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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Predictive Maintenance Using Machine Learning for Textile Industry\n",
        "# Based on AI4I 2020 Predictive Maintenance Dataset"
      ],
      "metadata": {
        "id": "rUNLj94I3cH-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install --upgrade shap"
      ],
      "metadata": {
        "id": "lPdnExiSORq4"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "602760f3"
      },
      "source": [
        "# # Uninstall the current NumPy version and install a compatible one\n",
        "# # This is to resolve the compatibility issue with NumPy 2.x and other libraries like SHAP.\n",
        "# !pip uninstall -y numpy\n",
        "# !pip install numpy<2"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import required libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import (classification_report, confusion_matrix,\n",
        "                             roc_auc_score, roc_curve, precision_recall_curve,\n",
        "                             auc, f1_score, precision_score, recall_score)\n",
        "import xgboost as xgb\n",
        "import lightgbm as lgb\n",
        "import shap\n",
        "from sklearn.model_selection import cross_val_score\n",
        "from scipy import stats\n",
        "import itertools\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# Set visualization style\n",
        "sns.set_style('whitegrid')\n",
        "plt.rcParams['figure.figsize'] = (12, 6)"
      ],
      "metadata": {
        "id": "XJUxftUt3hSU"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1. DATA LOADING AND INITIAL EXPLORATION\n"
      ],
      "metadata": {
        "id": "R2ScafP93jZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset\n",
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "\n",
        "df = pd.read_csv('ai4i2020.csv')\n",
        "print(df.shape)\n",
        "print(len(df))\n",
        "\n",
        "print(f\"Dataset Shape: {df.shape}\")\n",
        "print(f\"Number of Records: {len(df)}\")\n",
        "print(f\"Number of Features: {len(df.columns)}\")\n",
        "\n",
        "print(\"\\nFirst 5 rows:\")\n",
        "print(df.head())\n",
        "\n",
        "print(\"\\nDataset Info:\")\n",
        "print(df.info())\n",
        "\n",
        "print(\"\\nBasic Statistics:\")\n",
        "print(df.describe())\n",
        "\n",
        "print(\"\\nMissing Values:\")\n",
        "print(df.isnull().sum())\n",
        "\n",
        "print(\"\\nTarget Variable Distribution:\")\n",
        "print(df['Machine failure'].value_counts())\n",
        "print(f\"\\nFailure Rate: {df['Machine failure'].mean()*100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "3yucHpQP3ouc",
        "outputId": "150ce13d-b7de-41db-ed88-7baa77a446a3"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-ebad7b0d-1195-4428-acae-51e6519b2974\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-ebad7b0d-1195-4428-acae-51e6519b2974\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving ai4i2020.csv to ai4i2020.csv\n",
            "(10000, 14)\n",
            "10000\n",
            "Dataset Shape: (10000, 14)\n",
            "Number of Records: 10000\n",
            "Number of Features: 14\n",
            "\n",
            "First 5 rows:\n",
            "   UDI Product ID Type  Air temperature [K]  Process temperature [K]  \\\n",
            "0    1     M14860    M                298.1                    308.6   \n",
            "1    2     L47181    L                298.2                    308.7   \n",
            "2    3     L47182    L                298.1                    308.5   \n",
            "3    4     L47183    L                298.2                    308.6   \n",
            "4    5     L47184    L                298.2                    308.7   \n",
            "\n",
            "   Rotational speed [rpm]  Torque [Nm]  Tool wear [min]  Machine failure  TWF  \\\n",
            "0                    1551         42.8                0                0    0   \n",
            "1                    1408         46.3                3                0    0   \n",
            "2                    1498         49.4                5                0    0   \n",
            "3                    1433         39.5                7                0    0   \n",
            "4                    1408         40.0                9                0    0   \n",
            "\n",
            "   HDF  PWF  OSF  RNF  \n",
            "0    0    0    0    0  \n",
            "1    0    0    0    0  \n",
            "2    0    0    0    0  \n",
            "3    0    0    0    0  \n",
            "4    0    0    0    0  \n",
            "\n",
            "Dataset Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 14 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   UDI                      10000 non-null  int64  \n",
            " 1   Product ID               10000 non-null  object \n",
            " 2   Type                     10000 non-null  object \n",
            " 3   Air temperature [K]      10000 non-null  float64\n",
            " 4   Process temperature [K]  10000 non-null  float64\n",
            " 5   Rotational speed [rpm]   10000 non-null  int64  \n",
            " 6   Torque [Nm]              10000 non-null  float64\n",
            " 7   Tool wear [min]          10000 non-null  int64  \n",
            " 8   Machine failure          10000 non-null  int64  \n",
            " 9   TWF                      10000 non-null  int64  \n",
            " 10  HDF                      10000 non-null  int64  \n",
            " 11  PWF                      10000 non-null  int64  \n",
            " 12  OSF                      10000 non-null  int64  \n",
            " 13  RNF                      10000 non-null  int64  \n",
            "dtypes: float64(3), int64(9), object(2)\n",
            "memory usage: 1.1+ MB\n",
            "None\n",
            "\n",
            "Basic Statistics:\n",
            "               UDI  Air temperature [K]  Process temperature [K]  \\\n",
            "count  10000.00000         10000.000000             10000.000000   \n",
            "mean    5000.50000           300.004930               310.005560   \n",
            "std     2886.89568             2.000259                 1.483734   \n",
            "min        1.00000           295.300000               305.700000   \n",
            "25%     2500.75000           298.300000               308.800000   \n",
            "50%     5000.50000           300.100000               310.100000   \n",
            "75%     7500.25000           301.500000               311.100000   \n",
            "max    10000.00000           304.500000               313.800000   \n",
            "\n",
            "       Rotational speed [rpm]   Torque [Nm]  Tool wear [min]  Machine failure  \\\n",
            "count            10000.000000  10000.000000     10000.000000     10000.000000   \n",
            "mean              1538.776100     39.986910       107.951000         0.033900   \n",
            "std                179.284096      9.968934        63.654147         0.180981   \n",
            "min               1168.000000      3.800000         0.000000         0.000000   \n",
            "25%               1423.000000     33.200000        53.000000         0.000000   \n",
            "50%               1503.000000     40.100000       108.000000         0.000000   \n",
            "75%               1612.000000     46.800000       162.000000         0.000000   \n",
            "max               2886.000000     76.600000       253.000000         1.000000   \n",
            "\n",
            "                TWF           HDF           PWF           OSF          RNF  \n",
            "count  10000.000000  10000.000000  10000.000000  10000.000000  10000.00000  \n",
            "mean       0.004600      0.011500      0.009500      0.009800      0.00190  \n",
            "std        0.067671      0.106625      0.097009      0.098514      0.04355  \n",
            "min        0.000000      0.000000      0.000000      0.000000      0.00000  \n",
            "25%        0.000000      0.000000      0.000000      0.000000      0.00000  \n",
            "50%        0.000000      0.000000      0.000000      0.000000      0.00000  \n",
            "75%        0.000000      0.000000      0.000000      0.000000      0.00000  \n",
            "max        1.000000      1.000000      1.000000      1.000000      1.00000  \n",
            "\n",
            "Missing Values:\n",
            "UDI                        0\n",
            "Product ID                 0\n",
            "Type                       0\n",
            "Air temperature [K]        0\n",
            "Process temperature [K]    0\n",
            "Rotational speed [rpm]     0\n",
            "Torque [Nm]                0\n",
            "Tool wear [min]            0\n",
            "Machine failure            0\n",
            "TWF                        0\n",
            "HDF                        0\n",
            "PWF                        0\n",
            "OSF                        0\n",
            "RNF                        0\n",
            "dtype: int64\n",
            "\n",
            "Target Variable Distribution:\n",
            "Machine failure\n",
            "0    9661\n",
            "1     339\n",
            "Name: count, dtype: int64\n",
            "\n",
            "Failure Rate: 3.39%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2. DATA PREPROCESSING AND ANALYSIS"
      ],
      "metadata": {
        "id": "NiqcHJ9P3qxW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for duplicates\n",
        "print(f\"Duplicate rows: {df.duplicated().sum()}\")\n",
        "\n",
        "# Encode categorical variables\n",
        "le = LabelEncoder()\n",
        "df['Type_encoded'] = le.fit_transform(df['Type'])\n",
        "print(f\"\\nProduct Types: {df['Type'].unique()}\")\n",
        "print(f\"Encoding: {dict(zip(le.classes_, le.transform(le.classes_)))}\")\n",
        "\n",
        "# Feature correlation analysis\n",
        "print(\"\\n2.1 Correlation Analysis with Machine Failure\")\n",
        "print(\"-\" * 80)\n",
        "\n",
        "numeric_cols = ['Air temperature [K]', 'Process temperature [K]',\n",
        "                'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]',\n",
        "                'Type_encoded']\n",
        "\n",
        "correlation_with_failure = df[numeric_cols + ['Machine failure']].corr()['Machine failure'].sort_values(ascending=False)\n",
        "print(correlation_with_failure)\n",
        "\n",
        "# Failure type analysis\n",
        "print(\"\\n2.2 Failure Type Analysis\")\n",
        "print(\"-\" * 80)\n",
        "failure_types = ['TWF', 'HDF', 'PWF', 'OSF', 'RNF']\n",
        "for failure in failure_types:\n",
        "    count = df[failure].sum()\n",
        "    percentage = (count / len(df)) * 100\n",
        "    print(f\"{failure}: {count} occurrences ({percentage:.2f}%)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CHALnZxj3vEG",
        "outputId": "5618d171-cdc8-4f34-fa45-147b79c4dd34"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate rows: 0\n",
            "\n",
            "Product Types: ['M' 'L' 'H']\n",
            "Encoding: {'H': np.int64(0), 'L': np.int64(1), 'M': np.int64(2)}\n",
            "\n",
            "2.1 Correlation Analysis with Machine Failure\n",
            "--------------------------------------------------------------------------------\n",
            "Machine failure            1.000000\n",
            "Torque [Nm]                0.191321\n",
            "Tool wear [min]            0.105448\n",
            "Air temperature [K]        0.082556\n",
            "Process temperature [K]    0.035946\n",
            "Type_encoded              -0.005152\n",
            "Rotational speed [rpm]    -0.044188\n",
            "Name: Machine failure, dtype: float64\n",
            "\n",
            "2.2 Failure Type Analysis\n",
            "--------------------------------------------------------------------------------\n",
            "TWF: 46 occurrences (0.46%)\n",
            "HDF: 115 occurrences (1.15%)\n",
            "PWF: 95 occurrences (0.95%)\n",
            "OSF: 98 occurrences (0.98%)\n",
            "RNF: 19 occurrences (0.19%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3. DATA VISUALIZATION"
      ],
      "metadata": {
        "id": "PJlVU1xA3ykl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Correlation heatmap\n",
        "plt.figure(figsize=(10, 8))\n",
        "correlation_matrix = df[numeric_cols + ['Machine failure']].corr()\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, fmt='.2f')\n",
        "plt.title('Feature Correlation Matrix')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "2-idxP-G35Qj",
        "outputId": "dc57104b-c1fe-4d0f-9a09-72a2c4ba1813"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature distributions by failure status\n",
        "fig, axes = plt.subplots(2, 3, figsize=(15, 10))\n",
        "for idx, col in enumerate(numeric_cols):\n",
        "    row, col_idx = idx // 3, idx % 3\n",
        "    df.boxplot(column=col, by='Machine failure', ax=axes[row, col_idx])\n",
        "    axes[row, col_idx].set_title(f'{col}')\n",
        "    axes[row, col_idx].set_xlabel('Machine Failure')\n",
        "plt.suptitle('Feature Distributions by Failure Status', y=1.00)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Dk33VWje32xg",
        "outputId": "4c0a9b55-3bdd-4484-c20f-bf35bf9fbd17"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4. PREPARE DATA FOR MODELING\n"
      ],
      "metadata": {
        "id": "cuf5a3tS394l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select features for modeling\n",
        "feature_cols = ['Type_encoded', 'Air temperature [K]', 'Process temperature [K]',\n",
        "                'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']\n",
        "\n",
        "X = df[feature_cols]\n",
        "y = df['Machine failure']\n",
        "\n",
        "# Split the data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,\n",
        "                                                      random_state=42, stratify=y)\n",
        "\n",
        "print(f\"Training set size: {len(X_train)}\")\n",
        "print(f\"Test set size: {len(X_test)}\")\n",
        "print(f\"Training set failure rate: {y_train.mean()*100:.2f}%\")\n",
        "print(f\"Test set failure rate: {y_test.mean()*100:.2f}%\")\n",
        "\n",
        "# Scale features\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M33uEm494AR2",
        "outputId": "fa24b7bb-591f-44dd-fb8a-792322cb7917"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training set size: 8000\n",
            "Test set size: 2000\n",
            "Training set failure rate: 3.39%\n",
            "Test set failure rate: 3.40%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5. MODEL DEVELOPMENT AND EVALUATION\n"
      ],
      "metadata": {
        "id": "khRRjEjr4HBY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = {}\n",
        "\n",
        "# 5.1 Random Forest\n",
        "print(\"\\n5.1 RANDOM FOREST CLASSIFIER\")\n",
        "print(\"-\" * 80)\n",
        "rf_model = RandomForestClassifier(n_estimators=100, random_state=42,\n",
        "                                   class_weight='balanced', max_depth=10)\n",
        "rf_model.fit(X_train_scaled, y_train)\n",
        "y_pred_rf = rf_model.predict(X_test_scaled)\n",
        "y_proba_rf = rf_model.predict_proba(X_test_scaled)[:, 1]\n",
        "\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_rf))\n",
        "print(f\"ROC-AUC Score: {roc_auc_score(y_test, y_proba_rf):.4f}\")\n",
        "\n",
        "results['Random Forest'] = {\n",
        "    'model': rf_model,\n",
        "    'predictions': y_pred_rf,\n",
        "    'probabilities': y_proba_rf,\n",
        "    'precision': precision_score(y_test, y_pred_rf),\n",
        "    'recall': recall_score(y_test, y_pred_rf),\n",
        "    'f1': f1_score(y_test, y_pred_rf),\n",
        "    'roc_auc': roc_auc_score(y_test, y_proba_rf)\n",
        "}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0zt2t_Nx4J6_",
        "outputId": "d73703c9-38f1-467e-9d2a-8bde6ab5d999"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "5.1 RANDOM FOREST CLASSIFIER\n",
            "--------------------------------------------------------------------------------\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      1932\n",
            "           1       0.68      0.69      0.69        68\n",
            "\n",
            "    accuracy                           0.98      2000\n",
            "   macro avg       0.84      0.84      0.84      2000\n",
            "weighted avg       0.98      0.98      0.98      2000\n",
            "\n",
            "ROC-AUC Score: 0.9738\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.2 XGBoost\n",
        "print(\"\\n5.2 XGBOOST CLASSIFIER\")\n",
        "print(\"-\" * 80)\n",
        "scale_pos_weight = (y_train == 0).sum() / (y_train == 1).sum()\n",
        "xgb_model = xgb.XGBClassifier(n_estimators=100, random_state=42,\n",
        "                               scale_pos_weight=scale_pos_weight, max_depth=5)\n",
        "xgb_model.fit(X_train_scaled, y_train)\n",
        "y_pred_xgb = xgb_model.predict(X_test_scaled)\n",
        "y_proba_xgb = xgb_model.predict_proba(X_test_scaled)[:, 1]\n",
        "\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_xgb))\n",
        "print(f\"ROC-AUC Score: {roc_auc_score(y_test, y_proba_xgb):.4f}\")\n",
        "\n",
        "results['XGBoost'] = {\n",
        "    'model': xgb_model,\n",
        "    'predictions': y_pred_xgb,\n",
        "    'probabilities': y_proba_xgb,\n",
        "    'precision': precision_score(y_test, y_pred_xgb),\n",
        "    'recall': recall_score(y_test, y_pred_xgb),\n",
        "    'f1': f1_score(y_test, y_pred_xgb),\n",
        "    'roc_auc': roc_auc_score(y_test, y_proba_xgb)\n",
        "}\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G69pZly34Mdy",
        "outputId": "0c0185f4-e2f0-4365-85bd-0fd1d63f1a1f"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "5.2 XGBOOST CLASSIFIER\n",
            "--------------------------------------------------------------------------------\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99      1932\n",
            "           1       0.72      0.79      0.76        68\n",
            "\n",
            "    accuracy                           0.98      2000\n",
            "   macro avg       0.86      0.89      0.87      2000\n",
            "weighted avg       0.98      0.98      0.98      2000\n",
            "\n",
            "ROC-AUC Score: 0.9656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5.3 LightGBM\n",
        "print(\"\\n5.3 LIGHTGBM CLASSIFIER\")\n",
        "print(\"-\" * 80)\n",
        "lgb_model = lgb.LGBMClassifier(n_estimators=100, random_state=42,\n",
        "                                class_weight='balanced', max_depth=5, verbose=-1)\n",
        "lgb_model.fit(X_train_scaled, y_train)\n",
        "y_pred_lgb = lgb_model.predict(X_test_scaled)\n",
        "y_proba_lgb = lgb_model.predict_proba(X_test_scaled)[:, 1]\n",
        "\n",
        "print(\"Classification Report:\")\n",
        "print(classification_report(y_test, y_pred_lgb))\n",
        "print(f\"ROC-AUC Score: {roc_auc_score(y_test, y_proba_lgb):.4f}\")\n",
        "\n",
        "results['LightGBM'] = {\n",
        "    'model': lgb_model,\n",
        "    'predictions': y_pred_lgb,\n",
        "    'probabilities': y_proba_lgb,\n",
        "    'precision': precision_score(y_test, y_pred_lgb),\n",
        "    'recall': recall_score(y_test, y_pred_lgb),\n",
        "    'f1': f1_score(y_test, y_pred_lgb),\n",
        "    'roc_auc': roc_auc_score(y_test, y_proba_lgb)\n",
        "}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MLsCF3954OJo",
        "outputId": "e92b5c30-5806-4aae-c1a5-b1e4bcb85eaa"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "5.3 LIGHTGBM CLASSIFIER\n",
            "--------------------------------------------------------------------------------\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.98      0.99      1932\n",
            "           1       0.56      0.88      0.68        68\n",
            "\n",
            "    accuracy                           0.97      2000\n",
            "   macro avg       0.78      0.93      0.83      2000\n",
            "weighted avg       0.98      0.97      0.98      2000\n",
            "\n",
            "ROC-AUC Score: 0.9666\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6. MODEL COMPARISON"
      ],
      "metadata": {
        "id": "XXFQQXSD4Pjj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Learning Curves for Model Evaluation\n",
        "from sklearn.model_selection import learning_curve\n",
        "\n",
        "def plot_learning_curves(model, model_name, X_train, y_train):\n",
        "    \"\"\"\n",
        "    Plot learning curves to detect overfitting/underfitting\n",
        "    \"\"\"\n",
        "    train_sizes, train_scores, val_scores = learning_curve(\n",
        "        model, X_train, y_train,\n",
        "        cv=5,\n",
        "        n_jobs=-1,\n",
        "        train_sizes=np.linspace(0.1, 1.0, 10),\n",
        "        scoring='f1',\n",
        "        random_state=42\n",
        "    )\n",
        "\n",
        "    train_mean = np.mean(train_scores, axis=1)\n",
        "    train_std = np.std(train_scores, axis=1)\n",
        "    val_mean = np.mean(val_scores, axis=1)\n",
        "    val_std = np.std(val_scores, axis=1)\n",
        "\n",
        "    return train_sizes, train_mean, train_std, val_mean, val_std\n",
        "\n",
        "# Create subplots for all three models\n",
        "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
        "\n",
        "model_configs = [\n",
        "    ('Random Forest', RandomForestClassifier(n_estimators=100, random_state=42,\n",
        "                                             class_weight='balanced', max_depth=10)),\n",
        "    ('XGBoost', xgb.XGBClassifier(n_estimators=100, random_state=42,\n",
        "                                  scale_pos_weight=(y_train == 0).sum() / (y_train == 1).sum(),\n",
        "                                  max_depth=5)),\n",
        "    ('LightGBM', lgb.LGBMClassifier(n_estimators=100, random_state=42,\n",
        "                                    class_weight='balanced', max_depth=5, verbose=-1))\n",
        "]\n",
        "\n",
        "for idx, (model_name, model) in enumerate(model_configs):\n",
        "\n",
        "    train_sizes, train_mean, train_std, val_mean, val_std = plot_learning_curves(\n",
        "        model, model_name, X_train_scaled, y_train\n",
        "    )\n",
        "\n",
        "    # Plot learning curves\n",
        "    axes[idx].plot(train_sizes, train_mean, 'o-', color='r', label='Training Score')\n",
        "    axes[idx].fill_between(train_sizes, train_mean - train_std, train_mean + train_std,\n",
        "                           alpha=0.1, color='r')\n",
        "\n",
        "    axes[idx].plot(train_sizes, val_mean, 'o-', color='g', label='Validation Score')\n",
        "    axes[idx].fill_between(train_sizes, val_mean - val_std, val_mean + val_std,\n",
        "                           alpha=0.1, color='g')\n",
        "\n",
        "    axes[idx].set_title(f'{model_name} Learning Curve', fontsize=12, fontweight='bold')\n",
        "    axes[idx].set_xlabel('Training Set Size')\n",
        "    axes[idx].set_ylabel('F1 Score')\n",
        "    axes[idx].legend(loc='lower right')\n",
        "    axes[idx].grid(True, alpha=0.3)\n",
        "    axes[idx].set_ylim([0, 1])\n",
        "\n",
        "plt.suptitle('Learning Curves: Overfitting/Underfitting Analysis',\n",
        "             fontsize=14, fontweight='bold', y=1.02)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "id": "Ss9Pq3oGAYYl",
        "outputId": "0939943f-5e97-45d7-8d6f-bdd31666edac"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x500 with 3 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "comparison_df = pd.DataFrame({\n",
        "    'Model': list(results.keys()),\n",
        "    'Precision': [results[m]['precision'] for m in results.keys()],\n",
        "    'Recall': [results[m]['recall'] for m in results.keys()],\n",
        "    'F1-Score': [results[m]['f1'] for m in results.keys()],\n",
        "    'ROC-AUC': [results[m]['roc_auc'] for m in results.keys()]\n",
        "})\n",
        "print(comparison_df.to_string(index=False))\n",
        "\n",
        "# Plot model comparison\n",
        "# Metrics comparison\n",
        "plt.figure(figsize=(8, 6))\n",
        "comparison_df.set_index('Model')[['Precision', 'Recall', 'F1-Score']].plot(\n",
        "    kind='bar', rot=45)\n",
        "plt.title('Model Performance Metrics Comparison')\n",
        "plt.ylabel('Score')\n",
        "plt.legend(loc='lower right')\n",
        "plt.ylim([0, 1])\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "W2xnMyz24Rld",
        "outputId": "accd604f-5f63-401d-eb1a-cc069a69bc92"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "        Model  Precision   Recall  F1-Score  ROC-AUC\n",
            "Random Forest   0.681159 0.691176  0.686131 0.973770\n",
            "      XGBoost   0.720000 0.794118  0.755245 0.965557\n",
            "     LightGBM   0.555556 0.882353  0.681818 0.966554\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ROC Curves\n",
        "plt.figure(figsize=(8, 6))\n",
        "for model_name in results.keys():\n",
        "    fpr, tpr, _ = roc_curve(y_test, results[model_name]['probabilities'])\n",
        "    plt.plot(fpr, tpr, label=f\"{model_name} (AUC={results[model_name]['roc_auc']:.3f})\")\n",
        "\n",
        "plt.plot([0, 1], [0, 1], 'k--', label='Random')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curves Comparison')\n",
        "plt.legend()\n",
        "plt.grid(True)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "uG5ga2wi4Tex",
        "outputId": "1ab9ed85-6bf0-47fa-98ce-0ec7fb682788"
      },
      "execution_count": 14,
      "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": [
        "# Confusion matrices\n",
        "fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n",
        "for idx, model_name in enumerate(results.keys()):\n",
        "    cm = confusion_matrix(y_test, results[model_name]['predictions'])\n",
        "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=axes[idx])\n",
        "    axes[idx].set_title(f'{model_name} Confusion Matrix')\n",
        "    axes[idx].set_ylabel('Actual')\n",
        "    axes[idx].set_xlabel('Predicted')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "S2Dl0iMV4Vhp",
        "outputId": "1bbd85bf-9487-457d-976a-21820d7028af"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x400 with 6 Axes>"
            ],
            "image/png": 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5y9mzZ63LVqxYYfH29rbUrl3bcuPGDevy999/3+Lt7W2z7aOOU3yPBz9LzZo1LVevXrUuCw0NtZQuXdoydOhQ67IZM2ZYvL29LQMHDrTZx7lz5yxlypSxzJkzx2b58ePHLWXLlrUuP3r0qMXb29uyadOmR7bZ19fXMmzYsEduE6dly5aWqlWrJmrb6OhoS82aNS0tWrSw3L5927r8hx9+sHh7e1s++ugj67Jhw4ZZvL29LbNmzbLbX6tWrWyW1a9f39KzZ0+bZXHH/eG/z6+//mrx9va2/PrrrxaLJfHHpH79+jbH5N1337V4e3tb9u7da10WGRlpadCggaV+/fqWe/fu2eyvadOmNv8f+Oyzzyze3t6W48ePP3K/cZ/j0KFDlqVLl1oqV65suXXrlsVisVj69etn6dy5c4LHIG67ONHR0ZYWLVpYunTpYrM8ob93Qv/eHlz3oMuXL1uqV69u6datm+XOnTuWli1bWurVq2fz/xsA6VNcZpYpU8Zy+PBhu/Vvvvmmxdvb2ybTLRaL5fbt25aIiAjr49q1a9Z1cb8Z4nuMGTPGEhsba902IiLCUq5cOUv37t2t388Wi8WydOlSi7e3t2X16tUWiyXxOXXt2jWLt7e3ZeHChY/83M2bN7d06tQpUcco7hg8+BkfJam/HR78vRW3v+rVq9ss69Spk6V58+Y2yx7OzThxx3/NmjUWiyXxx6RTp042x+TTTz+1eHt7W7766ivrsujoaEtAQIDF19fXmiFJ+Y0Yn7jPsWnTJssPP/xg8fHxsVy4cMFisVgsU6ZMsTz//PMJHoOH89RisVi6d+9ufU2chP7ecVnevn17y927d+Nd9+DvlaioKMsLL7xgad68ueXOnTuWnj17WqpUqWI5f/78Iz8jACTVg+caCXn4+95isVhatGhhqVu3riUyMtK6bPfu3Xbn4kn57h43bpzd+YXFkvq/ESwW2+/+1q1bW0aOHGmxWO5nW7ly5Szr1q2zyZFH4fyY8+M4nB/D2Zgyxcm6du1qHfrZr18/Zc6cWXPmzLHpqe3m5madYyo2Nlb//vuv7t69q/Lly+vo0aN279msWTN5enpanz88Dcvly5cVGhqqVq1a2VyhrF27tt1cWT/99JPc3Nzshud2795dFotFP/30k83ymjVr2vRiqlSpkiSpUaNGypYtm3V53LxiD04N8yijR49WSEiIzePhz/LUU09Zty9durRq1aoV71CaV1991eb55s2bFRsbq6ZNm+rKlSvWR9xcmHHDn+Lav2PHDusw6ZSKjIxU1qxZE7Xt4cOHFRERofbt29vMQ1evXj0VL17cbjiVJLueUFWrVnXoVDXJPSY//vijKlasaDMMLmvWrAoICND58+d18uRJm+1bt25tM89afFMLPU7Tpk11584d/fDDD4qMjNT27dsTHA4m3e8FF+fatWu6ceOGqlatGu//5x7l4X9vCcmTJ49Gjx6tX375RR07dlRoaKgmTZpk8/8bAOlTzpw5JUl58+ZVqVKl7NZHRkZKkt09MT7//HPVrFnT+nhwmrA4AQEB1lydOXOmOnbsqC+++ELBwcHWbXbu3KmYmBh16dJFrq7//VRs27atsmXLZs3axOZUpkyZ5O7urj179ujatWvJPCrxH4PEZKojfjtUq1ZN//77r3W/KZXcY/LTTz8pT548atGihXWZu7u7OnfurKioKO3du9dm+8f9RkyM2rVry9PTU998840sFos2btyo5s2bJ7j9g3l648YNXblyRdWrV9fZs2dtho8/Trt27RI1X3jmzJkVHBysP//8Ux07dtT27ds1YsQIFShQINH7AoDUcunSJf3xxx9q2bKlTWZVr15d3t7e8b4mJd/dqf0b4WEvvviiNm/ebJ1+w83NLUk3V+T8mPPjOJwfw9mYMsXJRo8erWLFiunGjRtas2aN9u7dG+8NFtatW6dPPvlEp06dUkxMjHV5fENo8+fPb/M8Ljzj5vm8cOGCJKlo0aJ2ry1WrJjNF9r58+eVN29euy+dEiVKWNc/at9xr3t4Kpa4Qnxib7xUsWLFeG+qGfdZihUrZreuRIkS2rFjh92NMx8+Zn///bcsFkuCc6rFzZFWuHBhdevWTSEhIfr6669VrVo1NWjQQC+99FKCQ58eJ1u2bIkOrUd91uLFi2v//v02yzJmzGg3vMvT09NhxQcp+cfkwoUL1oslDypevLh1/YM/CB8+iY0b+paUG3flypVLNWvW1IYNG3T79m3du3cv3nlm4/zwww+aM2eOQkNDFR0dbV2e1LuBJzTMPT7NmzfX+vXrtX37dgUEBFiHGgJIvy5evKgZM2bI29tbf/zxhxYuXKi+ffvabBN34hgVFWXz3du4cWPrd+nkyZNt5vSMU7RoUdWqVcv6vFGjRnJxcdFnn32mNm3ayMfHx5o/cd/RcTw8PFS4cGHrb4HE5pSHh4cGDx6sKVOmqHbt2qpUqZLq1aunli1bKk+ePEk7QP8v7vfGzZs3H3tjy+T8dkgoh65du+aQE7PkHpPz58+raNGiNhcq4j6H9N9njfO434iJ4e7uriZNmlhvnHbx4sVHnkDv379fM2fO1MGDB+2KAzdu3Ej0b6ik5GnVqlXVvn17LVu2TH5+fnrllVcS/VoASE1x38vxTatZtGjReIuLKfnuTu3fCA9r1qyZpkyZop9++knr169XvXr1kpSTnB/b4vyY82M4Dz3EnaxixYqqVauWGjdurDlz5ljnEbt586Z1m6+++krDhw9XkSJFNHHiRC1cuFAhISF67rnn4p1zKaHeM/Ft62gJ7dvINj3s4bt8x8bGWudie7gXekhIiMaPH2/ddvjw4Vq/fr169eql27dva+LEiWrevLn++eefZLWlePHiunHjht3NOx0hMb2oEpJQqMX3Q8nRxyQ+D5/ox0nqv58WLVrop59+0ooVK1S3bt0Eiyb79u1Tnz59lDFjRo0ZM0bz589XSEiIWrRokeR9JuWu8levXtXhw4clSSdPnoz3eANIX+IyaMGCBWrSpInmzp1rd6IYd7L0xx9/2CzPnz+/atWqpVq1atn0LHucuJONffv2paTpj9S1a1d99913GjhwoDJmzKiPPvpIzZo1S3IvozgJHQNHSW4OJSVPHX1M4uOo32MvvviiQkNDNXPmTJUuXdpuhGGcM2fOqGvXrrp69aqGDx9uzdOuXbtKiv84JCQpeRodHa09e/ZIut9bzlEj+wDACCn57nb2b4S8efOqevXqCgkJ0b59+x55wTSh9nJ+/HicHwOOR0HcQG5ubho4cKAuX75svUu0JH333XcqXLiwZs2apZYtW6pOnTqqVauW7ty5k6z9xF1NjO8uxKdOnbJ5XrBgQV2+fNluSPBff/1lXW+kuM/ycLul+23MmTOn3fCwhxUpUkQWi0WFChWy/ih48OHr62uzvY+Pj/r27atly5Zp2bJlunTpkj7//PNktb9+/fqSpPXr1z9220d91lOnTjl0KHBcED48lPnhEQFxknpMChQokODfLG59anjhhRfk6uqqgwcP2gwvf9h3332njBkzatGiRdabdjzYOyK1jB8/Xjdv3tSgQYO0f/9+ffbZZ6m+TwBp1+bNm7Vt2zb1799fTz/9tEaOHCl3d3eNGzfOZru4Gxl9/fXXDtnv3bt3Jcl6cT7uOznuOzpOdHS0zp07Z/0tkNScKlKkiLp3765PPvlEGzZsUExMjD755BPr+qT0OHJUnib2t0NiJTVPH3dMHlawYEGdPn3a7gQxtfO0atWqKlCggPbs2fPIPN22bZuio6M1Z84cvfrqq9Y8fXDYdZyk9jB7lBkzZujPP//UsGHDdO7cOb3//vsOe28ASIm47+UzZ87YrYvv/DyxEvoOTe3fCPFp0aKF9u3bp2zZsqlu3bpJen/Oj21xfsz5MZyHgrjBatSooYoVK+qzzz6zFrzjrmQ+eOXtt99+08GDB5O1j7x586pMmTJat26dzRf6L7/8Yjc3Vd26dXXv3j2bAr0kffrpp3JxcUlywDla3Gf58ssvbYYH/fHHH/rll18SdffhRo0ayc3NTbNmzbK7ummxWHT16lVJ9+czi/sBEMfb21uurq42Q4ayZMmS6KFKcUPV5s6dqwMHDtitj4yM1IcffihJKl++vHLnzq0VK1bY7O/HH3/Un3/+af2x4whxQ/genHv03r17WrlypV37EnNMHubv769Dhw7ZfOaoqCitXLlSBQsWTLCnWUplzZpVY8eOVVBQkBo0aJDgdm5ubnJxcdG9e/esy86dO2e96/mDkvL3fpRvv/1WGzdu1KBBg9SzZ081b95c06dPj/eHEYAnX2RkpCZOnKiyZcta7+ORL18+9e/fXz///LM2bdpk3bZq1aqqXbu2Vq5cqS1btsT7fknpvfPDDz9Iuj+ntiTVqlVL7u7uWrJkic37rF69Wjdu3LBmbWJz6tatW3YX9YsUKaKsWbPavC5z5syJ/n6tXLmy6tSpo1WrVsV7DKKjozVlyhRJjvntkFgFCxaUm5ub3VzeD58UJ/aYPKxu3boKCwvTxo0brcvu3r2rJUuWKEuWLHr22Wcd8Cnsubi4aNSoUQoMDNTLL7+c4Hbx/YaNmybwYUn5ez/Kb7/9pk8++USvvfaaunfvrh49emjp0qXWHuMAYKR8+fLJ29tbX375pU1Rec+ePSka5ZQ5c2ZJ9lNmpPZvhPg0adJEgYGBGjNmTLzTwT4K58ecH8fh/BjOxhziaUCPHj3Uv39/rV27Vu3bt1e9evX0/fff680331S9evV07tw5rVixQiVLllRUVFSy9jFw4ED16tVLHTp0UJs2bfTvv/9q6dKlKlWqlM17NmjQQDVq1NCHH36o8+fPy8fHR7/88ou2bt2q1157Ld65z5xt6NCheuONNxQQEKBXXnlFt2/f1tKlS5U9e3YFBgY+9vVFihTRW2+9pffff1/nz59Xw4YNlTVrVp07d05btmxRu3bt1KNHD/36668aP368mjRpomeeeUb37t3TV199JTc3N5u5tsqVK6ddu3YpJCREefPmVaFCheKdD0y6Pw/nrFmz1K1bN3Xq1ElNmjRRlSpV5O7urhMnTmjDhg3KkSOHBgwYIHd3dw0ePFgjRoxQp06d1Lx5c0VERGjx4sUqWLCgdfixI5QqVUq+vr764IMPdO3aNXl6emrjxo124Z7YY/Kwnj176ptvvtEbb7yhzp07y9PTU19++aXOnTunmTNnJjgEzBFatWr12G38/f0VEhKi119/XS1atFBERISWL1+uIkWK6Pjx4zbbJuXvnZCIiAiNHTtWNWrUUKdOnSRJ77zzjnbv3q0RI0Zo+fLlqXpMAKQ906dP1+XLlzVz5kybIb4dO3bUl19+qUmTJqlOnTrWeTnfe+89vf7663rzzTdVt25d1apVSzly5FB4eLh27typvXv3xnsR++jRo/rqq68k3e/t9euvv+q7775T5cqV5efnJ+n+HJO9evXSrFmz9Prrr6tBgwY6deqUli9frgoVKuill16SpETn1N9//62uXbuqSZMmKlmypNzc3LRlyxaFh4fb3JyxXLly+vzzz/Xxxx+raNGi1rkuEzJ16lR1795dgYGBql+/vmrWrKnMmTPr9OnT2rhxoy5fvqxhw4ZJSvlvh8TKnj27mjRpoqVLl8rFxUWFCxfW9u3bFRERYbNdYo/JwwICAvTFF19o+PDhOnLkiAoWLKjvvvtO//vf/zRy5MhUvfFUw4YNH3ujtNq1a8vd3V29e/fWq6++qps3b2rVqlXKnTu3wsLCbLZN6t87Pnfu3NGwYcNUtGhRDRgwQJIUFBSkH374QSNGjNDXX3/tsN7/ABBnzZo1+vnnn+2Wd+nSJd7tBwwYoL59+6p9+/Zq3bq1rl+/rmXLlsnb2/uRPa8fpVy5cpKkiRMnys/PT25ubtb8SM3fCPHJnj27goKCkvU5OD/m/Fji/BjGoCCeBjRq1EhFihTRJ598onbt2ql169YKDw/XF198oR07dqhkyZJ677339O233ya7t0vdunX10Ucfafr06Xr//fdVpEgRBQcHa+vWrTbv6erqqjlz5mjGjBnauHGj1q5dq4IFC2ro0KHq3r27oz5yitSqVUsLFy7UjBkzNGPGDGXIkEHPPvushgwZosKFCyfqPXr27KlnnnlGn376qWbPni3p/o1Aa9eubb1S6uPjIz8/P/3www+6dOmSMmfOLB8fHy1YsMBmWpXhw4dr9OjRmj59um7fvq1WrVo9MgCKFi2qL7/8Up9++qk2b96srVu3KjY2VkWLFlXbtm2tPQOl+3eTzpQpkxYsWKBp06YpS5YsatiwoYYMGfLYm4gl1bRp0zR69GjNnz9fOXLk0CuvvKIaNWqoW7du1m0Se0we5uXlpRUrVui9997T0qVLdefOHfn4+Gju3LkOvZKfXDVr1tS7776rBQsWaNKkSSpUqJAGDx6s8+fP2wV+Uv/e8Rk7dqyio6MVHBxsHe6YM2dOjR8/Xn379tWiRYv0xhtvOOzzAUjbDh8+rOXLl6tDhw6qWLGizTo3NzeNHTtWAQEBmj59ut5++21JsvaQWrFihTZt2qRZs2bp9u3bypkzp8qXL69p06apWbNmdvvasGGDNmzYIOn+TaTz58+vHj166M0337Q50QgKClKuXLm0dOlSBQcHy9PTU+3atdPAgQPl7u5u3S4xOfX000+refPm2rVrl9avXy83NzcVL15c06dPtzlZfPPNN3XhwgUtXLhQN2/eVPXq1R9ZIM2VK5dWrFih5cuXa+PGjfrwww8VExOjggULqkGDBjaFCUf8dkist99+W3fv3tWKFSvk4eGhJk2aaOjQoTZDkxN7TB6WKVMmLVmyRNOmTdO6desUGRmpYsWKKTg4WK1bt3bo50iO4sWLa8aMGZo+fbqmTJkiLy8vtW/fXrly5dLIkSNttk3q3zs+H3zwgc6cOaMVK1ZY5yj18PDQ5MmTFRAQoKlTp2rs2LGO+ngAIMl+1E+chL6HGzRooA8++EAzZ87U+++/r2eeeUbBwcH68ssvdeLEiWS1oVGjRurcubO++eYbrV+/XhaLxVoQT+3fCI7G+THnx5wfwwguFiPucggAAAAAAJBOvfzyy8qVK5dCQkKMbgoApDuMOQAAAAAAAEgFMTExdlNt7N69W8eOHVP16tUNahUApG9MmQIAAAAAAJAKLl26pG7duumll15S3rx59ddff2nFihXKkyePXn31VaObBwDpEgVxAAAAAACAVODp6aly5cpp1apVunLlirJkySJ/f38NHjxYOXPmNLp5AJAuMYc4AAAAAAAAACBdYA5xAAAAAAAAAEC6QEEcAAAAAAAAAJAuUBAHAAAAAAAAAKQLT+RNNTNXDjS6CUCiXN07y+gmAImSKZXSIiXf17cO8P+fJwnZDbMgu2EWZDdSG9kNswj7dabRTQASJVtGl1R5X7LbHj3EAQAAAAAAAADpwhPZQxwAYBIuXJcFAMBUyG4AAMyF7LZDQRwAYByX1BkSBgAAUgnZDQCAuZDddiiIAwCMw5VqAADMhewGAMBcyG47FMQBAMbhSjUAAOZCdgMAYC5ktx0K4gAA43ClGgAAcyG7AQAwF7LbDgVxAIBxuFINAIC5kN0AAJgL2W2HSwQAAAAAAAAAgHSBHuIAAOMwdAsAAHMhuwEAMBey2w4FcQCAcRi6BQCAuZDdAACYC9lth4I4AMA4XKkGAMBcyG4AAMyF7LZDQRwAYByuVAMAYC5kNwAA5kJ226EgDgAwDleqAQAwF7IbAABzIbvtcEQAAAAAAAAAAOkCPcQBAMZh6BYAAOZCdgMAYC5ktx0K4gAA4zB0CwAAcyG7AQAwF7LbDgVxAIBxCGYAAMyF7AYAwFzIbjscEQCAcVxdkv8AAADOR3YDAGAuTsruvXv3qnfv3vLz85OPj4+2bNlis97Hxyfex8KFC63bNGjQwG79/Pnzbd7n2LFj6tChgypUqCB/f38tWLAgyYeEHuIAAONwpRoAAHMhuwEAMBcnZXdUVJR8fHzUpk0bBQYG2q3fsWOHzfOffvpJo0aNUuPGjW2W9+vXT+3atbM+z5o1q/W/IyMj1aNHD9WsWVPjxo3TH3/8oZEjRypHjhwKCAhIdFspiAMAAAAAAAAAks3f31/+/v4Jrs+TJ4/N861bt6pGjRoqXLiwzfKsWbPabRtn/fr1iomJ0aRJk+Th4aFSpUopNDRUISEhSSqIc3kfAGAcF5fkPwAAgPOR3QAAmEsKsjs6OlqRkZE2j+jo6BQ3KTw8XD/++KNeeeUVu3ULFixQjRo11LJlSy1cuFB37961rjt48KCqVasmDw8P6zI/Pz+dOnVK165dS/T+6SEOADCOk4Zu7d27V4sWLdLhw4cVFham2bNnq2HDhtb1Pj4+8b5uyJAhev311yXdn8vs/PnzNusHDRqknj17Wp8fO3ZM48eP1++//65cuXKpU6dOeuONN1LhEwEAYBCyGwAAc0lBds+bN0+zZs2yWRYYGKigoKAUNWndunXKmjWrGjVqZLO8c+fOKlu2rDw9PXXgwAF98MEHCgsL04gRIyTdL6QXKlTI5jVeXl7WdZ6enonaPwVxAIBxnNRbzExzmQEAkKaR3QAAmEsKsrtXr17q1q2bzbIHe2cn15o1a/Tiiy8qY8aMNssf3Ffp0qXl7u6uMWPGaNCgQQ7ZbxwK4gAA4zipl5mZ5jIDACBNI7sBADCXFGS3h4eHQwvRkrRv3z6dOnVK06dPf+y2lSpV0t27d3Xu3DkVL15cXl5eCg8Pt9km7nlcT/HEYA5xAIBxmMsMAABzIbsBADCXNHb/j9WrV6tcuXIqXbr0Y7cNDQ2Vq6urcufOLUny9fXVvn37FBMTY91m586dKlasWKKnS5HoIQ4AMBJzmQEAYC5kNwAA5uKk0V03b97UmTNnrM/PnTun0NBQeXp6qkCBApLuT1f27bffatiwYXavP3DggH777Tc999xzypo1qw4cOKDg4GC99NJL1kx+8cUXNXv2bI0aNUpvvPGGTpw4ocWLF1uzPbEoiAMATOlJncsMAIAnFdkNAMCT6/Dhw+rSpYv1eXBwsCSpVatWmjx5siTpm2++kcViUYsWLexe7+HhoY0bN2rWrFmKjo5WoUKF1LVrV5s8z549uxYtWqTx48erdevWypkzp/r27Zvkqc4oiAMAjJOCIVhP6lxmAACkaWQ3AADm4qQbYteoUUPHjx9/5DYBAQEJFq/LlSunlStXPnY/pUuX1vLly5PVxjjMIQ4AMI6La/IfqSAtzGUGAECaRnYDAGAuaSy704In95MBANI+J93c4+bNmwoNDVVoaKik/+Yyu3DhgnWbuLnM2rZta/f6AwcO6NNPP9WxY8d09uxZrV+/Pt65zNzd3TVq1CidOHFCGzdu1OLFi+2GhgMAYGpkNwAA5pLGbqqZFjBlCgDAOE664mymucwAAEjTyG4AAMzlCe7pnVwuFovFYnQjHC1z5UCjmwAkytW9s4xuApAomVLp8mnmFz9O9mtvfd3XgS2B0chumAXZDbMgu5HayG6YRdivM41uApAo2TKmTo9sstselwgAAAAAAAAAAOkCU6YAAIzzBM9JBgDAE4nsBgDAXMhuOxTEAQDGYS4zAADMhewGAMBcyG47FMQBAMbhSjUAAOZCdgMAYC5ktx0K4gAA43ClGgAAcyG7AQAwF7LbDgVxAIBxuFINAIC5kN0AAJgL2W2HSwQAAAAAAAAAgHSBHuIAAMO4cKUaAABTIbsBADAXstseBXEAgGEIZgAAzIXsBgDAXMhuexTEAQDGIZcBADAXshsAAHMhu+1QEAcAGIYr1QAAmAvZDQCAuZDd9iiIAwAMQzADAGAuZDcAAOZCdttzNboBAAAAAAAAAAA4Az3EAQCG4Uo1AADmQnYDAGAuZLc9CuIAAMMQzAAAmAvZDQCAuZDd9iiIAwCMQy4DAGAuZDcAAOZCdtuhIA4AMAxXqgEAMBeyGwAAcyG77VEQBwAYhmAGAMBcyG4AAMyF7LZHQRwAYBiCGQAAcyG7AQAwF7LbnqvRDQAAAAAAAAAAwBnoIQ4AMAxXqgEAMBeyGwAAcyG77VEQBwAYh1wGAMBcyG4AAMyF7LZDQRwAYBiuVAMAYC5kNwAA5kJ226MgDgAwDMEMAIC5kN0AAJgL2W2PgjgAwDAEMwAA5kJ2AwBgLmS3PVejGwAAAAAAAAAAMK+9e/eqd+/e8vPzk4+Pj7Zs2WKzfvjw4fLx8bF59OjRw2abf//9V4MGDVKVKlVUrVo1jRw5Ujdv3rTZ5tixY+rQoYMqVKggf39/LViwIMltpYc4AMA4XKgGAMBcyG4AAMzFSdkdFRUlHx8ftWnTRoGBgfFuU6dOHQUHB1ufe3h42KwfPHiwwsLCFBISopiYGI0cOVKjR4/W+++/L0mKjIxUjx49VLNmTY0bN05//PGHRo4cqRw5ciggICDRbaUgDgAwDEO3AAAwF7IbAABzcVZ2+/v7y9/f/5HbeHh4KE+ePPGu+/PPP/Xzzz9r9erVqlChgiTp7bffVs+ePTV06FDly5dP69evV0xMjCZNmiQPDw+VKlVKoaGhCgkJSVJBnClTAACGcXFxSfYDAAA4H9kNAIC5pKXs3rNnj2rWrKnGjRtrzJgxunr1qnXdgQMHlCNHDmsxXJJq1aolV1dXHTp0SJJ08OBBVatWzaZnuZ+fn06dOqVr164luh0UxAEAhnFWMJtpLjMAANIyshsAAHNJSXZHR0crMjLS5hEdHZ2sdtSpU0dTpkzRp59+qiFDhmjv3r164403dO/ePUlSeHi4cuXKZfOaDBkyyNPTU2FhYdZtvLy8bLaJex4eHp7otjBlCgDAMM7qLWamucwAAEjLyG4AAMwlJdk9b948zZo1y2ZZYGCggoKCkvxezZs3t/533MXshg0bWnuNOxMFcQDAE89Mc5kBAACyGwCAtKBXr17q1q2bzbKHL0AnV+HChZUzZ06dPn1aNWvWlJeXl65cuWKzzd27d3Xt2jVr3nt5edn1BI97/nDP8UdhyhQAgHFckv9w5NAtKe3MZQYAQJpGdgMAYC4pyG4PDw9ly5bN5uGogvg///yjf//911rsrly5sq5fv67Dhw9bt/n1118VGxurihUrSpJ8fX21b98+xcTEWLfZuXOnihUrJk9Pz0Tvm4I4AMAwKZnLbN68eapatarNY968eclqR1qaywwAgLSM7AYAwFycdf+PmzdvKjQ0VKGhoZKkc+fOKTQ0VBcuXNDNmzc1ZcoUHTx4UOfOndOuXbvUt29fFS1aVHXq1JEklShRQnXq1NE777yjQ4cOaf/+/ZowYYKaN2+ufPnySZJefPFFubu7a9SoUTpx4oQ2btyoxYsX2/VifxymTAEAGCYlc5k5cuhWWprLDACAtIzsBgDAXJx1/4/Dhw+rS5cu1udx9/lo1aqVxo4dqz/++ENffvmlbty4obx586p27drq37+/zW+BadOmacKECXrttdfk6uqqRo0a6e2337auz549uxYtWqTx48erdevWypkzp/r27Zvkqc4oiAMADJOSYPbw8HDYUK2HGTmXGQAAaRnZDQCAuTirIF6jRg0dP348wfWLFi167Hs89dRT1ptfJ6R06dJavnx5ktv3IKZMAQAYJwVzmaUmI+cyAwAgTSO7AQAwlzSa3UaiIJ6O1K5SQqun99Jf37+rWwdm6cV6FW3W582VXfPHddJf37+riJ0f6KtZfVWiiO1d27u3rq3vFvTXpZ/f060Ds+SZLbPdfnLmyKKQd1/TpZ/f08WfpmrOmA7Kmjl1eoIgfVq0YJ46tGujms9WVr06NfVWUF/9feov6/pr//6r4Hcn6KXmjVW9SkU1fr6eJk+aqBs3bhjYasSHucyAR3NWdg/t0Vg/fDpQETs/0MWfpqbqZ0L69LjslqTVK79Qj66dVat6FVUq56Pr168b1Fo8CtkNPNrjsjtrZg99OKytTn47QVd2faD/rRml11/xs9kmX+7sWjShi05tnqTwne9r5/Jhavm8b7z783DPoF9XDNetA7NU0btgan0sQCGL5qtqxdKaNmWSdVl4eJjeGTlUjer7qXb1yurQrrW2bv7OwFYiPs7KbjOhIJ6OZM2cUb//cV5vBX8R7/qVH/ZUsUJeavvWPD3XfrLOXLyijXODlCXTf8XsLJnctXnnUb33yfcJ7idk0msqUyK/WvSZpTb95sqvSknNfqeDwz8P0q99e/cooH1HLfl8peYtCNHdu3fV+40eioqKkiRdDrussMuXNXDwMK35coPGvxusX3b8rLHvjDK45TDK4cOH1bJlS7Vs2VLS/bnMWrZsqRkzZsjNzU1//PGH+vTpoyZNmmjUqFEqV66cli1bZjeXWfHixfXaa6+pZ8+eqlKlisaPH29dHzeX2blz59S6dWtNnjw5WXOZAQ9yVnZ7uLtp7eYDWrD6Z4d/BkB6fHZL0u3bt1Srdh31eKO3gS1FWkF2w6wel91TBrXRC7XKqtuoxfJtPVGzlm3Xh8Paqrl/Bes2Cyd0kfczedX2rXmq1naSvtp2UEundFcln0J27zfprZd1Mexaqn0eQJKOHP5da1d9oVLePjbLR48aptN/n9IHMz7WF2vXq0HDFzR8yAAdCz1qUEuBxGEO8XTk+1+O6vtf4v9SKlkkr2pULKYqbSYq9K9/JEn9Jn2hv7dMUrumVfXpul2SpFnLt0uS6lQtFe/7+BTLp8a1y6l2x6n639EzkqSBU1bpy5l9NOLDdQQ1HGLOfNt5p8a/O1n169RU6NEjqlrtWZUq5a0PPpppXV+4SBEF9X9LI4cN0d27d5UhA199aQVzmQGP5ozslqSJczdKkjq9WMOBrQf+87jslqROXbpKkvbu2e3s5iEJyG7g0R6V3ZL0XKViWrpht37ef0KS9MnaX9SjTW1VK1dU3/z4+/9vU1z9Jq3QviOnJUlTFn6noI4NVLlsYf12/Jz1vRrVLqvnnyuj9kMWqolfuVT8VEjPoqJu6u0Rg/X22AlaNH+OzbpDBw9qxNtjVL7C/ZEQr/fso+VLPlXo0SMqXaasEc1FPJ7knt7JRQ9xSJIyetwvEN6OvmtdZrFYFB19V7V8SyT6fWpULKar16OsxXBJ2rb7uGJjLXq2fFHHNRh4QOT/T4WS4xFzPUbeiFS2bNkohqcxDN0Cks9R2Q0YITHZjbSJ7AZS5tffTqmFfwUVyHP/+69utVIqVTSvtvwa+sA2f+mVRlWVM0cWubi4qG3jqsqUMYN+2nfCuk3eXNn18Tvt1eOdxYq6Fe30z4H0Y/K74+VXp55qPFfLbl1FX199/91GXbv2r2JjY/Xdpm905060qj1b3YCWIiFktz1DK0NXrlzRmjVrdPDgQZu7eVeuXFmtW7dWrly5jGxeunL873905uIVTQh6SYETP9fNW9Hq16m+Cj2dU097Jf5EJV/uHAq7YjtP8717sbpyPUr5vHI4utmAYmNjNXXKJPlWrqJSpbzj3ebq1SuaP/djtWnL8Ne05kkO2CcV2Z12OCq7AWdLTHYj7SK7zYfsTlsGTlml2e+015/fv6uYmHuKtcSq74TP9cv//rRu02noJ1oypbsu/DhVMTH3FHU7WgEDF+ivs+HWbeaP76QFq3fof0fPqEh+/oZIHd9t+kbHQo9qyeer410/5b3pGj50gBrUeU5uGTIoU6ZMmjZ9pgoXoUNkWkJ22zOsh/ihQ4fUpEkTLVmyRNmzZ1e1atVUrVo1Zc+eXUuWLFHTpk31+++/G9W8dOfu3Vi9OmiBShbNq4s/vacruz5Q3Wre+nbHEcVaYo1uHpCgSRPH6c8TJzR12ofxro+MjFRgn14qXqKEevcNdHLr8Fjc7dpUyO60heyGWT0uu5HGkd2mQnanPX1f9Vf1Cs+oTf+5qtVxioZ/sE7Th7dT/Rr/zc085s0Weip7ZjXtNUO1O03VjKXbtHRqd5UrWeD+e7T3V/YsmR55fxAgpf7556KmTZmkdydPU8aMGePdZs7sj3Tj+g3NmR+ipZ+vVqfOXTV8yACd+CPhKa9gALLbjmE9xCdOnKgmTZpo3LhxdlcqLBaLxowZo4kTJ+qLL+K/EQUc70DoWT336mTlyJZJHu4ZFH41Uj8tHqz9D0x/8jiXIq4rT67sNsvc3FyVK0cWXQq/7ugmI52bNHG8fvpxuz75bKnyPf203fqbNyPVt9frypo1qz6cMVvu7u4GtBKPwpVqcyG70x5HZDfgTI/LbqR9ZLe5kN1pS6aM7hoX9KICBi7QtzuOSJIOn7igij6F9Fbn5/XD7uMqVshLfV71t7lHyO9/nFftKiXUK6Cu+r27QvWe9VaNisV0bfd0m/f/ZdlQrdi0T2+MXuLsj4YnUOjRI7pyJUIdA1pbl927d0//279PK1cs05r1m/TF58u0cu3XKlHy/r1qvH1K68D/9mvVF8s18p1xRjUdDyG77RlWED927JiCg4Pj/aO4uLjotddeU6tWrQxoGa5H3pYklSiSR1XKFtG4jzck+rW7D51SzhxZVLlMYR0IPStJqvest1xdXbT38OlUaS/SH4vFouB3J2jb1s1a9OkSFSpU2G6byMhI9enZQx4eHvpo1pwEr2gDSDyyO+1KSXYDzpCY7AbgeGR32uKewU0e7hkUa7HYLL93L1aurvf/RlkyeUhSPNtY5Pr/f8dBU1dr7Oz/sj5/Hk9tmBOozsNDtPf3v1PxEyA9qV7jOX2xZr3NsnGjR+qZYsX1WrfXdfvWLUmSq6vt5BOubq6KjWW0ItI2wwriXl5e+v3331WiRPw3ffr999/l5eXl5FY92bJm9lCJwnmsz58pmFsVvQvq6vUonf3nqlo3rKywq5E6+88VlS9VQNOGvKKvtx/S1l+PWV+TL3d25cudQyWK3P/blC9VQDdu3tbZf67q6vUoHT91Sd/9ckSz3+mgfu+ukHsGN304vJ1Wffc/XQy75vTPjCfTpAnjtGnjBk2f+bGyZsmq8LAwSVK27NmVKVMmRUZGqvcb3XX79i1NmvyebkZG6mZkpCQpZ65ccnNzM7L5eABXqs2F7HY+Z2S3JBV+Oqdy5siiwvlzys3VVRW9C0qS/jwbppvcqAsO8LjslqTwsDCFh4fr7Jn7IxxOnvhDWbJkVf78+eX51FNGNR0PIbvNhex2vsdl90/7TmjSWy1163aMzly8ojpVS6pji+oa9sFaSffvEXLyzGXNeru9RnywThHXbuql+hX1/HM+at1/riTp7D9XbfYZGXVHkvTX2TCdv/yvcz4onnhZs2ZTyYfu9ZE5c2Z5ej6lkqW8FRMTo8JFiurd8WP01qCh8nzqKW3ftkW7d+3U9FlzDWo14kN22zOsIN6jRw+98847Onz4sGrWrGkN4fDwcO3atUurVq3S0KFDjWreE6lK2aL6fmF/6/Opg9tIkpas/1U9xyzV03lyaMqg1sqbO7v+Cb+uZRt2K3j+tzbv8fordfR272bW51s+GSBJemP0Ei39erckqdvIz/Th8HbaOC9IsbEWfbn1oAZNXZXaHw/pyMovPpck9eja2Wb5+InBerlVa4UePaLfD/0mSWrR9AWbbTZ+v1UFCxZyTkPxWOSyuZDdzues7H6nT3N1fuk56za7vxghSWr0+kf6ef+J1PlwSFcel92StGrlCs39eJZ1XbcuHe22gfHIbnMhu53vcdndZfgnGh/0sj6d9Jpy5siiMxevaOzsDVqwaoek+/cIaRk0RxP7vazVH/VStiwZ9efZML0+eom+23HUkM8ExMfd3V0zZs/TzOnva0BQH0VFRalwkSIaN3Gy/Or4G908PIDstudisTw0DseJNm7cqE8//VRHjhzRvXv3JElubm4qV66cunbtqmbNmj3mHeKXuTI3zoM5XN076/EbAWlAplS6fFpqyLeP3ygBJ95r4sCWILHIbqR3ZDfMguxGHLIb6V3YrzONbgKQKNkypk7lmuy2Z1gPcUlq1qyZmjVrppiYGF29en/IT86cObnxHQCkE1ypNh+yGwDSN7LbfMhuAEjfyG57hhbE47i7uytv3rxGNwMA4GTMZWZeZDcApE9kt3mR3QCQPpHd9lwfvwkAAAAAAAAAAOaXJnqIAwDSJy5UAwBgLmQ3AADmQnbboyAOADCMqyvJDACAmZDdAACYC9ltj4I4AMAwXKkGAMBcyG4AAMyF7LZHQRwAYBhu7gEAgLmQ3QAAmAvZbY+COADAMOQyAADmQnYDAGAuZLc9V6MbAAAAAAAAAACAM9BDHABgGIZuAQBgLmQ3AADmQnbboyAOADAMwQwAgLmQ3QAAmAvZbY+COADAMOQyAADmQnYDAGAuZLc9CuIAAMNwpRoAAHMhuwEAMBey2x4FcQCAYchlAADMhewGAMBcyG57FMQBAIbhSjUAAOZCdgMAYC5ktz1XoxsAAAAAAAAAAIAz0EMcAGAYLlQDAGAuZDcAAOZCdtujIA4AMAxDtwAAMBeyGwAAcyG77TFlCgDAMC4uyX8AAADnI7sBADAXZ2X33r171bt3b/n5+cnHx0dbtmyxrouJidF7772nF198Ub6+vvLz89PQoUN16dIlm/do0KCBfHx8bB7z58+32ebYsWPq0KGDKlSoIH9/fy1YsCDJx4Qe4gAAw3ClGgAAcyG7AQAwF2dld1RUlHx8fNSmTRsFBgbarLt9+7aOHj2qPn36qHTp0rp+/breffdd9enTR2vXrrXZtl+/fmrXrp31edasWa3/HRkZqR49eqhmzZoaN26c/vjjD40cOVI5cuRQQEBAottKQRwAYBjOqQEAMBeyGwAAc3FWdvv7+8vf3z/eddmzZ1dISIjNsnfeeUdt27bVhQsXVKBAAevyrFmzKk+ePPG+z/r16xUTE6NJkybJw8NDpUqVUmhoqEJCQpJUEGfKFADAE89MQ7cAAADZDQBAWhAdHa3IyEibR3R0tEPeOzIyUi4uLsqRI4fN8gULFqhGjRpq2bKlFi5cqLt371rXHTx4UNWqVZOHh4d1mZ+fn06dOqVr164let/0EAcAGIahWwAAmAvZDQCAuaQku+fNm6dZs2bZLAsMDFRQUFCK2nTnzh1NmzZNzZs3V7Zs2azLO3furLJly8rT01MHDhzQBx98oLCwMI0YMUKSFB4erkKFCtm8l5eXl3Wdp6dnovZPQRwAYBiGbgEAYC5kNwAA5pKS7O7Vq5e6detms+zB3tnJERMTo/79+8tisWjcuHE26x7cV+nSpeXu7q4xY8Zo0KBBKd7vg5gyBQBgGBcXl2Q/UpORQ7cAAEjLUpLdT+qwawAA0rKUZLeHh4eyZctm80hJYTomJkZvvfWWLly4oE8++cSmd3h8KlWqpLt37+rcuXOS7vcGDw8Pt9km7nlcT/HEoIc4AMAwKalrR0dH251Ee3h4pPiqsdFDtwAASMtSkt1P6rBrAADSsrRyQ+y4Yvjp06e1ePFi5cyZ87GvCQ0Nlaurq3Lnzi1J8vX11fTp0xUTEyN3d3dJ0s6dO1WsWLEk5TYFcQCAYdLaXGZpYegWAABpWUqy+0kddg0AQFrmrPt/3Lx5U2fOnLE+P3funEJDQ+Xp6ak8efKoX79+Onr0qObNm6d79+4pLCxMkuTp6SkPDw8dOHBAv/32m5577jllzZpVBw4cUHBwsF566SVrsfvFF1/U7NmzNWrUKL3xxhs6ceKEFi9ebL3YnVgUxAEApuTok+oHh2599tlnSRq6Vbx4cYcN3QIA4EnliJFcDyK7AQBIOw4fPqwuXbpYnwcHB0uSWrVqpcDAQG3btk2S9PLLL9u8bvHixapRo4Y8PDy0ceNGzZo1S9HR0SpUqJC6du1qc96fPXt2LVq0SOPHj1fr1q2VM2dO9e3bN8n3/qAgDgAwTEquVDvypDotDd0CACAtc1Yvs8chuwEASBxnZXeNGjV0/PjxBNc/ap0klStXTitXrnzsfkqXLq3ly5cnuX0PoiAOADCMs86pzTR0CwCAtIzsBgDAXNLItew0hYI4AMAwzrpSbaahWwAApGVkNwAA5pJWRnelJRTEAQCGcVYum2noFgAAaRnZDQCAuVAPt0dBHABgGK5UAwBgLmQ3AADmQnbboyAOADAMuQwAgLmQ3QAAmAvZbc/V6AYAAAAAAAAAAOAM9BAHABjGlUvVAACYCtkNAIC5kN32KIgDAAxDLgMAYC5kNwAA5kJ226MgDgAwDDf3AADAXMhuAADMhey2R0EcAGAYV3IZAABTIbsBADAXstseBXEAgGG4Ug0AgLmQ3QAAmAvZbc/V6AYAAAAAAAAAAOAM9BAHABiGC9UAAJgL2Q0AgLmQ3fYoiAMADOMikhkAADMhuwEAMBey2x4FcQCAYbi5BwAA5kJ2AwBgLmS3PQriAADDcHMPAADMhewGAMBcyG57FMQBAIYhlwEAMBeyGwAAcyG77bka3QAAAAAAAAAAAJyBHuIAAMO4cqkaAABTIbsBADAXstseBXEAgGHIZQAAzIXsBgDAXMhuexTEAQCG4eYeAACYC9kNAIC5kN32KIgDAAxDLgMAYC5kNwAA5kJ226MgDgAwDHOZAQBgLmQ3AADmQnbbS1RBfOvWrYl+w+effz7ZjQEAAI5BdgMAYC5kNwAAzpGogvibb76ZqDdzcXFRaGhoihoEAEg/uE6deshuAEBqILtTD9kNAEgNZLe9RBXEjx07ltrtAACkQ9zcI/WQ3QCA1EB2px6yGwCQGshue8whDgAwjCu5DACAqZDdAACYC9ltL1kF8aioKO3du1cXLlxQTEyMzbouXbo4pGEAgCcfV6qdh+wGADgC2e08ZDcAwBHIbntJLogfPXpUPXv21K1bt3Tr1i15enrq6tWrypw5s3LlykUwAwASjVx2DrIbAOAoZLdzkN0AAEchu+25JvUFwcHBql+/vvbu3auMGTNq5cqV+uGHH1SuXDkNGzYsNdoIAHhCubi4JPuBxCO7AQCOQnY7B9kNAHAUZ2X33r171bt3b/n5+cnHx0dbtmyxWW+xWPTRRx/Jz89PFStWVNeuXfX333/bbPPvv/9q0KBBqlKliqpVq6aRI0fq5s2bNtscO3ZMHTp0UIUKFeTv768FCxYk+ZgkuSAeGhqqbt26ydXVVW5uboqOjlb+/Pk1ZMgQffDBB0luAAAASF1kNwAA5kJ2AwDMJioqSj4+PhozZky86xcsWKAlS5Zo7NixWrlypTJnzqwePXrozp071m0GDx6skydPKiQkRHPnztW+ffs0evRo6/rIyEj16NFDBQoU0Nq1azV06FDNmjVLX3zxRZLamuSCeIYMGeTqev9luXPn1oULFyRJ2bJl0z///JPUtwMApGOuLsl/IPHIbgCAo5DdzkF2AwAcxVnZ7e/vrwEDBuiFF16wW2exWLR48WL16dNHDRs2VOnSpTV16lRdvnzZ2pP8zz//1M8//6yJEyeqUqVKqlatmt5++2198803unTpkiRp/fr1iomJ0aRJk1SqVCk1b95cnTt3VkhISNKOSdI+mlS2bFn9/vvvkqRnn31WM2bM0Pr1660NAQAgsRi65RxkNwDAUchu5yC7AQCOkhamOzt37pzCwsJUq1Yt67Ls2bOrUqVKOnDggCTpwIEDypEjhypUqGDdplatWnJ1ddWhQ4ckSQcPHlS1atXk4eFh3cbPz0+nTp3StWvXEt2eJBfEBwwYoDx58lj/O0eOHBo7dqyuXr2qCRMmJPXtAADpmEsKHklhpqFbqYHsBgA4CtntHGQ3AMBRUpLd0dHRioyMtHlER0cnuQ1hYWGS7o96elDu3LkVHh4uSQoPD1euXLls1mfIkEGenp7W14eHh8vLy8tmm7jnce+TGBmS1nzZVOlz586tRYsWJfUtAACQJLk66QZb/v7+8vf3j3fdw0O3JGnq1KmqVauWtmzZoubNm1uHbq1evdqag2+//bZ69uypoUOHKl++fDZDtzw8PFSqVCmFhoYqJCREAQEBTvmcCSG7AQCOQnY7B9kNAHCUlGT3vHnzNGvWLJtlgYGBCgoKSmmzDJXkHuIAADiKi0vyH46S1oZuAQCQlpHdAACYS0qyu1evXtq/f7/No1evXkluQ9yop4iICJvlERER1h7eXl5eunLlis36u3fv6tq1a9bXe3l52fUEj3v+cM/xR0lyD/EGDRo8cg6ZrVu3JvUtAQBIsujoaLuhWh4eHjYntYnhyKFbhQoVstnmwaFbnp6eSWqXI5HdAIC0gOxOPLIbAJAWJCen41OoUCHlyZNHu3btUpkyZSTdn7rst99+U/v27SVJlStX1vXr13X48GGVL19ekvTrr78qNjZWFStWlCT5+vpq+vTpiomJkbu7uyRp586dKlasWJJyO8kF8ddee83m+d27d3X06FHt2LFDPXr0SOrbAQDSsZTcpONJHbqVGshuAICjkN3OQXYDABzFkTfHfJSbN2/qzJkz1ufnzp1TaGioPD09VaBAAXXp0kVz5sxR0aJFVahQIX300UfKmzevdfqzEiVKqE6dOnrnnXc0btw4xcTEaMKECWrevLny5csnSXrxxRc1e/ZsjRo1Sm+88YZOnDihxYsXa8SIEUlqa4oL4nGWLVumw4cPJ/XtAADpWEpyuVevXurWrZvNsuRcuX5w6FbevHmtyyMiIlS6dGlJzh26lRrIbgCAo5DdzkF2AwAcxUn1cB0+fFhdunSxPg8ODpYktWrVSpMnT9Ybb7yhW7duafTo0bp+/bqqVq2qhQsXKmPGjNbXTJs2TRMmTNBrr70mV1dXNWrUSG+//bZ1ffbs2bVo0SKNHz9erVu3Vs6cOdW3b98k3/sjyQXxhNStW1fvv/++9cMCAPA4Kbm5x5M6dMuZyG4AQFKR3cYiuwEASeWsG2LXqFFDx48fT3C9i4uL+vfvr/79+ye4zVNPPaX333//kfspXbq0li9fnux2Sg68qea3336rp556ylFvBwBIB5x1Y66bN28qNDRUoaGhkv4bunXhwgW5uLhYh25t3bpVx48f19ChQxMcunXo0CHt378/3qFb7u7uGjVqlE6cOKGNGzdq8eLFdj3h0hKyGwCQVGS3schuAEBSpYUbYqc1Se4h3rJlS5u5ZywWi8LDw3XlyhWNGTPGoY0DADzZnDWXmZmGbqUGshsA4Chkt3OQ3QAAR3FWdpuJi8VisSTlBTNnzrQ5kC4uLsqVK5eqV6+uEiVKOLyByZG5cqDRTQAS5ereWY/fCEgDMjlsgi1bb64LTfZrZ7cq48CWPNnIbsBxyG6YBdltbmQ34Dhhv840uglAomTLmDqFa7LbXpIL4mZw+67RLQASJzb2ifu/H55QWTxSJ5iDUhDMM5/QYE6vyG6Yxd17ZDfMIbVOqsluxCG7YRYxd2ONbgKQKNkzOWxmaxtkt70kH+kyZcooIiLCbvnVq1etNzQBACAxXFxckv1A4pHdAABHIbudg+wGADgK2W0vyQPpEupQHh0dbb0zNwAAieH65OZrmkJ2AwAchex2DrIbAOAoZLe9RBfEFy9eLOn+VYVVq1YpS5Ys1nWxsbHau3evihcv7vgWAgCeWARz6iK7AQCORnanLrIbAOBoZLe9RBfEP/30U0n3r1SvWLFCrq7/zbbi7u6uQoUKady4cQ5vIADgyfUkD8FKC8huAICjkd2pi+wGADga2W0v0QXxbdu2SZI6d+6sWbNmydPTM9UaBQBIH7hSnbrIbgCAo5HdqYvsBgA4GtltL8lziC9ZsiQ12gEAAFIJ2Q0AgLmQ3QAApB7Xx29iKygoSPPnz7dbvmDBAvXr188hjQIApA8uLsl/IPHIbgCAo5DdzkF2AwAchey2l+SC+N69e+Xv72+3vG7dutq3b59DGgUASB9cXVyS/UDikd0AAEchu52D7AYAOArZbS/JU6ZERUXJ3d3d/o0yZFBkZKRDGgUASB+SfFUWyUJ2AwAchex2DrIbAOAoZLe9JB8Tb29vbdy40W75xo0bVbJkSYc0CgCQPjB0yznIbgCAo5DdzkF2AwAchey2l+Qe4n379lVQUJDOnj2r5557TpK0a9cubdiwQTNmzHB4AwEAT64neQhWWkJ2AwAchex2DrIbAOAoZLe9JBfEGzRooNmzZ2vu3Ln67rvvlDFjRpUuXVqfffaZPD09U6ONAAAgBchuAADMhewGACD1JLkgLkn16tVTvXr1JEmRkZHasGGDpkyZoiNHjig0NNSR7QMAPMG4UO08ZDcAwBHIbuchuwEAjkB220tWQVy6f9fr1atX6/vvv1fevHn1wgsvaPTo0Y5sGwDgCedKMDsV2Q0ASCmy27nIbgBASpHd9pJUEA8LC9O6deu0evVqRUZGqmnTpoqOjtbs2bO5sQcAIMmYyyz1kd0AAEciu1Mf2Q0AcCSy216iC+K9e/fW3r17Va9ePY0cOVJ16tSRm5ubVqxYkZrtAwA8wcjl1EV2AwAcjexOXWQ3AMDRyG57iS6I//TTT+rcubPat2+vZ555JhWbBABILxi6lbrIbgCAo5HdqYvsBgA4GtltzzWxGy5fvlw3b95U69at1bZtWy1dulRXrlxJzbYBAIAUILsBADAXshsAgNSX6IK4r6+vJk6cqB07diggIEDffPON6tatq9jYWP3yyy+KjIxMzXYCAJ5ALin4Hx6P7AYAOBrZnbrIbgCAo5Hd9lwsFosluS/+66+/tHr1aq1fv17Xr19XrVq1NHfuXEe2L1lu3zW6BUDixMYm+/9+gFNl8UidIJy87c9kv3Z4gxIObEn6QXYDKXP3HtkNc8iWkex+UpDdQMrE3I01uglAomTPlOh+y0lCdttL0ZEuXry4hg4dqh9//FEffPCBo9oEAEgnXF2S/0DykN0AgJQgu52P7AYApATZbS/RN9V8FDc3NzVs2FANGzZ0xNsBANIJF253bRiyGwCQHGS3cchuAEBykN32HFIQBwAgOZ7kK84AADyJyG4AAMyF7LZHQRwAYBguVAMAYC5kNwAA5kJ220ud2doBAAAAAAAAAEhj6CEOADCMK5eqAQAwFbIbAABzIbvtURAHABiGucwAADAXshsAAHNxVnY3aNBA58+ft1veoUMHjRkzRp07d9aePXts1gUEBGj8+PHW5xcuXNDYsWO1e/duZcmSRS1bttSgQYOUIYNjS9gUxAEAhuFCNQAA5kJ2AwBgLs7K7tWrV+vevXvW5ydOnFC3bt3UpEkT67J27dqpX79+1ueZM2e2/ve9e/fUq1cveXl5acWKFbp8+bKGDRsmd3d3DRw40KFtpSAOADCMqzirBgDATJyV3WbqZQYAQFrmrOzOlSuXzfP58+erSJEiql69unVZpkyZlCdPnnhfv2PHDp08eVIhISHy8vJSmTJl1L9/f02bNk2BgYHy8PBwWFv5JQAAMIyzrlRzUg0AgGPQywwAAHNJSXZHR0crOjraZpmHh8dji9PR0dFav369unXrJpcHGvD1119r/fr1ypMnj+rXr6++ffta8/vgwYPy9vaWl5eXdXs/Pz+NHTtWJ0+eVNmyZZP/QR7CWTwA4InHSTUAAOZipl5mAAA8qebNm6dZs2bZLAsMDFRQUNAjX7dlyxbduHFDrVq1si5r0aKFChQooLx58+r48eOaNm2aTp06ZX3/8PBwm2K4JOvzsLAwR3wcKwriAADDOOvmHpxUAwDgGEbcVDOt9zIDACAtS0l2v96rl7p162azLDHnv2vWrFHdunWVL18+67KAgADrf/v4+ChPnjzq2rWrzpw5oyJFiiS/kclAQRwAYBjXFIzdelKHbgEAkJYZkd1pvZcZAABpWUqyOzE5/bDz589r586dmjlz5iO3q1SpkiTp9OnTKlKkiLy8vHTo0CGbbcLDwyUpwc5ryUVBHABgmJTMZfakDt0CACAtMyK703ovMwAA0jJn3f8jztq1a5U7d27Vq1fvkduFhoZK+q/Y7evrq7lz5yoiIkK5c+eWJO3cuVPZsmVTyZIlHdpGCuIAAMOk5Ep1ryd06BYAAGmZs7PbDL3MAABIy1KS3UkVGxurtWvXqmXLlsqQ4b+y85kzZ/T111/L399fTz31lI4fP67g4GA9++yzKl26tKT7o7BLliypoUOHasiQIQoLC9P06dPVsWNHh09TSkEcAGCYlOTykzp0CwCAtMzZ2W2GXmYAAKRlzuwhvnPnTl24cEFt2rSxWe7u7q5du3Zp8eLFioqKUv78+dWoUSP17dvXuo2bm5vmzp2rsWPHKiAgQJkzZ1arVq3Ur18/h7eTgjgAIN3gpBoAAPMwSy8zAABwn5+fn44fP263PH/+/Fq6dOljX1+wYEEtWLAgNZpmg4I4AMAwrk7cFyfVAACknDOz2yy9zAAASMucmd1mQUEcAGAYFyeO3eKkGgCAlHNmdpullxkAAGmZM7PbLCiIAwAM48xY5qQaAICU45QaAABzIbvtURAHABjGmXe7BgAAKUd2AwBgLmS3PQriAADDEMsAAJgL2Q0AgLmQ3faYVx0AAAAAAAAAkC7QQxwAYBhGbgEAYC5kNwAA5kJ226MgDgAwDHe7BgDAXMhuAADMhey2R0EcAGAY5u0CAMBcyG4AAMyF7LZHQRwAYBiuVAMAYC5kNwAA5kJ226MgDgAwDLEMAIC5kN0AAJgL2W2PgjgAwDBcqQYAwFzIbgAAzIXstsc0MgAAAAAAAACAdIEe4gAAw3BVFgAAcyG7AQAwF7LbHgVxAIBhGLoFAIC5kN0AAJgL2W2PgjgAwDDEMgAA5kJ2AwBgLmS3PQriAADDcKEaAABzIbsBADAXstseBXEAgGFcuVYNAICpkN0AAJgL2W2PedUBAAAAAAAAAOkCPcQBAIZh6BYAAOZCdgMAYC5ktz0K4gAAw7gwdAsAAFMhuwEAMBey2x4FcQCAYbhSDQCAuZDdAACYC9ltj4I4AMAw3NwDAABzIbsBADAXstseBXEAgGG4Ug0AgLmQ3QAAmAvZbc/V6AYAAAAAAAAAAOAM9BAHABiGK9UAAJgL2Q0AgLmQ3fYoiAMADMPdrgEAMBeyGwAAcyG77VEQBwAYxpVcBgDAVMhuAADMhey2R0EcAGAYrlQDAGAuZDcAAOZCdtvjppoAAMO4uCT/AQAAnI/sBgDAXJyV3TNnzpSPj4/No0mTJtb1d+7c0bhx41SjRg1VrlxZQUFBCg8Pt3mPCxcuqGfPnqpUqZJq1qypKVOm6O7du444DDboIQ4AMAxXqgEAMBeyGwAAc3FmdpcqVUohISHW525ubtb/njRpkn788UdNnz5d2bNn14QJExQYGKgVK1ZIku7du6devXrJy8tLK1as0OXLlzVs2DC5u7tr4MCBDm0nPcQBAAAAAGmKmXqZAQCA+9zc3JQnTx7rI1euXJKkGzduaM2aNRo+fLhq1qyp8uXLa9KkSTpw4IAOHjwoSdqxY4dOnjyp9957T2XKlJG/v7/69++vZcuWKTo62qHtpCAOq0UL5qlDuzaq+Wxl1atTU28F9dXfp/6y2Wb1yi/Uo2tn1apeRZXK+ej69esGtRb4zycL56tyhdJ6b8okSdKF8+dUuULpeB+bv/vW4NbiQa4uyX8kBSfVeJLt37dXQX17q2E9P1Uq56NtW7ckuO2EcaNVqZyPli7+1HkNBCTN+3imqlYsbfNo/VJTu+0sFouC+ryhqhVL64dtCf9bhnGcld3S/V5mO3bssD6WL19uXTdp0iT98MMPmj59upYsWaLLly8rMDDQuj6ul1lMTIxWrFihyZMna926dZoxY4YjDgPgMPfu3dOsGdPVtFEDVa9SUc2bNNS8ObNlsViMbhqgy5cu6Z0RQ/V83edUu7qvAtq8pKNHDlvXWywWzZ09Q42fr6Pa1X3Vt2c3nTn9t3ENRrycmd2nT5+Wn5+fnn/+eQ0aNEgXLlyQJB0+fFgxMTGqVauWddsSJUqoQIEC1oL4wYMH5e3tLS8vL+s2fn5+ioyM1MmTJ1N0DB7GlCmw2rd3jwLad1S5ChV07+49zfzoA/V+o4fWrv9GWbJkkSTdvn1LtWrXUa3adTRj+vsGtxiQjhz+XWtWf6FS3j7WZfmezq/NP/xss92aVSu1+NNFql2njrObiEdg6BaQcrduRcnHx0ctW7fRwP6BCW63dctm/f7bb8qTN68TWwf8p0SJUvp4wSfW525u9qciy5d+Jhcmm07TnJndcb3MHhbXy2zatGmqWbOmpPtZ3qxZMx08eFC+vr7WXmYhISHy8vJSmTJl1L9/f02bNk2BgYHy8PBw2ucAHiVk0QKt+uJzTZg0RSVKltTRw4c1+u0RypY9uzp26mJ085COXb9+TT26dlC1ajX00ez5ypkzl86eOa0cOXJYt/ksZKFWfL5UYycEq2DBQpoze4aC+ryhles2KGPGjAa2Hg9KSXZHR0fb9c728PCIN0crVqyo4OBgFStWTGFhYZo9e7Y6duyor7/+WuHh4XJ3d7f59yNJuXPnVlhYmCQpPDzcphguyfo8bhtHoSAOqznzF9k8H//uZNWvU1OhR4+oarVnJUmdunSVJO3ds9vZzQPsREXd1Mjhg/XOmAlaOH+Odbmbm5u8vGxPnn7YtkUvNG6qLFmyOruZeARn1jw4qcaTyq+Ov/zq+D9ym0uXLmnypAmaM3+Rgvr0clLLAFtuGezz+UHHj4Vq6WchWrJitRo34AJ2WuXM7I7rZZYxY0b5+vpq0KBBKlCgwGN7mfn6+ibYy2zs2LE6efKkypYt67wPAjzCwYMHVK/B86rrX0+SVLBgIW3a+I0O/37I2IYh3fvsk4XKly+/xkyYZF1WsFAh639bLBZ9vmyxerzRW/XqPy9JGj9xsho18NP2bVvUuGlzp7cZ8UtJds+bN0+zZs2yWRYYGKigoCC7bf39/zsnKV26tCpVqqT69etr06ZNypQpU/IbkQqYMgUJirxxQ5KUw9PT4JYA8Qt+d7zq1Kmn52rWeuR2R48c1vFjoWrZuo2TWobEcknBI6nMMnQLcLTY2FiNGj5EXbv1UMmSpYxuDtKxM6dPq/HzdfRS04YaNXywLl68YF1369YtjRo+WMNGjX5k0RzGS0l2R0dHKzIy0uaR0Jygcb3MFi5cqLFjx+r8+fPq2LGjIiMj01wvMyAlfH0ra8+vv+rvv09Jko4fO6YDB/bLr05dg1uG9O6nH39QmXLlNGzwW3qhXm11aNda69astK4/f/6cIsLDVb1GTeuybNmzq3yFivr90G9GNBkJSEl29+rVS/v377d59OqVuA42OXLk0DPPPKMzZ87Iy8tLMTExdlMvR0REWDuueXl52U1dGvc8vs5tKZGmC+IXL17UiBEjjG5GuhQbG6upUybJt3IVlSrlbXRzADvfbvpGx44eVdBbj5+u4st1a1SseAn5+lZxQsuQFK4uLsl+cFKdNpHdaU/IogVyy5BBHRh2DQOVr1BJYycGa9achRr+9hhdOH9Or3ftpJs3IyVJH7wXrIqVKlt7mCHtSkl2z5s3T1WrVrV5zJs3L979+Pv7q2nTpipdurTq1Kmj+fPn6/r169q0aZOTP/GTj+w2VvfXe6px02Zq2aKpqlYqp4BXWqpT59fUvMVLRjcN6dz5c2e1ZuUKFSlSVDPnLNAr7V7VtCmTtGH9l5KkiP8vVObOndvmdblyeykinHOktCQl2e3h4aFs2bLZPBI7QvrmzZs6e/as8uTJo/Lly8vd3V27du2yrv/rr7904cIF+fr6SpJ8fX31xx9/KCIiwrrNzp07lS1bNpUsWdKhxyRNT5ly7do1ffnllwoODja6KenOpInj9OeJE/p0yfLHbww42T//XNR7kydpzvxPHjsv2e3bt7Vp4wa90auPk1oHZ3lSh26ZHdmdthw9cljLlizWitVrmZcZhqr9QE/HUt4+qlChkpo3aaDN332rnDlzae+e3Vq+cq2BLYQz9OrVS926dbNZltiT6gd7mdWqVcvay+zBC9oP9zI7dMh2yonU6mVmdmS3sb77dpM2fvO1gqe+r5IlS+rYsVC9NzlYefLk1UstWxndPKRjsbEWlS1XTm/2GyBJKl2mrP48eUJrVq1Qi5daGts4pElTpkxR/fr1VaBAAV2+fFkzZ86Uq6urWrRooezZs6tNmzaaPHmyPD09lS1bNk2cOFGVK1e2FsT9/PxUsmRJDR06VEOGDFFYWJimT5+ujh07OnyaUkML4lu3bn3k+rNnzzqpJXjQpInj9dOP2/XJZ0uV7+mnjW4OYCf0yBFduRKhDgGtrcvu3bun/+3fpy8+X6bd+w9Zb5i4ZfN3un3rtlq82NKg1uJRUlKe46TaGGS3ufxv/z5duRKhJg3rW5fdu3dP7783RcuWLNamzdsMbB3Ss+w5cqho0Wd09uxpnTzxh86dPaN6tavbbDN0YD9VrlJV8z9ZYlArEZ+UZHdCN+FKjIR6mTVu3FhS/L3M5s6dq4iICGvvxdTqZZbWkd1p24fvT1X3Hj3VtNn9+ZZLefvo4oULWrRwHgVxGMorj5eKFS9hs6xY8eLatuV7SVLu/x8xGxERIa88/920/UpEuLx9yjivoXgsZ3WL+eeffzRw4ED9+++/ypUrl6pWraqVK1cqV65ckqSRI0fK1dVV/fr1U3R0tPz8/DRmzBjr693c3DR37lyNHTtWAQEBypw5s1q1aqV+/fo5vK2GFsTffPNNubi4yGKxJLgNvZmcx2KxKPjdCdq2dbMWfbpEhQoVNrpJQLyqP/ecVq1db7NszDsjVaxYcXXt/rq1GC5JX65dLf/69a1fwEhjUvAVz0m1Mchuc2nx0suq8dB9Fvr07KEWL76slq1aJ/AqIPVFRd3UubNn1azFS3qhcVO1bP2KzfqANi9p4JDhquvfwKAWIkFO+oo3Uy+ztI7sTttu37otV1fb4+/m5qbY2IT/XoAzVPKtotN//22z7PTpv5W/QAFJ928Am9vLS3t3/yqf0vcL4JGRkTr8+yG1afuqs5uLR3HSV/yHH374yPUZM2bUmDFjbIrgDytYsKAWLFjg6KbZMbQgnidPHo0ZM0YNGzaMd31oaKhat+ZkzVkmTRinTRs3aPrMj5U1S1aF//+8uNmyZ7dOKRAeFqbw8HCdPXNGknTyxB/KkiWr8ufPL8+nnjKq6UhnsmbNppIPzW2fOXNmeT71lM3yM2dO63/792nmx/Od3UQkkouTkpmTaschu9OeqJs3deb/c1mSzp87p2OhofL09FT+AgX01FM5bbZ3z+AuLy8vPVOsuLObinTsw2lTVLdefeXPX0BhYZc17+NZcnVzVZOmLZQzV654b6T5dP4CKliokAGtxaM4K7vN1MssrSO70zb/evW1YP5cPZ2/gEqULKljoaFa8lmIXm7VxuimIZ3r0Ok1dX+tgz5ZOE8vNGqiI4d/17rVqzRq9DhJ9y+kte/YRYsWzFXhokVVsGAhzZk9Q3ny5FW9BvF/38AYzspuMzG0IF6uXDkdOXIkwWB+3FVsONbKLz6XJPXo2tlm+fiJwXr5/3uRrVq5QnM//m/O3m5dOtptA6QVX61bo3z5nlbNWrWNbgoS4KzOSJxUOw7ZnfYcOXJYr3f774aZ06benwP2pZdbacKkyUY1C7Bx+fIljRw2SNf+/Vc5c+aSb5Wq+nTpF8rJCC7TcVZ2m6mXWVpHdqdtw0e9rdkzPtKkCeN05UqE8uTNq1faBqhXnzeNbhrSuXLlK2jaBzM0a8aHWjjvYxUoWEiDhg5X0+YvWrd5rdvrun3rliaNH6MbN67Lt3IVzfh4/mPv9QXnYhCQPReLgcm3b98+RUVFqW7duvGuj4qK0uHDh1W9evV41yfk9l1HtA5IfQyDg1lk8UidBN3717Vkv/bZ4p4ObAkSi+xGenf3HtkNc8iWkezGfWQ30ruYu7FGNwFIlOyZXFPlfclue4YWxFMLwQyzoCAOs6AgjtRGdsMsKIjDLCiII7WR3TALCuIwCwrizmPolCkAgHSOoVsAAJgL2Q0AgLmQ3XYoiAMADMPNPQAAMBeyGwAAcyG77VEQBwAYhpt7AABgLmQ3AADmQnbboyAOADAMuQwAgLmQ3QAAmAvZbY+COADAOCQzAADmQnYDAGAuZLed1Ll9KQAAAAAAAAA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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Statistical Testing for Model Comparison"
      ],
      "metadata": {
        "id": "sEZWIFF_CXpq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define models with same configurations\n",
        "models_for_testing = {\n",
        "    'Random Forest': RandomForestClassifier(n_estimators=100, random_state=42,\n",
        "                                           class_weight='balanced', max_depth=10),\n",
        "    'XGBoost': xgb.XGBClassifier(n_estimators=100, random_state=42,\n",
        "                                scale_pos_weight=(y_train == 0).sum() / (y_train == 1).sum(),\n",
        "                                max_depth=5),\n",
        "    'LightGBM': lgb.LGBMClassifier(n_estimators=100, random_state=42,\n",
        "                                  class_weight='balanced', max_depth=5, verbose=-1)\n",
        "}\n",
        "\n",
        "# Perform cross-validation for each model\n",
        "print(\"\\n1. Cross-Validation F1 Scores (10-fold CV):\")\n",
        "print(\"-\" * 80)\n",
        "\n",
        "cv_scores = {}\n",
        "cv_folds = 10\n",
        "\n",
        "for model_name, model in models_for_testing.items():\n",
        "    print(f\"\\nEvaluating {model_name}...\")\n",
        "    scores = cross_val_score(model, X_train_scaled, y_train,\n",
        "                            cv=cv_folds, scoring='f1', n_jobs=-1)\n",
        "    cv_scores[model_name] = scores\n",
        "\n",
        "    print(f\"  Fold Scores: {[f'{s:.4f}' for s in scores]}\")\n",
        "    print(f\"  Mean F1: {scores.mean():.4f} (+/- {scores.std() * 2:.4f})\")\n",
        "    print(f\"  Min F1:  {scores.min():.4f}\")\n",
        "    print(f\"  Max F1:  {scores.max():.4f}\")\n",
        "\n",
        "# Statistical comparison using paired t-test\n",
        "print(\"\\n\\n2. Paired t-test Results (Pairwise Comparisons):\")\n",
        "print(\"-\" * 80)\n",
        "print(\"H0 (Null Hypothesis): No significant difference between models\")\n",
        "print(\"H1 (Alternative): Significant difference exists (α = 0.05)\")\n",
        "print()\n",
        "\n",
        "comparison_results = []\n",
        "model_names = list(cv_scores.keys())\n",
        "\n",
        "for model1, model2 in itertools.combinations(model_names, 2):\n",
        "    scores1 = cv_scores[model1]\n",
        "    scores2 = cv_scores[model2]\n",
        "\n",
        "    # Paired t-test\n",
        "    t_stat, p_value = stats.ttest_rel(scores1, scores2)\n",
        "\n",
        "    mean_diff = scores1.mean() - scores2.mean()\n",
        "\n",
        "    comparison_results.append({\n",
        "        'Model 1': model1,\n",
        "        'Model 2': model2,\n",
        "        'Mean Diff': mean_diff,\n",
        "        't-statistic': t_stat,\n",
        "        'p-value': p_value,\n",
        "        'Significant': 'Yes' if p_value < 0.05 else 'No'\n",
        "    })\n",
        "\n",
        "    print(f\"{model1} vs {model2}:\")\n",
        "    print(f\"  Mean Difference: {mean_diff:+.4f}\")\n",
        "    print(f\"  t-statistic: {t_stat:.4f}\")\n",
        "    print(f\"  p-value: {p_value:.4f}\")\n",
        "\n",
        "    if p_value < 0.05:\n",
        "        better_model = model1 if mean_diff > 0 else model2\n",
        "        print(f\"  ✓ SIGNIFICANT: {better_model} performs significantly better\")\n",
        "    else:\n",
        "        print(f\"  ✗ NOT SIGNIFICANT: No statistical difference detected\")\n",
        "    print()\n",
        "\n",
        "# Create comparison dataframe\n",
        "comparison_df = pd.DataFrame(comparison_results)\n",
        "\n",
        "# Friedman test (non-parametric alternative for multiple models)\n",
        "print(\"\\n3. Friedman Test (Overall Comparison):\")\n",
        "print(\"-\" * 80)\n",
        "print(\"Non-parametric test for comparing multiple models simultaneously\")\n",
        "\n",
        "friedman_stat, friedman_p = stats.friedmanchisquare(\n",
        "    cv_scores['Random Forest'],\n",
        "    cv_scores['XGBoost'],\n",
        "    cv_scores['LightGBM']\n",
        ")\n",
        "\n",
        "print(f\"  Friedman Statistic: {friedman_stat:.4f}\")\n",
        "print(f\"  p-value: {friedman_p:.4f}\")\n",
        "\n",
        "if friedman_p < 0.05:\n",
        "    print(f\"  ✓ SIGNIFICANT: At least one model differs significantly from others\")\n",
        "else:\n",
        "    print(f\"  ✗ NOT SIGNIFICANT: No statistical difference among models\")\n",
        "\n",
        "# Visualize CV scores distribution\n",
        "print(\"\\n\\n4. Visual Comparison of CV Scores:\")\n",
        "print(\"-\" * 80)\n",
        "\n",
        "fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
        "\n",
        "# Box plot\n",
        "axes[0].boxplot([cv_scores[name] for name in model_names],\n",
        "               labels=model_names, patch_artist=True)\n",
        "axes[0].set_ylabel('F1 Score')\n",
        "axes[0].set_title('Cross-Validation F1 Score Distribution', fontweight='bold')\n",
        "axes[0].grid(True, alpha=0.3)\n",
        "axes[0].set_ylim([0, 1])\n",
        "\n",
        "# Mean scores with error bars\n",
        "means = [cv_scores[name].mean() for name in model_names]\n",
        "stds = [cv_scores[name].std() for name in model_names]\n",
        "x_pos = np.arange(len(model_names))\n",
        "\n",
        "axes[1].bar(x_pos, means, yerr=stds, alpha=0.7, capsize=10,\n",
        "           color=['steelblue', 'orange', 'green'])\n",
        "axes[1].set_xticks(x_pos)\n",
        "axes[1].set_xticklabels(model_names, rotation=45, ha='right')\n",
        "axes[1].set_ylabel('Mean F1 Score')\n",
        "axes[1].set_title('Mean F1 Scores with Standard Deviation', fontweight='bold')\n",
        "axes[1].grid(True, alpha=0.3, axis='y')\n",
        "axes[1].set_ylim([0, 1])\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "# Final recommendation\n",
        "print(\"\\n\\n5. STATISTICAL RECOMMENDATION:\")\n",
        "print(\"=\" * 80)\n",
        "\n",
        "# Find best model based on mean CV score\n",
        "best_model_cv = max(cv_scores.items(), key=lambda x: x[1].mean())\n",
        "print(f\"Best Model (by Mean CV F1): {best_model_cv[0]}\")\n",
        "print(f\"  Mean F1 Score: {best_model_cv[1].mean():.4f}\")\n",
        "print(f\"  Std Deviation: {best_model_cv[1].std():.4f}\")\n",
        "\n",
        "# Check if significantly better than others\n",
        "significantly_better = True\n",
        "for model_name in model_names:\n",
        "    if model_name != best_model_cv[0]:\n",
        "        _, p_val = stats.ttest_rel(best_model_cv[1], cv_scores[model_name])\n",
        "        if p_val >= 0.05:\n",
        "            significantly_better = False\n",
        "            break\n",
        "\n",
        "print()\n",
        "if significantly_better:\n",
        "    print(f\"✓ {best_model_cv[0]} is STATISTICALLY SIGNIFICANTLY better than all other models\")\n",
        "    print(\"  Recommendation: Use this model for deployment\")\n",
        "else:\n",
        "    print(f\"⚠ {best_model_cv[0]} has highest mean score but differences are NOT statistically significant\")\n",
        "    print(\"  Recommendation: Consider other factors (speed, interpretability, resource usage)\")\n",
        "\n",
        "print(\"\\n\" + \"=\" * 80)\n",
        "print(\"Statistical Analysis Complete!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oxO5ibcICJp3",
        "outputId": "fce4dd66-5bea-4f3f-c5fc-af9ec5c17e7f"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "1. Cross-Validation F1 Scores (10-fold CV):\n",
            "--------------------------------------------------------------------------------\n",
            "\n",
            "Evaluating Random Forest...\n",
            "  Fold Scores: ['0.6522', '0.6296', '0.7451', '0.5000', '0.6909', '0.5172', '0.6038', '0.5769', '0.6667', '0.6296']\n",
            "  Mean F1: 0.6212 (+/- 0.1430)\n",
            "  Min F1:  0.5000\n",
            "  Max F1:  0.7451\n",
            "\n",
            "Evaluating XGBoost...\n",
            "  Fold Scores: ['0.6316', '0.7213', '0.8571', '0.6275', '0.7500', '0.7018', '0.7037', '0.7143', '0.7451', '0.7869']\n",
            "  Mean F1: 0.7239 (+/- 0.1291)\n",
            "  Min F1:  0.6275\n",
            "  Max F1:  0.8571\n",
            "\n",
            "Evaluating LightGBM...\n",
            "  Fold Scores: ['0.6301', '0.6286', '0.7143', '0.5758', '0.5854', '0.5556', '0.5556', '0.6154', '0.6562', '0.5946']\n",
            "  Mean F1: 0.6111 (+/- 0.0932)\n",
            "  Min F1:  0.5556\n",
            "  Max F1:  0.7143\n",
            "\n",
            "\n",
            "2. Paired t-test Results (Pairwise Comparisons):\n",
            "--------------------------------------------------------------------------------\n",
            "H0 (Null Hypothesis): No significant difference between models\n",
            "H1 (Alternative): Significant difference exists (α = 0.05)\n",
            "\n",
            "Random Forest vs XGBoost:\n",
            "  Mean Difference: -0.1027\n",
            "  t-statistic: -5.6839\n",
            "  p-value: 0.0003\n",
            "  ✓ SIGNIFICANT: XGBoost performs significantly better\n",
            "\n",
            "Random Forest vs LightGBM:\n",
            "  Mean Difference: +0.0101\n",
            "  t-statistic: 0.6171\n",
            "  p-value: 0.5525\n",
            "  ✗ NOT SIGNIFICANT: No statistical difference detected\n",
            "\n",
            "XGBoost vs LightGBM:\n",
            "  Mean Difference: +0.1128\n",
            "  t-statistic: 6.2209\n",
            "  p-value: 0.0002\n",
            "  ✓ SIGNIFICANT: XGBoost performs significantly better\n",
            "\n",
            "\n",
            "3. Friedman Test (Overall Comparison):\n",
            "--------------------------------------------------------------------------------\n",
            "Non-parametric test for comparing multiple models simultaneously\n",
            "  Friedman Statistic: 13.4000\n",
            "  p-value: 0.0012\n",
            "  ✓ SIGNIFICANT: At least one model differs significantly from others\n",
            "\n",
            "\n",
            "4. Visual Comparison of CV Scores:\n",
            "--------------------------------------------------------------------------------\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "5. STATISTICAL RECOMMENDATION:\n",
            "================================================================================\n",
            "Best Model (by Mean CV F1): XGBoost\n",
            "  Mean F1 Score: 0.7239\n",
            "  Std Deviation: 0.0646\n",
            "\n",
            "✓ XGBoost is STATISTICALLY SIGNIFICANTLY better than all other models\n",
            "  Recommendation: Use this model for deployment\n",
            "\n",
            "================================================================================\n",
            "Statistical Analysis Complete!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7. MODEL EXPLAINABILITY (SHAP VALUES)"
      ],
      "metadata": {
        "id": "b93ei8LB4Xcd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select best model\n",
        "best_model_name = max(results.keys(), key=lambda x: results[x]['f1'])\n",
        "best_model = results[best_model_name]['model']\n",
        "print(f\"Best Model: {best_model_name}\")\n",
        "print(f\"F1-Score: {results[best_model_name]['f1']:.4f}\")\n",
        "\n",
        "# Prepare the data\n",
        "print(\"\\nFeatures being analyzed:\", feature_cols)\n",
        "\n",
        "# Use a sample of training data as background\n",
        "np.random.seed(42)\n",
        "background = X_train_scaled[np.random.choice(X_train_scaled.shape[0], 100, replace=False)]\n",
        "\n",
        "# Create explainer\n",
        "explainer = shap.KernelExplainer(\n",
        "    lambda x: best_model.predict_proba(x)[:, 1],\n",
        "    background\n",
        ")\n",
        "\n",
        "# Calculate SHAP values\n",
        "print(\"\\nCalculating SHAP values for 300 samples...\")\n",
        "print(\"This may take 2-3 minutes...\")\n",
        "n_samples = 300\n",
        "X_explain = X_test_scaled[:n_samples]\n",
        "shap_values = explainer.shap_values(X_explain)\n",
        "\n",
        "# Verify shape\n",
        "print(f\"\\nSHAP values shape: {shap_values.shape}\")\n",
        "print(f\"X_explain shape: {X_explain.shape}\")\n",
        "print(f\"Number of features: {len(feature_cols)}\")\n",
        "\n",
        "# Create DataFrame for better handling\n",
        "X_explain_df = pd.DataFrame(X_explain, columns=feature_cols)\n",
        "\n",
        "# Create the SHAP summary plot\n",
        "fig, ax = plt.subplots(figsize=(12, 8))\n",
        "shap.summary_plot(\n",
        "    shap_values,\n",
        "    X_explain_df,\n",
        "    plot_type='dot',\n",
        "    max_display=len(feature_cols),\n",
        "    show=False\n",
        ")\n",
        "plt.xlabel('SHAP value (impact on model output)', fontsize=12)\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()\n",
        "\n",
        "# Print summary statistics\n",
        "print(\"\\n\" + \"=\"*70)\n",
        "print(\"SHAP FEATURE IMPORTANCE SUMMARY\")\n",
        "print(\"=\"*70)\n",
        "mean_abs_shap = np.abs(shap_values).mean(axis=0)\n",
        "feature_importance = pd.DataFrame({\n",
        "    'Feature': feature_cols,\n",
        "    'Mean |SHAP|': mean_abs_shap,\n",
        "    'Mean SHAP': shap_values.mean(axis=0),\n",
        "    'Std SHAP': shap_values.std(axis=0)\n",
        "}).sort_values('Mean |SHAP|', ascending=False)\n",
        "\n",
        "print(feature_importance.to_string(index=False))\n",
        "print(\"=\"*70)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 828,
          "referenced_widgets": [
            "51b8242bd28b469ba38481af68b570ef",
            "0aa97735c91744f3858297fdc3e8d1f0",
            "af296a876a6a4c48856120c646b1ade9",
            "460bc9df5408446c8e309687b8d60600",
            "19193534cea14e9893edbdd15585529d",
            "243062f7802a462fb2cd8aa8b6a87401",
            "9d6d0fda76fc4abd9b213580eb6343c6",
            "39f84dec78e04d6694d9a5286fdcad4e",
            "be444e457e1e41368d97cc97c232ff76",
            "dc3bd8fdd0e6452d857eca2a7691e9f1",
            "4233113eef9444fc9f6a668c73fc6350"
          ]
        },
        "id": "gD6W4HVY4av7",
        "outputId": "17abfe96-279b-4bba-f5a4-128a15d872e6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Model: XGBoost\n",
            "F1-Score: 0.7552\n",
            "\n",
            "Features being analyzed: ['Type_encoded', 'Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]']\n",
            "\n",
            "Calculating SHAP values for 300 samples...\n",
            "This may take 2-3 minutes...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/300 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51b8242bd28b469ba38481af68b570ef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "SHAP values shape: (300, 6)\n",
            "X_explain shape: (300, 6)\n",
            "Number of features: 6\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x390 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "======================================================================\n",
            "SHAP FEATURE IMPORTANCE SUMMARY\n",
            "======================================================================\n",
            "                Feature  Mean |SHAP|  Mean SHAP  Std SHAP\n",
            "            Torque [Nm]     0.055230  -0.014791  0.117621\n",
            "    Air temperature [K]     0.029673   0.006173  0.063983\n",
            "        Tool wear [min]     0.028338  -0.004920  0.065271\n",
            " Rotational speed [rpm]     0.025089   0.001914  0.061891\n",
            "Process temperature [K]     0.019892   0.004605  0.039733\n",
            "           Type_encoded     0.003441  -0.001169  0.008495\n",
            "======================================================================\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature importance from SHAP\n",
        "shap_importance = np.abs(shap_values).mean(axis=0)\n",
        "feature_importance_df = pd.DataFrame({\n",
        "    'Feature': feature_cols,\n",
        "    'SHAP Importance': shap_importance\n",
        "}).sort_values('SHAP Importance', ascending=False)\n",
        "\n",
        "print(\"\\nFeature Importance (SHAP):\")\n",
        "print(feature_importance_df.to_string(index=False))\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.barh(feature_importance_df['Feature'], feature_importance_df['SHAP Importance'])\n",
        "plt.xlabel('Mean |SHAP value|')\n",
        "plt.title(f'Feature Importance - {best_model_name}')\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "plt.close()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "H-E-sp9e4ct_",
        "outputId": "e8b5d1df-4400-4012-92b3-55d1f82b3f8a"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Feature Importance (SHAP):\n",
            "                Feature  SHAP Importance\n",
            "            Torque [Nm]         0.055230\n",
            "    Air temperature [K]         0.029673\n",
            "        Tool wear [min]         0.028338\n",
            " Rotational speed [rpm]         0.025089\n",
            "Process temperature [K]         0.019892\n",
            "           Type_encoded         0.003441\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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TkzVmzBitWrVKAwYMUPny5e/43gOAEbinGwCAbFC/fn25u7tr/vz5SkxMTLU+5SnOd7oq+N+vjpJk/y7p/045d3d3V5EiRbR7926H5Z9++mmG623cuLHMZrPmzJmT6oqszWZz+Pqy+23VqlUO53DlypVKSkqyh+66desqV65cWrFihUPtq1ev1tWrV9WgQYMM7Sdv3rxpTm83m82pzsnGjRuzfE91tWrVVKZMGS1fvjzV/lL2U6xYMdWuXVurVq3Sv//+m6qP9J5YX7NmTdWtW9f+k17oXrFihX777Te9+eab9inkgwcP1mOPPaa3335bSUlJ9rYDBw6U1WrVsGHDdP369VR9ZeaK/qlTp3Tq1ClVqVLFvqxBgwY6cOCA9u7da18WFxenzz//XKVLl9YTTzwhSXr66ad17tw5bdiwwd4uKSlJK1asUL58+VSrVi1JSjV2XVxcZLFYJN36YEa6858tADACV7oBAMgG7u7uGjNmjEaMGKG2bduqWbNmKlq0qGJiYrRt2zb5+/tr9OjRcnd3V61atbRo0SIlJiaqZMmS+uGHHxQdHZ2qz2rVqkmSZsyYoWbNmilXrlxq2LCh8uXLpw4dOmjBggUaNWqUqlevrt27d6d5tfBOypUrp5CQEE2fPl1//vmnnnvuOeXPn1/R0dHasmWLXnzxRfXq1Svbzk9mJCYmqkePHmratKlOnTqlTz/9VDVr1tSzzz4r6dbXpvXr109z5sxR7969FRQUZG+X8t3NGVGtWjWtXLlSH3zwgcqXL6+iRYvqySef1DPPPKO5c+dq5MiR8vPz07Fjx7Ru3bp0g+yduLi4aMyYMXrllVf0wgsv2L8q7uTJkzp+/LgWL14sSXr77bfVpUsXtWzZUi+++KLKli2r8+fPa9++ffr777+1du3aLO3/v/766y+9//77atiwocPXkOXLl0+jRo3SoEGDtHz5cvXs2VPSrQfDvfXWW5owYYKaNGmili1bqlKlSkpISNDp06e1bt065cqVy/6VaymSkpL05ZdfSroVzKOjo/XZZ58pOTlZAwcOtLfr27ev1q9frz59+qhbt24qVKiQIiMjFR0drdmzZ9uvanfs2FGrVq1SWFiYDh06pNKlS2vz5s3as2eP3njjDfuHB2+++aauXLmiOnXqqGTJkoqJidHHH3+sqlWr2u//rlq1qsxmsxYuXKirV6/Kzc1NderUUbFixbLlHAPA7QjdAABkk5YtW6pEiRJasGCBFi9erISEBJUsWVIBAQEOT8+ePn26xo8fr08//VQ2m01PPfWUFi5cmOpeXh8fHw0dOlSfffaZtm/fruTkZH377bfKly+fBg4cqIsXL2rz5s3auHGjnn76aS1atEhPPvlkhuvt27evKlSooKVLl2ru3LmSbt1z+9RTTykoKCh7TkoWjB49WuvWrdOsWbOUmJio5s2b680333SYEjx48GAVLVpUH3/8sSZPnqxChQrpxRdf1Guvvebw5PO7GThwoGJiYrRo0SJdv35dtWvX1pNPPqn+/fsrPj5e69at04YNG+Tl5aX58+dr+vTpWT6m+vXra9myZZo7d64++ugj2Ww2lS1bVi+++KK9zRNPPKE1a9Zozpw5ioiI0OXLl1W0aFF5eXk5hNR7lXLP9ujRo1Ota9SokRo2bKjZs2eradOmKlWqlKRbDyfz8/PT0qVLtWnTJp07d065cuVS2bJl1aZNG3Xu3DnVPecJCQkaMWKE/bW7u7u8vb317rvvOozT4sWL67PPPtO7776rjz/+WDdv3pTFYtG8efP0zDPP2NvlyZNHK1as0LRp0xQREaFr166pYsWKmjx5ssOfr1atWunzzz/Xp59+qtjYWHl4eKhp06YaPHiwPcB7eHho7Nixmj9/vkaNGiWr1arly5cTugEYwmS7H08wAQAASEd4eLhGjhyp1atXp/mEeAAAciLu6QYAAAAAwCCEbgAAAAAADELoBgAAAADAINzTDQAAAACAQbjSDQAAAACAQQjdAAAAAAAYhO/pxiMjOTlZSUlJcnFxcfiuVwAAAADILJvNpuTkZLm6usrF5c7XswndeGQkJSXp4MGDzi4DAAAAwEPE29tbbm5ud1xP6MYjI+XTJy8vr7v+ocCjy2q16uDBg/L29pbZbHZ2OXjAMD6QHsYI7obxgfQwRnKelPfsble5JUI3HiEpU8rNZjN/keGuGCO4G8YH0sMYwd0wPpAexkjOk96tqzxIDQAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbjxyz2ezsErLEmmxzdgkAAAAAMsnV2QUA91vomv06/Pd1Z5eRKU+UcNf7nfycXQYAAACATCJ045Fz8tx1HYqJdXYZAAAAAB4BTC8HAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuOEV4eLgCAgLuuR+LxaItW7ZkQ0UAAAAAkP1cnV1ATmexWO66ftCgQRo8ePB9qgYAAAAA8CAhdN+jqKgo++8bNmzQrFmztGnTJvuyfPnyOaMsAAAAAMADgOnl98jDw8P+U6BAAZlMJnl4eCh//vxq0qSJtm/f7tB+y5Yt8vX11bVr1xQdHS2LxaL169erU6dO8vb2VosWLbRr1y6HbY4dO6bevXvLz89PdevW1fDhw3Xx4sUM1ZecnKz58+crKChIPj4+atWqlcOHAjt37pTFYtGOHTvUtm1b1ahRQ506ddLJkycd+vnuu+/Url07eXt7KzAwUAMHDrSvu3LlikaMGKFatWqpRo0a6t27t06fPu2wfXh4uJ555hnVqFFDAwcO1OXLl1PVumXLFrVp00be3t569tlnNWfOHCUlJdnXnz59Wl27dpW3t7eaNWumH374IUPnAAAAAACchdBtkHz58ql58+YKDw93WL5mzRo1adJE7u7u9mVTp07Vyy+/rMjISPn6+qp///66dOmSJCk2Nlbdu3eXl5eXVq9erUWLFunChQsKCQnJUB3z589XZGSkxo4dq/Xr16tHjx4aPnx4qmA/Y8YMhYWFac2aNTKbzXrjjTfs67Zu3apBgwapQYMGioyM1LJly+Tj42NfHxYWpl9//VUffvihVq1aJZvNpr59+yoxMVGStH//fo0aNUpdu3ZVZGSkAgMD9eGHHzrsf/fu3QoNDVVwcLA2bNigcePGKTw8XPPmzZN068ODwYMHK1euXPriiy80duxYTZs2LUPnAAAAAACchenlBurQoYM6deqkf//9VyVKlNCFCxf0/fffa8mSJQ7tunbtqiZNmkiSxowZo+3bt2v16tXq06ePPv74Y3l5eem1116zt580aZIaNGigU6dOqWLFinfcf0JCgubPn68lS5bIz89PklS2bFn98ssvWrVqlWrXrm1v++qrr9pf9+3bV3379tXNmzeVO3duzZs3T82aNdOQIUPs7atUqSLp1tXn7777TitXrpS/v78kadq0aXrmmWe0ZcsWNW3aVMuXL1f9+vXVp08fSVLFihW1d+9eh1kAc+bMUd++fdWmTRt7nUOHDtW7776rQYMG6ccff9TJkye1aNEilSxZ0l5zSp+PCqvV6uwSHmop55fzjLQwPpAexgjuhvGB9DBGcp6MvleEbgP5+PjoiSeeUGRkpPr27au1a9fq8ccfV61atRzapQRiSXJ1dVX16tXt07uPHDminTt3OrRJcebMmbuG7j/++EPx8fHq2bOnw/LExERVrVrVYdntD4Tz8PCQJF24cEGPP/64Dh8+rA4dOqS5jxMnTsjV1VU1atSwLytSpIgqVqyoEydO2Ns899xzDtv5+vo6hO4jR45oz5499ivb0q1BfPPmTcXHx+vEiRN67LHH7IFbUprn5GF39OhRxcfHO7uMh97BgwedXQIeYIwPpIcxgrthfCA9jJGHD6HbYB06dNAnn3yivn37Kjw8XG3btpXJZMrw9nFxcWrYsKGGDRuWal1KOL7bttKtKea3h1VJcnNzc3jt6vq/oZBSX3JysiQpT548Ga43q+Li4jR48GA1btw41brcuXMbvv+cIr2n5ePeWK1WHTx4UN7e3jKbzc4uBw8YxgfSwxjB3TA+kB7GSM6T8p6lh9BtsFatWundd9/V8uXLdfz4cfv06dvt27fPfvU7KSlJhw4dUteuXSVJ1apV0+bNm1W6dGmHYJwRlStXlpubm2JiYhymkmeWp6enduzYoXbt2qW5j6SkJO3fv98+vfzSpUs6deqUnnjiCXubAwcOOGy3f/9+h9deXl46deqUypcvf8dj+fvvv+1T9aVb5+1Rw1/A94fZbOZc444YH0gPYwR3w/hAehgjDx8epGawQoUKqVGjRpo6daqeeuopPfbYY6nafPrpp/rmm2904sQJjRs3TleuXLEH3C5duujKlSt67bXXdODAAZ05c0bbt2/XyJEj072HwN3dXT179tTkyZMVERGhM2fO6NChQ1qxYoUiIiIyfAyDBg3S+vXrNWvWLJ04cUJHjx7VggULJEkVKlTQs88+q7feeku7d+/WkSNHNHz4cJUsWVLPPvusJKlbt27avn27Fi9erNOnT+vjjz9O9VT3gQMH6ssvv9ScOXP0+++/68SJE1q/fr1mzJghSapbt64qVKigsLAwHTlyRLt377avAwAAAIAHFaH7Pmjfvr0SExPTvFIsSa+//roWLFig1q1b65dfftGHH36ookWLSpJKliyplStXKjk5Wb169VLLli01adIkFShQQC4u6b99ISEhGjBggObPn69mzZqpd+/e2rp1q8qUKZPh+gMDA/X+++/ru+++U+vWrdW9e3eHaRSTJ09WtWrV1L9/f3Xs2FE2m00LFixQrly5JN26f3v8+PFavny5WrduraioKL3yyisO+6hfv77mzZunqKgotW/fXi+++KKWLl2q0qVLS5JcXFw0Z84c3bhxQ+3bt9eoUaP06quvZvgYAAAAAMAZTDabzebsIh52kZGRmjx5srZv3+5wL3V0dLSeffZZRUZGpnqwGbKf1WrVvn37NPGnOO05G+vscjKl2uMFtX5IfWeX8dBLGSO+vr5M60IqjA+khzGCu2F8ID2MkZwno+8Z93QbKD4+XufOndPChQvVqVOnVA8vAwAAAAA83AjdBlq0aJHmzZungIAA9e3bN9v7j4mJUfPmze+4fv369Xr88cezfb8AAAAAgIwhdBto8ODBGjx48B3XlylTRkePHs1y/yVKlFBkZORd1wMAAAAAnIfQnYO5urre8Su2AAAAAADOx9PLAQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDuDq7AOB+q+SRXzetzq4ic54o4e7sEgAAAABkAaEbj5wp7WrIbDY7u4xMsybbZHYxObsMAAAAAJnA9HI8cqzWHHaZ+/8hcAMAAAA5D6EbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoB4DZ58+Z1dgkAAAB4iLg6uwDgfjObzc4uAQ8os9ksLy8vZ5eBB5XJRbly5XJ2FQAAIIchdOORE7pmvw7/fd3ZZQDIQZ4o4a73O/nJ1ZV/NgEAQObwvwc8ck6eu65DMbHOLgMAAADAI4B7ugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRtOZbFYZLFYFBAQkKntwsPD7dtOnDjRoOoAAAAA4N5kKnSHhYXZg0716tXVqFEjzZkzR0lJSUbVd98FBQVp6dKlzi7jvggLC9OAAQOcXYYmT56szZs321+Hh4enCuEnTpxQgwYNNGTIECUkJKhZs2aKioqSn5/f/S4XAAAAADIs01e669evr6ioKG3evFkvv/yy5syZo8WLF6fZNiEh4Z4LROYlJibe1/1ZrVYlJydnefuCBQuqWLFid1x/4MABdenSRfXr19fMmTPl5uamPHnyyMPDQ7ly5cryfgEAAADAaJkO3W5ubvLw8FDp0qXVpUsX1a1bV999952k/105/fDDD1WvXj09//zzkqSjR48qODhYPj4+CgwM1FtvvaXr16879Lt69Wo1b95c1atXV7169TRu3Dj7utjYWI0aNUp16tSRv7+/goODdeTIEfv6I0eOqFu3bvLz85O/v7/atm2rgwcPSpL+/PNP9e/fX7Vq1ZKvr6+aN2+ubdu2pXls3bp1059//qnJkyfbr+in2L17t7p06SIfHx81aNBAEyZMUFxcnH19UFCQPvjgA40YMUJ+fn5q2LChvv32W128eFGvvPKK/Pz81LJlS3td0v+u6G7ZskWNGzeWt7e3evXqpb/++suhri1btqhNmzby9vbWs88+m2p2gcVi0aeffqr+/fvL19dX8+bNk9Vq1RtvvKGgoCD5+PioSZMmWrZsmX2b2bNnKyIiQt9++639WHfu3KmdO3fKYrEoNjbW3vbw4cOyWCyKjo52qPvbb79Vs2bN5O3trZiYGCUkJGjKlCmqX7++fH191aFDB+3cuTPNc51RO3bsUPfu3dW+fXtNmDBBLi7cEQEAAAAg57jnBJM7d26HK6s7duzQqVOntGTJEs2fP19xcXHq1auXChUqpNWrV2vmzJn68ccfNX78ePs2n376qcaNG6cXX3xR69at0wcffKBy5crZ1w8dOlQXLlzQwoULFR4ermrVqql79+66fPmyJGnYsGF67LHHtHr1aoWHh6tPnz72K6Djxo1TQkKCPv74Y61bt07Dhg1Tvnz50jyW2bNn67HHHtOQIUMUFRWlqKgoSdKZM2fUp08fNW7cWGvXrtWMGTP0yy+/OByDJC1btkz+/v6KiIhQgwYNNGLECI0YMUKtWrVSeHi4ypUrp9DQUNlsNvs2N27c0IcffqgpU6Zo5cqVio2N1auvvmpfv3v3boWGhio4OFgbNmzQuHHjFB4ernnz5jnse86cOWrUqJHWrVundu3aKTk5WY899pjef/99rV+/XgMHDtSMGTO0YcMGSVLPnj3VtGlT+8yFzE7VvnHjhhYuXKgJEyboq6++UrFixTRu3Djt3btXM2bM0Nq1a/X888+rd+/eOn36dIb7vd0333yjfv366ZVXXtHw4cOz1AcAAAAAOJNrVje02WzasWOHoqKi9NJLL9mX58uXTxMmTJCbm5sk6fPPP7dfAU0Ju6NHj1b//v01bNgwFS9eXB9++KFefvllde/e3d6Pj4+PpFuh88CBA9qxY4e9z9DQUG3ZskWbN29Wx44dFRMTo169eqly5cqSpAoVKtj7iYmJUZMmTexXrcuWLXvHYypcuLDMZrPy588vDw8P+/L58+erZcuW6tGjh73/UaNGqVu3bhozZoxy584tSXr66afVqVMnSdLAgQO1cuVKeXt7q2nTppKkPn36qGPHjjp//ry9/8TERI0ePVo1atSQJL3zzjtq1qyZDhw4IB8fH82ZM0d9+/ZVmzZt7PUPHTpU7777rgYNGmSvsUWLFmrXrp3D8QwZMsT+e9myZbVv3z5t2rRJzZo1U/78+ZUnTx4lJCQ4HGtGJSYmasyYMapSpYqkW+c5PDxc//d//6eSJUtKknr16qXt27crPDxcr732Wqb6j4uL09ChQ9WvXz/17ds30/UBgFGsVquzS8ADKmVsMEaQFsYH0sMYyXky+l5lOnRv3bpVfn5+SkxMlM1mU4sWLTR48GD7ek9PT3s4lm49AMtisThcXfb391dycrJOnTolk8mkf//9V08++WSa+zt69Kji4uIUGBjosPzGjRs6c+aMJOnll1/Wm2++qS+//FJ169bV888/b79SHhwcrDFjxigqKkp169ZV48aN7UExo44cOaKjR49q3bp19mU2m03JycmKjo62h/3bp6MXL17cfj5SpNy3fOHCBXvQdXV1lbe3t71N5cqVVbBgQZ04cUI+Pj46cuSI9uzZ43Bl22q16ubNm4qPj1fevHklSdWrV09V9yeffKI1a9YoJiZGN2/eVGJiYqaP/U5y5crlcLzHjh2T1Wq131KQIiEhQYULF850/7lz51bNmjX1xRdfqEWLFvZzDADOdvttQkBaGCO4G8YH0sMYefhkOnQHBgZqzJgxypUrl0qUKCFXV8cuUkJgRqVcJb6T69evy8PDQytWrEi1rkCBApKkwYMHq0WLFtq2bZu+//57zZo1SzNmzFCjRo3UoUMH1atXT1u3btUPP/ygBQsWKDQ0VN26dctwjXFxcerUqVOa25QqVcr+++3nwmQySZLDg75Slt0+vTwj+x48eLAaN26cat3t5+6/U+bXr1+vKVOmKDQ0VH5+fsqfP78WL16s/fv333V/KfdM315jWg9my5Mnj/14Uuo0m81as2aNzGazQ9s7Tee/G7PZrA8++ECDBg1ScHCwli9fTvAG8EDw9vZO9fccIN36UPzgwYOMEaSJ8YH0MEZynpT3LD2ZDt158+ZV+fLlM9y+cuXKioiIUFxcnD187dmzRy4uLqpYsaLc3d1VunRp7dixQ3Xq1Em1fbVq1XT+/HmZzWaVKVPmjvupWLGiKlasqB49eui1117TmjVr1KhRI0m3gnHnzp3VuXNnTZ8+XZ9//vkdQ3euXLlSPYnby8tLx48fz9RxZ1RSUpJ+/fVX+3T6kydPKjY21h4wvby8dOrUqUzve8+ePfLz81PXrl3ty1JmBqRI61iLFi0qSTp37pwKFSokSQ4PrbuTqlWrymq16uLFi5n+zu07cXNz05w5czRkyBAFBwdr2bJleuKJJ7KlbwDIKrPZzH+GcFeMEdwN4wPpYYw8fAx/FHTLli3l5uamsLAwHTt2TD/99JPGjx+v1q1b26dgDx48WEuWLNHy5ct1+vRpHTp0yH5lu27duvL19dXAgQMVFRWl6Oho7dmzRzNmzNDBgwd148YNjRs3Tjt37tSff/6pX375RQcPHrSH1okTJ2r79u06e/asDh06pJ07d971imnp0qX1888/659//tHFixcl3boXe+/evRo3bpwOHz6s06dPa8uWLQ5PWM+qXLlyafz48dq/f79+/fVXjRw5Ur6+vvYQPnDgQH355ZeaM2eOfv/9d504cULr16/XjBkz7tpv+fLl9euvv2r79u06deqUZs6cmepTmNKlS+vo0aM6efKkLl68qMTERJUrV06lSpXS7Nmzdfr0aW3dulUfffRRusdRsWJFtWzZUiNGjNDXX3+ts2fP6sCBA5o/f762bt2a5fPj5uamWbNmycfHR8HBwfr999+z3BcAAAAA3G9ZfpBaRuXNm1eLFy/WxIkT1b59e+XNm1eNGzdWWFiYvU2bNm108+ZNLV26VFOnTlXhwoXt9wabTCYtWLBAM2fO1MiRI3Xp0iUVL15cAQEBKl68uFxcXHT58mWFhobq/PnzKlKkiBo3bmx/iFhycrLGjRunv//+W+7u7qpfv75Gjhx5x3qHDBmi0aNH67nnnlNCQoKOHj2qKlWqaMWKFZo5c6a6dOki6daDyZo1a3bP5ydPnjzq06ePXn/9df3zzz8KCAjQxIkT7evr16+vefPmae7cuVq4cKFcXV1VqVIldejQ4a79durUSYcPH9arr74qk8mk5s2bq0uXLvr+++/tbV588UXt2rVL7dq1U1xcnJYvX67AwEBNnz5dY8aMUatWreTt7a2QkBANHTo03WOZPHmyPvzwQ73zzjv6999/VbhwYfn6+uqZZ57J8vmR/he8Q0JC7Fe8b79XHgAAAAAeVCZbZm4wRrYKDw/XpEmTtHv3bmeX4jQWi0Vz587Vc889l6Xtu3XrpipVqmjUqFHptrVardq3b58m/hSnPWdj020PACmqPV5Q64fU12+//SaLxcK0P6Qp5d8ZX19fxghSYXwgPYyRnCej75nh08uB9Lz22mt6+umnM7XN2rVr5efn90h/YAEAAADgwWf49HLgbr7++mtJ/3tqekYFBQXZv9s85Sn2AAAAAPCgIXQ7Udu2bdW2bVtnl+FUWX0ivLu7u9zd3bO5GgAAAADIXkwvBwAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgrs4uALjfKnnk102rs6sAkJM8UcLd2SUAAIAcitCNR86UdjVkNpudXQaAHMaabFNSUpKzywAAADkM08vxyLFaucyNtFmtVv3222+MEaTNlqzExERnVwEAAHIYQjcA3CY+Pt7ZJQAAAOAhQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGgNvkzZvX2SXgAcb4AAAAmeXq7AKA+81sNju7BDygzGazvLy8nF0GHlCMjzuzJttkdjE5uwwAAB5IhG48ckLX7Nfhv687uwwAeCg8UcJd73fyc3YZAAA8sAjdeOScPHddh2JinV0GAAAAgEcA93QDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgkIc+dIeFhWnAgAH3fb9BQUFaunTpfd/vvUrvfIWHh8tischisWjixIn3sTJHYWFh9jq2bNnitDoAAAAA4G6yNXTfHoSqVaumoKAgTZ06VTdv3sxwHzt37pTFYlFsbGym9h0dHS2LxaLDhw87LB81apTeeeedTPWFu3N3d1dUVJSGDh3qtBpGjRqlqKgop+0fAAAAADLCNbs7rF+/viZPnqykpCQdOnRIoaGhMplMGj58eHbvKkMKFCjglP0+zEwmkzw8PO7aJiEhQW5ubobVUKBAAd5bAAAAAA+8bJ9e7ubmJg8PD5UqVUrPPfec6tatqx9//NG+PiEhQRMmTNCTTz4pb29vde7cWQcOHJB062p1cHCwJKlWrVqyWCwKCwuTJH3//ffq3LmzAgICFBgYqH79+unMmTP2fp999llJ0gsvvCCLxaJu3bpJSj1d+m77l/53pX3Hjh1q27atatSooU6dOunkyZP2NmfOnNErr7yiunXrys/PT+3atXM4xozYuXOn2rdvL19fXwUEBKhTp076888/JUmzZ89W69at9dlnn6lBgwaqUaOGhg4dqqtXrzr08cUXX6hp06by9vbW888/r08++cRh/V9//aWhQ4cqICBAtWvX1iuvvKLo6Gj7eqvVqsmTJ9vP6dSpU2Wz2TJ1HCmCgoI0d+5cjRgxQv7+/ho9erR99sH69evVqVMneXt7q0WLFtq1a5fDebBYLNq+fbteeOEF+fj4KDg4WBcuXNC2bdvUtGlT+fv76/XXX1d8fHyWagMAAAAAZzH0nu5jx45p7969ypUrl33Z1KlTtXnzZr3zzjuKiIhQ+fLl1bt3b12+fFmlSpXS7NmzJUmbNm1SVFSURo0aJUmKj4/Xyy+/rDVr1mjp0qUymUwaOHCgkpOTJd0KoJK0dOlSRUVF2fv5r7vt/3YzZsxQWFiY1qxZI7PZrDfeeMO+Li4uTg0aNNDSpUsVERGh+vXrq3///oqJicnQeUlKStLAgQNVq1YtrV27VqtWrVLHjh1lMpnsbc6cOaONGzdq3rx5WrRokQ4fPqwxY8bY169du1bvv/++Xn31VW3YsEGvvfaaZs2apYiICElSYmKievXqpfz58+uTTz7RypUrlS9fPvXu3VsJCQmSpI8++kgRERGaNGmSPv30U125ckXffPNNho4hLR999JGqVKmiyMhIhw86pk6dqpdfflmRkZHy9fVV//79denSJYdt58yZo7feekufffaZ/v77b4WEhGj58uWaPn26FixYoKioKK1YsSLLtQEAAACAM2T79PKtW7fKz89PSUlJSkhIkIuLi9566y1Jt8LqZ599psmTJ6tBgwaSpPHjx+uHH37Q6tWr1bt3bxUqVEiSVKxYMRUsWNDeb5MmTRz2M2nSJD355JM6fvy4PD09VbRoUUlS4cKF7zj1OSP7T/Hqq6+qdu3akqS+ffuqb9++unnzpnLnzq0qVaqoSpUq9rYhISHasmWLvvvuO7300kvpnqNr167p6tWratiwocqVKydJqly5skObmzdvaurUqSpZsqQk6c0331S/fv0UFhYmDw8PzZ49W2FhYWrcuLEkqWzZsjp+/LhWrVqlNm3aaMOGDUpOTtbEiRPtYX7y5MmqVauWdu3apXr16mnZsmXq27evvY+xY8fe033SderUUc+ePe2vU66qd+3a1f7+jRkzRtu3b9fq1avVp08fe9uQkBDVrFlTktS+fXtNnz5dW7ZsUdmyZSXdev937typvn37Zrk+AIBxrFars0twupRzwLlAWhgfSA9jJOfJ6HuV7aE7MDBQY8aMUXx8vJYuXSqz2WwPXGfOnFFiYqL8/f3t7XPlyiUfHx+dOHHirv2ePn1as2bN0v79+3Xp0iX7NOi//vpLnp6eGaotM/u3WCz231NC/IULF/T444/r+vXrmjNnjrZu3apz587JarXqxo0bGb7SXbhwYbVt21a9evXSU089pSeffFJNmzZViRIl7G1KlSplD9yS5Ofnp+TkZJ06dUr58+fXmTNnNGrUKPsHGtKtK+gp9zkfOXJEZ86ccThW6VaYP3PmjK5evapz586pRo0a9nWurq6qXr16lqeYV69ePc3lfn5+qfZx+3R9yfF8FytWTHnz5rUHbkkqXry4Dh48mKW6AADGO3r0KLcB/T/8e4W7YXwgPYyRh0+2h+68efOqfPnykm5djW7durW++OILdejQ4Z767d+/v0qXLq0JEyaoRIkSSk5OVosWLZSYmJgdZafi6vq/U5NypThlKvuUKVP0448/KjQ0VOXKlVOePHk0ZMiQTNUyefJkdevWTdu3b9fGjRs1c+ZMLVmyRL6+vuluGxcXJ+nWVfrbQ7Mkubi42NtUq1ZN06ZNS7V9yqyA7JY3b94sb/vf833765RlKecfAPDguf3D00eV1WrVwYMH5e3tLbPZ7Oxy8IBhfCA9jJGcJ+U9S0+2h+7bubi4qF+/fnrnnXfUsmVLlStXTrly5dKePXtUunRpSbfuPT548KC6d+8uSfb7v2+/VH/p0iWdOnVKEyZMUEBAgCRp9+7dDvtKa7v/ysj+M2Lv3r1q06aNGjVqJEm6fv26/SFomeHl5SUvLy/169dPHTt21FdffWUP3X/99Zf++ecf+9Xuffv2ycXFRRUrVlTx4sVVokQJnT17Vq1atUqz72rVqmnjxo0qVqyY3N3d02zj4eGh/fv3q1atWpJkf+K8l5dXpo/lbvbt25dqH127ds3WfQAAnIv/IP6P2WzmfOCOGB9ID2Pk4WNo6Jak559/XlOnTtUnn3yiXr16qXPnzpo6daoKFSqkxx9/XIsWLdKNGzfUvn17SVLp0qVlMpm0detWNWjQQLlz51ahQoVUuHBhrVq1Sh4eHoqJidH06dMd9lOsWDHlyZNH27dv12OPPabcuXOn+kqpfPnypbv/jChfvry++eYbBQUFyWQyaebMmZm6Cnv27Fl9/vnnCgoKUokSJXTq1CmdPn1arVu3trfJnTu3wsLCFBoaqmvXrmnChAlq2rSpfar7kCFDNGHCBBUoUED169dXQkKCfv31V8XGxurll19Wy5YttXjxYr3yyisaOnSoSpYsqZiYGH3zzTfq3bu3HnvsMQUHB2vhwoWqUKGCKlasqKVLl2b6+9Ez4tNPP1WFChVUqVIlLVu2TFeuXFG7du2yfT8AAAAA8KAxPHS7urrqpZde0qJFi9S5c2cNGzZMNptNI0aM0PXr11W9enUtWrTI/gC1kiVLavDgwZo+fbpGjhypF154Qe+8845mzJihCRMmqEWLFqpYsaLefPNN+9eCpeznzTff1Ny5czVr1iwFBASk+bTr9PafEWFhYXrjjTfUqVMnFSlSRH369NH169czvH3evHl18uRJRURE6PLlyypRooS6du2qTp062duUK1dOjRo1Up8+fXTlyhU988wzevvtt+3rO3TooDx58mjx4sWaOnWq8uXLJ09PT/sV+7x58+rjjz/WtGnTNGjQIF2/fl0lS5bUk08+ab/y3bNnT507d06hoaFycXFRu3bt1KhRo1RfTXavXn/9dS1YsECHDx9W+fLl9eGHHxo2xR0AAAAAHiQmW1afmgXDzJ49W1u2bNGXX37p7FJSCQ8P16RJk1JN709LdHS0nn32WUVGRqpq1aqG1GOxWDR37lw999xz6ba1Wq3at2+fJv4Upz1ns/+KPgA8iqo9XlDrh9R3dhkPhJR/Z3x9fZkailQYH0gPYyTnyeh7Zuj3dOPhdPXqVfn5+endd991Wg2jR492eCo6AAAAADyIDJ9ejodL48aN7d+n/d975u+noUOHqlevXpJ0x+9lBwAAAABnI3Q/gAYPHqzBgwc7u4w0ubu73/Fp6P9VpkwZHT161JA6ihUrpmLFihnSNwAAAABkF6aXAwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQV2cXANxvlTzy66bV2VUAwMPhiRLuzi4BAIAHGqEbj5wp7WrIbDY7uwwAeGhYk20yu5icXQYAAA8kppfjkWO1cpkbabNarfrtt98YI0gT4+POCNwAANwZoRsAbhMfH+/sEvAAY3wAAIDMInQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDwG3y5s3r7BLwAGN8AACAzHJ1dgHA/WY2m51dAh5QZrNZXl5ezi4DDyjGx8PDmmyT2cXk7DIAAI8IQjceOaFr9uvw39edXQYAwAmeKOGu9zv5ObsMAMAjhNCNR87Jc9d1KCbW2WUAAAAAeARwTzcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQ7WVhYmAYMGODsMrKkW7duslgsslgsOnz48D33NXHixExtk7LvgICAe9o3AAAAABjF1dkFPOgsFstd1w8aNEiDBw++T9U8eF588UUNGTJERYoUuad+Zs+eLVfXzA3HqKgobdiwQbNnz76nfQMAAACAUQjd6YiKirL/vmHDBs2aNUubNm2yL8uXL58zyrqvEhIS5Obmlua6PHnyyMPD4573Ubhw4Uxv4+HhoQIFCtzzvgEAAADAKEwvT4eHh4f9p0CBAjKZTPbXxYoV05IlS/T000+revXqat26tb7//nuH7Y8eParg4GD5+PgoMDBQb731lq5fv56hfdtsNtWpU8ch5Ldu3Vr16tWzv969e7eqV6+u+Ph4SVJsbKxGjRqlOnXqyN/fX8HBwTpy5Ii9/ZkzZ/TKK6+obt268vPzU7t27fTjjz867DcoKEhz587ViBEj5O/vr9GjR2f4fO3cuVMWi0Xbt2/XCy+8IB8fHwUHB+vChQvatm2bmjZtKn9/f73++uv2mqXU08uDgoI0b948jRw5Un5+fnrmmWe0atWqDNcBAAAAAA8CQvc9WL58uZYsWaLQ0FCtXbtW9erV04ABA3T69GlJUlxcnHr16qVChQpp9erVmjlzpn788UeNHz8+Q/2bTCbVqlVLu3btkiRduXJFJ06c0I0bN3TixAlJ0s8//yxvb2/lzZtXkjR06FBduHBBCxcuVHh4uKpVq6bu3bvr8uXL9poaNGigpUuXKiIiQvXr11f//v0VExPjsO+PPvpIVapUUWRkZJbuOZ8zZ47eeustffbZZ/r7778VEhKi5cuXa/r06VqwYIGioqK0YsWKu/axZMkSVa9eXZGRkerSpYvGjBmjkydPZroWAAAAAHAWppffg8WLF6tPnz5q3ry5JGn48OHauXOnli1bprfffltfffWVEhISNGXKFPs09NGjR6t///4aNmyYihcvnu4+ateubb/C+/PPP8vLy0vFixfXrl27VLlyZe3atUu1a9eWdOuq94EDB7Rjxw77dPDQ0FBt2bJFmzdvVseOHVWlShVVqVLF3n9ISIi2bNmi7777Ti+99JJ9eZ06ddSzZ88sn5uQkBDVrFlTktS+fXtNnz5dW7ZsUdmyZSVJTZo00c6dO9W3b9879vH000+ra9eukqQ+ffpo6dKl2rlzpypVqpTlugAAkCSr1WpYn0b0jZyP8YH0MEZynoy+V4TuLLp27Zr+/fdf+fv7Oyz39/e3T+c+ceKELBaLw33f/v7+Sk5O1qlTpzIUumvVqqWJEyfq4sWL+vnnn1W7dm176G7fvr327t2r3r17S7o1lT0uLk6BgYEOfdy4cUNnzpyRJF2/fl1z5szR1q1bde7cOVmtVt24cSPVle7q1atn/qTc5vYH0BUrVkx58+a1B25JKl68uA4ePJjhPkwmk4oXL64LFy7cU10AAEi3/s28/Tan7JTev294tDE+kB7GyMOH0P2As1gsKlSokHbt2qWff/5ZISEh8vDw0KJFi3Tw4EElJSXJz89P0q1A7eHhkea07ZQHjk2ZMkU//vijQkNDVa5cOeXJk0dDhgxRYmKiQ/uU6epZdfuTyE0mU6onk5tMJiUnJ2e4j5RtbDbbPdUFAICU/reTZIXVatXBgwfl7e0ts9mc7f0jZ2N8ID2MkZwn5T1LD6E7i9zd3VWiRAnt2bPHPr1bkvbs2SMfHx9JUuXKlRUREaG4uDj71e49e/bIxcVFFStWzNB+TCaTAgIC9O233+r3339XzZo1lTdvXiUkJGjVqlWqXr26ve9q1arp/PnzMpvNKlOmTJr97d27V23atFGjRo0k3Qrqf/75Z5bPAwAAOZGR/6E1m838hxl3xPhAehgjDx8epHYPevXqpYULF2rDhg06efKkpk2bpiNHjig4OFiS1LJlS7m5uSksLEzHjh3TTz/9pPHjx6t169YZmlqeonbt2lq/fr2qVq2q/Pnzy8XFRQEBAVq3bp1q1aplb1e3bl35+vpq4MCBioqKUnR0tPbs2aMZM2bYP4EpX768vvnmGx0+fFhHjhzR66+/nu4VZwAAAABA1nCl+x4EBwfr2rVreuedd3Tx4kVVrlxZH3zwgSpUqCDp1hTtxYsXa+LEiWrfvr3y5s2rxo0bKywsLFP7qV27tqxWq8MV9dq1a+vbb791WGYymbRgwQLNnDlTI0eO1KVLl1S8eHEFBATYQ35YWJjeeOMNderUSUWKFFGfPn0y/BVmAAAAAIDMMdm4SRZZ1K1bN1WpUkWjRo1yWg3h4eGaNGmSdu/enW5bq9Wqffv2aeJPcdpzNvY+VAcAeNBUe7yg1g+pb0jfKf/O+Pr6MjUUqTA+kB7GSM6T0feM6eW4JytXrpSfn5+OHj163/ft5+ent99++77vFwAAAAAyiunlyLJp06bpxo0bkqRSpUrd9/1HRkZKklxc+OwIAAAAwIOJ0I0sK1mypFP3X758eafuHwAAAADSwyVCAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAzi6uwCgPutkkd+3bQ6uwoAgDM8UcLd2SUAAB4xhG48cqa0qyGz2ezsMgAATmJNtsnsYnJ2GQCARwTTy/HIsVq5zI20Wa1W/fbbb4wRpInx8fAgcAMA7idCNwDcJj4+3tkl4AHG+AAAAJlF6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AaA2+TNm9fZJeABxvgAAACZ5ersAoD7zWw2O7sEPKDMZrO8vLycXQYeUIwP/Jc12Sazi8nZZQAAHnCEbjxyQtfs1+G/rzu7DABADvZECXe938nP2WUAAHIAQjceOSfPXdehmFhnlwEAAADgEcA93QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABjkoQzdO3fulMViUWxsrLNLwV2Eh4fLYrHIYrFo4sSJmdq2W7du9m0PHz5sUIUAAAAAcG9ybOjeu3evqlatqr59+6Za5+fnp6ioKBUoUCBTfT5qYd1isWjLli1OrcHd3V1RUVEaOnSofVm3bt1ShfBly5apevXqWr9+vSRp9uzZ+uKLL+5rrQAAAACQWTk2dK9evVovvfSSfv75Z/3zzz8O69zc3OTh4SGTyZTmtlarVcnJyfejzPvOGceWkJCQ5W1NJpM8PDzk7u5+xzazZs3SjBkz9MEHH6h58+aSpMKFC6to0aJZ3i8AAAAA3A85MnRfv35dGzZsUOfOnfXMM88oIiLCYf1/r1iHh4crICBA3377rZo1ayZvb2/FxMQ4bBMdHa3g4GBJUq1atWSxWBQWFiZJSk5O1vz58xUUFCQfHx+1atVKmzZtSrW/7du364UXXpCPj4+Cg4N14cIFbdu2TU2bNpW/v79ef/11xcfH27fr1q2bxo0bp3HjxqlmzZoKDAzUzJkzZbPZ7G0SEhI0ZcoU1a9fX76+vurQoYN27txpX3+nYztw4IBefvllBQYGqmbNmnrppZd06NAh+3ZBQUGSpIEDB8pisdhfh4WFacCAAQ7nZuLEierWrVuquidOnKjAwED16tVLknTs2DH17t1bfn5+qlu3roYPH66LFy9m6D1Ni81m0/jx47VixQp99NFHevrpp7PcFwAAAAA4Q44M3Rs3blSlSpVUqVIltWrVSmvWrHEIqmm5ceOGFi5cqAkTJuirr75SsWLFHNaXKlVKs2fPliRt2rRJUVFRGjVqlCRp/vz5ioyM1NixY7V+/Xr16NFDw4cP165duxz6mDNnjt566y199tln+vvvvxUSEqLly5dr+vTpWrBggaKiorRixQqHbSIiImQ2m/XFF19o1KhRWrp0qcO06XHjxmnv3r2aMWOG1q5dq+eff169e/fW6dOn73ps169f1wsvvKBPP/1Un3/+ucqXL6++ffvq2rVrkm7NFJCkyZMnKyoqyv46oyIiIpQrVy6tXLlSY8eOVWxsrLp37y4vLy+tXr1aixYt0oULFxQSEpKpflNYrVYNGzZMmzdv1ooVK+Tv75+lfgAAAADAmVydXUBWrF69Wq1atZIk1a9fX1evXtWuXbsUGBh4x20SExM1ZswYValSJc31ZrNZhQoVkiQVK1ZMBQsWlHTrSvP8+fO1ZMkS+fn5SZLKli2rX375RatWrVLt2rXtfYSEhKhmzZqSpPbt22v69OnasmWLypYtK0lq0qSJdu7c6XAfeqlSpfTGG2/IZDKpUqVKOnbsmJYuXaoXX3xRMTExCg8P1//93/+pZMmSkqRevXpp+/btCg8P12uvvXbHY3vyyScdjm/8+PEKCAjQzz//rIYNG9qnZhcsWFAeHh53Pd9pqVChgkaMGGF//cEHH8jLy8tekyRNmjRJDRo00KlTp1SxYsVM9f/5559Lkr788ktVrlw50/UBAHA/WK3WVL/fvgxIwfhAehgjOU9G36scF7pPnjypgwcPau7cuZIkV1dXNWvWTKtXr75r6M6VK5csFkum9/fHH38oPj5ePXv2dFiemJioqlWrOiy7vf9ixYopb9689sAtScWLF9fBgwcdtqlRo4bDvee+vr5asmSJrFarjh07JqvVqueff95hm4SEBBUuXPiux3b+/HnNnDlTu3bt0oULF5ScnKz4+PhU0+qzqlq1ag6vjxw5op07d9o/mLjdmTNnMh26a9asqSNHjuj999/Xe++9J1fXHDdUAQCPgKNHjzrcOiYp1b/1wO0YH0gPY+Thk+OSzOrVq5WUlKT69evbl9lsNrm5uWn06NF3fGJ5njx57vhgtbuJi4uTdGuKecrV5hRubm4Or28PhiaTKVVQNJlMmXrIWVxcnMxms9asWSOz2eywLl++fPbf0zq20NBQXb58WaNGjdLjjz8uNzc3dezYUYmJiXfdp8lkSjVVPykpKVW7vHnzpqq1YcOGGjZsWKq2WbmS7unpqbCwMPXo0UOvvvqqZsyYQfAGADxwbv/Q22q16uDBg/L29k717zbA+EB6GCM5T8p7lp4clWKSkpL05ZdfKiwsTE899ZTDuoEDB+qrr75S586ds9x/rly5JDlOE6hcubLc3NwUExPjMJU8uxw4cMDh9f79+1W+fHmZzWZVrVpVVqtVFy9eVEBAQKb63bNnj95++201aNBAkvTXX3/p0qVLDm1y5cqVakpE0aJF9fvvvzssO3z4sP3c3Em1atW0efNmlS5dOtvCcdWqVbVs2TL16NFDISEhmjFjRrp1AABwP6X1H2Oz2cx/mHFHjA+khzHy8MlRD1LbunWrrly5ovbt28vT09Php3Hjxpl+GNh/lS5dWiaTSVu3btXFixd1/fp1ubu7q2fPnpo8ebIiIiJ05swZHTp0SCtWrEj11PSsiImJ0eTJk3Xy5El99dVX+vjjj+1PUa9YsaJatmypESNG6Ouvv9bZs2d14MABzZ8/X1u3br1rvxUqVNDatWt14sQJ7d+/X8OGDVOePHlSHe+OHTt07tw5XblyRZJUp04d/frrr4qMjNTp06c1a9asVCE8LV26dNGVK1f02muv6cCBAzpz5oy2b9+ukSNH3tN9KVWqVNGyZcv0yy+/KCQkJN0r9QAAAADwIMlRoXv16tWqW7dumlPImzRpol9//VVHjhzJcv8lS5bU4MGDNX36dNWtW1fjx4+XdOsBaQMGDND8+fPVrFkz9e7dW1u3blWZMmWyvK8UL7zwgm7cuKEOHTpo3LhxCg4OVseOHe3rJ0+erBdeeEHvvPOOmjZtqgEDBujgwYMqVarUXfudOHGirly5ojZt2mjEiBHq1q1bqie2h4aG6scff9QzzzyjNm3aSLr1YLoBAwbo3XffVfv27e1PQU9PyZIltXLlSiUnJ6tXr15q2bKlJk2apAIFCsjF5d6GmcVi0bJly7R3714NHTr0nr4XHAAAAADuJ5Mtve/agmG6deumKlWq2L+a7FETHh6uSZMmaffu3VnaPjo6Ws8++6wiIyNTPdQuLVarVfv27dPEn+K052xslvYJAIAkVXu8oNYPqe+wLOXfGV9fX6aGIhXGB9LDGMl5Mvqe5agr3Xj4XL16VX5+fnr33XcztV3v3r3VokULg6oCAAAAgOyRox6khodL48aN7d9rfqenzt/JxIkTdePGDUlKd6o9AAAAADgLoduJVqxY4ewSnMrd3V3u7u5Z2va/X98GAAAAAA8ippcDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBXZxcA3G+VPPLrptXZVQAAcrInSrg7uwQAQA5B6MYjZ0q7GjKbzc4uAwCQw1mTbTK7mJxdBgDgAcf0cjxyrFYucyNtVqtVv/32G2MEaWJ84L8I3ACAjCB0A8Bt4uPjnV0CHmCMDwAAkFmEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgC4Td68eZ1dAh5gjA+khzGCu2F8ID2MkYeTyWaz2ZxdBHA/WK1W7du3T76+vjKbzc4uBwAAAEAGWZNtMruYnF2Gg4zmC9f7WBPwQAhds1+H/77u7DIAAAAAZMATJdz1fic/Z5eRZYRuPHJOnruuQzGxzi4DAAAAwCOAe7oBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEb9yQ6OloWi0UWi0WtW7e+b/sNCwuz73fLli33bb8AAAAAkBmEbidICYt3+pk9e7azS8y0pUuXaunSpfbXs2fPlsVi0ejRox3aHT58WBaLRdHR0fe0v1GjRikqKuqe+gAAAAAAo7k6u4BH0e1hccOGDZo1a5Y2bdpkX5YvX75M9ZeYmKhcuXJlW31ZUbhwYRUpUsRhWe7cubVmzRr17NlTFSpUyNb9FShQQAUKFMjWPgEAAAAgu3Gl2wk8PDzsPwUKFJDJZLK/LlasmJYsWaKnn35a1atXV+vWrfX999/bt02Zzr1hwwa99NJL8vb21rp162S1WjV58mQFBAQoMDBQU6dOVWhoqAYMGGDfNigoyOFqtCS1bt3a4cp6bGysRo0apTp16sjf31/BwcE6cuRIlo6zYsWKCgwM1IwZM+7YZufOnbJYLNq+fbteeOEF+fj4KDg4WBcuXNC2bdvUtGlT+fv76/XXX1d8fHyW6gAAAAAAZyF0P2CWL1+uJUuWKDQ0VGvXrlW9evU0YMAAnT592qHdtGnTFBwcrA0bNqhevXr66KOPFBERoUmTJunTTz/VlStX9M0332R6/0OHDtWFCxe0cOFChYeHq1q1aurevbsuX76cpeN5/fXX9fXXX+vgwYN3bTdnzhy99dZb+uyzz/T3338rJCREy5cv1/Tp07VgwQJFRUVpxYoVWaoBAAAAAJyF0P2AWbx4sfr06aPmzZurUqVKGj58uKpUqaJly5Y5tOvevbsaN26ssmXLqkSJElq2bJn69u2rxo0bq3Llyho7dmymp1/v3r1bBw4c0KxZs+Tt7a0KFSooNDRUBQsW1ObNm7N0PNWqVVPTpk01bdq0u7YLCQlRzZo15eXlpfbt22vXrl0aM2aMvLy8FBAQoCZNmmjnzp1ZqgEAAABAzme1Wh+4n4zgnu4HyLVr1/Tvv//K39/fYbm/v3+qKd7Vq1e3/3716lWdO3dONWrUsC9zdXVV9erVZbPZMrz/o0ePKi4uToGBgQ7Lb9y4oTNnzmTmUByEhISoWbNmioqKUrFixdJsY7FY7L8XK1ZMefPmVdmyZe3Lihcvnu7VcgAAAAAPr6NHj+bIW04J3TlUZh+2JkkmkynVsqSkJPvv169fl4eHR5rTuO/loWXlypVThw4dNH36dE2cODHNNq6u/xuKJpPJ4XXKsuTk5CzXAAAAACBnu/1C3YPAarVm6MIgofsB4u7urhIlSmjPnj2qXbu2ffmePXvk4+Nzx+0KFCggDw8P7d+/X7Vq1ZJ0K0wfOnRIXl5e9nZFixbVv//+a3997do1h6/uqlatms6fPy+z2awyZcpk56Fp4MCBatSokdavX5+t/QIAAAB4NJjNZmeXkCWE7gdMr169NHv2bJUrV05VqlRReHi4jhw5ku490cHBwVq4cKEqVKigihUraunSpYqNjXVoU6dOHUVERCgoKEgFChTQrFmz5OLyv9v669atK19fXw0cOFDDhw9XhQoV9O+//2rbtm167rnn5O3tneXjKl68uHr06KHFixdnuQ8AAAAAyGkI3Q+Y4OBgXbt2Te+8844uXryoypUr64MPPkj3e6579uypc+fOKTQ0VC4uLmrXrp0aNWqkq1ev2tv069dP0dHR6tevnwoUKKChQ4c6XOk2mUxasGCBZs6cqZEjR+rSpUsqXry4AgICVLx48Xs+tl69emnlypW6efPmPfcFAAAAADmByZaZJ20hRwkLC1NsbKw++OADw/YRHR2tZ599VpGRkapataph+7kTi8WiuXPn6rnnnku3rdVq1b59+zTxpzjtORubbnsAAAAAzlft8YJaP6S+s8tIJSVf+Pr63nXqO18ZhmzRqVMnderU6b7tb/To0fLz87tv+wMAAACArGB6Oe7JY489pq+//lqS5Obmdt/2O3ToUPXq1UuS5OHhcd/2CwAAAACZQeh+iL3zzjuG78PV1VXly5c3fD//VaxYsTt+5zcAAAAAPCiYXg4AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQVydXQBwv1XyyK+bVmdXAQAAACAjnijh7uwS7gmhG4+cKe1qyGw2O7sMAAAAABlkTbbJ7GJydhlZwvRyPHKsVi5zI21Wq1W//fYbYwRpYnwgPYwR3A3jA+lhjNxdTg3cEqEbABzEx8c7uwQ8wBgfSA9jBHfD+EB6GCMPJ0I3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGcXV2AcD9YrPZJElWq1VWq9XJ1eBBlDIuGB9IC+MD6WGM4G4YH0gPYyTnSXmvUnLGnZhs6bUAHhIJCQk6ePCgs8sAAAAA8BDx9vaWm5vbHdcTuvHISE5OVlJSklxcXGQymZxdDgAAAIAczGazKTk5Wa6urnJxufOd24RuAAAAAAAMwoPUAAAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELqRo33yyScKCgqSt7e3OnTooAMHDty1/caNG/X888/L29tbLVu21LZt2xzW22w2vf/++6pXr558fHzUo0cPnT592sAjgJGye3x8/fXX6tmzpwIDA2WxWHT48GEjy8d9kJ1jJDExUe+++65atmwpX19f1atXTyNGjNA///xj9GHAQNn998js2bP1/PPPy9fXV7Vq1VKPHj20f/9+Iw8BBsru8XG70aNHy2KxaOnSpdlcNe6X7B4fYWFhslgsDj+9evUy8hCQXWxADrV+/XpbtWrVbKtXr7b9/vvvtjfffNMWEBBgO3/+fJrtf/nlF1vVqlVtCxcutB0/ftw2Y8YMW7Vq1WxHjx61t5k/f76tZs2atm+++cZ2+PBhW//+/W1BQUG2Gzdu3K/DQjYxYnxERETYZs+ebfv8889tnp6ett9+++1+HQ4MkN1jJDY21tajRw/b+vXrbSdOnLDt3bvX1r59e1ubNm3u52EhGxnx98jatWttP/zwg+3MmTO2Y8eO2d544w2bv7+/7cKFC/frsJBNjBgfKb7++mtbq1atbPXq1bMtWbLE4COBEYwYH6GhobZevXrZ/v33X/vP5cuX79ch4R4QupFjtW/f3jZ27Fj7a6vVaqtXr55t/vz5abYfOnSorW/fvg7LOnToYHvrrbdsNpvNlpycbHvqqadsixYtsq+PjY21Va9e3fbVV18ZcAQwUnaPj9udPXuW0P0QMHKMpNi/f7/N09PT9ueff2ZP0biv7scYuXr1qs3T09P2448/Zk/RuG+MGh9///23rX79+rZjx47ZGjZsSOjOoYwYH6GhobZXXnnFmIJhKKaXI0dKSEjQoUOHVLduXfsyFxcX1a1bV3v37k1zm3379unJJ590WFavXj3t27dPkhQdHa1z58459FmgQAHVqFHjjn3iwWTE+MDD5X6NkWvXrslkMqlgwYLZUjfun/sxRhISErRq1SoVKFBAFosl22qH8YwaH8nJyRo+fLh69eql/+//+/8MqR3GM/Lvj127dunJJ59UkyZN9Pbbb+vSpUvZXj+yn6uzCwCy4tKlS7JarSpWrJjD8mLFiunkyZNpbnP+/HkVL148Vfvz589Lks6dO2dfdqc2yBmMGB94uNyPMXLz5k1NmzZNzZs3l7u7e/YUjvvGyDHyf//3f3rttdcUHx8vDw8PffTRRypatGj2HgAMZdT4WLhwoVxdXRUcHJz9ReO+MWp81K9fX40aNVKZMmV09uxZvffee+rTp49WrVols9mc/QeCbEPoBgAgmyUmJmro0KGy2WwaO3ass8vBAyYwMFCRkZG6dOmSPv/8c4WEhOiLL75I9R90PFp+/fVXLV++XOHh4TKZTM4uBw+g5s2b239PeZDac889Z7/6jQcX08uRIxUpUkRms1kXLlxwWH7hwoVUnxKmKF68eKqrDbe39/DwsC/LaJ94MBkxPvBwMXKMJCYmKiQkRDExMfroo4+4yp1DGTlG8uXLp/Lly8vX11eTJk2Sq6urVq9enb0HAEMZMT52796tCxcuqGHDhvLy8pKXl5f+/PNPTZkyRUFBQcYcCAxxv/4fUrZsWRUpUkR//PHHvRcNQxG6kSO5ubmpWrVq2rFjh31ZcnKyduzYIT8/vzS38fX11U8//eSw7Mcff5Svr68kqUyZMvLw8HDo89q1a9q/f/8d+8SDyYjxgYeLUWMkJXD/8ccfWrp0qYoUKWJI/TDe/fx7JDk5WQkJCfdcM+4fI8ZH69attXbtWkVGRtp/SpQooV69emnRokWGHQuy3/36++Pvv//W5cuX7ReO8ABz9pPcgKxav369rXr16rbw8HDb8ePHbW+99ZYtICDAdu7cOZvNZrMNHz7cNm3aNHv7X375xebl5WVbvHix7fjx47ZZs2al+ZVhAQEBti1bttiOHDlie+WVV/jKsBzKiPFx6dIl22+//WbbunWrzdPT07Z+/Xrbb7/9Zvv333/v+/Hh3mX3GElISLD179/f9vTTT9sOHz7s8JUuN2/edMox4t5k9xi5fv26bfr06ba9e/faoqOjbQcPHrSFhYXZqlevbjt27JhTjhFZZ8S/M//F08tzruweH9euXbO98847tr1799rOnj1r+/HHH21t2rSxNW7cmH9jcgDu6UaO1axZM128eFGzZs3SuXPnVLVqVS1atMg+Deevv/6Si8v/JnP4+/tr2rRpmjlzpt577z1VqFBBc+fOlaenp71Nnz59FB8fr9GjRys2NlY1a9bUokWLlDt37vt+fLg3RoyP7777TiNHjrS/fvXVVyVJgwYN0uDBg+/TkSG7ZPcY+eeff/Tdd99JunXF6nbLly9XYGDgfToyZJfsHiNms1knT55URESELl26pMKFC8vb21uffPIJT6rOgYz4dwYPDyP+/jh27JgiIyN19epVlShRQk899ZSGDh0qNzc3pxwjMs5ks9lszi4CAAAAAICHEfd0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAIAczWKxKDo62tll3HdhYWEaMGBAtvf7qJ5PADAKoRsAAKQSFhYmi8Wi0aNHp1o3duxYWSwWhYWFOaGy9J09e1avv/666tWrJ29vbz399NN65ZVXdOLECXsbi8WiLVu2pNr2TkF27969qlq1qvr27ZtqXXR0tCwWi/0nMDBQPXv21G+//Za9BwYAyJEI3QAAIE2lSpXShg0bdOPGDfuymzdv6quvvtLjjz/uxMruLDExUT179tTVq1c1Z84cbdq0STNmzJCnp6euXr2a5X5Xr16tl156ST///LP++eefNNssXbpUUVFRWrRokeLi4tSnTx/FxsZmeZ8AgIcDoRsAAKTJy8tLpUqV0tdff21f9vXXX6tUqVKqWrWqQ9vk5GTNnz9fQUFB8vHxUatWrbRp0yb7eqvVqjfeeMO+vkmTJlq2bJlDHylXmRcvXqx69eopMDBQY8eOVWJiYoZrPn78uM6cOaO3335bvr6+Kl26tGrWrKlXX31Vvr6+WToP169f14YNG9S5c2c988wzioiISLNd4cKF5eHhIW9vb40YMULnz5/X/v37U7U7deqULBaLw5V36VZof+655yRl7Hz9V1BQkJYuXeqwrHXr1po9e7b9dWxsrEaNGqU6derI399fwcHBOnLkSEZOAwAgiwjdAADgjtq1a6fw8HD76zVr1qht27ap2s2fP1+RkZEaO3as1q9frx49emj48OHatWuXpFuh/LHHHtP777+v9evXa+DAgZoxY4Y2bNjg0M/OnTt15swZLVu2TO+8844iIiLuGHLTUrRoUbm4uGjz5s2yWq1ZPGpHGzduVKVKlVSpUiW1atVKa9askc1mu+s2efLkkaQ0PzCoWLGiqlevrnXr1jksX7t2rVq0aCEp4+crs4YOHaoLFy5o4cKFCg8PV7Vq1dS9e3ddvnz5nvoFANyZq7MLAAAAD65WrVpp+vTp+vPPPyVJe/bs0XvvvWcP05KUkJCg+fPna8mSJfLz85MklS1bVr/88otWrVql2rVrK1euXBoyZIh9m7Jly2rfvn3atGmTmjVrZl9eqFAhjR49WmazWZUrV1aDBg20Y8cOvfjiixmqt2TJknrzzTf17rvvas6cOapevboCAwPVqlUrlS1b1qHta6+9JrPZ7LAsISFBDRo0cFi2evVqtWrVSpJUv359Xb16Vbt27VJgYGCaNcTGxuqDDz5Qvnz55OPjk2abli1b6pNPPlFISIikW1e/Dx06pHfffVeSMny+MmP37t06cOCAduzYITc3N0lSaGiotmzZos2bN6tjx45Z6hcAcHeEbgAAcEdFixa1T6m22Wx65plnVLRoUYc2f/zxh+Lj49WzZ0+H5YmJiQ7T0D/55BOtWbNGMTExunnzphITE1WlShWHbZ544gmHIOzh4aFjx45lquauXbuqdevW2rVrl/bv369NmzZp/vz5+vDDD/XUU0/Z240cOVJ169Z12HbatGkOV8hPnjypgwcPau7cuZIkV1dXNWvWTKtXr04Vujt16iQXFxfFxcWpbNmymjlzpooXL55mjc2bN9fUqVO1b98++fr6at26dapWrZoqV66cqfOVGUePHlVcXFyqum/cuKEzZ85kuV8AwN0RugEAwF21a9dO48aNkyS9/fbbqdbHxcVJujXFvGTJkg7rUq6orl+/XlOmTFFoaKj8/PyUP39+LV68ONU9z66ujv81MZlM6U7lTou7u7uCgoIUFBSkkJAQ9erVK1Xo9vDwUPny5R22y58/v8PDz1avXq2kpCTVr1/fvsxms8nNzU2jR49WgQIF7MtnzJihJ554QoULF1bBggXvWp+Hh4fq1Kmjr776Sr6+vvrqq6/UuXNn+/qMnq/bmUymVMuSkpLsv1+/fl0eHh5asWJFqna3HwcAIHsRugEAwF3Vr19fiYmJMplMqlevXqr1lStXlpubm2JiYlS7du00+9izZ4/8/PzUtWtX+7L7dXXVZDKpUqVK2rNnT6a2S0pK0pdffqmwsDCHsC5JAwcOTBWUS5UqpXLlymW4/5YtW+rdd99V8+bNdfbsWYdp41k5X0WLFtW///5rf33t2jWH79uuVq2azp8/L7PZrDJlymS4TgDAvSF0AwCAuzKbzdq4caP99/9yd3dXz549NXnyZNlsNtWsWVNXr17Vnj175O7urjZt2qh8+fKKjIzU9u3bVaZMGX355Zc6ePBgtoe/w4cPa9asWWrdurWeeOIJ5cqVS7t27dKaNWvUu3fvTPW1detWXblyRe3bt091Jbhx48ZavXq1Q+jOrEaNGmnMmDEaM2aMAgMDHWYJZOV81alTRxEREQoKClKBAgU0a9Ysubj875m5devWla+vrwYOHKjhw4erQoUK+vfff7Vt2zY999xz8vb2zvKxAADujNANAADS5e7uftf1ISEhKlq0qObPn6/o6GgVKFBAXl5e6t+/v6Rb9zsfPnxYr776qkwmk5o3b64uXbro+++/z9Y6S5YsqdKlS2vu3LmKjo6WyWRS6dKlNXjwYPXo0SNTfa1evVp169ZNc+p1kyZNtGjRIh05ciTdc3Mn7u7uatiwoTZu3KhJkyY5rMvK+erXr5+io6PVr18/FShQQEOHDnW40m0ymbRgwQLNnDlTI0eO1KVLl1S8eHEFBATc8d5zAMC9M9mycqMUAADAA8Jisejbb79lynQ24XwCQPbie7oBAAAAADAIoRsAAAAAAIMQugEAQI42aNCgdL+iCxnH+QSA7MU93QAAAAAAGIQr3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQf5/607xP3zHUV4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8. SUMMARY\n"
      ],
      "metadata": {
        "id": "ASYOabEq4eap"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u4lTJ-BUsgMT",
        "outputId": "da0c5c9b-3d6a-4bea-f706-2032f8b7d297"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "✓ Dataset: 10000 records analyzed\n",
            "✓ Overall failure rate: 3.39%\n",
            "✓ Best performing model: XGBoost\n",
            "✓ Best F1-Score: 0.7552\n",
            "✓ Best ROC-AUC: 0.9656%\n",
            "\n",
            "Top 3 Critical Features for Predicting Failures:\n",
            "  5. Torque [Nm]\n",
            "  2. Air temperature [K]\n",
            "  6. Tool wear [min]\n"
          ]
        }
      ],
      "source": [
        "print(f\"\\n✓ Dataset: {len(df)} records analyzed\")\n",
        "print(f\"✓ Overall failure rate: {df['Machine failure'].mean()*100:.2f}%\")\n",
        "print(f\"✓ Best performing model: {best_model_name}\")\n",
        "print(f\"✓ Best F1-Score: {results[best_model_name]['f1']:.4f}\")\n",
        "print(f\"✓ Best ROC-AUC: {results[best_model_name]['roc_auc']:.4f}%\")\n",
        "\n",
        "print(\"\\nTop 3 Critical Features for Predicting Failures:\")\n",
        "for idx, row in feature_importance_df.head(3).iterrows():\n",
        "    print(f\"  {idx+1}. {row['Feature']}\")"
      ]
    }
  ]
}