{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Student name: Muhammad Haroon**\n",
        "# **Student ID: 2434025**"
      ],
      "metadata": {
        "id": "tEXmtiWbNFlx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Setup\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score, classification_report, confusion_matrix\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.svm import SVC\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n"
      ],
      "metadata": {
        "id": "ZoLgtV4DNg-k"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "df = pd.read_csv('/content/drive/MyDrive/Colab Notebooks/Dataset/Haroon/diabetes.csv')\n",
        "\n",
        "# Display basic information about the dataset\n",
        "print(\"Dataset Shape:\", df.shape)\n",
        "print(\"\\nFirst 5 rows:\")\n",
        "print(df.head())\n",
        "print(\"\\nDataset Info:\")\n",
        "print(df.info())\n",
        "print(\"\\nStatistical Summary:\")\n",
        "print(df.describe())\n",
        "print(\"\\nClass Distribution:\")\n",
        "print(df['Outcome'].value_counts())\n",
        "\n",
        "# Check for missing values\n",
        "print(\"\\nMissing Values:\")\n",
        "print(df.isnull().sum())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4B_qc5VbNhDO",
        "outputId": "cd3dff23-ea79-4379-8b64-f424f617d0bc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset Shape: (768, 9)\n",
            "\n",
            "First 5 rows:\n",
            "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
            "0            6      148             72             35        0  33.6   \n",
            "1            1       85             66             29        0  26.6   \n",
            "2            8      183             64              0        0  23.3   \n",
            "3            1       89             66             23       94  28.1   \n",
            "4            0      137             40             35      168  43.1   \n",
            "\n",
            "   DiabetesPedigreeFunction  Age  Outcome  \n",
            "0                     0.627   50        1  \n",
            "1                     0.351   31        0  \n",
            "2                     0.672   32        1  \n",
            "3                     0.167   21        0  \n",
            "4                     2.288   33        1  \n",
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 768 entries, 0 to 767\n",
            "Data columns (total 9 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Pregnancies               768 non-null    int64  \n",
            " 1   Glucose                   768 non-null    int64  \n",
            " 2   BloodPressure             768 non-null    int64  \n",
            " 3   SkinThickness             768 non-null    int64  \n",
            " 4   Insulin                   768 non-null    int64  \n",
            " 5   BMI                       768 non-null    float64\n",
            " 6   DiabetesPedigreeFunction  768 non-null    float64\n",
            " 7   Age                       768 non-null    int64  \n",
            " 8   Outcome                   768 non-null    int64  \n",
            "dtypes: float64(2), int64(7)\n",
            "memory usage: 54.1 KB\n",
            "None\n",
            "\n",
            "Statistical Summary:\n",
            "       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\n",
            "count   768.000000  768.000000     768.000000     768.000000  768.000000   \n",
            "mean      3.845052  120.894531      69.105469      20.536458   79.799479   \n",
            "std       3.369578   31.972618      19.355807      15.952218  115.244002   \n",
            "min       0.000000    0.000000       0.000000       0.000000    0.000000   \n",
            "25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n",
            "50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n",
            "75%       6.000000  140.250000      80.000000      32.000000  127.250000   \n",
            "max      17.000000  199.000000     122.000000      99.000000  846.000000   \n",
            "\n",
            "              BMI  DiabetesPedigreeFunction         Age     Outcome  \n",
            "count  768.000000                768.000000  768.000000  768.000000  \n",
            "mean    31.992578                  0.471876   33.240885    0.348958  \n",
            "std      7.884160                  0.331329   11.760232    0.476951  \n",
            "min      0.000000                  0.078000   21.000000    0.000000  \n",
            "25%     27.300000                  0.243750   24.000000    0.000000  \n",
            "50%     32.000000                  0.372500   29.000000    0.000000  \n",
            "75%     36.600000                  0.626250   41.000000    1.000000  \n",
            "max     67.100000                  2.420000   81.000000    1.000000  \n",
            "\n",
            "Class Distribution:\n",
            "Outcome\n",
            "0    500\n",
            "1    268\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Missing Values:\n",
            "Pregnancies                 0\n",
            "Glucose                     0\n",
            "BloodPressure               0\n",
            "SkinThickness               0\n",
            "Insulin                     0\n",
            "BMI                         0\n",
            "DiabetesPedigreeFunction    0\n",
            "Age                         0\n",
            "Outcome                     0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Perform 80/20 train-test split\n",
        "X = df.drop('Outcome', axis=1)\n",
        "y = df['Outcome']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)\n",
        "\n",
        "print(f\"\\nTraining set size: {X_train.shape[0]}\")\n",
        "print(f\"Test set size: {X_test.shape[0]}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Sg42eK-NOSvT",
        "outputId": "5429d309-069b-4af5-938a-38be96e7c5f5"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Training set size: 614\n",
            "Test set size: 154\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Train and Evaluate Models\n",
        "models = {\n",
        "    'Decision Tree': DecisionTreeClassifier(random_state=42),\n",
        "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42),\n",
        "    'Naïve Bayes': GaussianNB(),\n",
        "    'SVM (Linear)': SVC(kernel='linear', random_state=42),\n",
        "    'SVM (RBF)': SVC(kernel='rbf', random_state=42)\n",
        "}\n",
        "\n",
        "# Dictionary to store results\n",
        "results = {}\n",
        "\n",
        "print(\"=\" * 60)\n",
        "print(\"MODEL TRAINING AND EVALUATION\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "for name, model in models.items():\n",
        "    print(f\"\\n{name}\")\n",
        "    print(\"-\" * 40)\n",
        "\n",
        "    # Train the model\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions\n",
        "    y_train_pred = model.predict(X_train)\n",
        "    y_test_pred = model.predict(X_test)\n",
        "\n",
        "    # Calculate accuracies\n",
        "    train_accuracy = accuracy_score(y_train, y_train_pred)\n",
        "    test_accuracy = accuracy_score(y_test, y_test_pred)\n",
        "\n",
        "    # Generate classification report and get metrics\n",
        "    report = classification_report(y_test, y_test_pred, output_dict=True)\n",
        "    precision = report['weighted avg']['precision']\n",
        "    recall = report['weighted avg']['recall']\n",
        "    f1 = report['weighted avg']['f1-score']\n",
        "\n",
        "    # Store results\n",
        "    results[name] = {\n",
        "        'train_accuracy': train_accuracy,\n",
        "        'test_accuracy': test_accuracy,\n",
        "        'precision': precision,\n",
        "        'recall': recall,\n",
        "        'f1_score': f1,\n",
        "        'model': model,\n",
        "        'predictions': y_test_pred\n",
        "    }\n",
        "\n",
        "    # Print results\n",
        "    print(f\"Training Accuracy: {train_accuracy:.4f}\")\n",
        "    print(f\"Testing Accuracy: {test_accuracy:.4f}\")\n",
        "    print(f\"Precision: {precision:.4f}\")\n",
        "    print(f\"Recall: {recall:.4f}\")\n",
        "    print(f\"F1-Score: {f1:.4f}\")\n",
        "\n",
        "    # Confusion Matrix\n",
        "    cm = confusion_matrix(y_test, y_test_pred)\n",
        "    print(\"\\nConfusion Matrix:\")\n",
        "    print(cm)\n",
        "\n",
        "    # Plot confusion matrix\n",
        "    plt.figure(figsize=(6, 4))\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n",
        "    plt.title(f'Confusion Matrix - {name}')\n",
        "    plt.ylabel('Actual')\n",
        "    plt.xlabel('Predicted')\n",
        "    plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "VhAFfdjqOSzN",
        "outputId": "7f2ee7f0-065f-4975-f750-d97ac695e65d"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "MODEL TRAINING AND EVALUATION\n",
            "============================================================\n",
            "\n",
            "Decision Tree\n",
            "----------------------------------------\n",
            "Training Accuracy: 1.0000\n",
            "Testing Accuracy: 0.7273\n",
            "Precision: 0.7182\n",
            "Recall: 0.7273\n",
            "F1-Score: 0.7179\n",
            "\n",
            "Confusion Matrix:\n",
            "[[85 15]\n",
            " [27 27]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest\n",
            "----------------------------------------\n",
            "Training Accuracy: 1.0000\n",
            "Testing Accuracy: 0.7597\n",
            "Precision: 0.7546\n",
            "Recall: 0.7597\n",
            "F1-Score: 0.7555\n",
            "\n",
            "Confusion Matrix:\n",
            "[[85 15]\n",
            " [22 32]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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WMDMzQ1FRUal/T55nYmKC3r17o3fv3igoKEBQUBC+++47REZGwsjIqEKvRXp9cPi9ivniiy9gYmKCgQMHIi0trcT6K1euYP78+QCeDB8DwLx581T6zJ07F8CTf5grSr169ZCVlYV//vlH2ZaSklJihv39+/dLbPv0JSzPP2b3lL29PZo3b441a9ao/OLw77//Ys+ePcrz1IaOHTti6tSpWLRoEezs7F7YT19fv0Q1t2nTJty+fVul7WliqYi3eI0bNw7JyclYs2YN5s6dCxcXF+ULYV7G29u71OFhTY5/7949rFy5EsD/qtxnPwdBEJTXoya6dOmCx48fY+nSpcq2oqIiLFy4UKWfLq+RsurSpQuKioqwaNEilfbo6GjIZDL4+/u/dHt9fX10794dW7ZsKXW07uk7JIAnT1E8y9DQEA0bNoQgCCgsLARQsdcivT5YqVcx9erVw/r165WP4zz7RrmjR49i06ZNCAkJAQA0a9YMwcHBWLFiBTIzM+Hj44MTJ05gzZo1CAwMRMeOHSssrj59+mDcuHH48MMPMWLECOTl5WHp0qV44403VCaKTZkyBYcPH0bXrl3h7OyMu3fvYsmSJahTpw7atWv3wv3PmjUL/v7+8PLyQmhoqPJxJQsLi1cOHZeHnp4evvnmm1f2e//99zFlyhT0798fbdu2xdmzZ7Fu3boSCbNevXqwtLTEsmXLYGZmBhMTE7Ru3brE/dNX2b9/P5YsWYKJEycqH7GLiYlBhw4dMH78eMycOVOt/WnC398fjRs3xty5cxEWFgZ3d3fUq1cPY8aMwe3bt2Fubo4tW7aUa05Gt27d4O3tjS+//BLXr19Hw4YN8euvv5Y6T0FX10hZdevWDR07dsTXX3+N69evo1mzZtizZw9+++03jBo1CvXq1XvlPqZPn44DBw6gdevWGDRoEBo2bIj79+/j1KlT2Ldvn/KX5nfffRd2dnbw9vaGra0tzp8/j0WLFqFr167KUQJPT08AwNdff40+ffrAwMAA3bp10/mIBmmZbibd06tcvHhRGDRokODi4iIYGhoKZmZmgre3t7Bw4UIhPz9f2a+wsFCYPHmy4OrqKhgYGAiOjo5CZGSkSh9BePI4TteuXUsc5/lHqV70SJsgCMKePXuExo0bC4aGhoKbm5vw008/lXikLS4uTggICBAcHBwEQ0NDwcHBQejbt69w8eLFEsd4/rGvffv2Cd7e3oKxsbFgbm4udOvWTfjvv/9U+jw93vOPzMXExJTp8Z1nH2l7kRc90jZ69GjB3t5eMDY2Fry9vYX4+PhSH0X77bffhIYNGwrVqlVTOU8fH58XPob07H6ys7MFZ2dn4c033xQKCwtV+oWHhwt6enpCfHz8S89BHS+6NgRBEFavXq1yDv/995/g6+srmJqaCrVq1RIGDRoknDlzpsT/zxd9zs9fL4IgCPfu3RM++eQTwdzcXLCwsBA++eQT4fTp0xV+jbwoppf9f3nWyz6npx4+fCiEh4cLDg4OgoGBgdCgQQNh1qxZQnFxsUo/lPK44FNpaWlCWFiY4OjoKBgYGAh2dnZC586dhRUrVij7LF++XHj77beFmjVrCnK5XKhXr54wduxYISsrS2VfU6dOFWrXri3o6enx8TaJkAmCDmaIEBERUYXjPXUiIiKRYFInIiISCSZ1IiIikWBSJyIiEgkmdSIiIpFgUiciIhIJJnUiIiKREOUb5YxbDNN1CERa9+Dkold3InrNGWk5S5UnXzw6XfX+DooyqRMREZWJTFwD1kzqREQkXSL7NjsmdSIiki6RVeriOhsiIiIJY6VORETSxeF3IiIikRDZ8DuTOhERSRcrdSIiIpFgpU5ERCQSIqvUxfUrChERkYSxUiciIuni8DsREZFIiGz4nUmdiIiki5U6ERGRSLBSJyIiEgmRVeriOhsiIiIJY6VORETSxUqdiIhIJPRkmi9qKCoqwvjx4+Hq6gpjY2PUq1cPU6dOhSAIyj6CIGDChAmwt7eHsbExfH19cenSJfVOR63eREREYiLT03xRw4wZM7B06VIsWrQI58+fx4wZMzBz5kwsXLhQ2WfmzJlYsGABli1bhuPHj8PExAR+fn7Iz88v83E4/E5ERNJVSbPfjx49ioCAAHTt2hUA4OLigg0bNuDEiRMAnlTp8+bNwzfffIOAgAAAwI8//ghbW1vExsaiT58+ZToOK3UiIpKuclTqCoUC2dnZKotCoSj1MG3btkVcXBwuXrwIADhz5gyOHDkCf39/AMC1a9eQmpoKX19f5TYWFhZo3bo14uPjy3w6TOpEREQaiIqKgoWFhcoSFRVVat8vv/wSffr0gbu7OwwMDNCiRQuMGjUKH3/8MQAgNTUVAGBra6uyna2trXJdWXD4nYiIpKscw++RkZGIiIhQaZPL5aX2/eWXX7Bu3TqsX78ejRo1QmJiIkaNGgUHBwcEBwdrHMPzmNSJiEi6yvFIm1wuf2ESf97YsWOV1ToANGnSBDdu3EBUVBSCg4NhZ2cHAEhLS4O9vb1yu7S0NDRv3rzMMXH4nYiIpEsm03xRQ15eHvT0VFOuvr4+iouLAQCurq6ws7NDXFyccn12djaOHz8OLy+vMh+HlToREUlXJb18plu3bvjuu+/g5OSERo0a4fTp05g7dy4GDBjwJAyZDKNGjcK3336LBg0awNXVFePHj4eDgwMCAwPLfBwmdSIikq5KeqRt4cKFGD9+PD7//HPcvXsXDg4OGDx4MCZMmKDs88UXXyA3NxefffYZMjMz0a5dO+zatQtGRkZlPo5MePZ1NiJh3GKYrkMg0roHJxfpOgQirTPSculp7B+t8baPdoZXYCQVg5U6ERFJl8je/c6kTkRE0sXvUyciIhIJVupEREQiwaROREQkEiIbfhfXryhEREQSxkqdiIiki8PvREREIiGy4XcmdSIiki5W6kRERCLBSp2IiEgcZCJL6uIadyAiIpIwVupERCRZYqvUmdSJiEi6xJXTmdSJiEi6WKkTERGJBJM6ERGRSIgtqXP2OxERkUiwUiciIskSW6XOpE5ERNIlrpzOpE5ERNLFSp2IiEgkmNSJiIhEQmxJnbPfiYiIRIKVOhERSZbYKnUmdSIiki5x5XQmdSIiki5W6kRERCLBpE5ERCQSYkvqnP1OREQkEqzUiYhIusRVqDOpExGRdIlt+J1JnYiIJEtsSZ331ImISLJkMpnGizpcXFxK3UdYWBgAID8/H2FhYahZsyZMTU3RvXt3pKWlqX0+TOpERCRZlZXUT548iZSUFOWyd+9eAEDPnj0BAOHh4di2bRs2bdqEQ4cO4c6dOwgKClL7fDj8TkREpGXW1tYqP0+fPh316tWDj48PsrKysGrVKqxfvx6dOnUCAMTExMDDwwPHjh1DmzZtynwcVupERCRdMs0XhUKB7OxslUWhULzykAUFBfjpp58wYMAAyGQyJCQkoLCwEL6+vso+7u7ucHJyQnx8vFqnw6RORESSVZ7h96ioKFhYWKgsUVFRrzxmbGwsMjMzERISAgBITU2FoaEhLC0tVfrZ2toiNTVVrfPh8DsREUlWeWa/R0ZGIiIiQqVNLpe/crtVq1bB398fDg4OGh/7RZjUiYhIssqT1OVyeZmS+LNu3LiBffv24ddff1W22dnZoaCgAJmZmSrVelpaGuzs7NTaP4ffiYiIKklMTAxsbGzQtWtXZZunpycMDAwQFxenbEtKSkJycjK8vLzU2j8rdSIikq5KfPdMcXExYmJiEBwcjGrV/pd+LSwsEBoaioiICFhZWcHc3BzDhw+Hl5eXWjPfASZ1KiM9PRm+GdIFfbu0gm1Nc6SkZ2HttuOYvnKXss+Kyf3wyQeqF+Cev/5DwLAllR0ukcYS/j6J1T+swvn//kV6ejqiFyxGp87/m5U8/qsv8ftvW1W2aevdDktXrKrsUKkCVOYb5fbt24fk5GQMGDCgxLro6Gjo6emhe/fuUCgU8PPzw5Il6v/byaROZTI65B0M6tEegyasxX9XUuDZyAnLJ/VDds4jLNlwSNlv91/nMHjiT8qfFQWPdREukcYePcqDm5sbAoO6I2LksFL7eLdrjynf/m+Ws6GhYWWFRxWsMpP6u+++C0EQSl1nZGSExYsXY/HixeU6BpM6lUmbZnWx/dA/2HXkHAAgOeU+er3XEi0bOav0Kyh4jLR7D3URIlGFaNfeB+3a+7y0j6GhIWo99zIRej3x3e8kScfOXEXHt9xQ38kGANDkjdrwal4Xe/76T6Vf+5YNcCMuCme2jsf8r3rDysJEF+ESadXfJ0+gQ3svfNDVD99OmYjMzAe6Dok0VFmvia0sOq3UMzIy8MMPPyA+Pl75gL2dnR3atm2LkJCQEq/VI92ZHbMX5qZGOLP1GxQVCdDXl2Hi4u34eeffyj57j57Hb/vP4Prte6hbpxYmD++G3xYNhU/wHBQXlz7kRPS6aduuPTr7voPaderg5s2bWDhvLj4fPAhr12+Evr6+rsMjidNZUj958iT8/PxQvXp1+Pr64o033gDw5Lm8BQsWYPr06di9ezdatmz50v0oFIoSr+UTiosg0+NfrorU49030ce/FUK+WoP/rqSgqVttzBrTAynpWVi37TgAYNPuBGX/c5fv4Oyl2zi/fTLebtkAB09c1FXoRBXKv8v/HkVq8IYb3njDDV3f88XfJ0+gdRv1Hj+iKqBqFtwa01lSHz58OHr27Illy5aVGMYQBAFDhgzB8OHDX/ne26ioKEyePFmlTd+2FQzs36rwmKVs2qhAzI7Zq0zc5y7fgZO9Fcb2f0eZ1J93/fY9pD94iHqO1kzqJFp1HB1Ro0YNJCffYFJ/DVXVYXRN6eye+pkzZxAeHl7qByqTyRAeHo7ExMRX7icyMhJZWVkqSzVbTy1ELG3GRoYoFopV2oqKBejpvfgSqm1jiZoWJkjNyNZ2eEQ6k5aaiszMTFjX4u3C1xHvqVcQOzs7nDhxAu7u7qWuP3HiBGxtbV+5n9Je08eh94r3x+GzGBfqh5spD/DflRQ0d6+DEf064sfYYwAAE2NDfD24C2LjEpGakY26jrXw3chAXLmZgb1Hz+s4eqKyy8vNRXJysvLn27du4cL588ov7Fi2dBF83/FDzVq1cOvmTUTPmQVHJ2e0bddeh1GTpqpobtaYzpL6mDFj8NlnnyEhIQGdO3dWJvC0tDTExcVh5cqVmD17tq7Co+dEzNiEiZ+/j/lf9YZ1DVOkpGdh1ea/MG3FTgBPqvbGDWrj426tYWlmjJT0LOyLv4ApS7ajoJDPqtPr49y5fzGw/6fKn2fPfPI8+gcBH+LrCZNwMekifv8tFg+zH8LGxgZebb0RNnwkn1V/TVXViltTMuFFT8JXgo0bNyI6OhoJCQkoKioCAOjr68PT0xMRERHo1auXRvs1blH6CyOIxOTByUW6DoFI64y0XHo2GLvr1Z1e4NKs9yowkoqh00faevfujd69e6OwsBAZGRkAgFq1asHAwECXYRERkUSIrFCvGm+UMzAwgL29va7DICIiiRHb8HuVSOpERES6ILKczqRORETSpacnrqzOpE5ERJIltkqdX+hCREQkEqzUiYhIsjhRjoiISCREltOZ1ImISLpYqRMREYkEkzoREZFIiCync/Y7ERGRWLBSJyIiyeLwOxERkUiILKczqRMRkXSxUiciIhIJkeV0JnUiIpIusVXqnP1OREQkEqzUiYhIskRWqDOpExGRdIlt+J1JnYiIJEtkOZ1JnYiIpIuVOhERkUiILKdz9jsREZFYMKkTEZFkyWQyjRd13b59G/369UPNmjVhbGyMJk2a4O+//1auFwQBEyZMgL29PYyNjeHr64tLly6pdQwmdSIikiyZTPNFHQ8ePIC3tzcMDAywc+dO/Pfff5gzZw5q1Kih7DNz5kwsWLAAy5Ytw/Hjx2FiYgI/Pz/k5+eX+Ti8p05ERJJVWRPlZsyYAUdHR8TExCjbXF1dlX8WBAHz5s3DN998g4CAAADAjz/+CFtbW8TGxqJPnz5lOg4rdSIikqzyDL8rFApkZ2erLAqFotTj/P7772jZsiV69uwJGxsbtGjRAitXrlSuv3btGlJTU+Hr66tss7CwQOvWrREfH1/m82FSJyIiySrP8HtUVBQsLCxUlqioqFKPc/XqVSxduhQNGjTA7t27MXToUIwYMQJr1qwBAKSmpgIAbG1tVbaztbVVrisLDr8TERFpIDIyEhERESptcrm81L7FxcVo2bIlpk2bBgBo0aIF/v33XyxbtgzBwcEVFhMrdSIikqzyDL/L5XKYm5urLC9K6vb29mjYsKFKm4eHB5KTkwEAdnZ2AIC0tDSVPmlpacp1ZcGkTkREklVZs9+9vb2RlJSk0nbx4kU4OzsDeDJpzs7ODnFxccr12dnZOH78OLy8vMp8HA6/ExGRZFXW7Pfw8HC0bdsW06ZNQ69evXDixAmsWLECK1asUMYxatQofPvtt2jQoAFcXV0xfvx4ODg4IDAwsMzHYVInIiLJqqzXxLZq1Qpbt25FZGQkpkyZAldXV8ybNw8ff/yxss8XX3yB3NxcfPbZZ8jMzES7du2wa9cuGBkZlfk4MkEQBG2cgC4Ztxim6xCItO7ByUW6DoFI64y0XHq+s+iYxtvuHdamAiOpGLynTkREJBIcficiIskS27e0MakTEZFk8fvUiYiIREJPXDmdSZ2IiKSLlToREZFIiCync/Y7ERGRWLBSJyIiyZJBXKU6kzoREUkWJ8oRERGJBCfKERERiYTIcjqTOhERSZeeyLI6Z78TERGJBCt1IiKSLJEV6kzqREQkXZwoR0REJBIiy+lM6kREJF1imyjHpE5ERJIlrpRexqT++++/l3mHH3zwgcbBEBERkebKlNQDAwPLtDOZTIaioqLyxENERFRpJDlRrri4WNtxEBERVTq++52IiEgkJFmpPy83NxeHDh1CcnIyCgoKVNaNGDGiQgIjIiLSNpHldPWT+unTp9GlSxfk5eUhNzcXVlZWyMjIQPXq1WFjY8OkTkRErw2xVepqv/s9PDwc3bp1w4MHD2BsbIxjx47hxo0b8PT0xOzZs7URIxEREZWB2kk9MTERo0ePhp6eHvT19aFQKODo6IiZM2fiq6++0kaMREREWqEn03ypitRO6gYGBtDTe7KZjY0NkpOTAQAWFha4efNmxUZHRESkRTKZTOOlKlL7nnqLFi1w8uRJNGjQAD4+PpgwYQIyMjKwdu1aNG7cWBsxEhERaUXVTM2aU7tSnzZtGuzt7QEA3333HWrUqIGhQ4ciPT0dK1asqPAAiYiItEVPJtN4qYrUrtRbtmyp/LONjQ127dpVoQERERGRZvjyGSIikqwqWnBrTO2k7urq+tIJAlevXi1XQERERJWlqk5405TaSX3UqFEqPxcWFuL06dPYtWsXxo4dW1FxERERaZ3Icrr6SX3kyJGlti9evBh///13uQMiIiKqLJU14W3SpEmYPHmySpubmxsuXLgAAMjPz8fo0aPx888/Q6FQwM/PD0uWLIGtra1ax1F79vuL+Pv7Y8uWLRW1OyIiIq2TyTRf1NWoUSOkpKQolyNHjijXhYeHY9u2bdi0aRMOHTqEO3fuICgoSO1jVNhEuc2bN8PKyqqidkdERCQq1apVg52dXYn2rKwsrFq1CuvXr0enTp0AADExMfDw8MCxY8fQpk2bsh9D3aBatGihMrFAEASkpqYiPT0dS5YsUXd3REREOlOeiXIKhQIKhUKlTS6XQy6Xl9r/0qVLcHBwgJGREby8vBAVFQUnJyckJCSgsLAQvr6+yr7u7u5wcnJCfHy8dpN6QECAyoegp6cHa2trdOjQAe7u7uruTisu7Z+r6xCItC7xRqauQyDSujb1LLW6//Lcg46Kiipxn3zixImYNGlSib6tW7fG6tWr4ebmhpSUFEyePBnt27fHv//+i9TUVBgaGsLS0lJlG1tbW6SmpqoVk9pJvbRgiYiIXkflqdQjIyMRERGh0vaiKt3f31/556ZNm6J169ZwdnbGL7/8AmNjY41jeJ7av6To6+vj7t27Jdrv3bsHfX39CgmKiIioMpTnW9rkcjnMzc1Vlhcl9edZWlrijTfewOXLl2FnZ4eCggJkZmaq9ElLSyv1HvxLz0et3nhyD700CoUChoaG6u6OiIhIZ3T11as5OTm4cuUK7O3t4enpCQMDA8TFxSnXJyUlITk5GV5eXmrtt8zD7wsWLADwZKji+++/h6mpqXJdUVERDh8+XGXuqRMREVUlY8aMQbdu3eDs7Iw7d+5g4sSJ0NfXR9++fWFhYYHQ0FBERETAysoK5ubmGD58OLy8vNSaJAeokdSjo6MBPKnUly1bpjLUbmhoCBcXFyxbtkytgxMREelSZb0m9tatW+jbty/u3bsHa2trtGvXDseOHYO1tTWAJzlWT08P3bt3V3n5jLpkwovG01+gY8eO+PXXX1GjRg21D1ZZbj0o0HUIRFp3636erkMg0jptz34fuz1J421nve9WgZFUDLVnvx84cEAbcRAREVU6sb37Xe2Jct27d8eMGTNKtM+cORM9e/askKCIiIgqg55MpvFSFamd1A8fPowuXbqUaPf398fhw4crJCgiIqLKoFeOpSpSO66cnJxSH10zMDBAdnZ2hQRFRERE6lM7qTdp0gQbN24s0f7zzz+jYcOGFRIUERFRZajMb2mrDGpPlBs/fjyCgoJw5coV5bfJxMXFYf369di8eXOFB0hERKQtVfXeuKbUTurdunVDbGwspk2bhs2bN8PY2BjNmjXD/v37+dWrRET0WhFZTtfs+9S7du2Krl27AgCys7OxYcMGjBkzBgkJCSgqKqrQAImIiLSlvK97rWo0nsB3+PBhBAcHw8HBAXPmzEGnTp1w7NixioyNiIhIq8T2SJtalXpqaipWr16NVatWITs7G7169YJCoUBsbCwnyREREelYmSv1bt26wc3NDf/88w/mzZuHO3fuYOHChdqMjYiISKskO/t9586dGDFiBIYOHYoGDRpoMyYiIqJKIdl76keOHMHDhw/h6emJ1q1bY9GiRcjIyNBmbERERFolK8d/VVGZk3qbNm2wcuVKpKSkYPDgwfj555/h4OCA4uJi7N27Fw8fPtRmnERERBVOT6b5UhWpPfvdxMQEAwYMwJEjR3D27FmMHj0a06dPh42NDT744ANtxEhERKQVkk/qz3Jzc8PMmTNx69YtbNiwoaJiIiIiIg1o9PKZ5+nr6yMwMBCBgYEVsTsiIqJKIauq09g1VCFJnYiI6HVUVYfRNcWkTkREkiWyQp1JnYiIpKuqvu5VU0zqREQkWWIbfi/X7HciIiKqOlipExGRZIls9J1JnYiIpEuvir7uVVNM6kREJFms1ImIiERCbBPlmNSJiEiyxPZIG2e/ExERiQQrdSIikiyRFepM6kREJF1iG35nUiciIskSWU5nUiciIukS28QyJnUiIpIssX2futh+SSEiIqrSpk+fDplMhlGjRinb8vPzERYWhpo1a8LU1BTdu3dHWlqa2vtmUiciIsmSlWPRxMmTJ7F8+XI0bdpUpT08PBzbtm3Dpk2bcOjQIdy5cwdBQUFq759JnYiIJEtPJtN4UVdOTg4+/vhjrFy5EjVq1FC2Z2VlYdWqVZg7dy46deoET09PxMTE4OjRozh27Jh656N2VERERCJRnkpdoVAgOztbZVEoFC88VlhYGLp27QpfX1+V9oSEBBQWFqq0u7u7w8nJCfHx8WqdD5M6ERFJlkym+RIVFQULCwuVJSoqqtTj/Pzzzzh16lSp61NTU2FoaAhLS0uVdltbW6Smpqp1Ppz9TkREklWe2e+RkZGIiIhQaZPL5SX63bx5EyNHjsTevXthZGSk8fHKgkmdiIhIA3K5vNQk/ryEhATcvXsXb775prKtqKgIhw8fxqJFi7B7924UFBQgMzNTpVpPS0uDnZ2dWjExqRMRkWRVxj3ozp074+zZsypt/fv3h7u7O8aNGwdHR0cYGBggLi4O3bt3BwAkJSUhOTkZXl5eah2LSZ2IiCSrMl4+Y2ZmhsaNG6u0mZiYoGbNmsr20NBQREREwMrKCubm5hg+fDi8vLzQpk0btY7FpE5ERJJVVd4nFx0dDT09PXTv3h0KhQJ+fn5YsmSJ2vuRCYIgaCE+nbr1oEDXIRBp3a37eboOgUjr2tSz1Or+N59J0XjbHs3sKzCSisFKnYiIJEtsz3WL7XyIiIgki5U6ERFJlti+pY1JnYiIJEtcKZ1JnYiIJExkhTqTOhERSZeeyGp1JnUiIpIssVXqnP1OREQkEqzUiYhIsmQcficiIhIHsQ2/M6kTEZFkcaIcERGRSLBSJyIiEgmxJXXOficiIhIJVupERCRZnP1OREQkEnriyulM6kREJF2s1ImIiESCE+WIiIioSmKlTkREksXhd5Ks9Wu+x5GD+5B84xrkciM0bNIMn4WFw9HZFQCQnZWFNSsX4+8T8biblgJLyxrwfrsTQgYPg6mpmY6jJyqbuB1bsH/Hr8hIuwMAqO1cFwF9Q9GsVVvkPMzC1p9W4t9Tx3EvPQ1mFpbw9PJB0CeDUd3EVMeRkyY4UY4k65/Tf+OD7n3g3rAxioqKsGrpfHwxcjB+2BALY+PquJdxF/cy0jF4+Gi4uNZDWuodRM+YioyMdEyKmqvr8InKxKqWDXr1/xy2Do6AAByJ24H5U8diysK1gCAg8146+gwcAQcnV9xLS8XqRdPx4F46hn89XdehkwbEVqnLBEEQdB1ERbv1oEDXIUhC5oP76O7vg+ilMWjaomWpfQ7F7UbUpEjsOHAC+tX4O2RFunU/T9chSMbnvd5B79Dh8PH7oMS6E3/GYfmsiVix9SD09XmNV7Q29Sy1uv8jlx5ovG27BjUqMJKKwSuQNJabkwMAMDO3eGGfnJwcVDcxZUKn11JxURFOHImDIv8R6ns0LrVPXm4OjKubMKG/psRVpzOpk4aKi4uxeN4MNG7aAq71GpTaJyvzAX6KWY6uAT0qOTqi8rl57TKmjh6IwoICGBkbY8T4GajtVLdEv4dZmfh9ww/o4B9Y+UESlaJKP9J28+ZNDBgw4KV9FAoFsrOzVRaFQlFJEUrXglnf4fqVy/jm25mlrs/NzcFXEWFwdqmL4EFDKzk6ovKxr+OMqYvWYkL0KnTsEoSVc6bgdvJVlT6P8nIwd2IEHJxcEfjxIB1FSuWlJ5NpvFRFVTqp379/H2vWrHlpn6ioKFhYWKgsi6NLTzRUMRbM/g7H/jqEOUtWwdrGrsT6vNxcfDlqCKpXr44pM+ajWjUDHURJpLlqBgawdXCEawMP9OofBse6DbDnt43K9Y/ycjF7/CgYVa+OEeNnoBpvL722ZOVYqiKdXom///77S9dfvXr1pesBIDIyEhERESpt6XlV9eN+vQmCgIVzpuHIof2Yu/gH2DvUKdEnNzcH40YOhqGBIabOXghDuVwHkRJVLKG4GI8LCwE8qdBnfTMSBgaGGDVhNgwNeY2/1kSWLnSa1AMDAyGTyfCyCfiyVwxxyOVyyJ9LHNlFnP2uDQtmfYe4PX9g6sz5qG5igvv3MgAAJiamkBsZPUnoIwYjP/8Rvpo0HXm5ucjLzQUAWFjWgL6+vi7DJyqTX2IWo2nLtqhpY4v8vDzEH9yNC2dPYczU+U8S+tcjoFAoMHjsZDzKy8WjvCfXuLmFJfR4jb92+EhbBapduzaWLFmCgICAUtcnJibC09MTRUVFau2Xj7RpR+c2TUptH/vNVLz3fiASE05idFjpcyDW/boLdg61tRme5PCRNu1YNe9b/Jf4NzLvZ8DYxBSOrvXRtccnaPxma5z/JwHTv/y81O1mx2yFta1DJUcrftp+pO3E1SyNt32r7ouf/NEVnSb1Dz74AM2bN8eUKVNKXX/mzBm0aNECxcXFau2XSZ2kgEmdpIBJXT06HX4fO3Yscv9/eLY09evXx4EDByoxIiIikhJxDb7rOKm3b9/+petNTEzg4+NTSdEQEZHkiCyrV+lH2oiIiLRJVo7/1LF06VI0bdoU5ubmMDc3h5eXF3bu3Klcn5+fj7CwMNSsWROmpqbo3r070tLS1D4fJnUiIpIsmUzzRR116tTB9OnTkZCQgL///hudOnVCQEAAzp07BwAIDw/Htm3bsGnTJhw6dAh37txBUFCQ+ufDL3Qhej1xohxJgbYnyp26nq3xtm+6mJfr2FZWVpg1axZ69OgBa2trrF+/Hj16PHmt9oULF+Dh4YH4+Hi0adOmzPtkpU5ERKQBTV9TXlRUhJ9//hm5ubnw8vJCQkICCgsL4evrq+zj7u4OJycnxMfHqxUTkzoREUlXOd4TW9pryqOiol54qLNnz8LU1BRyuRxDhgzB1q1b0bBhQ6SmpsLQ0BCWlpYq/W1tbZGamqrW6fCFxUREJFnleaNcaa8pf/4Np89yc3NDYmIisrKysHnzZgQHB+PQoUMaH780TOpERCRZ5fmytdJeU/4yhoaGqF+/PgDA09MTJ0+exPz589G7d28UFBQgMzNTpVpPS0uDnV3JL816GQ6/ExGRZOnyW9qKi4uhUCjg6ekJAwMDxMXFKdclJSUhOTkZXl5eau2TlToREUlXJb18JjIyEv7+/nBycsLDhw+xfv16HDx4ELt374aFhQVCQ0MREREBKysrmJubY/jw4fDy8lJr5jvApE5ERKR1d+/exaeffoqUlBRYWFigadOm2L17N9555x0AQHR0NPT09NC9e3coFAr4+flhyZIlah+Hz6kTvab4nDpJgbafU//nZo7G2zZ1NK3ASCoGK3UiIpKs8kyUq4qY1ImISLJEltOZ1ImISMJEltWZ1ImISLLK8/KZqojPqRMREYkEK3UiIpIsTpQjIiISCZHldCZ1IiKSMJFldSZ1IiKSLLFNlGNSJyIiyRLbPXXOficiIhIJVupERCRZIivUmdSJiEjCRJbVmdSJiEiyOFGOiIhIJMQ2UY5JnYiIJEtkOZ2z34mIiMSClToREUmXyEp1JnUiIpIsTpQjIiISCU6UIyIiEgmR5XQmdSIikjCRZXXOficiIhIJVupERCRZnChHREQkEpwoR0REJBIiy+lM6kREJF2s1ImIiERDXFmds9+JiIhEgpU6ERFJFoffiYiIREJkOZ1JnYiIpIuVOhERkUiI7eUznChHRETSJSvHooaoqCi0atUKZmZmsLGxQWBgIJKSklT65OfnIywsDDVr1oSpqSm6d++OtLQ0tY7DpE5ERKRlhw4dQlhYGI4dO4a9e/eisLAQ7777LnJzc5V9wsPDsW3bNmzatAmHDh3CnTt3EBQUpNZxZIIgCBUdvK7delCg6xCItO7W/Txdh0CkdW3qWWp1/2nZhRpva2tuoPG26enpsLGxwaFDh/D2228jKysL1tbWWL9+PXr06AEAuHDhAjw8PBAfH482bdqUab+s1ImISLJkMs0XhUKB7OxslUWhUJTpuFlZWQAAKysrAEBCQgIKCwvh6+ur7OPu7g4nJyfEx8eX+XyY1ImISLJk5fgvKioKFhYWKktUVNQrj1lcXIxRo0bB29sbjRs3BgCkpqbC0NAQlpaWKn1tbW2Rmppa5vPh7HciIpKuckx+j4yMREREhEqbXC5/5XZhYWH4999/ceTIEc0P/gJM6kREJFnleaBNLpeXKYk/a9iwYdi+fTsOHz6MOnXqKNvt7OxQUFCAzMxMlWo9LS0NdnZ2Zd4/h9+JiIi0TBAEDBs2DFu3bsX+/fvh6uqqst7T0xMGBgaIi4tTtiUlJSE5ORleXl5lPg4rdSIikqzKeqNcWFgY1q9fj99++w1mZmbK++QWFhYwNjaGhYUFQkNDERERASsrK5ibm2P48OHw8vIq88x3gI+0Eb22+EgbSYG2H2m7n1uk8bZWJvpl7it7wW8PMTExCAkJAfDk5TOjR4/Ghg0boFAo4OfnhyVLlqg1/M6kTvSaYlInKdB2Un+Qp3lSr1G97Em9svCeOhERkUjwnjoREUmW2L6ljZU6ERGRSLBSJyIiyRLbV68yqRMRkWSJbfidSZ2IiCRLZDmdSZ2IiCRMZFmdE+WIiIhEgpU6ERFJFifKERERiQQnyhEREYmEyHI6kzoREUmYyLI6kzoREUmW2O6pc/Y7ERGRSLBSJyIiyRLbRDlRfp86VS6FQoGoqChERkZCLpfrOhwireB1Tq8DJnUqt+zsbFhYWCArKwvm5ua6DodIK3id0+uA99SJiIhEgkmdiIhIJJjUiYiIRIJJncpNLpdj4sSJnDxEosbrnF4HnChHREQkEqzUiYiIRIJJnYiISCSY1ImIiESCSZ2IiEgkmNSp3BYvXgwXFxcYGRmhdevWOHHihK5DIqowhw8fRrdu3eDg4ACZTIbY2Fhdh0T0QkzqVC4bN25EREQEJk6ciFOnTqFZs2bw8/PD3bt3dR0aUYXIzc1Fs2bNsHjxYl2HQvRKfKSNyqV169Zo1aoVFi1aBAAoLi6Go6Mjhg8fji+//FLH0RFVLJlMhq1btyIwMFDXoRCVipU6aaygoAAJCQnw9fVVtunp6cHX1xfx8fE6jIyISJqY1EljGRkZKCoqgq2trUq7ra0tUlNTdRQVEZF0MakTERGJBJM6aaxWrVrQ19dHWlqaSntaWhrs7Ox0FBURkXQxqZPGDA0N4enpibi4OGVbcXEx4uLi4OXlpcPIiIikqZquA6DXW0REBIKDg9GyZUu89dZbmDdvHnJzc9G/f39dh0ZUIXJycnD58mXlz9euXUNiYiKsrKzg5OSkw8iISuIjbVRuixYtwqxZs5CamormzZtjwYIFaN26ta7DIqoQBw8eRMeOHUu0BwcHY/Xq1ZUfENFLMKkTERGJBO+pExERiQSTOhERkUgwqRMREYkEkzoREZFIMKkTERGJBJM6ERGRSDCpExERiQSTOhERkUgwqRO9BkJCQhAYGKj8uUOHDhg1alSlx3Hw4EHIZDJkZmZW+rGJ6NWY1InKISQkBDKZDDKZDIaGhqhfvz6mTJmCx48fa/W4v/76K6ZOnVqmvkzERNLBL3QhKqf33nsPMTExUCgU+OOPPxAWFgYDAwNERkaq9CsoKIChoWGFHNPKyqpC9kNE4sJKnaic5HI57Ozs4OzsjKFDh8LX1xe///67csj8u+++g4ODA9zc3AAAN2/eRK9evWBpaQkrKysEBATg+vXryv0VFRUhIiIClpaWqFmzJr744gs8/xUNzw+/KxQKjBs3Do6OjpDL5ahfvz5WrVqF69evK7+MpEaNGpDJZAgJCQHw5Gtyo6Ki4OrqCmNjYzRr1gybN29WOc4ff/yBN954A8bGxujYsaNKnERU9TCpE1UwY2NjFBQUAADi4uKQlJSEvXv3Yvv27SgsLISfnx/MzMzw559/4q+//oKpqSnee+895TZz5szB6tWr8cMPP+DIkSO4f/8+tm7d+tJjfvrpp9iwYQMWLFiA8+fPY/ny5TA1NYWjoyO2bNkCAEhKSkJKSgrmz58PAIiKisKPP/6IZcuW4dy5cwgPD0e/fv1w6NAhAE9++QgKCkK3bt2QmJiIgQMH4ssvv9TWx0ZEFUEgIo0FBwcLAQEBgiAIQnFxsbB3715BLpcLY8aMEYKDgwVbW1tBoVAo+69du1Zwc3MTiouLlW0KhUIwNjYWdu/eLQiCINjb2wszZ85Uri8sLBTq1KmjPI4gCIKPj48wcuRIQRAEISkpSQAg7N27t9QYDxw4IAAQHjx4oGzLz88XqlevLhw9elSlb2hoqNC3b19BEAQhMjJSaNiwocr6cePGldgXEVUdvKdOVE7bt2+HqakpCgsLUVxcjI8++giTJk1CWFgYmjRponIf/cyZM7h8+TLMzMxU9pGfn48rV64gKysLKSkpKt9HX61aNbRs2bLEEPxTiYmJ0NfXh4+PT5ljvnz5MvLy8vDOO++otBcUFKBFixYAgPPnz6vEAQBeXl5lPgYRVT4mdaJy6tixI5YuXQpDQ0M4ODigWrX//bUyMTFR6ZuTkwNPT0+sW7euxH6sra01Or6xsbHa2+Tk5AAAduzYgdq1a6usk8vlGsVBRLrHpE5UTiYmJqhfv36Z+r755pvYuHEjbGxsYG5uXmofe3t7HD9+HG+//TYA4PHjx0hISMCbb75Zav8mTZqguLgYhw4dgq+vb4n1T0cKioqKlG0NGzaEXC5HcnLyCyt8Dw8P/P777yptx44de/VJEpHOcKIcUSX6+OOPUatWLQQEBODPP//EtWvXcPDgQYwYMQK3bt0CAIwcORLTp09HbGwsLly4gM8///ylz5i7uLggODgYAwYMQGxsrHKfv/zyCwDA2dkZMpkM27dvR3p6OnJycmBmZoYxY8YgPDwca9aswZUrV3Dq1CksXLgQa9asAQAMGTIEly5dwtixY5GUlIT169dj9erV2v6IiKgcmNSJKlH16tVx+PBhODk5ISgoCB4eHggNDUV+fr6ych89ejQ++eQTBAcHw8vLC2ZmZvjwww9fut+lS5eiR48e+Pzzz+Hu7o5BgwYhNzcXAFC7dm1MnjwZX375JWxtbTFs2DAAwNSpUzF+/HhERUXBw8MD7733Hnbs2AFXV1cAgJOTE7Zs2YLY2Fg0a9YMy5Ytw7Rp07T46RBRecmEF82+ISIiotcKK3UiIiKRYFInIiISCSZ1IiIikWBSJyIiEgkmdSIiIpFgUiciIhIJJnUiIiKRYFInIiISCSZ1IiIikWBSJyIiEgkmdSIiIpH4PzLDgjf5JWXuAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Naïve Bayes\n",
            "----------------------------------------\n",
            "Training Accuracy: 0.7736\n",
            "Testing Accuracy: 0.7078\n",
            "Precision: 0.7179\n",
            "Recall: 0.7078\n",
            "F1-Score: 0.7114\n",
            "\n",
            "Confusion Matrix:\n",
            "[[74 26]\n",
            " [19 35]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SVM (Linear)\n",
            "----------------------------------------\n",
            "Training Accuracy: 0.7915\n",
            "Testing Accuracy: 0.7208\n",
            "Precision: 0.7126\n",
            "Recall: 0.7208\n",
            "F1-Score: 0.7141\n",
            "\n",
            "Confusion Matrix:\n",
            "[[83 17]\n",
            " [26 28]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SVM (RBF)\n",
            "----------------------------------------\n",
            "Training Accuracy: 0.7785\n",
            "Testing Accuracy: 0.7208\n",
            "Precision: 0.7106\n",
            "Recall: 0.7208\n",
            "F1-Score: 0.7031\n",
            "\n",
            "Confusion Matrix:\n",
            "[[88 12]\n",
            " [31 23]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Compare Results\n",
        "print(\"=\" * 60)\n",
        "print(\"PERFORMANCE COMPARISON SUMMARY\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "# Create summary table\n",
        "summary_data = []\n",
        "for name, result in results.items():\n",
        "    summary_data.append({\n",
        "        'Model': name,\n",
        "        'Training Accuracy': f\"{result['train_accuracy']:.4f}\",\n",
        "        'Test Accuracy': f\"{result['test_accuracy']:.4f}\",\n",
        "        'Precision': f\"{result['precision']:.4f}\",\n",
        "        'Recall': f\"{result['recall']:.4f}\",\n",
        "        'F1-score': f\"{result['f1_score']:.4f}\"\n",
        "    })\n",
        "\n",
        "summary_df = pd.DataFrame(summary_data)\n",
        "print(\"\\nPerformance Comparison Table:\")\n",
        "print(summary_df.to_string(index=False))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G2QXACI1Oxh-",
        "outputId": "c18a02ef-1e9b-417d-a9b1-42732e3d8309"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "PERFORMANCE COMPARISON SUMMARY\n",
            "============================================================\n",
            "\n",
            "Performance Comparison Table:\n",
            "        Model Training Accuracy Test Accuracy Precision Recall F1-score\n",
            "Decision Tree            1.0000        0.7273    0.7182 0.7273   0.7179\n",
            "Random Forest            1.0000        0.7597    0.7546 0.7597   0.7555\n",
            "  Naïve Bayes            0.7736        0.7078    0.7179 0.7078   0.7114\n",
            " SVM (Linear)            0.7915        0.7208    0.7126 0.7208   0.7141\n",
            "    SVM (RBF)            0.7785        0.7208    0.7106 0.7208   0.7031\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Visualization\n",
        "# Plot comparison of test accuracies\n",
        "plt.figure(figsize=(10, 6))\n",
        "models_list = list(results.keys())\n",
        "test_accuracies = [results[model]['test_accuracy'] for model in models_list]\n",
        "\n",
        "plt.bar(models_list, test_accuracies, color=['skyblue', 'lightcoral', 'lightgreen', 'gold', 'violet'])\n",
        "plt.title('Model Comparison - Test Accuracy')\n",
        "plt.xlabel('Models')\n",
        "plt.ylabel('Test Accuracy')\n",
        "plt.xticks(rotation=45)\n",
        "plt.ylim(0, 1)\n",
        "for i, v in enumerate(test_accuracies):\n",
        "    plt.text(i, v + 0.01, f'{v:.4f}', ha='center', va='bottom')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 590
        },
        "id": "1u1HCMcTO4Wj",
        "outputId": "dabd9793-a022-4619-eab9-e1dd74d16faa"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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IkUObNm3SvXv3bHq7T5w4YV0f89/o6Ghr712MkydP2uwvZmbzqKgoeXl5Jchr8fb21u7du7Vs2TI1a9bsb9vG1Hvy5Em9/fbb1uURERE6d+5cgtX0tFOnTtn0Tp8+fVrR0dHWScp++uknhYeHa/Xq1TY9yTFDpf+LNGnSqHXr1mrdurXu37+vd999V0OGDFG7du1sfhfPOnHihNKlS5cgt5vy8fGxuS/8i540iEuuXLl05MgRffDBB/84BNvBwUEffPCBPvjgA40fP14jR45U//79tXXrVnl5ecV7CPf8+fOVIUMGTZs2Lda65cuXa8WKFfL391eSJEmUK1cuHTt27B9fy549exQZGWkzed/TYkYSPDuz/rMjMv7O77//rr/++ktz586Vj4+Pdfmzs9jH/P/wT3VLUtOmTeXn56cff/xRDx8+lLOzs5o0afLCNQHAm4bh5QDwBkqePLlmzJihIUOGyNvb+7ntatWqpaioqFi9WBMmTJDFYrHOgB7z32dnP584caLNY0dHRzVs2FDLli2L88v9vxl6/Omnnypz5sz6/PPP9ddff8Vaf/36dQ0fPlyS5OXlJRcXF02ePNmmdzYgIEAhISFxzrb+Xz0b0qZMmSLpf7+zmF7Xp+sJCQnR7Nmz/9Pz3rp1y+Zx8uTJlTt3buuttTJnzqxixYpp7ty5NqHu2LFj2rBhg2rVqvWfnj/G22+/LS8vL+tPhQoV/vW+GjdurCtXrsR53/WHDx9ah8Pfvn071vpixYpJkvX1x5xQeDbQxuXhw4davny56tSpo0aNGsX66dq1q+7du6fVq1dLkho2bKgjR47EeWutmL9zw4YNdfPmzTh7iGPa5MiRQ46OjtqxY4fN+unTp/9jzTHiOr4Mw9CkSZNs2qVPn17vvvuuZs2apYsXL8ZZT4x06dKpZs2amjdvnubPn68aNWooXbp0L1wTALxp6OkGgDfU84Z3P83b21vvv/+++vfvr/Pnz6to0aLasGGDVq1apR49eliv4S5WrJiaNWum6dOnKyQkROXLl9fmzZt1+vTpWPscPXq0tm7dqrJly6p9+/YqWLCgbt++rYMHD2rTpk1xBqa/kzp1aq1YsUK1atVSsWLF9Mknn6hkyZKSpIMHD+rHH3+03sYoffr06tu3r4YOHaoaNWqobt26OnnypKZPn67SpUubMhHUuXPnVLduXdWoUUO7d++23lataNGikqRq1arJxcVF3t7e6tixo+7fv6+ZM2cqQ4YMunbt2r9+3oIFC+q9995TyZIllSZNGu3fv19Lly5V165drW3Gjh2rmjVrytPTU23btrXeMixVqlQaMmTIf33pCa5FixZavHixPv30U23dulUVKlRQVFSUTpw4ocWLF1vvXz5s2DDt2LFDtWvXVo4cOXT9+nVNnz5d2bJls/a658qVS+7u7vL391eKFCmULFkylS1bNs5r5levXq179+6pbt26cdZVrlw5pU+fXvPnz1eTJk3Uq1cvLV26VB999JHatGmjkiVL6vbt21q9erX8/f1VtGhR+fj46Pvvv5efn5/27t2rSpUqKSwsTJs2bVLnzp1Vr149pUqVSh999JGmTJkii8WiXLlyac2aNfGa9yB//vzKlSuXevbsqStXrihlypRatmxZnNfhT548WRUrVlSJEiXUoUMH5cyZU+fPn9fatWt1+PBhm7Y+Pj7WuSC+/PLLF64HAN5IdpkzHQDwUj19y7C/8+wtwwzDMO7du2d89tlnRpYsWQxnZ2cjT548xtixY21uI2QYhvHw4UOje/fuRtq0aY1kyZIZ3t7exqVLl2LdMswwDCM4ONjo0qWLkT17dsPZ2dnIlCmT8cEHHxjffvuttc2L3jIsxtWrV43PPvvMyJs3r+Hm5mYkTZrUKFmypDFixAgjJCTEpu3UqVON/PnzG87OzkbGjBmNTp06GXfu3LFpU7lyZaNQoUIv9DsyDMOQZHTp0sX6OOY2W3/++afRqFEjI0WKFEbq1KmNrl27Gg8fPrTZdvXq1UaRIkUMNzc3w8PDwxgzZoz1llLnzp37x+eOWff0LcOGDx9ulClTxnB3dzeSJEli5M+f3xgxYoQRERFhs92mTZuMChUqGEmSJDFSpkxpeHt7G3/++adNm5jX8uwtuGKOq6drTEjP3jLMMAwjIiLCGDNmjFGoUCHD1dXVSJ06tVGyZElj6NCh1r/z5s2bjXr16hlZsmQxXFxcjCxZshjNmjUz/vrrL5t9rVq1yihYsKDh5OT0t8eat7e34ebmZoSFhT231latWhnOzs7WW+HdunXL6Nq1q5E1a1bDxcXFyJYtm9GyZUubW+U9ePDA6N+/v5EzZ07r/weNGjUyzpw5Y21z48YNo2HDhkbSpEmN1KlTGx07djSOHTsW5y3DkiVLFmdtf/75p+Hl5WUkT57cSJcundG+fXvjyJEjcb7mY8eOGQ0aNDDc3d0NNzc3I1++fMbAgQNj7TM8PNxInTq1kSpVqljHMwDAlsUwXmDWFwAAEC9DhgzR0KFDdePGDYbe4rXz+PFjZcmSRd7e3goICLB3OQDwSuOabgAAAMTLypUrdePGDZvJ2QAAceOabgAAALyQPXv26OjRo/ryyy9VvHhx6/2+AQDPR083AAAAXsiMGTPUqVMnZciQQd9//729ywGARMGuoXvHjh3y9vZWlixZZLFYtHLlyn/cZtu2bSpRooRcXV2VO3duzZkzx/Q6AQCIryFDhsgwDK7nxmtlzpw5evz4sfbv36933nnH3uUAQKJg19AdFhamokWLxrqH6fOcO3dOtWvX1vvvv6/Dhw+rR48eateundavX29ypQAAAAAAxN8rM3u5xWLRihUrVL9+/ee26dOnj9auXatjx45ZlzVt2lR3795VYGDgS6gSAAAAAIAXl6gmUtu9e7e8vLxsllWvXl09evR47jbh4eEKDw+3Po6Ojtbt27eVNm1aWSwWs0oFAAAAALzGDMPQvXv3lCVLFjk4PH8QeaIK3UFBQcqYMaPNsowZMyo0NFQPHz5UkiRJYm0zatQoDR069GWVCAAAAAB4g1y6dEnZsmV77vpEFbr/jb59+8rPz8/6OCQkRG+99ZYuXbqklClT2rEyAAAAAEBiFRoaquzZsytFihR/2y5Rhe5MmTIpODjYZllwcLBSpkwZZy+3JLm6usrV1TXW8pQpUxK6AQAAAAD/yT9dtpyo7tPt6empzZs32yzbuHGjPD097VQRAAAAAADPZ9fQff/+fR0+fFiHDx+W9OSWYIcPH9bFixclPRka7uPjY23/6aef6uzZs+rdu7dOnDih6dOna/Hixfrss8/sUT4AAAAAAH/LrqF7//79Kl68uIoXLy5J8vPzU/HixTVo0CBJ0rVr16wBXJJy5syptWvXauPGjSpatKjGjRun7777TtWrV7dL/QAAAAAA/J1X5j7dL0toaKhSpUqlkJAQrukGAAAAAPwrL5otE9U13QAAAAAAJCaEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMQugGAAAAAMAkhG4AAAAAAExC6AYAAAAAwCSEbgAAAAAATELoBgAAAADAJIRuAAAAAABMYvfQPW3aNHl4eMjNzU1ly5bV3r17/7b9xIkTlS9fPiVJkkTZs2fXZ599pkePHr2kagEAAAAAeHF2Dd2LFi2Sn5+fBg8erIMHD6po0aKqXr26rl+/Hmf7BQsW6IsvvtDgwYN1/PhxBQQEaNGiRerXr99LrhwAAAAAgH9m19A9fvx4tW/fXq1bt1bBggXl7++vpEmTatasWXG237VrlypUqKDmzZvLw8ND1apVU7Nmzf6xdxwAAAAAAHuwW+iOiIjQgQMH5OXl9b9iHBzk5eWl3bt3x7lN+fLldeDAAWvIPnv2rNatW6datWo993nCw8MVGhpq8wMAAAAAwMtgt9B98+ZNRUVFKWPGjDbLM2bMqKCgoDi3ad68uYYNG6aKFSvK2dlZuXLl0nvvvfe3w8tHjRqlVKlSWX+yZ8+eoK8DwOslPvNMvPfee7JYLLF+ateubW3TqlWrWOtr1Khhs5+DBw+qatWqcnd3V9q0adWhQwfdv3/fun7OnDlxPo/FYnnu5TgAAAB4Ndh9IrX42LZtm0aOHKnp06fr4MGDWr58udauXasvv/zyudv07dtXISEh1p9Lly69xIoBJCbxnWdi+fLlunbtmvXn2LFjcnR01EcffWTTrkaNGjbtfvzxR+u6q1evysvLS7lz59aePXsUGBioP/74Q61atbK2adKkic32165dU/Xq1VW5cmVlyJDBlN8FAAAAEoaTvZ44Xbp0cnR0VHBwsM3y4OBgZcqUKc5tBg4cqBYtWqhdu3aSpMKFCyssLEwdOnRQ//795eAQ+xyCq6urXF1dE/4FAHjtPD3PhCT5+/tr7dq1mjVrlr744otY7dOkSWPzeOHChUqaNGms0O3q6vrc97U1a9bI2dlZ06ZNs76H+fv7q0iRIjp9+rRy586tJEmSKEmSJNZtbty4oS1btiggIOA/vV4AAACYz2493S4uLipZsqQ2b95sXRYdHa3NmzfL09Mzzm0ePHgQK1g7OjpKkgzDMK9YAK+9fzPPxLMCAgLUtGlTJUuWzGb5tm3blCFDBuXLl0+dOnXSrVu3rOvCw8Pl4uJi894WE7B37twZ5/N8//33Spo0qRo1avTCrw8AAAD2Ydfh5X5+fpo5c6bmzp2r48ePq1OnTgoLC7P2Mvn4+Khv377W9t7e3poxY4YWLlyoc+fOaePGjRo4cKC8vb2t4RsA/o1/M8/E0/bu3atjx45ZR+LEqFGjhr7//ntt3rxZY8aM0fbt21WzZk1FRUVJkqpUqaKgoCCNHTtWERERunPnjrVX/dq1a3E+V0BAgJo3b27T+w0AAIBXk92Gl0tPrlO8ceOGBg0apKCgIBUrVkyBgYHWL70XL1606f0ZMGCALBaLBgwYoCtXrih9+vTy9vbWiBEj7PUSAEDSkyBcuHBhlSlTxmZ506ZNrf8uXLiwihQpoly5cmnbtm364IMPVKhQIc2dO1d+fn7q27evHB0d1b17d2XMmDHOS2Z2796t48eP64cffjD9NQEAAOC/sxhv2Ljs0NBQpUqVSiEhIUqZMqW9ywHwioiIiFDSpEm1dOlS1a9f37q8ZcuWunv3rlatWvXcbcPCwpQlSxYNGzZMvr6+//hc6dOn1/Dhw9WxY0eb5cHBwUqWLJksFotSpkyphQsXxro+vG3btjp48KAOHToUvxcIAACABPWi2TJRzV4OAGb5N/NMxFiyZInCw8P1ySef/OPzXL58Wbdu3VLmzJljrcuYMaOSJ0+uRYsWyc3NTVWrVrVZf//+fS1evFht27Z9wVcFAAAAe7Pr8HIAeJX4+fmpZcuWKlWqlMqUKaOJEyfGmmcia9asGjVqlM12AQEBql+/vtKmTWuz/P79+xo6dKgaNmyoTJky6cyZM+rdu7dy586t6tWrW9tNnTpV5cuXV/LkybVx40b16tVLo0ePlru7u83+Fi1apMePH79QuAcAAMCrgdANAP8vvvNMSNLJkye1c+dObdiwIdb+HB0ddfToUc2dO1d3795VlixZVK1aNX355Zc2tzLcu3evBg8erPv37yt//vz65ptv1KJFi1j7CwgI0IcffhgrjAMAAODVxTXdAAAAAADEE9d0AwAAAABgZ4RuAAAAAABMQugGAAAAAMAkhG6YZtq0afLw8JCbm5vKli2rvXv3Prfte++9J4vFEuundu3akqTIyEj16dNHhQsXVrJkyZQlSxb5+Pjo6tWr1n1s27Ytzn1YLBbt27dP0pNJr95//31lzJhRbm5uevvttzVgwABFRkaa+8sAAAAA8EZi9nKYYtGiRfLz85O/v7/Kli2riRMnqnr16jp58qQyZMgQq/3y5csVERFhfXzr1i0VLVpUH330kSTpwYMHOnjwoAYOHKiiRYvqzp078vX1Vd26dbV//35JUvny5XXt2jWb/Q4cOFCbN29WqVKlJEnOzs7y8fFRiRIl5O7uriNHjqh9+/aKjo7WyJEjzfp1AAAAAHhDMXs5TFG2bFmVLl1aU6dOlSRFR0cre/bs6tatm7744ot/3H7ixIkaNGiQrl27pmTJksXZZt++fSpTpowuXLigt956K9b6yMhIZc2aVd26ddPAgQOf+1x+fn7at2+ffvnllxd8dQAAAADedMxeDruJiIjQgQMH5OXlZV3m4OAgLy8v7d69+4X2ERAQoKZNmz43cEtSSEiILBbLc+9ZvHr1at26dUutW7d+7j5Onz6twMBAVa5c+YXqAgAACeNlX4YmSbdv39bHH3+slClTyt3dXW3bttX9+/dt2qxfv17lypVTihQplD59ejVs2FDnz59P8NePxInjFv8Gw8uR4G7evKmoqChlzJjRZnnGjBl14sSJf9x+7969OnbsmAICAp7b5tGjR+rTp4+aNWv23LNKAQEBql69urJlyxZrXfny5XXw4EGFh4erQ4cOGjZs2D/WhRcTMnSovUtAIpFq8GB7lwDATuxxGZokffzxx7p27Zo2btyoyMhItW7dWh06dNCCBQskSefOnVO9evXk5+en+fPnKyQkRJ999pk+/PBDHTx40OTfCl51HLf4txhejgR39epVZc2aVbt27ZKnp6d1ee/evbV9+3bt2bPnb7fv2LGjdu/eraNHj8a5PjIyUg0bNtTly5e1bdu2OP+Oly9fVo4cObR48WI1bNgw1vpLly7p3r17OnLkiHr16qXu3burd+/e8XyliAuhGy+K0A28uexxGdrx48dVsGBB7du3zzrXS2BgoGrVqqXLly8rS5YsWrp0qZo1a6bw8HA5ODwZEPrTTz+pXr16Cg8Pl7OzcwL9BpAYcdziWQwvh92kS5dOjo6OCg4OtlkeHBysTJky/e22YWFhWrhwodq2bRvn+sjISDVu3FgXLlzQxo0bn3twz549W2nTplXdunXjXJ89e3YVLFhQzZo10+jRozVkyBBFRUW9wKsDAAD/hb0uQ9u9e7fc3d2twUWSvLy85ODgYO0QKFmypBwcHDR79mxFRUUpJCREP/zwg7y8vAgubziOW/wXhG4kOBcXF5UsWVKbN2+2LouOjtbmzZtter7jsmTJEoWHh+uTTz6JtS4mcJ86dUqbNm1S2rRp49yHYRiaPXu2fHx8XuiNJjo6WpGRkYqOjv7HtgAA4L/5u8vQgoKC/nH7mMvQ2rVr99w2cV2GFhQUFGsIsJOTk9KkSWN93pw5c2rDhg3q16+fXF1d5e7ursuXL2vx4sXxfZl4zXDc4r8gdMMUfn5+mjlzpubOnavjx4+rU6dOCgsLs05q5uPjo759+8baLiAgQPXr148VqCMjI9WoUSPt379f8+fPV1RUlIKCghQUFGRzrYwkbdmyRefOnYvzTW3+/PlavHixjh8/rrNnz2rx4sXq27evmjRpwplAAAASgYCAABUuXFhlypSJc33MSXrDMDRjxox47TsoKEjt27dXy5YttW/fPm3fvl0uLi5q1KiR3rArMpHAOG7fbIRumKJJkyb6+uuvNWjQIBUrVkyHDx9WYGCg9ezgxYsXY91T++TJk9q5c2ecQ8uvXLmi1atX6/LlyypWrJgyZ85s/dm1a5dN24CAAJUvX1758+ePtR8nJyeNGTNGZcqUUZEiRTR06FB17dpV3333XQK+egB4uRJyNl3pyYihQYMGKXPmzEqSJIm8vLx06tQp6/pt27bFuQ+LxaJ9+/ZZ2zGbLuJir8vQMmXKpOvXr9u0f/z4sW7fvm193mnTpilVqlT66quvVLx4cb377ruaN2+eNm/e/I9z0uD1xnGL/4LQDdN07dpVFy5cUHh4uPbs2aOyZcta123btk1z5syxaZ8vXz4ZhqGqVavG2peHh4cMw4jz57333rNpu2DBAv36669x1tSkSRMdOHBA9+7d0/379/XHH3+ob9++cnNz+8+vFwDsIWY23cGDB+vgwYMqWrSoqlevHutLWozly5fr2rVr1p9jx47J0dHROpuuJH311VeaPHmy/P39tWfPHiVLlkzVq1fXo0ePJD25A8TT+7h27ZratWunnDlzWq87jJlNt0qVKjp8+LDWr1+vmzdv6sMPPzT/l4JXmr0uQ/P09NTdu3d14MAB67ItW7YoOjra+h3lwYMH1omoYjg6OlprxJuL4xb/BbOXA0hQzF6OF8Xs5QkjoWfTNQxDWbJk0eeff66ePXtKejKxT8aMGTVnzhw1bdo01j4iIyOVNWtWdevWTQMHDpQkZtPF31q0aJFatmypb775RmXKlNHEiRO1ePFinThxQhkzZpSPj4+yZs2qUaNG2WxXqVIlZc2aVQsXLrRZHnMZ2sGDB7VmzRqb627TpEkjFxcXSVLNmjUVHBwsf39/662XSpUqZb310pYtW+Tl5aUhQ4aoWbNmunfvnvr166cTJ07o+PHjSpIkicm/GbzKOG7xLGYvBwDgNWfGbLrnzp1TUFCQzT5TpUqlsmXLPnefq1ev1q1bt6zzdkjMpou/Z6/L0ObPn6/8+fPrgw8+UK1atVSxYkV9++231vVVqlTRggULtHLlShUvXlw1atSQq6urAgMDCS7guMW/Rk83gARFTzdeFD3d/93Vq1eVNWtW7dq1y2Z4Y+/evbV9+/Z/vJZv7969Klu2rPbs2WOd3GfXrl2qUKGCrl69qsyZM1vbNm7cWBaLRYsWLYq1n1q1akmS1q1bZ7N8+/btaty4sW7duqWoqCh5enpq3bp11lvhAACQmNHTDQAA/tY/zab7Ii5fvqz169fH6sVhNl0AAJ5wsncBAADg30mI2XSHDRtmszxmu+DgYJue7uDgYBUrVizWfmbPnq20adOqbt26Nsufnk03xrx585Q9e3bt2bNH5cqVe6HXCABAYkdPNwAAiZQZs+nmzJlTmTJlstlnaGio9uzZE2ufhmFo9uzZ8vHxiXWdNrPpAgDwBKEbAIBEzM/PTzNnztTcuXN1/PhxderUSWFhYdZJzXx8fNS3b99Y2wUEBKh+/fqxbk9jsVjUo0cPDR8+XKtXr9bvv/8uHx8fZcmSRfXr17dpu2XLFp07d07t2rWLtf/atWtr3759GjZsmE6dOqWDBw+qdevWypEjh4oXL55wvwAAAF5xDC9/hY0+dNPeJSCR+KJ4OnuXAMBOmjRpohs3bmjQoEEKCgpSsWLFYs2m+2yPc8xsuhs2bIhzn71791ZYWJg6dOigu3fvqmLFigoMDJSbm5tNu4CAAJUvX1758+ePtY+Y2XS/+uorffXVV0qaNKk8PT2ZTRcA8MZh9vJXGKEbL+pVCt3MXo4XxezlAAAgMWP2cgAAAAAA7Izh5QAAAInRCYu9K0Bikf/VGdh658s79i4BiUTqgantXUKCoacbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAkTqQEA3niT7kyydwlIJHxT+9q7BABAIkNPNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBK7h+5p06bJw8NDbm5uKlu2rPbu3fu37e/evasuXbooc+bMcnV1Vd68ebVu3bqXVC0AAAAAAC8u3qF78ODBunDhQoI8+aJFi+Tn56fBgwfr4MGDKlq0qKpXr67r16/H2T4iIkJVq1bV+fPntXTpUp08eVIzZ85U1qxZE6QeAAAAAAASUrxD96pVq5QrVy598MEHWrBggcLDw//1k48fP17t27dX69atVbBgQfn7+ytp0qSaNWtWnO1nzZql27dva+XKlapQoYI8PDxUuXJlFS1a9F/XAAAAAACAWeIdug8fPqx9+/apUKFC8vX1VaZMmdSpUyft27cvXvuJiIjQgQMH5OXl9b9iHBzk5eWl3bt3x7nN6tWr5enpqS5duihjxox65513NHLkSEVFRT33ecLDwxUaGmrzAwAAAADAy/CvrukuXry4Jk+erKtXryogIECXL19WhQoVVKRIEU2aNEkhISH/uI+bN28qKipKGTNmtFmeMWNGBQUFxbnN2bNntXTpUkVFRWndunUaOHCgxo0bp+HDhz/3eUaNGqVUqVJZf7Jnzx6/FwsAAAAAwL/0nyZSMwxDkZGRioiIkGEYSp06taZOnars2bNr0aJFCVWjVXR0tDJkyKBvv/1WJUuWVJMmTdS/f3/5+/s/d5u+ffsqJCTE+nPp0qUErwsAAAAAgLg4/ZuNDhw4oNmzZ+vHH3+Uq6urfHx8NG3aNOXOnVuSNGXKFHXv3l1NmjR57j7SpUsnR0dHBQcH2ywPDg5WpkyZ4twmc+bMcnZ2lqOjo3VZgQIFFBQUpIiICLm4uMTaxtXVVa6urv/mZQIAAAAA8J/Eu6e7cOHCKleunM6dO6eAgABdunRJo0ePtgZuSWrWrJlu3Ljxt/txcXFRyZIltXnzZuuy6Ohobd68WZ6ennFuU6FCBZ0+fVrR0dHWZX/99ZcyZ84cZ+AGAAAAAMCe4h26GzdurPPnz2vt2rWqX7++Ta9zjHTp0tkE4+fx8/PTzJkzNXfuXB0/flydOnVSWFiYWrduLUny8fFR3759re07deqk27dvy9fXV3/99ZfWrl2rkSNHqkuXLvF9GQAAAAAAmC7ew8sHDhyYYE/epEkT3bhxQ4MGDVJQUJCKFSumwMBA6+RqFy9elIPD/84LZM+eXevXr9dnn32mIkWKKGvWrPL19VWfPn0SrCYAAAAAABJKvEN3w4YNVaZMmVhB96uvvtK+ffu0ZMmSeO2va9eu6tq1a5zrtm3bFmuZp6enfvvtt3g9BwAAAAAA9hDv4eU7duxQrVq1Yi2vWbOmduzYkSBFAQAAAADwOoh36L5//36ck5Y5OzsrNDQ0QYoCAAAAAOB18K9mL4/rHtwLFy5UwYIFE6QoAAAAAABeB/9qIrUPP/xQZ86cUZUqVSRJmzdv1o8//hjv67kBAAAAAHidxTt0e3t7a+XKlRo5cqSWLl2qJEmSqEiRItq0aZMqV65sRo0AAAAAACRK8Q7dklS7dm3Vrl07oWsBAAAAAOC1Eu9rugEAAAAAwIuJd093VFSUJkyYoMWLF+vixYuKiIiwWX/79u0EKw4AAAAAgMQs3j3dQ4cO1fjx49WkSROFhITIz89PH374oRwcHDRkyBATSgQAAAAAIHGKd+ieP3++Zs6cqc8//1xOTk5q1qyZvvvuOw0aNEi//fabGTUCAAAAAJAoxTt0BwUFqXDhwpKk5MmTKyQkRJJUp04drV27NmGrAwAAAAAgEYt36M6WLZuuXbsmScqVK5c2bNggSdq3b59cXV0TtjoAAAAAABKxeIfuBg0aaPPmzZKkbt26aeDAgcqTJ498fHzUpk2bBC8QAAAAAIDEKt6zl48ePdr67yZNmihHjhzatWuX8uTJI29v7wQtDgAAAACAxCxeoTsyMlIdO3bUwIEDlTNnTklSuXLlVK5cOVOKAwAAAAAgMYvX8HJnZ2ctW7bMrFoAAAAAAHitxPua7vr162vlypUmlAIAAAAAwOsl3td058mTR8OGDdOvv/6qkiVLKlmyZDbru3fvnmDFAQAAAACQmMU7dAcEBMjd3V0HDhzQgQMHbNZZLBZCNwAAAAAA/y/eofvcuXNm1AEAAAAAwGsn3td0AwAAAACAFxPvnu42bdr87fpZs2b962IAAAAAAHidxDt037lzx+ZxZGSkjh07prt376pKlSoJVhgAAAAAAIldvEP3ihUrYi2Ljo5Wp06dlCtXrgQpCgAAAACA10GCXNPt4OAgPz8/TZgwISF2BwAAAADAayHBJlI7c+aMHj9+nFC7AwAAAAAg0Yv38HI/Pz+bx4Zh6Nq1a1q7dq1atmyZYIUBAAAAAJDYxTt0Hzp0yOaxg4OD0qdPr3Hjxv3jzOYAAAAAALxJ4h26t27dakYdAAAAAAC8duJ9Tfe5c+d06tSpWMtPnTql8+fPJ0RNAAAAAAC8FuIdulu1aqVdu3bFWr5nzx61atUqIWoCAAAAAOC1EO/QfejQIVWoUCHW8nLlyunw4cMJURMAAAAAAK+FeIdui8Wie/fuxVoeEhKiqKioBCkKAAAAAIDXQbxD97vvvqtRo0bZBOyoqCiNGjVKFStWTNDiAAAAAABIzOI9e/mYMWP07rvvKl++fKpUqZIk6ZdfflFoaKi2bNmS4AUCAAAAAJBYxbunu2DBgjp69KgaN26s69ev6969e/Lx8dGJEyf0zjvvmFEjAAAAAACJUrx7uiUpS5YsGjlyZELXAgAAAADAayXePd2zZ8/WkiVLYi1fsmSJ5s6dmyBFAQAAAADwOoh36B41apTSpUsXa3mGDBno/QYAAAAA4CnxDt0XL15Uzpw5Yy3PkSOHLl68mCBFAQAAAADwOoh36M6QIYOOHj0aa/mRI0eUNm3aBCkKAAAAAIDXQbxDd7NmzdS9e3dt3bpVUVFRioqK0pYtW+Tr66umTZuaUSMAAAAAAIlSvGcv//LLL3X+/Hl98MEHcnJ6snl0dLR8fHw0YsSIBC8QAAAAAIDEKt6h28XFRYsWLdLw4cN1+PBhJUmSRIULF1aOHDnMqA8AAAAAgETrX92nW5Ly5MmjPHnySJJCQ0M1Y8YMBQQEaP/+/QlWHAAAAAAAidm/Dt2StHXrVs2aNUvLly9XqlSp1KBBg4SqCwAAAACARC/eofvKlSuaM2eOZs+erbt37+rOnTtasGCBGjduLIvFYkaNAAAAAAAkSi88e/myZctUq1Yt5cuXT4cPH9a4ceN09epVOTg4qHDhwgRuAAAAAACe8cI93U2aNFGfPn20aNEipUiRwsyaAAAAAAB4LbxwT3fbtm01bdo01ahRQ/7+/rpz546ZdQEAAAAAkOi9cOj+5ptvdO3aNXXo0EE//vijMmfOrHr16skwDEVHR5tZIwAAAAAAidILh25JSpIkiVq2bKnt27fr999/V6FChZQxY0ZVqFBBzZs31/Lly82qEwAAAACARCdeoftpefLk0ciRI3Xp0iXNmzdPDx48ULNmzRKyNgAAAAAAErX/dJ9uSXJwcJC3t7e8vb11/fr1hKgJAAAAAIDXwr/u6Y5LhgwZEnJ3AAAAAAAkagkaugEAAAAAwP8QugEAAAAAMAmhGwAAAAAAk8Q7dL/99tu6detWrOV3797V22+/nSBFAQAAAADwOoh36D5//ryioqJiLQ8PD9eVK1cSpCgAAAAAAF4HL3zLsNWrV1v/vX79eqVKlcr6OCoqSps3b5aHh0eCFgcAAAAAQGL2wqG7fv36kiSLxaKWLVvarHN2dpaHh4fGjRuXoMUBAAAAAJCYvXDojo6OliTlzJlT+/btU7p06UwrCgAAAACA18ELh+4Y586di7Xs7t27cnd3T4h6AAAAAAB4bcR7IrUxY8Zo0aJF1scfffSR0qRJo6xZs+rIkSMJWhwAAAAAAIlZvEO3v7+/smfPLknauHGjNm3apMDAQNWsWVO9evVK8AIBAAAAAEis4j28PCgoyBq616xZo8aNG6tatWry8PBQ2bJlE7xAAAAAAAASq3j3dKdOnVqXLl2SJAUGBsrLy0uSZBhGnPfvBgAAAADgTRXvnu4PP/xQzZs3V548eXTr1i3VrFlTknTo0CHlzp07wQsEAAAAACCxinfonjBhgjw8PHTp0iV99dVXSp48uSTp2rVr6ty5c4IXCAAAAABAYhXv0O3s7KyePXvGWv7ZZ58lSEEAAAAAALwu4n1NtyT98MMPqlixorJkyaILFy5IkiZOnKhVq1YlaHEAAAAAACRm8Q7dM2bMkJ+fn2rWrKm7d+9aJ09zd3fXxIkTE7o+AAAAAAASrXiH7ilTpmjmzJnq37+/HB0drctLlSql33//PUGLAwAAAAAgMYt36D537pyKFy8ea7mrq6vCwsISpCgAAAAAAF4H8Q7dOXPm1OHDh2MtDwwMVIECBRKiJgAAAAAAXgsvHLqHDRumBw8eyM/PT126dNGiRYtkGIb27t2rESNGqG/fvurdu/e/KmLatGny8PCQm5ubypYtq717977QdgsXLpTFYlH9+vX/1fMCAAAAAGCmF75l2NChQ/Xpp5+qXbt2SpIkiQYMGKAHDx6oefPmypIliyZNmqSmTZvGu4BFixbJz89P/v7+Klu2rCZOnKjq1avr5MmTypAhw3O3O3/+vHr27KlKlSrF+zkBAAAAAHgZXrin2zAM678//vhjnTp1Svfv31dQUJAuX76stm3b/qsCxo8fr/bt26t169YqWLCg/P39lTRpUs2aNeu520RFRenjjz/W0KFD9fbbb/+r5wUAAAAAwGzxuqbbYrHYPE6aNOnf9kb/k4iICB04cEBeXl7/K8jBQV5eXtq9e/dztxs2bJgyZMjwQkE/PDxcoaGhNj8AAAAAALwMLzy8XJLy5s0bK3g/6/bt2y+8v5s3byoqKkoZM2a0WZ4xY0adOHEizm127typgICAOCdzi8uoUaM0dOjQF64JAAAAAICEEq/QPXToUKVKlcqsWv7RvXv31KJFC82cOVPp0qV7oW369u0rPz8/6+PQ0FBlz57drBIBAAAAALCKV+hu2rTpfxpO/qx06dLJ0dFRwcHBNsuDg4OVKVOmWO3PnDmj8+fPy9vb27osOjpakuTk5KSTJ08qV65cNtu4urrK1dU1wWoGAAAAAOBFvfA13f80rPzfcHFxUcmSJbV582brsujoaG3evFmenp6x2ufPn1+///67Dh8+bP2pW7eu3n//fR0+fJgebAAAAADAK+WFe7qfnr08Ifn5+ally5YqVaqUypQpo4kTJyosLEytW7eWJPn4+Chr1qwaNWqU3Nzc9M4779hs7+7uLkmxlgMAAAAAYG8vHLpjhnEntCZNmujGjRsaNGiQgoKCVKxYMQUGBlonV7t48aIcHOI1yToAAAAAAK+EeF3TbZauXbuqa9euca7btm3b3247Z86chC8IAAAAAIAEQBcyAAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACYhdAMAAAAAYBJCNwAAAAAAJiF0AwAAAABgEkI3AAAAAAAmIXQDAAAAAGASQjcAAAAAACZ5JUL3tGnT5OHhITc3N5UtW1Z79+59btuZM2eqUqVKSp06tVKnTi0vL6+/bQ8AAAAAgL3YPXQvWrRIfn5+Gjx4sA4ePKiiRYuqevXqun79epztt23bpmbNmmnr1q3avXu3smfPrmrVqunKlSsvuXIAAAAAAP6e3UP3+PHj1b59e7Vu3VoFCxaUv7+/kiZNqlmzZsXZfv78+ercubOKFSum/Pnz67vvvlN0dLQ2b978kisHAAAAAODv2TV0R0RE6MCBA/Ly8rIuc3BwkJeXl3bv3v1C+3jw4IEiIyOVJk0as8oEAAAAAOBfcbLnk9+8eVNRUVHKmDGjzfKMGTPqxIkTL7SPPn36KEuWLDbB/Wnh4eEKDw+3Pg4NDf33BQMAAAAAEA92H17+X4wePVoLFy7UihUr5ObmFmebUaNGKVWqVNaf7Nmzv+QqAQAAAABvKruG7nTp0snR0VHBwcE2y4ODg5UpU6a/3fbrr7/W6NGjtWHDBhUpUuS57fr27auQkBDrz6VLlxKkdgAAAAAA/oldQ7eLi4tKlixpMwlazKRonp6ez93uq6++0pdffqnAwECVKlXqb5/D1dVVKVOmtPkBAAAAAOBlsOs13ZLk5+enli1bqlSpUipTpowmTpyosLAwtW7dWpLk4+OjrFmzatSoUZKkMWPGaNCgQVqwYIE8PDwUFBQkSUqePLmSJ09ut9cBAAAAAMCz7B66mzRpohs3bmjQoEEKCgpSsWLFFBgYaJ1c7eLFi3Jw+F+H/IwZMxQREaFGjRrZ7Gfw4MEaMmTIyywdAAAAAIC/ZffQLUldu3ZV165d41y3bds2m8fnz583vyAAAAAAABJAop69HAAAAACAVxmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTELoBAAAAADAJoRsAAAAAAJMQugEAAAAAMAmhGwAAAAAAkxC6AQAAAAAwCaEbAAAAAACTvBKhe9q0afLw8JCbm5vKli2rvXv3/m37JUuWKH/+/HJzc1PhwoW1bt26l1QpAAAAAAAvzu6he9GiRfLz89PgwYN18OBBFS1aVNWrV9f169fjbL9r1y41a9ZMbdu21aFDh1S/fn3Vr19fx44de8mVAwAAAADw9+weusePH6/27durdevWKliwoPz9/ZU0aVLNmjUrzvaTJk1SjRo11KtXLxUoUEBffvmlSpQooalTp77kygEAAAAA+Ht2Dd0RERE6cOCAvLy8rMscHBzk5eWl3bt3x7nN7t27bdpLUvXq1Z/bHgAAAAAAe3Gy55PfvHlTUVFRypgxo83yjBkz6sSJE3FuExQUFGf7oKCgONuHh4crPDzc+jgkJESSFBoa+l9Kfyke3b9n7xKQSISGuti7BKvQR4/sXQISCcsr9D78KJTjFi8m1PHVOW51394FINF4hd5vQx+9OrXg1eYY6mjvEv5RTKY0DONv29k1dL8Mo0aN0tChQ2Mtz549ux2qAcwR+wgHEoHRo+1dARBvX+gLe5cA/Aup7F0AEH8j7V3Ai7t3755SpXr+/2d2Dd3p0qWTo6OjgoODbZYHBwcrU6ZMcW6TKVOmeLXv27ev/Pz8rI+jo6N1+/ZtpU2bVhaL5T++ArxsoaGhyp49uy5duqSUKVPauxzghXDcIjHiuEVixHGLxIjjNvEyDEP37t1TlixZ/radXUO3i4uLSpYsqc2bN6t+/fqSnoTizZs3q2vXrnFu4+npqc2bN6tHjx7WZRs3bpSnp2ec7V1dXeXq6mqzzN3dPSHKhx2lTJmSNyUkOhy3SIw4bpEYcdwiMeK4TZz+roc7ht2Hl/v5+ally5YqVaqUypQpo4kTJyosLEytW7eWJPn4+Chr1qwaNWqUJMnX11eVK1fWuHHjVLt2bS1cuFD79+/Xt99+a8+XAQAAAABALHYP3U2aNNGNGzc0aNAgBQUFqVixYgoMDLROlnbx4kU5OPxvkvXy5ctrwYIFGjBggPr166c8efJo5cqVeuedd+z1EgAAAAAAiJPdQ7ckde3a9bnDybdt2xZr2UcffaSPPvrI5KrwKnJ1ddXgwYNjXTIAvMo4bpEYcdwiMeK4RWLEcfv6sxj/NL85AAAAAAD4Vxz+uQkAAAAAAPg3CN0AAAAAAJiE0A0AAAAAgEkI3QAAAAAAmITQDQCvmejo6FjL7t27Z4dKgOd79jhlXlckRhy3eJVxfL46CN0A8JpxcHDQhQsXNHHiREnSkiVL5OPjo5CQEPsWBvy/6OhoOTg8+Qqyf/9+SZLFYrFnScA/ioiIkI+PjxYvXqzDhw9L4rjFq+Xhw4datGiRHj16pOjoaFksFoL3K4LQDcQh5g0qKipKjx49snM1QPw8fvxYM2bM0OzZs9WyZUs1adJE9erVU6pUqexdGmATuAcPHqxWrVpp0aJFdq4K+GfXr19Xnjx55O/vr44dO6p9+/YKCgpSVFSUvUsDJEmbNm1S//791ahRI7Vq1UoXLlzgxNArgvt0A88wDEMWi0Xr1q3TggULdPjwYdWvX19ly5aVt7e3vcsDXsjDhw/VpEkTrVmzRo0bN9bChQslPTmR5OjoaOfqAKlv374KCAjQwoULlS9fPmXNmtXeJQEv5MqVKzp58qTatGmjzJkzq2vXrmrUqJFcXV3tXRreQDHfW2NERERo5cqVmj9/vnbs2KGhQ4eqfv36euutt+xYJejpBp5hsVi0evVqffTRR/Lw8JCfn5927Nih3r17W4eTAa+qmPOoLi4ucnd3V9WqVXX58mWNGjVKkuTo6EivDOzu2LFjWrNmjZYuXaoqVaooRYoUOnPmjKZPn65Tp07p8ePH9i4RsPH0MZkqVSpVqVJFf/75pzJnzqwpU6YoICBAERERdqwQb6KnA/eaNWt0/vx5ubi4qHHjxlq1apV8fX01depUjRs3Tn/99Zedq32zEbqBZ9y8eVNff/21Ro4cqeHDh6t58+Y6fvy4atWqpWLFitm7POC5Yj58Dxw4oCtXrmju3LlatGiRihcvrlWrVtkEb+nJsQ7YQ3h4uC5cuKA0adLo6NGjGjBggOrUqaOBAwfqvffe0+nTp+1dImDDyclJktSqVSstX75cYWFhSpo0qRYsWKB8+fLp+++/1549e+xcJd4kTwfuLl266LPPPpObm5t1nSQNGTJEfn5+2rZtm+bOnau7d+/aq9w3HqEbeIabm5sePHig2rVr69y5c8qdO7caNGigcePGSXpyvcy5c+fsXCVgK+bDd8WKFapVq5amTJmiW7duyd3dXf3791fp0qW1evVqjRw5UpI0aNAgderUSeHh4XauHK+7uGbTL1GihCpWrKjKlSurYsWKioqK0vDhw3Xr1i05ODgoMDDQDpUCsT09MmjGjBn69ddf5eXlpWTJkik6Olpubm7y9/eXxWLR8OHD7Vgp3iRPB+7PPvtMP/74o5YsWaJMmTJJejJqM2Z0xqeffqrWrVvru+++08GDByXF/b4Mc3FNN6D/vXkZhqGrV6+qWrVq6t27t4YPH6733ntP/v7+cnR01NmzZzVgwAC1atVK1apVs3fZgI2ff/5ZDRs21NSpU1W3bl2lS5fOuu769esaO3asVqxYIRcXFwUHB2vdunUqW7asHSvG6+7pSdM2bNig27dvKzw8XC1bttT9+/cVGBioDBkyqHz58nJyclJkZKTef/99de7cWc2bN7dz9cD/jB07ViEhIfLw8FC7du2s3xti5sm4dOmSypQpoyFDhqhjx472LhdvCD8/P82fP1/r16+3jsaMjo7W3bt3lSZNGpv34DZt2ujXX3/VoUOHlDRpUjtW/WYidOONFvOh+ejRI7m5uVk/PAcMGKCRI0fK29tbq1atsrbv37+/fvrpJ61du1bZs2e3Y+WArYiICHXo0EEZMmTQV199pbCwMF28eFHz5s1Tzpw5Vbt2baVIkUK7d+/WyZMnVaNGDeXOndveZeMN0adPHy1fvlzu7u6Kjo7WrVu39PPPP6tAgQKSnkz8d/XqVfXo0UNXrlzR3r17rcN5AXu7ffu2ChUqpODgYA0cOFBDhw61WR/Ta/jll1/q/v37Gjt2rD3KxBtm165dqlixokaPHq3evXtLejIyI3fu3Kpbt64mTZok6X8nPy9evKgOHTrIz8+PjiM74BMNb6yYwL1hwwYFBATo3r17SpIkiaZNm6YePXro6tWrmj9/vqZMmaLIyEidOXNGP/zwg3755RcCN145FotF58+f14MHD6xfDE+dOqWrV68qJCREx44d08SJE1W1alVVrVrV3uXiDfLtt99q9uzZCgwMVIkSJTR//ny1aNFC586dU4ECBRQVFaWVK1dq5syZioyM1J49e+Tk5MRM+7Cbp4fuGoahNGnSaO/evWrUqJGWLl2qFi1a2Jy0jOlJLFWqlDp06KAePXowGz9MlzdvXvn5+WnQoEEqWrSoqlevrnLlyqlgwYIaNmyYtV3M8Zk1a1YlSZJEq1evJnTbAdd0441lsVi0atUqNWjQQHny5FHjxo114cIFlSpVSuHh4Ro2bJj69u2rGTNmaNGiRbp586Z27dqlokWL2rt0QM8OUnJ2dlavXr20ceNG5c6dW7du3VKHDh108uRJ9ejRQ7/99hv3nIddnDlzRr6+vipRooSWLl2qzp07y9/fX7Vq1dL9+/fl6Oio0qVLq0OHDtq2bZucnZ31+PFjAjfsIioqyub2SzHvtdmzZ9fSpUsVHR2tTz75RJcvX47Vpnbt2ho3bpxSpUr1covGay+ugcnp0qXTgAED1KVLF9WuXVvZsmVTtmzZtGDBgljHoGEYcnR01MiRI5UlSxZFRka+rNLx/xhejjfW3bt3VbduXXl7e6tXr166cuWKKlasKC8vL82cOdPa7vr168qQIYN1CDpgbzG9ML/++qt++eUX3bhxQ15eXqpZs6auXr2qs2fPqmLFitZ2vr6+unbtmr7//nuOYZjq2fvFSlL9+vWVJ08eVatWTQ0bNtSYMWPUqVMnGYahsWPHysXFRT169LC2p4cb9vL0sTd48GCdP39eQUFBGjRokMqUKSNnZ2ddunRJVatWVZo0abR48WJly5bNZh8PHz5UkiRJ7FE+XlNPv6/u3LnTemu6KlWqSHryfXbSpEkaOnSo5s6dqxYtWsT5XixJDx48UGRkJCeG7ICebrwxDMOwnimMioqyfnh+/PHHun79usqWLatq1apZA/eCBQsUERGhDBkySJJcXV3tVjvwNIvFouXLl6tevXratWuX7t69q9q1a6tv375KmzatKlasKEn6/fff1a9fP82dO1cDBgwgcMN0MV/yZsyYoVmzZkl6Erp/+eUX1a1b1xq4JSkkJEQ7duyIdQsbAjfsJebYa9iwoZYsWaKMGTPqrbfeUs2aNfXDDz8oNDRU2bNn18aNG3X79m1VrlxZd+7csdkHgRsJ6enw3K9fP7Vu3VqtW7dWu3btNH78eEmSu7u7unXrpp49e6pVq1ZaunSpdXLgZyVNmpTAbSeEbrzWnr0lgsVi0erVqzVs2DA5Ozsrb968mjdvnkqXLi1vb29NnTpVkhQUFKTFixdr3bp1NtsCr4KTJ0/Kz89PI0eO1OrVqzV58mTrpFMxJ4eOHDmicePG6aefftL27dtVpEgRe5aMN8idO3f0008/af/+/ZKkd999V8mSJVPu3LmVOXNmhYeH66+//lLz5s0VHBysAQMG2Lli4H++/PJLnTp1Sjt27NBXX30lT09P3b9/X126dLEJ3oGBgWrUqJFSp05t75Lxmno6cPfo0UP+/v6aO3euli9frjp16mjDhg3WtmnSpNHgwYPVo0cPNW/e3Bq88QoxgNdUVFSUYRiGcfToUWPt2rWGYRjGoUOHjEyZMhmzZ882Hjx4YHTu3NlIkSKFUatWLZttv/jiC6Nw4cLGpUuXXnrdwD/Zs2eP8e677xqGYRinT582smbNanTo0MG6/uLFi4ZhGMa+ffuMy5cv26VGvNl++OEHI0mSJMaxY8cMwzCM48ePG5UrVzby589vpE6d2ihdurRRoUIFIyIiwjAMw3j8+LE9ywUMwzCM+/fvGxMmTDCWL19uGIZhjBs3zkiTJo2xZ88eo3fv3kaqVKmMb7/91rhz547NdtHR0XaoFm+Kfv36GcmTJzdOnDhhXfbDDz8YlStXNqZPn26MGTPG+n31/v37Rs+ePQ2LxWLs3bvXXiUjDlzTjddSzO0Rjhw5olKlSmnGjBl69913tXr1al29etU6JCc4OFiNGzdWeHi4qlSpojx58mjXrl1asmSJtm/fzqRpeCUYT8207+7urqioKH388cf68ccf1axZM1WtWlXTp0+Xo6Ojtm/frrFjx+qbb75h9lyYznjmusGY997w8HB9+OGHKlCggIYPHy43NzfduHFDV65c0bFjx5Q7d26VLl1ajo6Oevz4MbcHg108fQ/jGH/88YeyZMmi06dPy8fHRyNGjNCHH36oLVu2qFq1aoqOjtaGDRvk5eVlp6rxJjl58qQKFCigpk2bauzYscqaNauio6NVuHBhRUVFKWPGjDp69Khy586txYsXK2fOnLp7964CAwPVtGlTe5ePpzC8HK+dmA/R33//XZ6enurdu7fatm0rb29v9e7dW+fPn7e2zZgxoxYsWKDSpUtr48aNmjp1qu7cuaOdO3cSuPHKsFgs2rlzpz788EOdPHlSefLkUaFChVSlShWVK1dO33zzjfWLY2BgoB48eMAcBHgpYgL3hAkTtH79el25ckXSk8scPD09tW7dOutlPunTp1exYsX0ySefqFy5cnJ0dFRUVBSBG3YRFRVlfd+8deuW9dgtVKiQUqdOrfPnzytZsmQqVaqUpCfH+pgxY7Rs2TICN16afPnyadasWdq1a5emTp2qkydPqly5csqWLZt27typwMBA7d69W3/++af8/f0lPbnGOyZwP3uZJeyHTzq8VmIC94kTJ/T++++rbt26GjFihCRp2bJlatWqlX7//Xdt2LDBeo/CrFmzauLEiTIMQ5GRkXJ0dJSLi4s9XwZg48KFC1q3bp369eunFi1aSJK8vb119uxZubi46I8//tDDhw+1ePFizZw5Uzt27FC6dOnsXDXeFHfv3tXOnTs1ZswY5cuXT9WrV9dnn32m3r17a+XKlfryyy81atSoOLdl0jTYQ3R0tPXY69y5sw4ePKjbt28rZ86c+vrrr1W4cGGFh4fr999/1759+3Tu3Dn5+vqqWrVq+vzzz637eLaXHPgvnh05FKNVq1aSpP79++u7775TwYIF9dNPP1m/q2bNmlVFixaN83jkGH11ELrx2oj5ADx8+LDKly8vR0dHHTp0SNu3b1fZsmX1zjvv6IcfflCjRo00efJkJUuWTBUqVJD05Ay2o6MjPS545Zw4cUJt2rTR1atX1adPH+vyDh066N69e1qzZo2KFCmiwoULy9HRUVu3blXhwoXtWDFed8+GDXd3dy1btky//vqr9u/fr+HDh2vTpk3KlSuXKlasqJMnT+revXtKkSKFHasG/ifm+G3atKn++OMPjR49WgULFlTevHk1cOBAzZ8/X5988ok2b96sZs2aKXPmzCpZsqS+/vrrWPsAEsLTgfvgwYPWkRglS5aU9CR4J0mSRF26dNE777yjq1evysPDQ5J079493b171/oYryg7Xk8OJLgjR44Yjo6OxvDhww3DMIwKFSoYHh4exrZt24zw8HDDMAzj999/NwoUKGDUqVPH+PXXX+1ZLvBCfH19jdSpUxv16tUz7t69a7MuNDTU+O2334wLFy4YN2/etFOFeFPETFBpGE/eS3fv3m2cPn3aps21a9eM6dOnG7Vq1TIsFothsViMJUuWvOxSgb91+PBho3jx4sYff/xhGIZhTJ061UidOrURGBho027v3r3G0aNHrY+f/n8ASAhPT8Q3cOBAo0CBAkaGDBmMDBkyGP7+/jZt58yZY2TNmtXw9fU1Ll++bDx48MAoUKCAUb9+/ZddNuKJidTw2njw4IE+/vhjFS5cWMOGDbMur1ixoq5cuaI5c+bI09NTLi4uOnbsmD7++GOlSpVKY8eOVdmyZe1YOfA/xnOGl/Xp00dr1qxRkyZN1L17d7m7u7/84vBGe/rY7N+/v1avXq2LFy+qbNmyKl26tPVSnqetXr1aP/zwg+7fv68FCxZweyW8MjZs2KDOnTvr9OnTGjdunEaMGKGFCxeqWrVqunDhglauXClfX1+bbRhSDjP16NFD33//vZYuXapHjx5p+vTp2rJli3bu3KnixYtb33/nzp2rQYMGqXbt2lq/fr1y5cplvX0Yx+iri7G0eG0kTZpUkyZN0ltvvSVJioyMlLOzs3bu3KmKFSuqVatW1uD9zjvvaO7cufr000+VJUsWO1cOPBETavbs2aNff/1VLi4uypkzp2rXrq0xY8bo8ePHWrVqlSwWi7p16yZ3d/fnhnQgocUcZ8OHD9d3332nH3/8UYULF1bv3r01bdo0hYSEaOrUqZKkiIgIubi4qG7dujIMQ127dtWNGzcI3bCLuN4nS5QooQwZMqhZs2Zav369lixZog8++EDSk3k0Fi9erPfee89mUlXCDMwycuRITZ48WZcvX7Z+Lz158qQ2bNige/fu2Ry/LVu2VFRUlNq1a6f69etr+fLlkgjcrzr+MngtxAzYiAnckuTs7KzHjx9Lknbu3KmsWbOqVatW2rNnj8LDw1WsWDHt2LFD2bNnt0vNwNNivhQuW7ZMVatW1cqVK/Xtt9+qfv368vPzkySNGzdO7777rtauXavRo0crJCSEwI2X6ujRo1q9erXmz5+vKlWq6MiRI1q8eLGqVaumwMBA9ejRQ5Lk4uKiyMhISVK9evWUJEkSHTlyxI6V400VFRVlfZ+8e/euHj16JOnJMVqoUCEFBgaqa9eu1sAdFBQkX19fFSlShLuY4KW4cuWK9u7dqxw5cujEiROSnhy38+bNU1RUlGbNmqVRo0Zp3rx5CgsLkyS1adNGv/32G4E7EeGvg9fC84KHk5OTTfD28PBQnTp1dODAAUlPgjlgD8/exsNisej06dPq1q2bxowZox07dmj79u2aN2+evvnmG/Xs2VPSk1szFS1aVHv27LGGGuBlKVKkiNq0aaPixYtr+/btatGihSZNmqSFCxcqb9688vf3V/PmzSX97/3V399fQUFB1lsvAS+LYRjWWcrbtGmjunXrqlixYlq5cqVSpkypPn36qHTp0lq1apWaNm0qX19fffDBB8qWLZtmzJhh3QeQ0B49eqRp06ZJejL7eL9+/VS5cmX16NFDP/30kypVqqQUKVJo8eLFqly5sq5evaouXbqoatWqKlGihG7evKkyZcpIInAnFgwvx2svJng7OTlp69atqlmzpjJkyCDp+WEdMNPT95K/evWqqlevLunJvWJTpEghb29vSVLq1KnVpEkT6zCyWrVqqUqVKvrmm290/fp1bgsGU23atEn79u3T7du31alTJ7399tuSpI4dO8pisWjhwoVq1KiRWrZsKQcHBxUqVEgPHjyQu7u7zZfAnDlz6rffflPOnDnt+XLwhomKirIGbl9fX+3atUu+vr7avn27mjdvrmHDhqlnz56aMWOGAgMDtXjxYqVOnVotWrTQF198IYkwA/Ps3r1b69atU5cuXSRJZcqUkcVi0eTJk/Xxxx/Lw8NDR48etdmmT58+Wrdunc6dO2fz+c8xmjgQuvFaed71rU8H759//tkOlQFPxHyJO3r0qIoVK6ahQ4daQ3fSpEl15swZ/fXXX8qWLZv1eH7vvfeUOXNmXbt2zbqfmBNHgBlmzpypgQMH6p133lFwcLAWLFigvXv3KmvWrNb32LNnzypp0qRydnZWVFSULly4oBYtWqhNmzayWCzW0BNzfANmezokxwTuQ4cO6fHjx1qyZIkKFy6sTp06aejQofryyy9lGIa6dOmiLl26qHPnzjbfHwjcMFO2bNm0e/durV+/3voeWbp0afn6+spisWj//v3WdYZh6PHjx8qWLZvat29vPU45RhMX/lJIlGKGe506dUonTpzQ2bNnJT3puX522G4M7sENe3v6XvLlypVTv379NHDgQOv6/Pnzq2bNmpo2bZoOHjxo/WBNly6d0qRJw3ByvBTffvutOnXqpOnTp2vNmjVasGCBUqZMqaCgIJuhtjVq1NC5c+dUq1YtVa5cWX/++adatWoli8ViM6wXeBnu378vX19f6/cBSQoICFDJkiW1dOlSPXz40Lp88ODB+vzzzzVq1CjNmDFDwcHBsU7YE2ZgFsMwlCdPHnXs2FEBAQE2J9RLlSqlbt26qWTJkvr888/1888/y2KxyNnZOVbHEsdo4sJfC4mSxWLR0qVLVaVKFb3//vv6+OOPNXnyZElP3oSeF7wBe3JwcNDJkydVrlw5DRgwQMOHD7euW7NmjcLDw9WuXTvdvHlTQ4YM0Zo1a/THH39o4MCBunDhgt577z37FY83wrJly/Tpp59q3rx5+vDDD+Xm5qbChQsrKipKkyZNUtmyZTV+/Hhdu3ZNPj4+atmypVKlSqVChQrp0KFDcnR0tJm4CnhZjh07pvDwcOtlEJLUtm1b9ezZUzdu3NCePXtsgvegQYPUo0cP9erVSwcPHrRHyXhDxbw/enp66q+//tK2bdtsvreWLl1a3bt3V8mSJdW7d2+tWLHCZjskTnT9IVGJOcsXFBSkAQMGaOjQocqQIYN27Nih8ePHKzQ0VAMGDLAGb84C4lXy6NEjDRkyRMmTJ5enp6d1+YgRI+Tv76+NGzeqXr16io6O1o8//qj69esrb968evz4sdavXy8PDw/7FY/XnmEY2rRpk3LkyKFbt25Z328//PBDPXr0SJkyZZKDg4N69uypq1ev6uuvv1aPHj1svgjGXMYDvEyGYahcuXIqV66cpCeT973zzjuqWLGivvrqK4WGhqpPnz5Kly6dGjRoIDc3N0lPgneRIkVUs2ZNe5aPN1TdunW1bt06de/eXTly5FD58uWt60qXLq1u3bppyJAh2rt3rxo0aGDHSpEQLAbTMiKR2b17t5YvX66wsDBNnjxZTk5OunbtmmbNmiV/f3917NhRAwYMkMT1Lnj1bN26VVOnTtWdO3c0ZswY/fbbbxoyZIjmz5+vGjVqWNtFRkbq/PnzioqKUtq0aZU+fXo7Vo03RWRkpHx9fXXw4EF98skn2rRpky5cuKAVK1ZYT/r07NlT06dP16lTp5Q1a1brttwzHq+CkydPqnjx4mrQoIF69Oih0qVLS5Lat2+vBQsWKCAgQA0aNJCrq6vNdnxfwMv09PFWr1497d27Vz/88IPKlSun5MmTW9udPXvWZvQGEi9CNxKVBw8eqE+fPpo/f74KFy6s7du3W9fFBO+AgAA1bdpUI0eOtGOlwPPFjMw4fvy4Lly4oG3btqlcuXLW62VjroklwOBlipn4LDIyUl27dtW6desUGRmprVu3qkCBAoqIiJCLi4u+/fZbffvtt1q3bh0T+uGVtH37drVt21Zly5aVr6+v9dZKHTt21Pz58zVp0iS1bNmSURkwTVyf4c+e2Il5bBiGmjRpol27dqlDhw6qX7++ihQp8o/7Q+LCKT0kCjFhJGnSpOrQoYNatGihPXv26Ntvv7W2yZw5s9q2batmzZpp9erVunnzJvfXxCsl5nh899131bNnT+XJk0cFCxZUWFiYJNvrtfhwxcsWcz22s7Ozpk6dqgYNGihLliwKDAzU/fv35eLioujoaK1cuVJvv/02oy/wynj6s94wDFWuXFkBAQHavXu3Jk2apL1790qSvvnmG9WsWVO7d+8mcMNUMZ/hI0eO1Pjx4yXFnnMoJnBbLBYtXrxYvr6+OnLkiKpVq6bhw4dr/fr1sfaHxIuebrzSYt6MHjx4IGdnZzk7O0uSzp07p6+//lqbN29Wr1691LZtW+s2wcHBcnJyUtq0ae1VNvBcT5+t/uWXXzRu3DiFhoaqV69e1usKOaMNe3q2x/vQoUNq1qyZOnTooObNm+vUqVM6evSonJycGJILu5k5c6bSp0+v+vXrS7J934z59/bt29W6dWtVqFBB3bp1s/Z4Ay/Dli1b5OXlJScnJ40YMUK9evWSFLvH++l7yt++fVs7duzQ2rVrlStXLnXo0EFp0qSxS/1IWIRuvLJiPjTXrl2rSZMm6d69e0qWLJmGDh2qChUq6MKFCxo7dqw2bdqkPn36qHXr1vYuGXghT385jBlq/uDBA3Xp0kX16tWzc3WAbfDu1q2bjhw5okuXLilZsmQ6duyYnJ2dmTQNdnPlyhV16dJFJ06c0MSJE63zYTz93hoTbHbs2KFWrVopb968mj59uvX6WE5uwmz79u1T27ZtVbhwYe3fv18tWrR47pxDzx6PkZGRcnJy4hh9jXB6Gq+smMDdoEEDlSxZUg0aNJCTk5MaNmyogIAA5ciRQ927d1eNGjXUp08fzZs3z94lA8/19PnNmGu2pf8NNX/8+LFmzZplHWoOmOXvbqkYs+7poeZTpkxR7ty5VaBAAQI37ObRo0eaNm2aJClr1qzq16+fypUrp549e+rnn3+WZPveGhNo3n33XS1cuFCFChWymZCKMAOzxByDpUuXVq5cuXT69Gm1a9dO3333nUaNGiUp9lDzZ49HZ2dnjtHXDD3deGXcuHHD5hrBhw8fWieTGDt2rHV5586dtWzZMq1du1alSpXS0aNHNX/+fHXo0EG5cuWyR+mAjZgz1ufOndPt27dVpEgR66URcbWTnszKnz17dmXLlu1ll4s3yNO9K4sXL9bp06fl6Oio0qVLq0qVKs9tH3PvbQcHBwI37GLr1q36+uuvtXbtWuuyffv2afLkyTp06JDGjh0b69Zff/75p3r16qVp06ZZZ9+nhxsJLa5jKmbZ77//roEDB6pNmzY6fPiwvvnmG3Xr1k1ffPGFJGbNf5PwV8YrYfDgwfrqq68UERFhXebg4KA7d+4oU6ZMkqTw8HBJ0vTp01WoUCF9+eWXkqQiRYpo+PDhBG68MiwWi5YvXy5PT095e3urSJEiWrlyZaxe7Kd7ZTw9PQncMF3Ml7vevXurR48eOnHihLZs2aKOHTvanNx8un10dLQcHR2t/yZwwx6yZcum3bt320wuVbp0aXXv3l3FixdXr169tG7dOuu6P//8U7Vq1ZIka+CW6OFGwos5psaMGaN+/frp4sWL1s/7tGnT6vbt2zp//rwGDRqkDh06aOrUqRozZoyk/02mhtcfoRuvhEKFCqlly5ZycXHRgwcPJEmurq5KkyaN1qxZY30cE7xLlSplE9Dj6kUE7MEwDF29elUjRozQgAEDFBgYqIIFC6pPnz5auHCh7t+/b9OeL4B42VasWKGFCxdqxYoV+v7779WsWTNdvnz5uSd9nu6FoUcG9mAYhvLkyaOOHTsqICBA165ds657Onj37t1bGzZs0I0bN1SnTh0VKlTI2jP+d5dVAP/G02H5559/Vt++fTV69GgNGTJELVq00KFDh5QlSxYNHTpUEydO1OnTp9WpUyd9+umnmjZtmvr37y+J7wFvCj498Upo3Lix3nnnHW3ZskW9e/fWH3/8IUnq27evLl++rA4dOkh6Erwl6fr160qZMqUiIyM5Q4hXQsxxaBiGUqdOrUqVKql169YqUqSIli1bJk9PT3311VdatGhRrOANvExnzpxR8eLFVbZsWS1btkzdu3fXhAkT1KxZM4WFhengwYP2LhGwERNKPD099ddff2nbtm02ITomeJcsWVJdu3ZVjhw5lD9/fpvAzQkjJLSY4zIsLEwlSpRQp06dlDZtWqVNm1alS5dWnTp11Lp1a+3atUvly5fX4cOHlSFDBrVp00ZNmzZlDpc3DGPE8Eq5fPmyvv/+ezk5OcnX11cVK1ZU7969NWbMGFWoUEHvvvuuLl++rBUrVui3336jhxuvjJiJ/+bMmaOLFy/Kzc1Njx8/tq6fM2eOWrZsqQkTJujRo0dq1aqVkiVLZseK8SZ4+lrDe/fuKUWKFHJ2dpaHh4c2bNigVq1aaezYsfr0008lSWvXrtXJkyeVK1cupUqVyp6lA7HUrVtX69atU/fu3ZUjRw6VL1/euq506dLq1q2bbt++LU9PT82dO1cSgRvmCggI0JQpU7R371716NFDjx8/VkBAgLZu3aqPPvpIq1at0tSpU3Xx4kU9fPhQjRo1UpYsWdSvXz+5u7tLYp6BNwXvQrCrmN7BS5cuyTAM+fj46JtvvtHSpUs1btw4Xbt2TW3bttW8efOUKVMmHTp0SJGRkfrtt9/0zjvv2Ll64H9+++031atXT6lTp5ajo6OOHj2qr776Snfu3LG2mTt3rvLmzau5c+faBHLALDFf5CZOnGgNIfnz59eUKVNUo0YNTZs2zRq4w8LCFBAQoOvXrxO48cqJ6dn29/dX+fLl1bBhQ23atMlm5FCpUqU0adIkAjdeCsMwFBkZqZCQEJ0/f1558uRRjx495O3trffff1/BwcHq2bOn9uzZo5kzZ8rf39+6LYH7zcPs5bCbmDean376SWPHjlWLFi3Uvn17SdKCBQvUu3dv1a9fX35+fja3+WDmXLxqTp48qeXLl8vV1VV+fn6SJD8/P+3cuVN169ZVt27dbELM1atXlSVLFnuVizdQ586dtWrVKv31119KliyZJk6cqJ49e2rq1KkqU6aMDMNQv379dP36de3bt09OTk58GcRLFdfx9mxojnlsGIaaNGmiXbt2qUOHDtY7nfzT/oCEFhERobx588rLy0vfffedJOmvv/7SyJEjtXr1av3444+qXr269djlRNCbi786XrqY8zwWi0UrVqxQ48aN1bBhQ1WqVMnapnnz5ho1apRWrFihyZMn69ixY9Z1BG68Ss6ePauOHTtq8uTJ1jkHJGn8+PGqWLGiVq5cqWnTptn0eBO4YaanR1HE9A726tVLHh4emjNnjiSpTZs2GjZsmPr27auaNWuqQ4cOMgxDe/fulZOTk/UWYcDLEnO8jRw5UuPHj5cU+17GMYHbYrFo8eLF8vX11ZEjR1StWjUNHz7cZmZzjl+YLSoqSi4uLurXr592796tffv2SZLy5s2r/v37q169evrkk0+0efNm67FL4H5z8ZfHS3Ps2DGbL3KXL1/W0KFDNX78ePn6+ip37tx6+PCh1q5dq1u3bqlFixYaO3asvvnmG82bN0+RkZF2fgVAbG+99ZaqVKkiNzc3rVq1ymZilPHjx+v9999XQECAAgICmPQPpmrdurWuXr1qPTH59Be8bNmy6e2339bSpUslSSlTplS/fv20d+9e/fzzz5o3b54CAwPl7Oysx48fy9HR0W6vA2+uLVu2aMCAAfriiy+st7B7NnhbLBZFRUVJenIyKWbY7oULF3To0CHdvn3bLrXjzRBz7EVFRVnfJ8uVK6fbt29rz5491nZ58uRR//795e3trapVq+rPP//kRNAbji5DvBRTp07VsmXLtGrVKqVMmVLSk/tuh4SEqFChQoqOjtZXX32ltWvX6tixY0qePLm2b9+u5s2by9nZWcWKFWPSNLwSnh2y6OTkpH79+ilJkiT68ccf1adPH40cOdJ6nI8dO1YuLi5q2LAhH7gwzaFDh+Tm5qY0adJIejJ/wJ49ezR69Gjr5GkjR45UkSJFNG3aNHXp0kXSky+GT+M+3LCnFClS6J133lHhwoX13XffKTw8XAMGDIg1LNfR0dH6XpwmTRrVr19ftWvXlpOTE++zMM3UqVMVGhoqHx8fm1ssFilSRO3atdOYMWNUt25dvfXWW5Kk3Llzq1evXvL09FTBggXtVTZeEVzTjZfi/v37CgoKUu7cuXX9+nWlSZNGkZGRatq0qU6cOKF79+6pTJkyKleunNq3by9PT0/Vrl1bEyZMsHfpgFXMl7xdu3Zp27Ztevz4sQoXLqwGDRooKipKX3/9tVasWKGSJUtq1KhR1uANmKlZs2YaMmSI8uXLJ0mKjIzUwIEDFRgYqOvXr6tjx4764IMPVLFiRfn5+enWrVuaOHGi3N3dCSh4JTx9MrNBgwa6evWqGjVqpGnTpqljx47q27evJCZGw8v19HFpGIZq166t8+fP6+rVq/Lz81PFihVVpUoVSdLBgwf18ccfa/DgwWratKkiIyNjdRZx/L7Z+MvDdFFRUUqePLly586tPXv2qHbt2lq9erWSJEmikSNHytfXV3379tV3332nPn36KG3atCpYsKA8PDzsXTpgw2KxaNmyZapevbo2bdqkn3/+WQ0bNlSnTp0UHh6unj17qm7dujp69Ki6deume/fu2btkvOYePHig5MmTK0eOHJKkhQsX6pdfftHo0aN1+PBhderUSQcOHFDt2rU1evRoubq6at26dTp27BiBG3bzbH+PxWKxLhs2bJgyZ86sfPnyqU2bNpo6dapGjx4tKfZQc8AsTwfuffv2KSgoSOvWrdP69evVq1cvLV26VM2aNdPHH3+sTZs2qUSJEipdurS1s8jZ2TnWcU7gfrMxhgyme/rawPz588swDH399ddydXVVjRo1VKhQIev6kJAQjRs3Trt27dJXX31lj3KB5zp37pz8/Pys9zWOjo7Whg0b1LBhQzk4OGjatGnq1auXHj58qD179igsLEwpUqSwd9l4jSVNmlQzZ86U9GRW/DFjxih9+vR68OCB6tSpo4EDB+ratWvWIJ4pUybdunVL33//vc3klcDLFBNmxowZo5CQEH366adKkyaNkidPrrRp0+r27ds6f/68Bg0aJMMwNHXqVFksFvXp08dmMjXADE8fXyNGjNCKFSv00UcfqUOHDsqePbv69++vJk2a6NixYxo4cKA6d+6sTJkyydvbWytXrtSSJUv00UcfcYzClgGYKDo62jAMw9i3b5+xd+9ewzAMIzQ01HjvvfeMMmXKGCtXrjQeP35sGIZh/PTTT4aPj4+RLVs24+DBg3arGTAMw/j222+NXbt2WY9hwzCM33//3ciVK5fx559/GoZhGFFRUYZhGMaaNWsMBwcHY926dYZhGMbjx4+Nmzdvvvyi8UZ5+tiMsWfPHuODDz4watWqZaxevdpm3eXLl40NGzYY3bt3NyIiIl5WmYDV08fsunXrDIvFYlgsFqN169ZG/fr1rZ/9W7ZsMXLmzGmcOnXKCA4ONr788ksje/bsRr9+/exVOt5AvXr1MjJlymQsWbLEOHPmjGEYsd937927Z6xdu9b48MMPjZQpUxoWi8WYMmWKPcrFK45xDjCN8f9nCpcvX6569erJ399fV69eVYoUKbR69WolTZpUo0aN0tq1ayVJmTJlUrFixbR161YVL17cztXjTWYYhoYOHao2bdrowIEDNre5O3v2rC5dumRtZxiG3nvvPRUsWFBnz56V9GR0R9q0ae1WP15/0dHR1l6Up4fblilTRiNGjNCDBw/k7+9vfX+VpKxZs6pq1aqaNGmSnJ2duSMEXrqYYzYsLEwlSpRQp06dlDZtWqVNm1alS5dWnTp11Lp1a+3atUvly5fX4cOHlSFDBrVp00ZNmza1uTsEYKaFCxdq2bJl+umnn9SoUSO9/fbbkp4cww8ePLC2S548uWrVqqVly5ZpxYoVmjNnjrp27WqvsvEKYyI1mGrr1q2qU6eOpk2bJm9vb6VNm9Y6kcS9e/dUt25dRUREqGfPnqpfv76io6O5VQ3sKuZkUUREhMqWLavHjx8rICBAJUqUkJOTkz7++GOdP39eEyZMUJkyZSQ9CT2enp5q1aqVOnXqZOdXgNfd05Px+Pv76/DhwwoNDVWjRo1UtWpVpUiRQnv27NEXX3yhZMmSqXPnzqpVq5adqwaeCAgI0JQpU7R3715duHBBX3/9tZYsWaKtW7cqadKkWrVqlaZOnaqLFy+qQYMGWrZsmSTp7t27cnd3lxT7LhJAQhs8eLCOHz+u77//Xm5uboqKitLatWu1evVqnT17Vjlz5tR3330ni8XCpGl4IRwNMNWGDRvUpEkTtWrVyubD0jAMa493WFiYpk2bprCwMAI37M5isSg8PFwuLi765Zdf9PDhQ33xxRc6cOCAJKldu3ZKnTq1unbtqlWrVmn37t3q16+fzpw5o+rVq9u5erwJYr7IffHFFxo4cKBSpUqlBw8eaPTo0Ro2bJhCQkJUtmxZjR49Wg8fPtSwYcO0e/duO1cNPPn8j4yMVEhIiM6fP688efKoR48e8vb21vvvv6/g4GD17NlTe/bssd5/OwaBGy9DzHfUM2fOKDQ0VNKTW9y2aNFCY8eO1YkTJ5QzZ05t3rxZjRo1kqQ4b2lL4Maz6OmGqWrVqiVHR0f99NNPkmw/LC9cuKAcOXLo3r17un37tnX2XcCeYo7RxYsXa+vWrTpx4oS2b9+uYsWKKSAgQMWLF9f27ds1Z84czZs3T7lz55aDg4PmzZvHZREwzbO9JnPmzNGXX36pJUuWqESJEvrpp59Uv359FShQQFWqVNHw4cOVMmVK/fLLL1q8eLEmTZrEl0C8EiIiIpQ3b155eXnpu+++kyT99ddfGjlypFavXq0ff/xR1atXtx7z9BjCHnbv3q0KFSqoUKFCOn/+vAoUKKDPP/9cDRs2lJOTk0aPHq1Zs2Zp586dypAhg73LRSLAuxhMEx0drVKlSik0NFSnTp2S9KQXMTo6WlevXtUXX3yhQ4cOKUWKFARuvDIsFot++eUXtWrVSqVKldLo0aO1Y8cOPXjwQJ988okOHTqkypUra/bs2Tp58qTWr1+vHTt2ELhhqqCgIElP3lejo6MVGRkpHx8flShRQitXrlTLli01YcIE1apVS/Pnz9fQoUN1584dVapUSVOmTOFWS3glREVFycXFRf369dPu3bu1b98+SVLevHnVv39/1atXT5988ok2b95snaWcwA178PT01KFDh9S6dWt9/fXX2r17txo3biwnpyc3fkqdOrWyZMkiV1dXO1eKxIKebiSImN7Ba9euKSIiQkmSJFGGDBl0+PBhVapUSS1atFC3bt1UoEABRUZGauTIkZo3b542b96st956y97lAzbGjx+vJUuWaMeOHdZhY6GhoSpdurSSJ0+u6dOnq2TJktYPX8BMhw8fVokSJbRkyRI1bNhQ0pPbKz58+FDR0dGqVauWWrRooc8//1xXrlxR6dKl5eTkpG7duqlXr14Mx4VdRUVFydHR0fpfSTp69P/au/egqOv9j+PPFUTXCyqIGBN4w2tejmiFaaUHTmjGMTVsxPB4QSHgeKRUUiZTU/CS6ZgOpoGEHkcrBHVy9CiMKV6S00WlEJUjHiDCkiwBLwj8/mh2f21ol5PrrvB6zDDMfr/s7vvL7Mx+X9/P+/v5nCIgIIDY2FiLSafOnz9PXFwcycnJ5OTk0KtXL1uVLXJHJSUljBw5Ej8/P1asWGHrcuQ+oTNG+cNMJ3Tp6enExsZiMBj47rvvCAkJYe7cuaSnpxMSEsLp06epra3FxcWFw4cPk5mZqcAtdsX0Wf7++++5cuWKOXBfu3YNZ2dn1qxZw4gRI5g+fTqbNm3Cx8fHxhVLQ/DAAw8wffp0goODee+99xg1ahQtW7akVatWHD58mB9++IERI0YAcOnSJYYMGcJf/vIXpk6dCqDALTazdu1afvjhByZOnMiDDz5o3t63b19CQ0NZtmwZf/3rX83nAt7e3syePZtBgwYpcIvdKS4upqCggIiICDp27GgO3LqwKb+FenbkDzMYDGRkZBASEkJYWBj//ve/efHFF1m+fDl79+7Fz8+P3bt3ExwcTOfOnfH19eX48eNqxxW7Y/rSHDduHMXFxcTHxwNgNBoBcHJyIjAwkCZNmpgn9RGxNnd3dxYuXEh4eDijR49m586dFi23RqOR3bt3c+bMGebPn0/z5s0JDQ2lUaNGVFdX27ByaWh+2jxZW1vLnj172LJlC71792bRokVkZmaa948ePZoWLVpw9OhRAPMSdj179mTatGkAuiVC7EZ5eTnh4eFER0fj6+vLzp07AcvlG0V+idrL5Q8xXd2LjIykpqaGhIQEioqKGDZsGH5+fhYzj4rYG9Pn9/PPP+eLL76gR48edOzYEVdXV5YsWUJSUhJTpkwhNjaW8vJy4uPjqaio4I033lBruVhVUVERRqPRYr33r7/+miVLlrBu3TrS0tIYNWoUV69eZdasWWRmZlJRUYGnpydZWVk0btxYoy9yT/3085adnc2DDz7IAw88QGFhISkpKWzfvp3S0lL8/f2ZPHky/v7+TJw4kby8PD7++OM6ryFib3JzcykuLsbf3x/QsmDy+yh0y+/y89lETV+Q48aNY9SoUYwZMwZvb2+eeeYZ1q9fb54F2s3NjWHDhtm6fJE6duzYweTJk3Fzc+O7774jODiY6Oho2rVrx9q1a4mLi8PV1ZUWLVpQVFREZmamujTEqlJTUwkNDcXDw4Np06bh7u7O+PHjgR9nfp49ezZvvfUW7733Hs899xzl5eXm5W0ee+wxHBwcuHXrli4MyT3z07C8ZMkS0tLSCAoKYvr06bRp0wb48X7tnJwcXn31VW7cuEH79u0JDAzk9ddfJzExkaCgIFsegsjvogtE8nspdMtv8vOQ/f3339OqVSvz/hkzZrB//34qKip49tlnWblyJY0bNzbPsNutWzdeffVVnQSKXTB9jgsLC4mMjCQwMJAJEyaYlwHr3LkzCxcupEuXLuTn57Nr1y5atWrFE088gbe3t63Ll3rs5s2bREdHk5KSQrNmzejRowcFBQU4OzvTrVs3IiIiaNSoERkZGcTHx7Nnz54668P/dMIqkXtpzpw5bN68mbfeegsfHx86d+5cJ5yUl5dz6NAhEhMTOXDgAFevXmXNmjUWE6qJiNQ3Ct3yq0yBu6CggC1btrBv3z4KCwsZPHgwTz/9NBMmTODixYuMHz+ewsJC8vLyaNasGdXV1cyfP5/NmzeTkZFB165dbX0oImbZ2dmkpKRQXFzMhg0baNu2LQApKSmsX7+eTp06ERMTQ9++fW1cqTQ0paWlxMfHc+HCBR566CGio6NJS0tj7969nDx5kuvXr+Pt7c3Ro0eprq4mOzubAQMG2LpsaeC2bdtGbGws27dvZ+DAgRb7KisradasWZ3nZGZmUlRUxMSJE+9VmSIiNqHQLb/IFLhPnz7N2LFjGThwIC1btsTLy4vExERu3LjB1KlTWbRoEampqSxYsIDy8nIefvhhKisrOXHiBPv27VM7rtiduLg4Vq9ejaOjI4cOHbIYwU5JSSEpKQlnZ2eWLl2qWXTlnvvqq6+Ii4vj448/ZtKkSURGRgJw5swZvv76a5KTkzlz5gyXL18mNzdXXURic6+99hq5ubmkpKTQtGlTqqur+fDDD9m1axf/+c9/6NSpE++88w4Gg4Gqqirz6hAmuj9WROozhW65I9MX4MmTJxkyZAgRERHMnTvXPGvz2bNnWbx4MXv37iU2NpZ//OMfnD17lqSkJC5fvkynTp0YN26c2nHFbq1bt44333yTgIAAYmJi6NChg3nfxo0b2bFjB4mJiXh4eNiwSmmoSkpKiIuL48SJE4waNYp58+aZ95ladk2/dQ+32IrpNDIkJIRvv/2W9PR0DAYDkydPprCwkNraWrp3705GRgYDBgwgNTXVxhWLiNx7Ct3yi86fP0+fPn2YNWsWr7/+uvleQdMJXn5+PlFRURQWFpKWlqYWcrFLpmBSWVlJTU0NLVq0MO9btmwZ27dvZ+jQocycOdNi7fifz10gcq+ZZizPzs5m9OjRxMTEAFiEbI0Qij04duwYgwcP5qGHHqKgoICePXvy8ssvM3bsWBwdHVm6dClJSUlkZWXRrl07W5crInJP6bK43FFNTQ1JSUm0bNkSNzc3ABwcHKiursbR0ZHa2lq6dOnCvHnzGDp0KDk5ORahWzM7ij0wfQ4//PBD3nnnHXJychgzZgxPPvkkTz/9NDExMdTU1PD+++/j6OhIREQEHTt2BFDgFptr3749sbGxxMXFsWvXLq5evcrixYstRrUVuMUeDBo0iM8++4yMjAyLteJN5wFt2rTBw8ODJk2a2LhSEZF7T6Fb7qhRo0ZERUVRWVnJ1q1bqays5JVXXsHBwYGamhrzF+mAAQNwdXWlpKTE4vkK3GIPDAYDu3btYvz48bz00ksMHz6cDz74gEOHDnHlyhWCg4OZO3cuDg4OJCQk4OTkxIIFC9SqK3ajffv2zJs3jzlz5nDp0iVd0BS71a9fP/r161dne0lJCW+//TZ+fn66mCkiDZLay+VX3am90dRqfuTIESIjI0lKSsLHx8fG1YpYysvL47nnniMqKoqwsDCuXbtGhw4dcHFxoXXr1kRHR/P8888DsGrVKp599lk6depk46pF6iorK6N169YWyzeK2LPi4mIKCgrMHUQ7d+4E1AknIg2PetLkV5naGx9++GHS0tJYtmwZgHkd2NTUVNzd3c0tuSK2cKfrh0ajkZEjRxIUFERRURG9e/cmKCiIrVu3cunSJZYtW0ZiYiIA0dHRCtxit1xcXGjUqJFFp5GIvSovLyc8PJzo6Gh8fX3NgVufXxFpiDTSLb/Z7Ua8Fy9ezJtvvsmhQ4fo3bu3rUuUBso0kdTly5cpLS2lurqaPn36AD92ZJSVleHm5kZYWBjl5eWsX7+eli1bEhwczOHDh/Hx8SElJQVnZ2edDIqI3CW5ubkUFxfj7+8PaNI/EWm4FLrldzEF75MnT3Ljxg1OnTrFkSNH1FYuNmM6icvJyWHKlCl888031NbW8tRTT7FhwwaLvx06dCgDBw7kjTfeACAsLIyePXsyfvx43N3dbVG+iEiDoJZyEWnIdLlRfhdTq7m3tzdlZWUcO3ZMgVts5qdryfv6+vLEE0+wadMmnnnmGd59910SEhKAH0e7Kysr8fLyIi8vjw0bNhATE8Pu3bsJCgpS4BYRsTIFbhFpyDTSLf+Tb775hpqaGoUVsbmfryUPcOHCBXr06MHf//5386g2wL/+9S9WrVrFuXPnaNq0KZs3b6Z///62Kl1EREREGgCtiSP/E9O63SK29NO15F1dXc3bt23bRlVVFefOnWP16tW4uLgwbtw4nnrqKYYNG0ZZWRkODg60bdvWhtWLiIiISEOgkW4Rua999dVXLF++nOPHj/O3v/2Nq1evsnTpUiIjI/nTn/7EP//5TwoLCykpKaF79+7MnDmTwMBAW5ctIiIiIg2EQreI3PdME/zt37+f/Px89u3bx5///GcAbt26haOjI2vXruXTTz9l1qxZ9OrVy8YVi4iIiEhDodAtIvVCaWkpcXFxHDx4kIkTJ/Lyyy8DcPPmTZycnID/D+AiIiIiIveKzj5FpF5wd3dn7ty51NTU8P7773Pr1i1iYmJwcnIyh20FbhERERG51zTSLSL1iqnV/LPPPsPPz4+FCxfauiQRERERacC0TreI1CumteS7du3K0aNHuXz5sq1LEhEREZEGTCPdIlIvlZaWAmgteRERERGxKYVuEREREREREStRe7mIiIiIiIiIlSh0i4iIiIiIiFiJQreIiIiIiIiIlSh0i4iIiIiIiFiJQreIiIiIiIiIlSh0i4iIiIiIiFiJQreIiIiIiIiIlSh0i4iIyB0dPHgQg8HAlStXfvNzOnbsyOrVq61Wk4iIyP1EoVtEROQ+NmnSJAwGA+Hh4XX2RUZGYjAYmDRp0r0vTERERACFbhERkfuep6cn27Zt49q1a+Zt169fZ+vWrXh5edmwMhEREVHoFhERuc/5+Pjg6enJjh07zNt27NiBl5cX/fv3N2+7ceMGM2bMoF27djRt2pQhQ4aQnZ1t8Vp79uyhW7duGI1Ghg0bRkFBQZ33y8rK4vHHH8doNOLp6cmMGTOoqKi4bW21tbUsWLAALy8vmjRpgoeHBzNmzLg7By4iInIfUOgWERGpB6ZMmcKmTZvMj5OSkpg8ebLF38yZM4fU1FTeffddPv30U7y9vQkICKCsrAyAwsJCxowZQ2BgIJ9//jmhoaG88sorFq+Rn5/P8OHDGTt2LKdOnWL79u1kZWURFRV127pSU1NZtWoVb7/9NufOnSM9PZ0+ffrc5aMXERGxXwrdIiIi9cALL7xAVlYWFy9e5OLFixw5coQXXnjBvL+iooKEhARWrFjBiBEj6NWrFxs3bsRoNJKYmAhAQkICXbp0YeXKlXTv3p0JEybUuR88Pj6eCRMmMHPmTLp27cpjjz3GmjVrSElJ4fr163Xq+u9//0v79u3x9/fHy8uLRx55hGnTpln1fyEiImJPFLpFRETqATc3N0aOHElycjKbNm1i5MiRtG3b1rw/Pz+fqqoqBg8ebN7WuHFjHnnkEXJzcwHIzc3l0UcftXjdQYMGWTw+efIkycnJtGjRwvwTEBBATU0NFy5cqFNXUFAQ165do3PnzkybNo20tDRu3bp1Nw9dRETErjnaugARERG5O6ZMmWJu8163bp1V3qO8vJywsLDb3pd9u0nbPD09ycvL48CBA+zfv5+IiAhWrFjBRx99ROPGja1So4iIiD3RSLeIiEg9MXz4cG7evElVVRUBAQEW+7p06YKTkxNHjhwxb6uqqiI7O5tevXoB0LNnT06cOGHxvOPHj1s89vHx4csvv8Tb27vOj5OT023rMhqNBAYGsmbNGg4ePMixY8c4ffr03ThkERERu6eRbhERkXrCwcHB3Cru4OBgsa958+a8+OKLzJ49GxcXF7y8vFi+fDmVlZVMnToVgPDwcFauXMns2bMJDQ3lk08+ITk52eJ1YmJi8PX1JSoqitDQUJo3b86XX37J/v37Wbt2bZ2akpOTqa6u5tFHH6VZs2Zs2bIFo9FIhw4drPNPEBERsTMa6RYREalHnJ2dcXZ2vu2+pUuXMnbsWEJCQvDx8eH8+fPs27ePNm3aAD+2h6emppKenk6/fv1Yv349cXFxFq/Rt29fPvroI86ePcvjjz9O//79mT9/Ph4eHrd9z9atW7Nx40YGDx5M3759OXDgALt378bV1fXuHriIiIidMtTW1tbauggRERERERGR+kgj3SIiIiIiIiJWotAtIiIiIiIiYiUK3SIiIiIiIiJWotAtIiIiIiIiYiUK3SIiIiIiIiJWotAtIiIiIiIiYiUK3SIiIiIiIiJWotAtIiIiIiIiYiUK3SIiIiIiIiJWotAtIiIiIiIiYiUK3SIiIiIiIiJWotAtIiIiIiIiYiX/B9GXRUdHmvdcAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Reflection Questions\n",
        "print(\"=\" * 60)\n",
        "print(\"REFLECTION QUESTIONS\")\n",
        "print(\"=\" * 60)\n",
        "\n",
        "# Answer reflection questions based on results\n",
        "best_model = max(results.items(), key=lambda x: x[1]['test_accuracy'])\n",
        "print(f\"\\n1. Which algorithm performed best on the test data?\")\n",
        "print(f\"   Answer: {best_model[0]} performed best with test accuracy of {best_model[1]['test_accuracy']:.4f}\")\n",
        "\n",
        "print(f\"\\n2. Did any algorithm show signs of overfitting?\")\n",
        "overfitting_models = []\n",
        "for name, result in results.items():\n",
        "    if result['train_accuracy'] - result['test_accuracy'] > 0.1:  # Threshold for overfitting\n",
        "        overfitting_models.append(name)\n",
        "if overfitting_models:\n",
        "    print(f\"   Answer: Yes, {', '.join(overfitting_models)} show signs of overfitting\")\n",
        "else:\n",
        "    print(\"   Answer: No significant overfitting detected\")\n",
        "\n",
        "print(f\"\\n3. How does Naïve Bayes handle correlated features?\")\n",
        "print(\"   Answer: Naïve Bayes assumes feature independence, so correlated features can negatively impact its performance compared to algorithms like Decision Trees or SVM that can handle feature correlations better.\")\n",
        "\n",
        "print(f\"\\n4. Why might Random Forest outperform a single Decision Tree?\")\n",
        "print(\"   Answer: Random Forest uses multiple decision trees and aggregates their predictions, reducing overfitting and variance through ensemble learning, which often leads to better generalization.\")\n",
        "\n",
        "print(f\"\\n5. If you had to deploy a model for predicting diabetes, which one would you choose?\")\n",
        "print(f\"   Answer: Based on the results, I would choose {best_model[0]} as it achieved the highest test accuracy. However, for medical applications, we should also consider precision and recall carefully to minimize false negatives.\")\n",
        "\n",
        "# Additional: Feature correlation analysis\n",
        "print(f\"\\nAdditional Analysis: Feature Correlations\")\n",
        "correlation_matrix = df.corr()\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0)\n",
        "plt.title('Feature Correlation Matrix')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nNote: High correlations between features (absolute value > 0.5) suggest that\")\n",
        "print(\"Naïve Bayes' independence assumption might be violated.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "m0ibykipO6kr",
        "outputId": "5b52e219-71fe-4172-9f0a-930e370bbb34"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "============================================================\n",
            "REFLECTION QUESTIONS\n",
            "============================================================\n",
            "\n",
            "1. Which algorithm performed best on the test data?\n",
            "   Answer: Random Forest performed best with test accuracy of 0.7597\n",
            "\n",
            "2. Did any algorithm show signs of overfitting?\n",
            "   Answer: Yes, Decision Tree, Random Forest show signs of overfitting\n",
            "\n",
            "3. How does Naïve Bayes handle correlated features?\n",
            "   Answer: Naïve Bayes assumes feature independence, so correlated features can negatively impact its performance compared to algorithms like Decision Trees or SVM that can handle feature correlations better.\n",
            "\n",
            "4. Why might Random Forest outperform a single Decision Tree?\n",
            "   Answer: Random Forest uses multiple decision trees and aggregates their predictions, reducing overfitting and variance through ensemble learning, which often leads to better generalization.\n",
            "\n",
            "5. If you had to deploy a model for predicting diabetes, which one would you choose?\n",
            "   Answer: Based on the results, I would choose Random Forest as it achieved the highest test accuracy. However, for medical applications, we should also consider precision and recall carefully to minimize false negatives.\n",
            "\n",
            "Additional Analysis: Feature Correlations\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Note: High correlations between features (absolute value > 0.5) suggest that\n",
            "Naïve Bayes' independence assumption might be violated.\n"
          ]
        }
      ]
    }
  ]
}