pd46KuubsyuX0zUtwc+OZJ6wdtCiGEkIRDlCJxcXH3nfZUr9ej1eb+sXZ0dLQYg1ES6XQ6kpPvP2e/r6/vfeuUFsnJyeh0uT8gzc7OTu4SC5t5pY0rg+ZpCY6O5Y6vNwqYkw0Ae6NikWxkcc/U069MJq8+6WO1TAjxkJEeVQUiCYcoNQYPHsz169dzrdOoUSOOHTuWa52uXbuaB0CXVFu3buXjjz++b70jR44UQTRFY8qUKaxbty7XOo0aNeK7774roojEo+h/3wTxbfd9GI1GVPc8G8jNYKCsLp1bjv8928LOaMRTl8H3L/oVdahCCFFqSMIhSo1PP/2U9PT0XOvk9HyGu5UpUybX5SXB448/zqxZs4o7jCI1ePBgOnfunGudrGe/CGErlcrYc75JVRz+icFbo7G6mVkrWUemRkOKRo2DUSEoVUeGklkssQohRGkhCYcoNRo0aFDcIRQZX1/fh6q7VF5Uq1aNatWqFXcYQrBsSmVaPp+JZ4oOLWD8txvVbUd7Yhzt8c/Qm+tmGjPZ0VPmXhFCiNyoizsAIYQQoiSx06rIcHbEJT2DO05O6FUqQMHRqOCToUenURPnaMcZb1ecdDp8OlUu7pCFEEVFpcrbS1iQFg4hhBDiHuUcFWId7bHPzCRNoyXZ2RFFUbjk4UyUu2mqUPuMTBrKzJdCCHFfknAIIYQQ95j/aQWeH53JH2W9mL1gMz6JqejVKqrUr8Y5P2/+KetBoGM6M2fUKO5QhRCixJMuVUIIIcQ9/Dy0zJtWmQopaXza7TFS7bU46I08ffQcHU9GsfZZO9Z8G4RGI10nhBDifqSFQwghhMhGRR8tf8zx5/vld/jMrgV1L96iliGVoT81wbWCS3GHJ4QQpYYkHEIIIUQuXujnywv9fIGqxR2KEEKUSpJwCCGEEEIIkRcyA1WByBgOIYQQQgghhM1IwiGEEEIIIYQxliTEAABcP0lEQVSwGUk4hBBCCCGEEDYjYziEEEKIfFh6Us/AjZZlF4ZDVS/5ShXioSdDOApEWjiEEEKIPGr6o3WyAVBtPsz+Q1/0AQkhRCkgCYcQQgiRR4du5rxs5Paii0MIIUoTaf8VQgghhBAiT6RPVUFIC4cQQgiRB7pMY3GHIIQQpZIkHEIIIUQe2GuKOwIhhCidpEuVEEIIkQcqecKwEEL+DBSItHAIIYQQeRD2g6G4QxBCiFJJWjiEEEKI+1BNkSlvhRCioKSFQwghhMjFT6ck2RBCiAchLRxCCCFELsbvKu4IhBAlhozhKBBp4RBCWIiIiCA0NJQjR44Udyg2c+TIEUJDQ4mIiCjuUEQpkJhR3BEIIUTpJi0cQtjImTNn2LlzJ926daNChQrFHY4oZkOGDOHkyZM8/fTTfPjhh9nWWbduHZs2beLChQvEx8fj7OxMxYoV6dWrF0899RQajczLWhzSM/Net8Y8PZHDNWjVchtUCCGySAuHEDZy9uxZ5s2bx7Vr14o7FFHMzp07x8mTJwkICGDbtm2kpaVlW+/06dO4ubnRp08f3nrrLYYPH46zszMff/wx//vf/4o4alEQ5xLgrZ0ym5UQDy9VHl/ibtLCIYQo8QwGA5mZmTg6OhZ3KAUSHh6Oi4sLn376KUOHDmXr1q08/fTTVvXGjRtnVda/f39ef/11IiIiGDlyJL6+vkUR8iMtU29g3TmFsTvhYnL+1596DL5uU9hRCSFE6SUJhxD/yszMZNmyZWzevJlLly6h1WqpVKkSXbt25dlnnwXg9u3bLFmyhMOHD3P9+nXS09Px9/enS5cuDBo0yNzlZe7cucybNw+AESNGmPfRtWtXJk6cWKhxr1u3jhUrVnD58mX0ej0+Pj7UrVuXsWPH4uXlBUC3bt0oX7483333ncW6R44cYcSIEXz00Ud069bNYpnBYGDu3LlEREQQExND5cqVGTp0KB07dixQnNu3b+f777/n0qVLeHl50b17d+rXr8+oUaMs9h8REcHHH3/MrFmz+Pvvv4mIiODGjRu8//77dOvWjQMHDhAeHs6pU6e4c+cOdnZ21KlTh2HDhtG4cWOr/e7cuZPvvvuOqKgovLy86Nq1Kw0bNsw2xoyMDJYsWcKmTZu4evUq9vb2NGzYkJdffpmaNWsW6LgzMzPZuHEjbdq0oW7dugQHBxMeHp5twpGT8uXLoygKycnJknAUIoNR4dlwA6vO22bbD63wQ/D1WkjWQY3ycPgfuBoDeiNoNeDtCnHJkP7v7F4qwAanwwloVPibBbUK3JzAxRF0GZCiA4MR1Gqw14JKZepnp1VDBS8I8oc/LkBiGgSWhylD4FosfPQz3E4APy+YNAD6NS/cOBNS4IOf4Le/oaY/fNwP6lQq3H2UFH9dhIk/w7kb0L6+6VjdnIo7KpEPknAIgemicPTo0Rw9epTHHnuMzp07Y29vz7lz59ixY4c54fjnn3/YsWMHrVq1IiAgAL1ez++//87MmTOJjo7mvffeA6BNmzbcuXOHNWvWMHToUKpWrQpAQEBAoca9fv16Jk6cSMOGDRkxYgQODg7cvHmTffv2ERsba044CmLGjBmkpaXxzDPPAKZE4L333iMjI8MqObmfLVu28N577xEQEMCLL76IRqNh3bp17NmzJ8d1pk+fjl6vp2fPnri4uFC5cmVzHAkJCTz11FOUK1eOW7duER4ezsiRI5kzZ45FMrFjxw7eeustKlSowAsvvIBGoyEiIoK9e/da7U+v1/Pqq69y/PhxnnrqKfr27UtycjJr1qxh+PDhzJs3j9q1a+fruAF27dpFfHw8Xbt2BUzJ35QpU4iKiqJKlSrZrpOcnIxerycxMZHff/+dtWvXUqlSJSpWrJjv/Yucjdhim2QDYPNFA+0fxmu/nSeg5xeg/JtBHLtguTxDDzfiLctslHvZrNOKUYGEVNPLcoHp+LJkAOduml5Z/oqCTp+aEpQs529A/6/BzRG6hBZenP2nwsZjpv+fvAK7T8G5b8HdufD2URLEJkHrj0xJLMCJy3DpNqx6q3jikd5SBSIJhxDAsmXLOHr0KEOHDmXUqFEWy4zG/744GjVqRHh4OCrVf39xBgwYwAcffEB4eDgvv/wyvr6+1KhRg3r16rFmzRqaNm1KaGghfsncZefOnbi4uDB79my02v9+ne9uVSmo+Ph4li9fjqurKwDPPPMM/fr1Y+rUqbRv3z7P3Zv0ej1Tp07Fy8uLxYsX4+7ubt5e//79c1xPp9OxbNkyq/28//77ODlZ3tnq3bs3ffv2ZeHCheaEw2AwMGXKFNzd3Vm8eDGenp7muv369bPa388//8zRo0eZMWMGjz/+uLn8mWee4dlnn2XatGlWLUR5sXbtWipUqECjRqZ7sZ06dWLatGmsXbuW1157Ldt1XnnlFSIjIwFQqVQ0adKEd955p0QNGk9PT8dgKN1jFZZE2mGrq4fJBww089WZP786nQ47Ozub7Kso2c/filZ5iFtvCsPdycZd9PO2ktE6/zctsnU9DuesZCPL7UTSV+3H8OwTD7TprDFmOY01K2qan/fiEGfZt1H59SBp0bfByyVP23B2fsiSsFJIEg4hgE2bNuHu7s4LL7xgtUyt/m9uhbsvfjMzM0lNTUVRFB5//HE2btzIqVOnaNGiRZHEDODq6opOp2Pv3r20bNnSIhF6UM8884w52cjaV+/evZk1axZHjx6lWbNmedrO6dOnuX37NoMHDzYnG2D6AujVqxczZszIcf/ZJTV3JxupqalkZGSg0WgICQnhxIkT5mWRkZHcvHmTQYMGmZONe4/jbhs3bqRKlSrUqlWL+Ph4i2VNmzZl/fr16HS6fI0juXHjBgcOHGD48OHm98bT05PmzZuzfv16Ro4caZEoZnn77bdJSUnhzp077N27l9jYWJKSkvK836Jw97kurdRKHWz1NahLS+XcuQtUq1YNgKtXr9pkP0WtUloKZYo7iFIqIT2VqH9vJDwoTXwq9TUqVAbL5O/qnZvEF9I+oqKiCmU7D8or9hbV7ilTNGrOnP8Ho4tDnraRXXdbUbQk4RACuHz5MsHBwTg45P7HS6/Xs2jRIjZs2MCVK1dQ7rnTl5iYaMswrQwdOpRjx44xbtw4PDw8aNSoEc2aNaN9+/a4uOTtzk9Osuvuk9U1LDo6Os/byaqb1SXqbtmVZalUKfv+KFevXmXWrFkcOHDA6iL87oQrt/1mHcfdLl68SHp6Ou3atcsxpvj4ePz8/HJcfq9169ZhNBqpX78+V65cMZeHhoayc+dO9u3bR8uWLa3WCwkJMf+/S5cuzJw5kxdffJHly5cXere8ggoJCSn1LRxjkmHSYVtsWeGLNo4E+gSyZcsWqlWrRkBAAG5ubrbYWZFSjXNGWf83Kl0+5gp+xCiO9pCegequrwdFo8blrWeoVatGoe3H0K852qX/dUs1BvpR/oWulHd4sJa0tLQ0c5fPe1uTi0Xlahjn7Ud96ba5yDCkFcGhDYovJpFvknAIkQ9Tp07l559/pn379gwbNgwvLy+0Wi2nT59mxowZVgmIrVWqVIlffvmFQ4cOcfjwYY4dO8akSZPMg9azLk5zavkoyReM2bUkpKam8uKLL5KWlkb//v0JDAzExcUFlUrFokWLOHz4wa4eAwMDefPNN3Ncnp8xMYqimB8sOHr06GzrrF27NtuE415du3Zl0aJFRERE8Morr+Q5Blu6X3JeGnzaEtwc9XywFzIK9VdXRZtqTmRmatHpdIDp8/xQdOt4rBbsnwyzNpoGjdevYho78OdFSEkHZwcIKg8Xb8L1eNM6jnamZYXs7resUDvGeblAWU/TgPDYJLidaBq7odWYlilAXIrpuEIqQqNqsP0EXI+FsEBUH/czLZ+4HM5eh5r+qCY+i2PToMKMEha+Ck2D/h00HoD6za44e3kU2uadnJxKxmfW2Rl+nwxT18E/16BjQ+xebIddsXUxlUEcBSEJhxCY7oJHRUWRkZGBvb19jvU2bNhAo0aNmDx5skX53XevsxRm96bc2Nvb07x5c5o3N82AsnfvXt544w2WLl3K22+/DYC7u3u2rS+5tVRk15x+8eJFAPz9/fMcX9ZDDy9dumS1LLuy3Bw6dIjbt2/z4YcfWs3yNHv2bIufs2LMbh9Zx3G3ihUrEhcXR1hYmEU3uoI6cuQI0dHR9O/fn/r161st37x5M7t37yYmJgYfH59ct5V10VrULWiPgreaanmrafbLPtijZ9LBoo2nVGhYDb6/a6zbO72LJYy01FQiIyOpVatW8V8Yf5JN2eaPbLtPOy282sX0etiV94YvBxd3FOIByIP/hMA0kDcxMZH58+dbLbu71UKtVlu1YqSlpbFs2TKr9bKaom15kXjvWAPAPH1rQkKCuaxSpUpERUVx69Ytc1lGRga//PJLjtteuXIlycn/DdRLTk5m1apVuLm55as/bK1atfD19WXdunUW5yI1NZXVq1fneTuAedD0ve/BgQMHrMYU1KpVi3LlyrF27VqL85R1HPfq0qULMTExLF26NNt9x8TE5CvW8PBwNBoNw4YNo127dlavfv36YTAYWL9+PWDqrpfd+wmmAe1g2dVK2N6nT2pRxuX/vtyEJjYIRgghSjFp4RAC08PV9uzZw/z58zl16hRNmzbFwcGBCxcucOnSJb799lsA2rZty+rVq3nnnXdo0qQJMTExRERE4OFh3Yxdp04d1Go1CxYsIDExEScnJ/z9/Qv1onHUqFG4ubnRsGFDypUrR1JSEhEREahUKp566ilzvb59+7JlyxZGjhxJ7969yczMZMOGDbkOgPb09GTIkCEWz8fIeh5GfgZOa7Va3njjDd5//32GDBlC9+7dzdPTenh4EB0dnefWoAYNGuDj48O0adO4fv06ZcuW5ezZs2zYsIHAwEDOnTtnrqvRaHjzzTd55513GDJkCD169ECj0bB27Vo8PDy4ceOGxbb79+/PwYMHmT59OocPHyYsLAwXFxdu3LjB4cOHsbe3Z+7cuXmKMykpiR07dtCgQYMcu2E1bNgQb29v1q5dy+DBg0lLS6NLly60atWK6tWr4+3tTUxMDLt27eLUqVM0adKETp065Wn/ovh0rAyTW8hXqxAPLelRVSDyV1EIwM7OjpkzZ7JkyRI2b97Mt99+i729PZUqVbJ45sSYMWNwcXFh69at7Nq1i3LlytGzZ09q167NyJEjLbbp5+fHhx9+yOLFi/n888/R6/V07dq1UBOOZ555hq1bt7J69WoSEhLw8PAgODiYt956y2Iq3gYNGjBx4kQWLFjA9OnTKVu2LL1796Z27do5jgl49dVX+fPPP/nll1+IjY2lUqVKTJo0qUAXvZ06dUKr1fL9998zd+5cvL296d69OzVq1GD8+PF5Hg/g5ubGzJkz+eabb/j5558xGAzUrFmT6dOnEx4ebpFwALRr1w61Ws3333/Pd999h7e3t/nBf/eOq9BqtUybNo2VK1eyYcMGc3JRpkwZ6tSpY36ORl5s3LiR9PR0WrdunWMdtVpNy5YtWbNmDX/99Re1a9emT58+HDt2jAMHDpCcnIyzszPVqlXjrbfeolevXiVqWtxHSX6eW7epj3ytCiHEvVRKUY9yFUKIfy1ZsoRp06axcOFC6tatW9zhCJEt16l6UvI4v8LdXbAyMzNZvnw5tWvXJjAwMNuWUFFwqSVpDMdDRM5r7lQT8jZFufJ56Z+VrjDJGA4hhM1lZmZazYiVmprKL7/8goeHh3nciRAl0afNizsCIYQo3aTtV4giZDAYiIuLu289vV6f7QPh7ubq6pqvsRSFLTk52Tx7Uk7s7OzM4zRee+01OnToQIUKFbhz5w7r168nOjqaCRMmlKonMN+5c+e+dYr7vRGF680wLWN26Ys7DCFESSBjOApEEg4hitDNmzetpnMtqI8++shifElRmzJlCuvWrcu1TqNGjfjuu+/w9PQkJCSEjRs3EhcXh0ajITAwkNGjR9O+ffsiirhw5GUMS3G/N6LwKeO0DF+nZ8Hp4o5ECCFKH0k4hChCPj4+zJo16771kpKS7vtU4urVqxdWWAUyePBgOnfunGsdd3d3wDTj1WeffVYUYdlcXt6/4n5vhG3M76plwWlp6RBCiPyShEOIIuTg4EDTpjk8ZayUqVatGtWqVSvuMIrcw/L+ifyTOVaEENKnqmBk0LgQQgiRB3qjJBxCCFEQknAIIYQQQgghbEa6VAkhhBB5YKdRA8biDkMIUZykR1WBSAuHEEIIkUf/1yLnZQf7FV0cQghRmkjCIYQQQuTRmCZaMt5UU/2uSeSe9APDWA1NAqTTgBBCZEf+OgohhBD5YKdRc+5luV8nhBB5JQmHEEIIIYQQeaGSQRwFIbdohBBCCCGEEDYjCYcQQgghhBDCZiThEEIIIYQQQtiMjOEQQggh8uCz/Xre229Z5qSG5Dc1qKVftxBC5EhaOIQQQoj7eG2LdbIBkGYEzf8Zij4gIYQoRSThEEIIIe5jxvHcl19L1BdNIEKI4qXK40tYkIRDCCGEeEAjtxZ3BEIIUXJJwiGEEEI8oH9iijsCIYQouSThEEIIIR5QgEdxRyCEECWXzFIlhBBCPKBa3sUdgRCiaMgAjYKQFg4hhBDiAZ2LK+4IhBCi5JKEQwghhHhA6y/DlQSZHlcIIbIjCYcQQgiRg6sJelRT8jblbaV5io2jEUIUO5kWt0Ak4RBCCCFyUHFe/upvv2S0TSBCCFGKScIhhBBCZCNBl/8uUssjJeEQQoh7ScIhhLBw5swZ5s6dy7Vr14o7FCIiIli2bNkDb2fnzp3MnTu3ECK6vyNHjhAaGkpoaChr1qzJtk5oaChvvPEGAOfPnyc0NJSxY8fmut21a9cSGhrKggULCjtkkYM0ff67SOnkgeNCPNykS1WBSMIhhLBw9uxZ5s2bV2ISjp9++umBt7Nz507mzctn35hC8N1336HT6XKtU716dUJCQti7dy+xsbE51ouIiECj0dC1a9fCDlPkICH3ty5bigzjEEIIK/IcDiGEsIHatWtz6tQpfvrpJ4YOHZpr3e7du3PixAk2bNjAwIEDrZZfuXKFP/74g2bNmlG2bFlbhfzQ0BsVdHpwtc/5NqNRUbiWZOR8rMLBa/DjCchQTC0UyXqIzSzYvu00BQz6YZaYCq6OpmwsNQPcnP4rt9eA3ggOdhB1CzycwckeLt2C20ng5QKZBqhaFu4kmu4cX4sFZ0e4dAOPk+fhQjKkK9ChARgMkJAKKpVp3dPXTLdWy3vDjVjT/m4mQaNqoBhN207JgCq+sPc0NK4Of16Esu7g5AjO9hCbDGo1BPrB9Xhw0JqOQw1ULmtalpAC7s6m/QLoDaDLAHut6ficHXI+L+p77v1m6iFDDy6ONntLSjSjEZJ1pvMpHhqScAhRymVmZrJs2TI2b97MpUuX0Gq1VKpUia5du/Lss88CcPv2bZYsWcLhw4e5fv066enp+Pv706VLFwYNGoRGY7pKmjt3rrklYMSIEeZ9dO3alYkTJxZq3OvWrWPFihVcvnwZvV6Pj48PdevWZezYsXh5edGtWzeuX78OmLogZZkzZw6hoaGcOHGClStXcvz4cW7evIlGoyEwMJBBgwbRunVrc/2XXnqJY8eOWW3no48+olu3bgDcuXOHefPmsXfvXmJiYvD09OTJJ5/klVdewdu7YE90a9euHYqisHjxYnr27Imnp2eOdTt06MDXX39NREREtgnH2rVrAXj66acLFMujZMphI/87YCQhHTpWUbG4s5qyLpaJx0+RBgatV7DFJLaLTkEtXwNvNZHMg5OXYcgMOHoePF3AqJgushtXN12Mn7xiSiAK2CrkDAQWZrwFoVaBnydci4PA8jB3BJyJhg+XmxIkjdp03D2bwsLRpovoyKsweDocOQ8VvOHr5+HZ5qbtfboCvgqHlHToFmpax8u1OI+waK05AK8vgCt3oGFVWPwa1K1c3FGJQiAJhxClWGZmJqNHj+bo0aM89thjdO7cGXt7e86dO8eOHTvMCcc///zDjh07aNWqFQEBAej1en7//XdmzpxJdHQ07733HgBt2rThzp07rFmzhqFDh1K1alUAAgICCjXu9evXM3HiRBo2bMiIESNwcHDg5s2b7Nu3j9jYWLy8vBg7diwzZ84kPj6eMWPGmNfNimnnzp1ERUXRrl07ypcvT0JCAuvWrWP8+PFMmjSJTp06ATBs2DAUReGPP/7gk08+MW+nXr16ANy4cYOhQ4eSmZlJ9+7dCQgI4MqVK6xatYojR47w448/4uqa/y98lUrF6NGjGTVqFAsWLLA4hnu5uLjQtm1b1q1bx8mTJ6lTp455mdFoZMOGDXh5edGyZct8x/Eo+e2ykfG7/hu0vSlKYdR2I788/d/F/8V4hefWKwW9xs2Tt3cr9A024v+o36DtM8V0cQ0Qn/Jf+dHz//2/tHdBMyqmZAPg3HV4+jNTspDF8O/ncfUBKOMOc0ZA3ylw4rKp/FosDJwOjwfDHxdNiUqW8EMwdhEsGF0kh1LsrsdCv69NrTtgOh/PfAWnZ/zXclQilKRYSg9JOIQoxZYtW8bRo0cZOnQoo0aNslhmNP534dWoUSPCw8NR3fVHe8CAAXzwwQeEh4fz8ssv4+vrS40aNahXrx5r1qyhadOmFi0ChWnnzp24uLgwe/ZstNr//gzd3arSqlUrli1bRnp6Ok899ZTVNoYPH87o0ZZfxP369WPAgAHMnz/fnHA89thjbNq0iT/++CPb7Xz55Zfo9XqWLl1KuXLlzOXt2rVj6NChLF26lJdffrlAx9m0aVOaNm3KypUr6d+/P+XLl8+xbvfu3Vm3bh0REREWCcfBgwe5efMmAwYMsDhXxS09PR2DoWQ96C78jAawbFlYf8FIaup/F4ARZ9UoRfDVt+CvDMbWT8fR0dQtRqfTYWdnZ/P9lhSqqFs4ZSUbj5K7k417GNcfJf3sFZyyko0segPp6w6hPnqBez8hyrojpKWmFn6cuUhLS7P4t6ho1h/BIeOeWRfOXiPt74sogX4PtG1n50c9+y9+JefbSwiRb5s2bcLd3Z0XXnjBapn6rn7BWRc9YGoVSU1NRVEUHn/8cTZu3MipU6do0aJFkcQM4Orqik6nY+/evbRs2dIiEcorJycn8/91Op15cHZYWBirVq0iOTn5vi0TycnJ7N27l27duuHg4EB8fLx5WYUKFQgICODgwYMFTjgAXn31VQYNGsTs2bMtWlju1bBhQypVqsTmzZt58803cXAw9fnO6k7VvXv3AsdgCydOnCjuEKw4pPoAlq1x5e3TiYw8Y/5ZFe8GVLN5LB6pVzh3LpFq1Uz7unr10br4VqdmUM/JDk1aAQfDPIRSyrrwz+1o6rvYo0nJsFh2SZWGi5OC/73rlHPlTGRk0QV5l6ioqCLdnyspBN9TZnTQcjruBsbIuAfaduPGjR9offHgJOEQohS7fPkywcHB5ovTnOj1ehYtWsSGDRu4cuUKyj1T6SQmJtoyTCtDhw7l2LFjjBs3Dg8PDxo1akSzZs1o3749Li4uedpGbGwss2fPZteuXdnO7pSXhCMqKgqj0Uh4eDjh4eHZ1vH3v/cSIH9q1qxJx44d2bRpE4MGDaJGjRo51n366aeZOXMmO3bsoFOnTiQmJrJr1y7q1KlD9erVHyiOwhYSElLiWjjGVocNMUZOxpqSba1a4fOWGmpVrWWuE6zADzeMHLllu0kaK7kZGfGkP3p9WbZs2UK1atUICAjAzc3NZvssiQzv9Ubz/nKrckWlQvWQTuelH9QCVWQ0miPnLcoVJ3vsPhtEzYa1MHzQB82EpeZlhvb1CBjcGeJSMG4+jfrcDdM6DnZoJw+mVq1aFKW0tDSioqKoUqWKxY0dm6tVC334KbTrjpqL9O/2IjisQdHFkBfSo6pAJOEQ4hEwdepUfv75Z9q3b8+wYcPw8vJCq9Vy+vRpZsyYYZWA2FqlSpX45ZdfOHToEIcPH+bYsWNMmjTJPGj9fmNGFEVh9OjRXLx4kX79+lG7dm1cXV1Rq9VERESwadMmiy5l99O5c+ccp5u9XzKXF6+88grbt29nxowZfPPNNznW69q1K7NnzyYiIoJOnTqxadMmMjIySlzrBhTOeSlszs5wZLDCqrMKN1Ohe6CK6p7W3Zh+H6iw6ISRSQcULhVyrl3TC/4eaodWbU9mZqa55c3R0fHR69bxXl/o2Ah++9s0oDpDD1fvoHqqMaToYPOfphmg/Lzg3DXY+IdpZikPFzhyzjRbEyrQqqGMBySnQVIaGBTQqDAqmMZQ8O8c/+5O4OsOsUmmfRkVyMg0bcNBC7p/W1tUKtPsUD5upkHsjnaQYfh3nIkK9HrQakyzaalUkJYOrk5QtxKcNU1kgcFgmlnr3V6mYzt0DhpXR9u2nmmfaw7CuRumQeXODqh6PYZjRV/Tum/3hvYNYNtxqOmPpktjnDUacHGBP7+Glb9DXDKqnk1xrFx8s9I5OTkV/Wc2/B3YcAxOXYHWIdiH1cC+aCMQNiIJhxClWOXKlYmKiiIjIwN7+5z/LG/YsIFGjRoxefJki/IrV65Y1S1I96aCsLe3p3nz5jRvbpqdZe/evbzxxhssXbqUt99+O9dY/vnnH86ePcuLL75o1d3p119/taqf03YCAgJQqVTo9XqaNm36AEeTO39/f5555hl++uknjhw5kmM9X19fnnjiCfbt28eNGzeIiIjA0dGRDh062Cy2h42jVsVztXP/DGvVKl6op+GFejnXSdQZKDtLIT2fuXifINP2xb9CA02v7ITl3NqXF7rUVCIjI6lVq1bxJ3Ot6v73f3u7/2adykmj6qbXvVwcYUhr6/JHhVoNXUNNL/FQkQf/CVGKZXW7mT9/vtWyu1st1Gq1VStGWlpatk/xzmpCt2U3q7vHSmSpWbMmAAkJCeYyZ2dnEhMTrWLPGp9yb/m5c+fYuXOn1bazjunubQN4enrSrFkzfvvtN/7++2+r9RRFIS7uwfoOZxk+fDguLi65tnCAaayG0Whk2rRpREZG0rZt2wLNkiUejLujhm/a5H+9v24XfixCCFHaSQuHEKVY//792bNnD/Pnz+fUqVM0bdoUBwcHLly4wKVLl/j2228BaNu2LatXr+add96hSZMmxMTEEBERgYeHh9U269Spg1qtZsGCBSQmJuLk5IS/vz8hISGFFveoUaNwc3OjYcOGlCtXjqSkJCIiIlCpVBYzSYWEhLBnzx6+/PJL6tWrh1qtJiwsjKpVq1KtWjV++OEHdDodlStX5vLly6xevZrAwEAi7xlkWbduXVasWMHnn39O8+bN0Wq1hISE4O/vz4QJE3jhhRd48cUX6dKlC8HBwRiNRqKjo9m9ezdPPfXUAw0az+Lp6cmgQYOYM2dOrvWaN2+Oj48P27ZtA+TZG8UpsACPYNn9aI0NF0KIPJGEQ4hSzM7OjpkzZ7JkyRI2b97Mt99+i729PZUqVTI/1A5gzJgxuLi4sHXrVnbt2kW5cuXo2bMntWvXZuTIkRbb9PPz48MPP2Tx4sV8/vnn6PV6unbtWqgJxzPPPMPWrVtZvXo1CQkJeHh4EBwczFtvvWUxFe9zzz1HdHQ027dvZ9WqVRiNRvOD/6ZPn860adNYt24daWlpVK9enYkTJ3L27FmrhKNjx46cOXOGLVu2sH37doxGIx999BH+/v74+fmxZMkSFi9ezK5du9i4cSP29vaUK1eOJ598kvbt2xfacQ8cOJCVK1dy586dHOtotVq6dOnCDz/8QEBAAI0aNSq0/Yv8aVNZC+jvW+9uTvKtKoQQVlRKUY8WFUIIIUoJ92l6kvKRc0x+EiY0NWUdmZmZLF++nNq1axMYGJhti6IouNSSNIbjISLnNXeqj/L2fBLl4yKc4asUkDEcQgghRA4S39DSsVLe6trxX7IhhHhIqfL4EhbkL6MQ4r4MBkOeBk/r9fr7Pg3b1dXV4kGEpUFmZqbVgPPseHl5odFo7ltPlC6b+mpRTcm9maOMHdx4Td57IYTIjiQcQoj7unnzZqENXv7oo48sxpeUBn/99RcjRoy4b721a9dSoUKFIohIlDSdq4O6iKaUFkKI0kYSDiHEffn4+DBr1qz71ktKSrrv05RL2hOz8yIoKChPx+/j41ME0YiSKDWjuCMQQhQJubFQIJJwCCHuy8HBwaYPxivp3N3dH+njF/fn51LcEQghRMklg8aFEEKIBxRZOM+HFEKIh5IkHEIIIcQDquZe3BEIIUTJJQmHEEIIcR+P++W+fE5nmaFKCCFyIgmHEEIIcR/7B2rJKaV4oY7MUCWEELmRQeNCCCFEHujHabmTYuDnMwpJadDYD9pXl69RIYS4H/lLKYQQQuSRr4uGUY2KOwohRLGRxswCkS5VQgghhBBCCJuRhEMIIYQQQghhM9KlSgghhBBCiDyRPlUFIS0cQgghhBBCCJuRFg4hhBAinxYuv82CTUnEazW4pWfQuo0XTzTzpF01DQ5auQMqhBB3k4RDCCGEyIcDhxP5fG8m56v6YdCYns5x+LKRkGO3eVel4a2B7jzXxLGYoxRC2ITcTygQ6VIlhBBC5MPEhXFEe7qZkw0AvUbNn+W88cnIZN6CO+iNSjFGKIQQJYskHEIIIUQeJcdnctTNHYMm+6/P3f4+VExOY9+faUUcmRBClFyScAghhBDZUBSFP3ff5NpPkRgz9Rw7n0b992NQFBVeukxUinUrhkGj5rK7C/+cSi6GiIUQomSSMRxCCCHEv25vu8q4mdfQo+ZgtSrEOTrimV6WKpsucrBcGXBxpnP0LWrExGNn0HPax4P1NSqSYm+HSlGofjuZFLWaq5lqpFeVEEKYSMIhhBDikXfjow04/W811zwq4fFkN6K9vMmw0xLn5IBPcjIVb93C80Yi5TUaymWkofm3daP+rTg8dRl8V7cGit7IeUd7nrwRw6UVScz4KpqouhWpXbuYD04IIYqZJBxCCCEeaTeeX4Ljkv3YG5zYH1ifW+6e/OnrzhU3Jz7duIkRvx9ArUC6Wst3rTuiUSx7I1dOTMElLZ0UOzsUlYp9lfzo9ecZ0uy0hByJZfFP7nz6QTEdnBBClAAyhkMIIcQj60LPH3FafJR0xZ0DTnUJOJVMu+1/csXZgbDLlxm535RsADgY9Tx59pTVNgwqFQa1Cv59GTRqLvq4Uz4plSteblTceI1D+xOK+MiEEDahyuNLWJAWDiGEEI+k2OD3KX/2Jhk48IdjPTLV9qiMClVuJrDusxXYO6WhYHntUCE+hjStFie93lx2x9WFGmmZ/O3oYCpQK1SKTQQgKMaUaOz74Axf/qZl61cViujohBCi5JCEQwghxCNHuZlA5j8JpGpcebtTb1bWb4CdwUDvg2cYtvMEGgw4JRm5RACOpOOhjudqmTKsadSEKF9fvFNScdTrSXJ0JM3Ojgpp6fzt5QqAxqjglp5psb+qMQkcuJLE05/cYu2HZYvjkIUQothIlyohHgEvvfQS3bp1K+4whCh+egMxbT/ntt+7+Co3mNsijGWhjcmw05DiaM8PLeuypV5lnMhE9W/fiFhHdxa17ER4WHPUGnsqJiQR5+LMFW8v4p2d0CgKOvV/7SAeunTsjUarXVeIS+RSdCYN37pB1B1DER60EEIUL0k4hBCihIqIiGDZsmXFHUappxiMZDR6j3Ttcxjt+uLy2wkcyECDgV+DQ63q7w/2t+hGdSbQnxQXJ/PPdkYj3qk6i5//cXM2/5zkYIdBZd2Ju+mFq5RJSuGm2pHq8/QMnp9UOAcohBAlnCQcQghRQkVERPDTTz8VdxilkpKuJ+611Zx2+JDjHp9w4bQBO4MOUGNEix2mJ4EHJMZareubZPmU8ERXJ6s69gZTC4W93oBnQhLVr9/GKVNP5ZgEup84zx0363Uc9QZCL13HLSUDp0wDP8Y6ovoyE+2UDBot1nM10UCKzrplRAghSjsZwyGEEKLkUBQ4cBYy9dC8FqjVGOLTiH1nGw47j6CyV8hIMZCRosY+RYeSmoad2oDWxZ5E5zLY3bqJoz4JPW6ogcokQ4YKA3bcoQIa9HhyGw1GFOD9Pb+ys0otdHb2AJRJSeCZU4e44eaCX1KKqSwmkdu+HhZhZmjUeKWmkaJR4xsTj4+TPX6pqVz29eBSWS9WNgxm3OYD1LkRY9Fa4pyWzmVfV3TOpv2hAgMq/rilUHGOYhqhruhxT07HJyUTBYV4Zzsy3ezQ2qnxsFOo7K2lQVnoWdlI3B8xqLwdqRjiiQpIyFDISNZz4KKePg3tqVOh6L/moxIUTscqNPFT4e0k0/UIISThEI+4iIgIPv74Y2bPns3p06dZuXIlt27donz58gwbNoyuXbsCcO3aNZ5++mlefPFFXn75ZYttzJ07l3nz5rF27VoqVDDNQDNx4kTWrVvHtm3bmDZtGnv27CEzM5OwsDDeeecdfH19Wb16NcuWLePatWuUL1+eV199lVatWhXp8R87dozvv/+ekydPotfrqVKlCn369KFHjx4W9U6cOMHKlSs5fvw4N2/eRKPREBgYyKBBg2jdurVF3axj37lzJzNmzOC3334jJSWFmjVrMmbMGEJCQvId5y+//MLOnTu5cOECcXFxeHh40KRJE1555RXzOc8SGhpK165d6dKlC99++y1nz57Fw8ODvn378vzzz5OYmGh+T1JTUwkLC+O9996jTJkyFtu5du0as2fP5uDBgyQlJVG2bFk6dOjA8OHDcXR0tDreI0eOWMWdFcvEiRPN28z6HNWuXZt58+Zx7tw53NzceOqppxg1ahRarenPcrdu3bh+/bp5O1nmzJlj8fNDJS4ZOn4Ch8+Zfq7pT8K4YZx+4QB6tARzE2/u4IapFSAVF+zJQGvIhHhwib/676xSCpBosWk9Wg65tqRF8k40/66vAppdOcvJb99iecjjOOkzeO74fnSpAVyo4M7IZ3tzxduThlHXaHf5Frd8vECl4h8vNzZXK0//E1FUTk0jsaw3AQpscHfDoFaj0WVi0BuZ8mRj/BJTeHX/n/j/m7ycLuuDztnO+thVKvN0WM46PX7xaaBSYW9QcE03EGWnJUGtIcEAl69B9LFYaqw/gLsuA4AFgf7MaR+KQ7wO3b/T+H68J5U3QrVMfcbZen828v5eA5MPKhgVcNLC4s5q+gRLZwrxEMmmu6S4P0k4hABmzZpFeno6vXr1wt7enpUrVzJx4kQCAgJo0KBBgbf72muvUbZsWUaMGMGVK1f4+eefGT9+PK1bt2bNmjV0794de3t7fv75Z95++21Wr16Nv79/4R1YLnbv3s348ePx8fFh4MCBODs7s2XLFiZNmkR0dDSjRo0y1925cydRUVG0a9eO8uXLk5CQwLp16xg/fjyTJk2iU6dOVtsfPXo0Xl5evPDCCyQkJLB06VJef/111q5di4uLS75iXbJkCSEhITz77LN4eHhw/vx5fv31Vw4fPszy5cvx9PS0qH/mzBn27NlDz5496dKlC1u3bmXmzJk4ODiwbt06KlSowEsvvWR+Tz766CO+/fZb8/rXr19nyJAhJCcn88wzz1CpUiWOHj3KwoUL+euvv/j222/NiUFB7Nu3j5UrV9K7d2+efvppdu3axY8//oibmxvDhg0DYOzYscycOZP4+HjGjBljXrdq1aoF3m9hSk9Px2Ao3IHPdp+vwi4r2QA4Hc2FUfvQ44Abcfhwy7K1gJR7tqDkOP29Fj11kk+jxTJmHc5Ui7/Nu3vXApCMBzEqV17u35+zZfwAuObtzmn/soReuMbWOtWIdXGi+eWb+KRnmLeTYqclQ6vBIzmdu5+4ccPdhe/DQnhv5yFOBpTjeDV/PJLSSXB3JCepTnbEuTtRLv6/aXm9ktNJzmoVAQbtPm5ONgCanovmaKVyHCp7V+KsUjHjiJ6xLZPxdrH9Rf/JGBX/O/BfMpWmh1e2GmhbXoejDa420tLSLP4VheNhPK/OzkWXdIvsScIhBJCRkcEPP/yAnZ3py7Jt27Z0796dFStWPFDCUadOHd5++22LsmXLlnHr1i1+/vlnXF1N02iGhYXRv39/1qxZw+jRowu8v7wyGAx8+eWXODk5sXjxYvPd/b59+/Lyyy+zePFiunXrRqVKlQAYPny4VVz9+vVjwIABzJ8/P9uEo2bNmkyYMMH8c7Vq1ZgwYQKbNm2id+/e+Yp3+fLlODlZ9olv0aIFI0eOJDw8nCFDhlgsO3fuHAsXLjS3pnTv3p2uXbvy9ddf07dvX8aPH29Rf9myZURFRVGlShXAlIDGxcUxbdo0mjdvDkCfPn2YPn06P/74I+vWrbNqBcqPCxcusGLFCnPrTO/evXn22Wf5+eefzQlHq1atWLZsGenp6Tz11FMF3petnDhxotC3Gbj3bzzuKUtLN/1OOvNgA6wzsaMcN63K4/FFhQpnEknBnZtUYXfdcuZkI8v5ct6c9/Uy/+x918U+gE9aOs4ZmdhnZIK9ZQvGRR8PpnVsBmo1KiAgNpU0Jzsy7DSmCopiddc01cH09WxUgUaBe58kVvl2vNWxVL0Vb5lwYHooYcT+yzQJsP3F44YbXkAli7IYnYodf12ginO6zfYbFRVls20/yh6m89q4cePiDuGRJwmHEJguJrOSDYCyZctSqVIlrly58kDb7d+/v8XPDRs2ZNmyZXTp0sWcbADUqFEDFxcXLl++/ED7y6vIyEhu3LjBgAEDLLoS2dnZMXjwYMaNG8euXbsYNGgQgMXFvk6nQ6czzdATFhbGqlWrSE5OtjgegAEDBlj8nNUNqCDnNGv/RqOR1NRU9Ho9QUFBuLq6ZnvhW7duXYuuW3Z2dtSpU4fdu3fTr18/i7pZ78mVK1eoUqUKRqOR3bt3ExwcbE42sjz//PMsXbqUnTt3PlDC0apVK4uuYCqVitDQUFasWEFqamqpuBsXEhJS6C0c2tYNYP8FizJnZz0pqfYk4v1A21ZQAxlW5R4kcJnaRBMIwN+VfXizd/cct6M2KtSIS7RqSbFTFOrfiiPayXqwuJ3RCOr/WhhUQLmbSSS6O+KoN3LTK5sB5hmmBwuqFFCAWDcHi+UXynkReDPOouyf8t5WyYu9YuSZFpVwcbB9C4fGDyZGWpaVdVJo26Aa9prC319aWpr5RsG9NyREwcl5vQ/pUVUgknAIAdl2Y/Lw8ODGjRuFul03NzcAq3EHAO7u7iQkJFiV28K1a9cAU6vDvapXrw5AdHS0uSw2NpbZs2eza9cuYmOtZ/XJLuG499izuj0V5BgPHz7MvHnzOHnyJOnplndKk5Ks73xn9366u7sD1uc+6z3JiisuLo7U1NRsz42Hhwe+vr4W56Ygcvq8ZcVRGhIOBweH+1fKr7d7w/6zsOPfJLJRNQI/bs+pXr+RlunKdSrhxxXUmAYpJOGBAzrsSf+3RI0KBeWerlUKEKvywU+5ls1OtZQhFh/i+MfHh+df6GkqNipw17M11AYjGBUGnLpExaRU83YVTNM9Rrk580d5b6rEpuCmN5CkNV1hqxSFChl67uWqN+IRm0qik51V64Zjhp4yCToMKkhxsCPGw5EUJ8tWkx9a1uf1DQfxSU7DCOypXZljNfxxjtehMygYVSrsFIXP29pTJpuExhYaOMPkJ418uM9IphE8HGBBJw2ebtmMWSlETk5OpeJ3prSR8yoKkyQcQgBqdfZ3/xTFdBmjymWQWG53eTWa7G/r5VSetb+SRFEURo8ezcWLF+nXrx+1a9fG1dUVtVpNREQEmzZtwpjNQ84K6xhPnjzJ6NGjCQgIYPTo0VSoUAEHBwdUKhXvvvtuvvZdmHFlyemzoddbX2Rmyenz9iBxPBTcnOC3T+DEJcg0QMNquAJhqYEkrPkH+832pKfVxPjPNdLvGElPdSRBUeNqn4jGQYOuemVUCfGoDp7DHiMOpJE1rsNZSUNBjQrLz0sSzqaJoVBx/e4Lc4Nibi2oFp+Chy4TnUZlTjbAdKPToFKxoYIvQ/6IpGxMHMlOjhwrX454Oy06tRqdix32GdbvqRpIcdAQ7eNs2g+gzTDgkp6Js07PbTcH4t0dMahBqwI/JyivzsBFpdCkugP1ynvSYnwHzh2OR+PjyOPVXZmggvh0V8poDRy5pKdDbTs8nG3QtJCLCU3VDA1R8U8cNCwLLvZyO1gIIQmHEHmSdXc8MTHRatmD3u0uDll32C9cuGC1LKssq84///zD2bNns52h69dff7VtoMCmTZswGAx88803Fi0DaWlp2bZuPCgvLy9cXFyyPTeJiYncuXOHoKAgc1nWZyMhIcHcSgGF87nILdF9qIVUtvhRpVXj2ScY+gSby7KbdsDtrv8rBiMxjb8h/a/b6HAggH8wYhpDof436UjEm7JEo2DkNkE8efk8HmlpJGR1IzGCo15Pg1uJqAH7DOsuWVpFoWZ8Epn2dlROSCYtRcey0CD0ajV6jRpFpSLFxZ7A6ATU/76fCgrpWg0xLvYYVCpcFT2j/FL5ZIAX9tqcB5Nn95VdtXOZbOtVLVN8X+/lXFSUy9+8EEKIh5zMVSdEHri4uODj48Phw4ct7kBfvXqVnTt3Fl9gBVSzZk38/PyIiIjgzp075nK9Xs+PP/6ISqWiZcuWwH934++9837u3LkiOfasFol7979gwYJsWzcelFqt5sknn+TMmTPs37/fYtmiRYswGo0W0xdnDaw/dOiQRd0lS5Y8cCzOzs4kJiY+2q0eBaTSqPH98w38lf9RXfcOqS/1IEFVlnScuUZ1ThNGEl6A0dQCQhyuGRmsXziHIUcOYafXU/3OHR6/Ef/fF6U6qzPXf4xA6PXbqNVqMh3sybDXkmZvR6ZWg/JvgpGh1eCVkkKSkxa1Qc8lHxduutjTvZIe41t2JL3lxOeDfbDXyleyEOLhJC0cQuRR3759mT17Nq+99hotW7bkzp07rFq1iurVq3Pq1KniDi9fNBoNb731FuPHj2fIkCH07NkTZ2dntm7dyt9//83QoUPNF9JVq1alWrVq/PDDD+h0OipXrszly5dZvXo1gYGBREZG3mdvDyZrtqbXX3+dnj17Ymdnx8GDBzl37pzVdLiFZdSoURw8eJBx48bxzDPPULFiRY4dO8bWrVtp1KiR+fksAB07duTbb7/lf//7H1FRUbi7u/P7778THx//wHGEhISwZ88evvzyS+rVq4darSYsLAxv7wcbQP3IcbDDa25vmNsb0jKw//08Xm0ncXf64EwMqfhT//pNZoav5pvw1WwrX4v5rduYZ51K02q55eqMT0oaWkVBp9XgG2c5JsnZYKRSXBKXve5qb1EU7ri7oaDitqM9TxiT+WGcL+W85CtYCPFokL92QuRR1nMZNmzYwNGjR6latSoffPABkZGRpS7hANO0st9++y3z58/nxx9/JDMzkypVqvD+++9bzMCk0WiYPn0606ZNY926daSlpVG9enUmTpzI2bNnbZ5wNGjQgC+//JLvv/+eOXPm4ODgQJMmTfjuu+948cUXbbLP8uXLs2jRIubMmcPGjRtJSkqiXLlyDB06lOHDh1s8g8PV1ZXp06fz9ddfs3DhQpycnGjTpg2ffvqp1UMR8+u5554jOjqa7du3s2rVKoxGI3PmzJGE40E42aNpUwun8/+Hrvqb5mI9rtzd6K8GGt28hs9PO9lSvxrL29TnqoczLaJjSXRwQK2AvT4Tv9h4q114pKTDXQmHd3I6dkaFTIORSc2NDOrtZ7WOEEI8zFSKtNULIYR4BMXX/AyHM6dwIhUdvujvGRmSjh2nqQFARONAvujVDC9dBk9ci8PJYERRFGpGX8c9I/O/dTRqPmvZCDedHrVKwSndgGu6HiPQp7mGif18ivIQH2qpqalERkZSq1YtmU2pEMl5zZ1qsvVYruwo79jfv9IjRFo4hBBCPJI8T79LTJs5ZOz4A5XWiPqeicVS+W/WqmanrwIQ52jPhqpl8UrLIE1vZI+nCz3PX6FKYjI3XZxYW6sKOjstvknp5pmtPO/E8ebL3vRqLcmGEOLRJAmHECVQXFzcfR+qptfrLbr2ZMfR0dHq+Rglxd2D1XPi6uqKo2Nus/YI8WB8fhtB2raLZLSfjIZUDJju6KbhwHXKmevddHMGvRE0KhQFYu20+OvSqBmfzJqgytzycIZ/n72hNRjJ0MAFXxdCz1yhQpgjbRvJ3U4hxKNLEg4hSqDBgwdz/fr1XOs0atSIY8eO5Vqna9euTJw4sRAjKzydOnW6b52PPvqIbt26FUE04lHm1K4q7HyP6B5LyTAk4Jmm42/7IFxTTUl/plrNTy1DePrcFfZWKEOCvR1+qTrUipGup87RCYXLri7otBque7ljdHbiYAVv/G7G4dQwg/GtUgCv4j1IIUQheUSnK39AknAIUQJ9+umnVk/Uvpe7u3u2zwW5W5ky2c3RXzLMmjXrvnWynnouhK05taxMYNy73DkXy7cjD+F2/TbeyXZcdffhurc7oddvcNXHh5dPXiRTrWJmwyCeioxCbTDiE59Mmdv//S7e9PXgjaFNafV4OZYv3wHULr4DE0KIEkASDiFKoAYNGhR3CDbXtGnT4g5BCCu+gd58uOW/1rfU2zr0aQbcK7kQ8XsyGz/7h2RnR3RaDahV2KdnoDZazr3iHZ9MnJ18vQohRBZ5ypAQQgiRA+cyjrhXMs1e1e1xVyb/UJtb9lrUisJv1QLQq62/RtUGI97edkUdqhCiKKjy+BIWJOEQQggh8sjDy4Eq1Z2ofyeBeCd71terYfX08chy3rQKlBYOIYTIIgmHEEIIkQ8z36uAe3om3S9cJ8nDjcO1q5Hg7Ei6Rs1pPx96ftcErUZucQohRBa5BSOEEELkg1ajYsvMCvR9+xqqVCN/VSxL/QCF7h/VoVwFmcZZCCHuJQmHEEIIkU/2WjW//l/AXSUBOdYVQohHnXSpEkIIIYQQQtiMJBxCCCGEEEIIm5EuVUIIIYQQQuSFzAdRINLCIYQQQgghhLAZSTiEEEIIIYQQNiMJhxBCCCGEEMJmJOEQQgghhBBC2IwkHEIIIYQQQgibkYRDCCGEEEIIYTMyLa4QQgghhBB5IdPiFoi0cAghhBBCCCFsRhIOIYQQQgghhM1IwiGEEEIIIYSwGUk4hBBCCCGEEDYjCYcQQgghhBDCZiThEEIIIYQQQtiMJBxCCCGEEELkhUqVt1cuZsyYQcOGDYso4JJBEg4hhBBCCCGEzUjCIYQQQgghhLAZSTiEEEIIIYTIC1UeXw/gzJkzDB8+nAYNGtC4cWNee+01rl27Zl7+7rvvMmDAAPPPsbGx1KxZk969e5vLUlJSqFOnDhs3bnywYAqJJBxCCCGEEEKUANevX2fgwIHExcXx1Vdf8fHHH3Py5EkGDhxIcnIyAGFhYfz999+kp6cDcOTIEezt7YmMjDTX+eOPP9Dr9YSFhRXbsdxNW9wBCCGEKF3OnDlDRkZGcYdRKgQGBgIQFRWFWi33+AqToigAnDt3DtV9BumKvHsYz6u9vT3BwcHFHUaeLFq0CL1ez4IFC/D09ASgVq1adOnShTVr1jBo0CBCQ0PJyMjgr7/+okmTJhw+fJj27duzd+9ejh07RosWLTh8+DBVqlTB19e3eA/oX5JwCCGEEDaSkpJCSkoK5cqVK+5QHjq3bt0CkHNbyOS85k4ZZ9tL5yNHjtC0aVNzsgFQvXp1atasydGjRxk0aBAVK1bEz8+Pw4cP06RJE44cOUK/fv3Q6XQcPnyYFi1acOTIkRLTugGScAghhMin0nKnsCR4//33AQgPDy/mSB4+cm5tQ85r8UpMTKRWrVpW5T4+PiQkJJh/DgsL48iRIyQnJ3P69GlCQ0NJS0tj06ZNZGRkcPz4cfr06VOUoedK2neFEEIIIYQoATw8PIiJibEqj4mJwcPDw/xzWFgYf/75JwcPHsTLy4vq1asTGhrKiRMnOHDgABkZGYSGhhZl6LmShEMIIYQQQogSoHHjxhw4cMCiNePChQucOXOGxo0bm8tCQ0NJTU1l0aJF5sSiVq1aODg4MG/ePMqXL09AQECRx58T6VIlhBBCCCFEETIYDGzatMmqfPDgwaxevZphw4bxyiuvkJ6ezrRp0yhfvjw9e/Y016tevTo+Pj4cOnTI3A1Oo9HQqFEjdu/eTbdu3YrsWPJCEg4hhBBCCCGKUHp6Oq+//rpV+ZdffsmPP/7Il19+ybhx41Cr1TRr1owJEybg6upqUTc0NJTNmzdbDA4PCwtj9+7dJWrAOIBKyZr/TAghhBCFqnv37oAMwLUFObe2IedV2IKM4RBCCCGEEELYjLRwCCGEEEIIIWxGWjiEEEIIIYQQNiMJhxBCCCGEEMJmJOEQQgghhBBC2IwkHEIIIYQQQgibkYRDCCGEuI+oqChGjhxJ8+bN6dixI9OnTyczM/O+6ymKwqJFi+jSpQvNmjVj6NCh/P3331b1bt++zfjx42nRogVt2rTh008/JTk52RaHUuLY8twePHiQd955h27dutGsWTP69OnDDz/8gF6vt9XhlBi2/sxmMRqNDBw4kNDQULZt21aYhyAeIpJwCCGEELlITExkxIgR6PV6vvrqK0aOHMmaNWv4+uuv77vu4sWLmTt3LgMGDGDq1Kn4+voyevRorl69aq6j1+sZPXo0ly9fZtKkSUyYMIEDBw6Ynx78MLP1uV29ejWpqam8/PLLTJ8+nS5dujB37lz+97//2fKwip2tz+vdVq9eze3btwv7EMTDRhFCCCFEjhYsWKA0b95ciY+PN5etWrVKadKkiXLr1q0c19PpdEqLFi2UmTNnmssyMjKUrl27KpMnTzaXbdy4UQkNDVUuXrxoLvv999+Vxo0bK3///XfhHkwJY+tzGxcXZ7Xu/PnzldDQ0GyXPSxsfV6zxMXFKW3atFHCw8OVxo0bK1u3bi3cAxEPDWnhEEIIIXKxf/9+mjRpgoeHh7msffv2GI1GDhw4kON6x48fJyUlhXbt2pnL7OzsaN26Nfv27bPYfo0aNahSpYq5rGnTpnh4eFjUexjZ+tx6enparRscHIyiKNy5c6dwDqIEsvV5zTJz5kwaN25MaGho4R6AeOhIwiGEEELkIioqyiIZAHBzc8PX15eoqKhc1wOs1q1atSo3btxAp9OZ61WuXNmijkqlonLlyrlu/2Fg63ObnT///BN7e3sqVKhQwKhLvqI4rydOnGDTpk288cYbhRO0eKhJwiGEEELkIjExETc3N6tyNzc3EhMTc13P3t4eBwcHq/UURSEpKQmApKSkbLfv7u6e6/YfBrY+t/e6fPkyy5cvp3fv3jg7Oz9Y8CWYrc+r0Wjkyy+/ZODAgQ914iYKjyQcQgghhHjoJScnM378eCpUqMDIkSOLO5xS7ddffyUmJobnn3++uEMRpYQkHEIIIUQu3N3ds52iNikpCXd391zXy8jIID093Wo9lUplvgPt5uaW7fYTExNz3f7DwNbnNktmZibjx48nKSmJ6dOn4+TkVDgHUELZ8rympqYya9Yshg0bRmZmJklJSaSkpACg0+kememcRf5IwiGEEELkokqVKlb93pOTk7lz545VX/d71wO4dOmSRXlUVBR+fn44OjrmuH1FUbh06VKu238Y2Prcgqn7z/vvv09kZCTffPMNfn5+hRV+iWXL8xofH09CQgKTJ0+mdevWtG7dmv79+wMwceJEevfuXZiHIh4S2uIOQAghhCjJnnjiCRYuXGgx1mLbtm2o1Woee+yxHNerV68eLi4ubNu2jaCgIMD0zI0dO3bQrFkzi+1v3LiRy5cvU6lSJQAOHTpEQkKCRb2Hka3PLcAXX3zBnj17mDFjBoGBgbY7mBLElufVx8eHOXPmWKwXExPDe++9x0svvUTTpk1tdFSiNJOEQwghhMhF7969+fnnnxk7dizDhg3j1q1bTJ8+nV69elGmTBlzvVdeeYXr16/z66+/AuDg4MDQoUP57rvv8PLyIjAwkF9++YWEhAQGDhxoXq9du3YsXLiQt956i1GjRqHT6Zg2bRrNmzcnJCSkqA+3SNn63C5YsIBVq1YxaNAg7O3tLZ6YXbVqVVxdXYvsWIuSLc+rg4OD1TS4165dA6BatWrUr1+/aA5SlCqScAghhBC5cHd3Z/bs2Xz11VeMHTsWFxcXevToYTXw2GAwYDAYLMqGDBmCoigsWbKEuLg4goKCmDFjBgEBAeY6Wq2WGTNm8NVXX/Hee++h0Who3bo1Y8aMKZLjK062PrdZz5z48ccf+fHHHy3WnzNnzkP7/Ahbn1ch8kulKIpS3EEIIYQQQgghHk4yaFwIIYQQQghhM5JwCCGEEEIIIWxGEg4hhBBCCCGEzUjCIYQQQgghhLAZSTiEEEIIIYQQNiMJhxBCCCGEEMJmJOEQQgghhBBC2IwkHEIIIYQQQgibkYRDCCGEyMaECRMIDg4u7jAAOHv2LLVr12bfvn3msoMHDxIcHMzq1auLMTJREqxevZrg4GAOHjxYoPXls5S9yMhIatasyaFDh4o7lFJPEg4hhHiEXLlyhQ8++IBOnTpRv359wsLC6Ny5M2+//TYHDhywqNumTRu6du2a47ayLshjY2OzXX7+/HmCg4MJDg7myJEjOW4nq07Wq27dunTo0IHJkycTHx9foON82Hz++ec0atSIZs2aFXcoReLq1avMmDGDyMjI4g5FFJHExERmzJhR4KSpoHL7rNWqVYt27drx+eefoyhKkcb1sNEWdwBCCCGKxt9//82gQYPQarX06NGDwMBAdDodly5dYt++fbi4uPDYY48V2v5WrlyJi4sLjo6OrFq1itDQ0Bzr1qpVi6FDhwKQkJDArl27WLRoEfv372fVqlXY29sXWlylzR9//MG+ffuYNWuWRXlYWBjHjx9Hq334vsqjo6OZOXMm/v7+1KpVq7jDEUUgMTGRmTNnMnr0aJo2bVpk+73fZ23IkCEMHDiQXbt20apVqyKL62Hz8P2VEkIIka1Zs2aRlpZGeHg4NWvWtFp++/btQttXZmYm4eHhdOrUCTc3N1asWMF7772Hq6trtvXLlStH9+7dzT8PHjyYESNGsGPHDrZv307nzp0LLbbSZtmyZXh5edGyZUuLcrVajYODQzFFJcSjITQ0FH9/f5YvXy4JxwOQLlVCCPGIiIqKwtPTM9tkA6BMmTKFtq8dO3YQExNDz5496dmzJ6mpqWzcuDFf22jevDkAly9fzrHOsmXLCA4OZvv27VbLjEYjLVq0sEhk9u7dyxtvvEHbtm2pV68eoaGhDBs2LM99tAcNGkSbNm2syq9evUpwcDAzZsywKFcUhWXLltGrVy/q169Pw4YNGTRokFX3tZzo9Xq2bdvGE088gZ2dncWy7Prd3122dOlSOnbsSN26denWrRs7duwA4MyZMwwfPpxGjRrRtGlTJk2aRGZmZrbHeeXKFV555RUaN25Mo0aNGDVqFFeuXLGoazQamT17Ns899xzNmjUjJCSEVq1a8dFHHxEXF5ftcW3evJlBgwYRGhpK/fr16dixI5MmTSIjI4PVq1czePBgAN555x1zV7tBgwbd93xdvXqV8ePH88QTTxASEkK7du34+uuvSUtLs6g3Y8YMgoODuXDhAl9//TUtWrQgJCSEp59+ml27dt13P/DfuInff/+dmTNn0rp1a+rVq0efPn34888/ATh06BD9+/enQYMGNG/e3KqVKsu2bdvo168fDRo0oGHDhvTr149t27ZlW3fFihV06tSJkJAQ2rdvz6JFi3Ls7pOUlMRXX31F+/btCQkJ4bHHHmPMmDFW72F+5fU85zYOKjg4mAkTJgCmz23btm0BmDlzpvk9z/pdu/v3a926dXTr1o26devSqlUrZsyYgV6vt9h2Xn9P8/JZU6lUNG/enD179pCSklKQ0yWQFg4hhHhkVKpUiYsXL7JlyxY6dOiQp3UMBkOOYzQyMjJyXG/lypUEBAQQGhqKSqWidu3arFq1ij59+uQ53qioKAC8vLxyrNOlSxcmT55MeHi4+YIly++//87NmzcZNmyYuWzNmjUkJCTQo0cP/Pz8uHnzJr/88gvPP/88P/zwQ67dvgpi/PjxrF+/no4dO9KrVy8yMjKIiIhg2LBhzJgxwyrme508eZLU1FTq1auXr/0uXbqUxMRE+vTpg729PT/++COjR49m+vTpvP/++3Tt2pV27dqxb98+fvzxR7y9vRk5cqTFNlJTUxk0aBD16tVjzJgxXLp0iWXLlvHXX3+xZs0ac4KamZnJ/Pnz6dChA23btsXJyYm///6bVatWcezYMasucVOnTmXOnDkEBgby/PPPU6ZMGS5fvsyWLVt47bXXCAsLY8SIEcyZM4dnn32Wxo0bA+Dr65vrMUdHR9OnTx+SkpIYMGAAlStX5tChQ8ydO5djx46xaNEiq+5nEyZMQKvVMmzYMDIzM1m8eDGjRo1i06ZNBAQE5OlcT5kyBaPRyODBg8nMzGTBggUMGzaML7/8kvfee4++ffvSrVs3Nm7cyDfffENAQIBFErx06VI++eQTqlWrZn4P1qxZw6hRo/jkk0949tlnzXUXLVrE5MmTqVmzJmPGjCEtLY0FCxbg4+NjFVdSUhL9+vXj2rVr9O7dmxo1anD79m2WLVtGnz59WLVqFf7+/nk6xgc9z/dTvXp13nnnHSZPnkz79u1p3749AC4uLhb1fvvtN65cucJzzz2Hr68vv/32GzNnzuTatWtMnjw538eS189aw4YN+fnnnzl69CgtWrTI934EoAghhHgkHDt2TKlTp44SFBSkdOjQQZkwYYKydOlS5dy5c9nWb926tRIUFHTfV0xMjMV6N27cUGrVqqV888035rJFixYpQUFB2e4rKChIGTZsmBITE6PExMQoFy9eVBYuXKjUqVNHady4sXLnzp1cj+vVV19VQkJClPj4eIvycePGKbVr17ZYPyUlxWr927dvK02aNFFeeOEFi/K3335bCQoKsigbOHCg0rp1a6ttXLlyRQkKCrI45i1btihBQUHK8uXLLepmZmYqPXv2VFq3bq0YjcZcj23lypVKUFCQsm3bNqtlBw4cUIKCgpRVq1ZZlTVv3lxJTEw0l0dGRipBQUFKcHCwsnnzZovt9OzZU2nWrJnVcQYFBSmTJk2yKM86pg8++MBcZjQalbS0NKv4VqxYoQQFBSnr1683l/31119KUFCQMmjQIEWn01nUNxqN5vOR3bHdz5gxY5SgoCBl586dFuWff/65EhQUpKxYscJc9s033yhBQUHKSy+9ZPEeZMU3ZcqU++5v1apVSlBQkNKjRw8lPT3dXL5t2zYlKChIqV27tnL8+HFzeXp6utKsWTOlb9++5rL4+HilQYMGSrt27ZSkpCRzeVJSktK2bVulQYMGSkJCgqIoipKQkKDUr19f6dy5s5Kammque/36daVBgwZKUFCQcuDAAXP5p59+qtStW1eJjIy0iPvq1atKw4YNlbfffttclp/znZ/znN3vUJagoCCLGLL7Hbp3Wc2aNZUTJ06Yy41GozJy5EglKChI+eOPP8zl+fk9zcuxHz58WAkKClLmz5+fYx2RO+lSJYQQj4iGDRuyatUqevbsSVJSEqtXr+bjjz/mqaee4rnnnsu2m4W/vz8LFy7M9pXV5elea9aswWg00qNHD3NZt27dsLOzY+XKldmus3fvXh5//HEef/xxOnbsyOTJk6levXqOd2/v1rNnTzIyMtiwYYO5LCUlhW3btvHkk09arO/s7GxRJy4uDrVaTf369Tl+/Hiu+8mvtWvX4uLiQrt27YiNjTW/EhMTadOmDdHR0eZWnJxktS55eHjka9+9evXCzc3N/HPNmjVxdXWlbNmyVq1bjRo14vbt29l2F3nppZcsfm7fvj1Vq1a16MKmUqlwdHQETC1iiYmJxMbGmicguPu8rl27FoCxY8dajT9RqVSoVKp8HWcWo9HIb7/9Ru3ata3Gurz88suo1epsuygNHjzYYp/16tXD2dmZS5cu5Xnf/fv3t2jByWolq1evHnXr1jWX29vbU7duXYv3fN++feaWpLvHN7m6ujJo0CBSU1PZv38/YPodSUtL47nnnsPJyclc18/Pj27dulnEpCgKERERhIWFUbZsWYvPn5OTEw0aNGDv3r15PsYsBT3PheWJJ56gTp065p9VKhUvvPACAFu3brXZfrNaWWNiYmy2j4eddKkSQohHSHBwMJ9//jlg6hpx+PBhfvnlF44cOcLIkSOtur84OzvzxBNPZLutrIvHuymKwqpVqwgODsZoNFpcuDVs2JC1a9cyduxYqy4X9evX54033gBMF2YVKlSgQoUKeTqmrKQiPDyc/v37A7BlyxZSU1Mtuq6AaTzI1KlT2bt3L4mJiRbLCnqxm5Pz58+TkpKS4/kD0wVM1apVc1xe0Jiy6w7k4eGBn59ftuUA8fHxFl1Y3N3dsx3XU716dbZt20Zqaqo5gduwYQMLFy4kMjLSajxIQkKC+f+XLl1CpVLlOI6ooGJjY0lNTSUwMNBqmaenJ2XKlMk2oa5YsaJVmZeXV45jT7Jz7zayzmdO78HdUz1fvXoVgBo1aljVzSrLijurbrVq1azqVq9e3eLn2NhY4uPjzYl8dtTq/N9zLuh5Liz3HidgjsWW+1X+HSNT2H8jHiWScAghxCPK398ff39/unfvzoABAzh27BjHjx9/oHEMhw4dMg/yzmmcyM6dO2nXrp1FmZeXV64X5rnRarV07dqVxYsXc+nSJSpXrsyvv/6Kh4eHxRiJlJQUnnvuOdLS0hgyZAhBQUG4uLigVquZO3dungdyZ8dgMFiVKYqCt7c3//d//5fjetldaN7N29sbIN/PI9FoNPkqBwr8nIEtW7bw5ptvUq9ePd59913Kly+Pg4MDBoOBF154wWq7D9KSUdgKctGd123kdq5tLeucP/HEE7z44ovFFkdO7/O9g7yLSna/p3mR9fuX9fso8k8SDiGEeMSpVCrq16/PsWPHuHXr1gNtK6uF5Isvvsj2Quyjjz5i5cqVVgnHg+rZsyeLFy/m119/pW/fvhw6dIi+fftatNb8/vvv3Lp1i88++4zevXtbrD9t2rQ87cfT05OTJ09alWd3d7Vy5cpERUVRv359q8GveZWVkOSni09hSUxM5Pbt21atHOfPn8fHx8fcuhEeHo6DgwM//PCDRVef8+fPW22zSpUq7N69m9OnT+c6ED6/CYm3tzcuLi6cO3fOallCQgK3b98ukc/zyGod+eeff6xaIrKOJatOVovJhQsXrOree669vb1xd3cnOTm5wIl8dvJ7nu9uPfP09DSXZ/f7kpf3PLvP1L3nCfL3e5qX/WbdRLnfDQKRMxnDIYQQj4h9+/Zle2dRp9Oxb98+IPsuC3mVlJTE5s2badasGU899RSdOnWyerVp04bdu3c/cGJzr1q1ahEcHMzatWsJDw/HaDTSs2dPizpZd5zvveO+d+9e/vrrrzztp0qVKqSkpFiMSzAajSxatMiqbo8ePTAajXz99dfZbuvOnTv33V/t2rVxdXXNc3yF7bvvvrP4eevWrVy8eNEiYdRoNKhUKoxGo7lMURRmz55ttb2ssQZff/11trOcZb03WcnM3d2xcqNWq2ndujWnTp1i9+7dVsdgNBoLPcktDM2aNcPZ2ZklS5aQnJxsLk9OTmbJkiU4Ozubny7frFkzHB0dWbp0qcX0szdu3CAiIsJiu2q1mm7dunH8+HE2bdqU7b4LMh4hv+e5SpUqAOZxKFkWLlxote28vOf79++3SCQUReH7778HsNpvXn9P87LfP//8E61WS6NGjXKsI3InLRxCCPGImDx5MvHx8bRp04agoCAcHR3NFytRUVH06NEjxznz82LdunXodDo6duyYY50OHTqwevVqfv31V6sByQ+qZ8+efP7558ybN48qVarQoEEDi+WNGzemTJkyfPHFF0RHR+Pn50dkZCTh4eEEBQVx9uzZ++6jb9++LFy4kFGjRjF48GDs7OzYvHlztl01OnXqRK9evViyZAknT56kdevWeHl5cePGDf78808uXbqU7fND7qbRaOjQoQPbtm0jIyOjSJ+47uXlxdatW7l16xZNmjQxT4vr6+vL6NGjzfU6duzI5s2bGTJkCD169DA/O+TeZzKAaSD1iy++yLx58+jVqxedO3emTJkyXL16lc2bN/PLL7/g7u5OYGAgLi4uLFu2DEdHR9zd3fH29s5xPALAmDFj2L9/P6NGjWLAgAFUqlSJI0eOsGHDBsLCwqwS0JLA3d2dcePG8cknn9C3b19zjGvWrOHSpUt88skn5sH/Hh4evP7663zxxRf069ePHj16kJaWxvLly6lSpQqnTp2y2Pabb77JsWPHeOONN+jcuTP169fHzs6Oa9eusXv3burUqWMez5Uf+TnPXbt2ZerUqXz44YdcuHABT09P9uzZk+0YGS8vLypXrsz69eupWLEivr6+ODk5WTxPo2bNmgwZMoTnnnuOMmXKsH37dvbv30/37t1p2LChuV5+fk/v91lTFIW9e/fy5JNPFrilUkjCIYQQj4wJEyawfft2jh49yubNm0lKSsLNzY2goCBefPFFevXq9UDbX7lyJVqtNtsHbmVp1qwZLi4urFq1qtATjm7dujFlyhSSk5PNM9fczd3dne+//56vvvqKJUuWoNfrCQkJYd68eaxcuTJPCUfFihWZNWsWX3/9NdOnT8fT05Pu3bvTu3fvbJ+GPnnyZJo2bcqKFSuYO3cumZmZlClThtq1azN27Ng8HVf//v1ZvXo1O3bsyDWZK2zOzs4sXryYzz77jP/7v/9DURSefPJJJkyYQNmyZc31unTpQkpKCosWLeKLL77Aw8OD1q1bM3bsWJo2bWq13XHjxlGzZk2WLFnC999/j6Io+Pn50aJFC/NsV46OjkydOpVp06bx2WefkZGRQZMmTXJNOPz9/VmxYgXffPMNa9euJSkpiXLlyvHyyy/zyiuv5PvZEEXlueeeo2zZssyfP9/8YMCaNWsya9Ysq1aZYcOG4ezszMKFC/m///s/ypcvz7Bhw3Bzc+Pdd9+1qOvm5sZPP/3EggUL2LRpE9u3b0ej0eDn50fjxo3z9Uycu+XnPLu6uvLdd98xefJk5s6di7OzMx06dOCrr74iLCzMattTpkzhs88+Y+rUqaSlpeHv72/x96RNmzZUrVqVuXPncvHiRXx8fBg5cqTVM2Ty83t6v8/a4cOHiY6O5sMPPyzQ+RImKqWgo8SEEEIIUSSGDx9OWloay5YtK5L9DRo0iOjoaH777bci2Z8Qubl69Spt27Zl9OjRvPrqq0W671GjRnH9+nVWrVpVYiY7KI1kDIcQQghRwk2YMIE///yzQM9OEEIUzKlTp9i+fTsTJkyQZOMBlcz2RSGEEEKY1ahRw6qPvhDCtmrXrs3p06eLO4yHgrRwCCGEEEIIIWxGxnAIIYQQQgghbEZaOIQQQgghhBA2IwmHEEIIIYQQwmYk4RBCCCGEEELYjCQcQgghhBBCCJuRhEMIIYQQQghhM5JwCCGEEEIIIWxGEg4hhBBCCCGEzUjCIYQQQgghhLAZSTiEEEIIIYQQNvP/Ps6BPDRQcM8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SHAP Summary Plot\n",
        "try:\n",
        "    shap.summary_plot(\n",
        "        shap_values,\n",
        "        X_train_processed,\n",
        "        feature_names=feature_names\n",
        "    )\n",
        "except:\n",
        "    shap.summary_plot(\n",
        "        shap_values[0],\n",
        "        X_train_processed,\n",
        "        feature_names=feature_names\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "mMr_KrhPeLQ1",
        "outputId": "7e8526a1-1e43-46a4-f597-21d9a078970d"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1150x660 with 6 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SHAP Feature Importance\n",
        "try:\n",
        "    shap.summary_plot(\n",
        "        shap_values,\n",
        "        X_train_processed,\n",
        "        feature_names=feature_names,\n",
        "        plot_type=\"bar\"\n",
        "    )\n",
        "except:\n",
        "    shap.summary_plot(\n",
        "        shap_values[0],\n",
        "        X_train_processed,\n",
        "        feature_names=feature_names,\n",
        "        plot_type=\"bar\"\n",
        "    )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 951
        },
        "id": "DLkkVXYMeN5e",
        "outputId": "f78daefa-b60e-4deb-fc99-27b5ed5d31e3"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x950 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Partial Dependence Plot\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(12, 6))\n",
        "\n",
        "PartialDependenceDisplay.from_estimator(\n",
        "    best_rf,\n",
        "    X_train,\n",
        "    features=[\"interest_rate\", \"annual_income\"],\n",
        "    target=0,\n",
        "    kind=\"average\",\n",
        "    grid_resolution=50,\n",
        "    ax=ax\n",
        ")\n",
        "\n",
        "plt.suptitle(\n",
        "    \"Partial Dependence Plot for 'Charged Off'\",\n",
        "    fontsize=18,\n",
        "    fontweight=\"bold\"\n",
        ")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "KN-jV9KgeQks",
        "outputId": "6fd2210d-3d6c-4443-c498-5b8921a9e5e5"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model Comparison"
      ],
      "metadata": {
        "id": "isoJBjfeeT82"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance Comparison Table\n",
        "comparison = pd.DataFrame({\n",
        "    \"Metric\": [\n",
        "        \"Accuracy\",\n",
        "        \"Precision\",\n",
        "        \"Recall\",\n",
        "        \"F1-Score\",\n",
        "        \"ROC-AUC\"\n",
        "    ],\n",
        "    \"Logistic Regression\": [\n",
        "        lr_accuracy,\n",
        "        lr_precision,\n",
        "        lr_recall,\n",
        "        lr_f1,\n",
        "        lr_auc\n",
        "    ],\n",
        "    \"Random Forest\": [\n",
        "        rf_accuracy,\n",
        "        rf_precision,\n",
        "        rf_recall,\n",
        "        rf_f1,\n",
        "        rf_auc\n",
        "    ]\n",
        "})\n",
        "\n",
        "comparison"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zj4qKdnneRTR",
        "outputId": "559d5df2-75af-4490-b342-7e090b7e9f31"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Metric  Logistic Regression  Random Forest\n",
              "0   Accuracy                 0.82           0.94\n",
              "1  Precision                 0.88           0.88\n",
              "2     Recall                 0.82           0.94\n",
              "3   F1-Score                 0.85           0.91\n",
              "4    ROC-AUC                 0.52           0.56"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-270e9d03-ff83-4f9d-a54b-8c681fff3175\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Metric</th>\n",
              "      <th>Logistic Regression</th>\n",
              "      <th>Random Forest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Precision</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Recall</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>F1-Score</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ROC-AUC</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.56</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-270e9d03-ff83-4f9d-a54b-8c681fff3175')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-270e9d03-ff83-4f9d-a54b-8c681fff3175 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-270e9d03-ff83-4f9d-a54b-8c681fff3175');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "  <div id=\"id_bb8172d1-284a-4acd-85f8-4141dae4a3dd\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('comparison')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_bb8172d1-284a-4acd-85f8-4141dae4a3dd button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('comparison');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "comparison",
              "summary": "{\n  \"name\": \"comparison\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Metric\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Precision\",\n          \"ROC-AUC\",\n          \"Recall\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Logistic Regression\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.14642006940699206,\n        \"min\": 0.5217893815604477,\n        \"max\": 0.8821887190822518,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8821887190822518,\n          0.5217893815604477,\n          0.8225\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Random Forest\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.15943190335676774,\n        \"min\": 0.5625912011802939,\n        \"max\": 0.937,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          0.8788769384692345,\n          0.5625912011802939,\n          0.937\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performance Comparison Chart\n",
        "comparison_plot = comparison.melt(\n",
        "    id_vars=\"Metric\",\n",
        "    var_name=\"Model\",\n",
        "    value_name=\"Score\"\n",
        ")\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "# Create comparison chart\n",
        "ax = sns.barplot(\n",
        "    data=comparison_plot,\n",
        "    x=\"Metric\",\n",
        "    y=\"Score\",\n",
        "    hue=\"Model\",\n",
        "    palette=\"Set2\"\n",
        ")\n",
        "\n",
        "# Add score labels\n",
        "for p in ax.patches:\n",
        "    ax.annotate(\n",
        "        f\"{p.get_height():.3f}\",\n",
        "        (\n",
        "            p.get_x() + p.get_width()/2,\n",
        "            p.get_height()\n",
        "        ),\n",
        "        ha=\"center\",\n",
        "        va=\"bottom\",\n",
        "        fontsize=9\n",
        "    )\n",
        "\n",
        "plt.ylim(0, 1.05)\n",
        "\n",
        "plt.title(\"Performance Comparison of Machine Learning Models\", fontsize=18)\n",
        "plt.xlabel(\"\")\n",
        "plt.ylabel(\"Score\")\n",
        "\n",
        "plt.legend(title=\"Model\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "WUiqlcnWeZic",
        "outputId": "ad0b437d-2b92-43f5-b681-5d9ed07e7768"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import label_binarize\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "# Binarize the target labels\n",
        "y_test_bin = label_binarize(\n",
        "    y_test,\n",
        "    classes=np.arange(len(label_encoder.classes_))\n",
        ")\n",
        "\n",
        "# Compute micro-average ROC for Logistic Regression\n",
        "fpr_lr, tpr_lr, _ = roc_curve(\n",
        "    y_test_bin.ravel(),\n",
        "    y_prob_lr.ravel()\n",
        ")\n",
        "\n",
        "roc_auc_lr = auc(fpr_lr, tpr_lr)\n",
        "\n",
        "fpr_rf, tpr_rf, _ = roc_curve(\n",
        "    y_test_bin.ravel(),\n",
        "    y_prob_rf.ravel()\n",
        ")\n",
        "\n",
        "roc_auc_rf = auc(fpr_rf, tpr_rf)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "\n",
        "plt.plot(\n",
        "    fpr_lr,\n",
        "    tpr_lr,\n",
        "    linewidth=2,\n",
        "    label=f\"Logistic Regression (AUC = {roc_auc_lr:.3f})\"\n",
        ")\n",
        "\n",
        "\n",
        "plt.plot(\n",
        "    fpr_rf,\n",
        "    tpr_rf,\n",
        "    linewidth=2,\n",
        "    linestyle=\"--\",\n",
        "    label=f\"Random Forest (AUC = {roc_auc_rf:.3f})\"\n",
        ")\n",
        "\n",
        "plt.plot(\n",
        "    [0, 1],\n",
        "    [0, 1],\n",
        "    \"k--\",\n",
        "    linewidth=1.5,\n",
        "    label=\"Random Classifier\"\n",
        ")\n",
        "\n",
        "plt.title(\"ROC Curve Comparison\", fontsize=18)\n",
        "plt.xlabel(\"False Positive Rate\")\n",
        "plt.ylabel(\"True Positive Rate\")\n",
        "\n",
        "plt.xlim(0, 1)\n",
        "plt.ylim(0, 1.05)\n",
        "\n",
        "plt.grid(alpha=0.3)\n",
        "\n",
        "plt.legend(loc=\"lower right\")\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "he62iHEoyL5R",
        "outputId": "006efe21-c805-4bbc-cf77-f18c4e945ffd"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Final Results Summary\n",
        "final_results = pd.DataFrame({\n",
        "    \"Model\": [\n",
        "        \"Logistic Regression\",\n",
        "        \"Random Forest\"\n",
        "    ],\n",
        "    \"Accuracy\": [\n",
        "        lr_accuracy,\n",
        "        rf_accuracy\n",
        "    ],\n",
        "    \"Precision\": [\n",
        "        lr_precision,\n",
        "        rf_precision\n",
        "    ],\n",
        "    \"Recall\": [\n",
        "        lr_recall,\n",
        "        rf_recall\n",
        "    ],\n",
        "    \"F1-Score\": [\n",
        "        lr_f1,\n",
        "        rf_f1\n",
        "    ],\n",
        "    \"ROC-AUC\": [\n",
        "        lr_auc,\n",
        "        rf_auc\n",
        "    ],\n",
        "    \"CV Mean\": [\n",
        "        cv_scores_lr.mean(),\n",
        "        cv_scores_rf.mean()\n",
        "    ],\n",
        "    \"Training Time (s)\": [\n",
        "        lr_training_time,\n",
        "        rf_training_time\n",
        "    ],\n",
        "    \"Prediction Time (s)\": [\n",
        "        lr_prediction_time,\n",
        "        rf_prediction_time\n",
        "    ]\n",
        "})\n",
        "\n",
        "final_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "oWbi5l01ecl-",
        "outputId": "454dc37d-51d2-451d-e0f1-cc127615597a"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Model  Accuracy  Precision  Recall  F1-Score  ROC-AUC  CV Mean  Training Time (s)  Prediction Time (s)\n",
              "0  Logistic Regression      0.82       0.88    0.82      0.85     0.52     0.85             904.53                 0.06\n",
              "1        Random Forest      0.94       0.88    0.94      0.91     0.56     0.91            2171.41                 0.23"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-74791e42-f49a-46fa-8333-98299436a77d\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "      <th>Recall</th>\n",
              "      <th>F1-Score</th>\n",
              "      <th>ROC-AUC</th>\n",
              "      <th>CV Mean</th>\n",
              "      <th>Training Time (s)</th>\n",
              "      <th>Prediction Time (s)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.85</td>\n",
              "      <td>904.53</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.94</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.91</td>\n",
              "      <td>2171.41</td>\n",
              "      <td>0.23</td>\n",
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          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    }
  ]
}