kidney_disease_prediction.ipynb

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    "colab": {
      "provenance": []
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Installing and import necessary libraries and packages"
      ],
      "metadata": {
        "id": "AhspMWhP6LPz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qTuS-WHLQMll",
        "outputId": "73f67a45-3e47-46b2-a1c9-d24f558fcfb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.10/dist-packages (from kaggle) (1.16.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from kaggle) (2023.11.17)\n",
            "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.8.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from kaggle) (4.66.1)\n",
            "Requirement already satisfied: python-slugify in /usr/local/lib/python3.10/dist-packages (from kaggle) (8.0.1)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from kaggle) (2.0.7)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.10/dist-packages (from kaggle) (6.1.0)\n",
            "Requirement already satisfied: webencodings in /usr/local/lib/python3.10/dist-packages (from bleach->kaggle) (0.5.1)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.10/dist-packages (from python-slugify->kaggle) (1.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->kaggle) (3.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5dJ-VLkhDZqI"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "import joblib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report, confusion_matrix, roc_curve, auc"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Fetching the disease dataset with the Kaggle API"
      ],
      "metadata": {
        "id": "oiTtnoA26Rac"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir ~/.kaggle\n",
        "!touch ~/.kaggle/kaggle.json\n",
        "\n",
        "api_token = {\"username\":\"leylaeminova\",\"key\":\"1208531f4079d9b739761ed6f33dceb1\"}\n",
        "\n",
        "\n",
        "with open('/root/.kaggle/kaggle.json', 'w') as file:\n",
        "    json.dump(api_token, file)\n",
        "\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "\n",
        "from kaggle.api.kaggle_api_extended import KaggleApi"
      ],
      "metadata": {
        "id": "TjwhSts8WcN3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fetch_disease_dataset(dataset_slug, destination_dir='./', unzip=True):\n",
        "\n",
        "    os.environ['KAGGLE_USERNAME'] = 'leylaeminova'\n",
        "    os.environ['KAGGLE_KEY'] = '1208531f4079d9b739761ed6f33dceb1'\n",
        "\n",
        "    api = KaggleApi()\n",
        "    api.authenticate()\n",
        "\n",
        "    try:\n",
        "        api.dataset_download_files(dataset_slug, path=destination_dir, unzip=unzip)\n",
        "\n",
        "        files = os.listdir(destination_dir)\n",
        "\n",
        "        return files\n",
        "\n",
        "    except Exception as e:\n",
        "        print(f\"Error downloading dataset: {str(e)}\")\n",
        "        return []\n",
        "\n",
        "\n",
        "dataset_slug = 'mansoordaku/ckdisease'\n",
        "destination_directory = './disease_dataset'\n",
        "\n",
        "downloaded_files = fetch_disease_dataset(dataset_slug, destination_directory)\n",
        "\n",
        "if downloaded_files:\n",
        "    print(f\"Download successful. Files: {downloaded_files}\")\n",
        "else:\n",
        "    print(\"Download failed.\")\n"
      ],
      "metadata": {
        "id": "LUiigAmZHqoJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "70a26cfd-cd14-49c9-bf23-50dd8e924ae3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Download successful. Files: ['kidney_disease.csv']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reading the dataset, checking its rows, datatypes and shape"
      ],
      "metadata": {
        "id": "BpdlT2yP6c2X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/disease_dataset/kidney_disease.csv')\n",
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHs_trY1H1n8",
        "outputId": "5b84ace4-538e-4b06-e8c6-e0cc2e77a92a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 26 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   id              400 non-null    int64  \n",
            " 1   age             391 non-null    float64\n",
            " 2   bp              388 non-null    float64\n",
            " 3   sg              353 non-null    float64\n",
            " 4   al              354 non-null    float64\n",
            " 5   su              351 non-null    float64\n",
            " 6   rbc             248 non-null    object \n",
            " 7   pc              335 non-null    object \n",
            " 8   pcc             396 non-null    object \n",
            " 9   ba              396 non-null    object \n",
            " 10  bgr             356 non-null    float64\n",
            " 11  bu              381 non-null    float64\n",
            " 12  sc              383 non-null    float64\n",
            " 13  sod             313 non-null    float64\n",
            " 14  pot             312 non-null    float64\n",
            " 15  hemo            348 non-null    float64\n",
            " 16  pcv             330 non-null    object \n",
            " 17  wc              295 non-null    object \n",
            " 18  rc              270 non-null    object \n",
            " 19  htn             398 non-null    object \n",
            " 20  dm              398 non-null    object \n",
            " 21  cad             398 non-null    object \n",
            " 22  appet           399 non-null    object \n",
            " 23  pe              399 non-null    object \n",
            " 24  ane             399 non-null    object \n",
            " 25  classification  400 non-null    object \n",
            "dtypes: float64(11), int64(1), object(14)\n",
            "memory usage: 81.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 412
        },
        "id": "Zi8F7wshH_NG",
        "outputId": "5d7ef18d-b1fa-4660-cd7e-2ca4e4413854"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "   id   age     bp     sg   al   su       rbc        pc         pcc  \\\n",
              "0   0  48.0   80.0  1.020  1.0  0.0       NaN    normal  notpresent   \n",
              "1   1   7.0   50.0  1.020  4.0  0.0       NaN    normal  notpresent   \n",
              "2   2  62.0   80.0  1.010  2.0  3.0    normal    normal  notpresent   \n",
              "3   3  48.0   70.0  1.005  4.0  0.0    normal  abnormal     present   \n",
              "4   4  51.0   80.0  1.010  2.0  0.0    normal    normal  notpresent   \n",
              "5   5  60.0   90.0  1.015  3.0  0.0       NaN       NaN  notpresent   \n",
              "6   6  68.0   70.0  1.010  0.0  0.0       NaN    normal  notpresent   \n",
              "7   7  24.0    NaN  1.015  2.0  4.0    normal  abnormal  notpresent   \n",
              "8   8  52.0  100.0  1.015  3.0  0.0    normal  abnormal     present   \n",
              "9   9  53.0   90.0  1.020  2.0  0.0  abnormal  abnormal     present   \n",
              "\n",
              "           ba  ...  pcv     wc   rc  htn   dm  cad appet   pe  ane  \\\n",
              "0  notpresent  ...   44   7800  5.2  yes  yes   no  good   no   no   \n",
              "1  notpresent  ...   38   6000  NaN   no   no   no  good   no   no   \n",
              "2  notpresent  ...   31   7500  NaN   no  yes   no  poor   no  yes   \n",
              "3  notpresent  ...   32   6700  3.9  yes   no   no  poor  yes  yes   \n",
              "4  notpresent  ...   35   7300  4.6   no   no   no  good   no   no   \n",
              "5  notpresent  ...   39   7800  4.4  yes  yes   no  good  yes   no   \n",
              "6  notpresent  ...   36    NaN  NaN   no   no   no  good   no   no   \n",
              "7  notpresent  ...   44   6900    5   no  yes   no  good  yes   no   \n",
              "8  notpresent  ...   33   9600  4.0  yes  yes   no  good   no  yes   \n",
              "9  notpresent  ...   29  12100  3.7  yes  yes   no  poor   no  yes   \n",
              "\n",
              "  classification  \n",
              "0            ckd  \n",
              "1            ckd  \n",
              "2            ckd  \n",
              "3            ckd  \n",
              "4            ckd  \n",
              "5            ckd  \n",
              "6            ckd  \n",
              "7            ckd  \n",
              "8            ckd  \n",
              "9            ckd  \n",
              "\n",
              "[10 rows x 26 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>age</th>\n",
              "      <th>bp</th>\n",
              "      <th>sg</th>\n",
              "      <th>al</th>\n",
              "      <th>su</th>\n",
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              "      <th>appet</th>\n",
              "      <th>pe</th>\n",
              "      <th>ane</th>\n",
              "      <th>classification</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>44</td>\n",
              "      <td>7800</td>\n",
              "      <td>5.2</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>ckd</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>7.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>38</td>\n",
              "      <td>6000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
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              "      <td>no</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>62.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>31</td>\n",
              "      <td>7500</td>\n",
              "      <td>NaN</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>48.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.005</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>32</td>\n",
              "      <td>6700</td>\n",
              "      <td>3.9</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>51.0</td>\n",
              "      <td>80.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "      <td>normal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>35</td>\n",
              "      <td>7300</td>\n",
              "      <td>4.6</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
              "      <td>good</td>\n",
              "      <td>no</td>\n",
              "      <td>no</td>\n",
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              "      <th>5</th>\n",
              "      <td>5</td>\n",
              "      <td>60.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.015</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>39</td>\n",
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              "      <td>4.4</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>6</td>\n",
              "      <td>68.0</td>\n",
              "      <td>70.0</td>\n",
              "      <td>1.010</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>7</td>\n",
              "      <td>24.0</td>\n",
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              "      <td>1.015</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>abnormal</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>notpresent</td>\n",
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              "      <td>44</td>\n",
              "      <td>6900</td>\n",
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              "      <td>no</td>\n",
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              "      <th>8</th>\n",
              "      <td>8</td>\n",
              "      <td>52.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>1.015</td>\n",
              "      <td>3.0</td>\n",
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              "      <td>33</td>\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>9</td>\n",
              "      <td>53.0</td>\n",
              "      <td>90.0</td>\n",
              "      <td>1.020</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>abnormal</td>\n",
              "      <td>present</td>\n",
              "      <td>notpresent</td>\n",
              "      <td>...</td>\n",
              "      <td>29</td>\n",
              "      <td>12100</td>\n",
              "      <td>3.7</td>\n",
              "      <td>yes</td>\n",
              "      <td>yes</td>\n",
              "      <td>no</td>\n",
              "      <td>poor</td>\n",
              "      <td>no</td>\n",
              "      <td>yes</td>\n",
              "      <td>ckd</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10 rows × 26 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b6bfd18-87c2-4e83-b844-fa333d4a9767')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7b6bfd18-87c2-4e83-b844-fa333d4a9767 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7b6bfd18-87c2-4e83-b844-fa333d4a9767');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-89075f95-682f-4f06-8cd6-70c32ff35e47\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-89075f95-682f-4f06-8cd6-70c32ff35e47')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-89075f95-682f-4f06-8cd6-70c32ff35e47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8Imi0PpAWUQ",
        "outputId": "2e9ae575-df55-4fd9-abba-9e58f32e82b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(400, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Data Preprocessing & EDA"
      ],
      "metadata": {
        "id": "mr-3K-uY7YFl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Identifying and handling missing values. Dropping identifier, unnecessary and highly imbalanced rows"
      ],
      "metadata": {
        "id": "fgbglFMQ6oaB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KT1DJS4H0-u",
        "outputId": "e36e80dc-673f-4551-f7cc-ae19410da766"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                  0\n",
              "age                 9\n",
              "bp                 12\n",
              "sg                 47\n",
              "al                 46\n",
              "su                 49\n",
              "rbc               152\n",
              "pc                 65\n",
              "pcc                 4\n",
              "ba                  4\n",
              "bgr                44\n",
              "bu                 19\n",
              "sc                 17\n",
              "sod                87\n",
              "pot                88\n",
              "hemo               52\n",
              "pcv                70\n",
              "wc                105\n",
              "rc                130\n",
              "htn                 2\n",
              "dm                  2\n",
              "cad                 2\n",
              "appet               1\n",
              "pe                  1\n",
              "ane                 1\n",
              "classification      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['age'] = df['age'].fillna(df['age'].mean())\n",
        "df.drop(['id', 'sg', 'su'], axis = 1, inplace = True)"
      ],
      "metadata": {
        "id": "OKoDWV9Syzp9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RMiaVT116klt",
        "outputId": "06a2788e-39f4-4bb1-e60a-cca2b1647483"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 23 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             400 non-null    float64\n",
            " 1   bp              388 non-null    float64\n",
            " 2   al              354 non-null    float64\n",
            " 3   rbc             248 non-null    object \n",
            " 4   pc              335 non-null    object \n",
            " 5   pcc             396 non-null    object \n",
            " 6   ba              396 non-null    object \n",
            " 7   bgr             356 non-null    float64\n",
            " 8   bu              381 non-null    float64\n",
            " 9   sc              383 non-null    float64\n",
            " 10  sod             313 non-null    float64\n",
            " 11  pot             312 non-null    float64\n",
            " 12  hemo            348 non-null    float64\n",
            " 13  pcv             330 non-null    object \n",
            " 14  wc              295 non-null    object \n",
            " 15  rc              270 non-null    object \n",
            " 16  htn             398 non-null    object \n",
            " 17  dm              398 non-null    object \n",
            " 18  cad             398 non-null    object \n",
            " 19  appet           399 non-null    object \n",
            " 20  pe              399 non-null    object \n",
            " 21  ane             399 non-null    object \n",
            " 22  classification  400 non-null    object \n",
            "dtypes: float64(9), object(14)\n",
            "memory usage: 72.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x) #removing unnecessary tab chars"
      ],
      "metadata": {
        "id": "KvLM46npFk8_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def drop_columns_with_nulls(dataset, threshold=50):\n",
        "\n",
        "    # Identify columns with more than the specified threshold of null values\n",
        "    columns_to_drop = df.columns[df.isnull().sum() > threshold]\n",
        "\n",
        "    # Drop the identified columns\n",
        "    df_dropped = df.drop(columns=columns_to_drop)\n",
        "\n",
        "    return df_dropped"
      ],
      "metadata": {
        "id": "7_RP0uxA3s0L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = drop_columns_with_nulls(df)"
      ],
      "metadata": {
        "id": "PgitLUWG4EBW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fill_nulls_with_mode(dataset):\n",
        "\n",
        "    df_filled = df.apply(lambda col: col.fillna(col.mode()[0]))\n",
        "\n",
        "    return df_filled\n",
        "df = fill_nulls_with_mode(df)"
      ],
      "metadata": {
        "id": "RUfGEusB4cwV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaAKab0k3gk0",
        "outputId": "458c149c-af61-4641-a5dc-3474af6ecb1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             400 non-null    float64\n",
            " 1   bp              400 non-null    float64\n",
            " 2   al              400 non-null    float64\n",
            " 3   pcc             400 non-null    object \n",
            " 4   ba              400 non-null    object \n",
            " 5   bgr             400 non-null    float64\n",
            " 6   bu              400 non-null    float64\n",
            " 7   sc              400 non-null    float64\n",
            " 8   htn             400 non-null    object \n",
            " 9   dm              400 non-null    object \n",
            " 10  cad             400 non-null    object \n",
            " 11  appet           400 non-null    object \n",
            " 12  pe              400 non-null    object \n",
            " 13  ane             400 non-null    object \n",
            " 14  classification  400 non-null    object \n",
            "dtypes: float64(6), object(9)\n",
            "memory usage: 47.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualizing numeric and categorical features to see their distribution"
      ],
      "metadata": {
        "id": "nVS-qm9E7G9C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_numeric_features(dataset):\n",
        "  plt.figure(figsize = (20, 15))\n",
        "  plotnumber = 1\n",
        "  num_cols = dataset.select_dtypes(include=['float64', 'int64'])\n",
        "  for column in num_cols:\n",
        "      if plotnumber <= 14:\n",
        "          ax = plt.subplot(3, 5, plotnumber)\n",
        "          sns.histplot(df[column], kde=True)\n",
        "          plt.xlabel(column)\n",
        "\n",
        "      plotnumber += 1\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "TBT6n5q2MR2P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_numeric_features(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        },
        "id": "Gqpa3Hr3MvRC",
        "outputId": "6c070e42-c8d8-48c9-f171-02c153231a6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 6 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_cols = df.select_dtypes(include=['object']).columns.tolist()\n",
        "\n",
        "def visualize_cat_cols(dataset):\n",
        "  fig, axes = plt.subplots(3, 3, figsize=(20, 5 * 3))\n",
        "\n",
        "  for i, column in enumerate(cat_cols):\n",
        "      ax = axes[i // 3, i % 3]\n",
        "      sns.countplot(dataset, x = column, palette='rocket', ax=ax)\n",
        "      ax.set_title(column)\n",
        "      ax.set_xlabel(column)\n",
        "      ax.set_ylabel('Count')\n",
        "\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "cbUGl5nrEkCX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "visualize_cat_cols(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "NHpatFANg-LJ",
        "outputId": "ca414e79-4078-4285-9673-f5c105110248"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 9 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking outliers with the help of box plots"
      ],
      "metadata": {
        "id": "kMAiEYbV7RZL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = df.select_dtypes(include=['float64', 'int64'])\n",
        "fig = px.box(df, y=num_cols.drop(['al', 'sc'], axis = 1).columns,\n",
        "             title='Box Plots for detecting outliers',\n",
        "             labels={'variable': 'Numerical Columns', 'value': 'Values'},\n",
        "             template='plotly_white')\n",
        "fig.show()"
      ],
      "metadata": {
        "id": "AEWchjUsRfYd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "888735fd-80f0-4a59-ecf2-e505e7a9ec51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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Columns\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Values\"}},\"legend\":{\"tracegroupgap\":0},\"title\":{\"text\":\"Box Plots for detecting outliers\"},\"boxmode\":\"group\"},                        {\"responsive\": true}                    ).then(function(){\n",
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_cols = df.select_dtypes(include=['float64', 'int64'])\n",
        "fig = px.box(df, y=num_cols.drop(['age', 'bp','bgr', 'bu'], axis = 1).columns,\n",
        "             title='Box Plots for detecting outliers',\n",
        "             labels={'variable': 'Numerical Columns', 'value': 'Values'},\n",
        "             template='plotly_white')\n",
        "fig.update_layout(yaxis=dict(range=[0, 10]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "VcK81if6MXxV",
        "outputId": "ed58919a-907e-422a-f97e-68d6841cd426"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "ckd       250\n",
              "notckd    150\n",
              "Name: classification, dtype: int64"
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        "plt.show()"
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          "data": {
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              "<Figure size 1500x800 with 2 Axes>"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting distribution of kidney disease according to age groups"
      ],
      "metadata": {
        "id": "0WXZ_HX67myn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "age_bins = [18, 30, 40, 50, 60, 70, 80, 90, 100]\n",
        "age_labels = ['18-30', '30-40', '40-50', '50-60', '60-70', '70-80', '80-90', '90-100']\n",
        "\n",
        "# Create a new column 'age_group' based on age bins\n",
        "df_age = df.copy()\n",
        "df_age['age_group'] = pd.cut(df_age['age'], bins=age_bins, labels=age_labels, right=False)\n",
        "\n",
        "# Plot the distribution\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.countplot(x='age_group', hue='classification', data=df_age, palette='viridis')\n",
        "plt.title('Chronic Kidney Disease Distribution According to Age Groups')\n",
        "plt.xlabel('Age Groups')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0qjOJIrlJsmo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 436
        },
        "outputId": "542df2bd-3c3b-4b15-897d-b5f79c9ed023"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plotting target column's distribution. There is an imbalance but it is not that horrifying"
      ],
      "metadata": {
        "id": "VB2NVXWc7vr9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "value_counts = df['classification'].value_counts()\n",
        "categories = value_counts.index\n",
        "counts = value_counts.values\n",
        "\n",
        "\n",
        "plt.pie(counts, labels=categories, autopct='%1.1f%%', startangle=90, pctdistance=0.85, colors=sns.color_palette('Set2'))\n",
        "\n",
        "inner_circle = plt.Circle((0, 0), 0.7, color='white')\n",
        "fig = plt.gcf()\n",
        "fig.gca().add_artist(inner_circle)\n",
        "\n",
        "plt.axis('equal')\n",
        "plt.title('Value Count')\n",
        "\n",
        "plt.legend(categories, title='Classes', loc='best')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "VcrqUlHLnHu7",
        "outputId": "ca5e77bd-3adb-4970-98c6-066812c643e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7bc9224f7730>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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/lEOVgZ8SGhDWDSImAYhJhOjRD8I3UHUqOsXWZQAApk+fjtTUVBw7duyMdQPV1dUICgrqVAYWL16Mt956C1lZWR3H3XPPPfjyyy87lYEfmzdvHhoaGvDNN9+ccd8TTzyBb7/9FikpKXjyySexfPlyHD58GG5u5x9ct1gs6NmzJx5++GE8/PDDF/eFXyBr/B1wmoDICnQpcby2DKmVBThW7fhv/k5F6kBZHmRZHnDoB0iI9tGChCEQ8YMhwrurTkg29uabb2L06NEYMWIEnnvuOQwaNAhmsxnr16/H22+/jbS0tE7HJyYmIjc3F59//jmuuuoqfPvtt1ixYkXH/U1NTXj00Udxyy23IC4uDvn5+di7dy/mzJkDAFi4cCGmT5+OpKQkVFVVYdOmTejXrx+A9sWF7777LubNm4fHHnsMISEhOH78OD7//HP885//xL59+7Bx40ZMnToVERER2L17N8rKyjo+31GxDBBdojbdgvTqYhwsz0dKZT7q2lpUR3IRsmP9gdz+FRAY1l4K4ocA3ZMgTHxZM5r4+HgcOHAAL7zwAh555BEUFRUhPDwcw4YNw9tvv33G8TNnzsRvf/tb/OY3v0FLSwuuv/56PPXUU3j22WcBACaTCRUVFbj99ttRUlKCsLAwzJ49G4sWLQLQ/tv8Aw88gPz8fAQEBGDatGlYsmQJACAmJgbbt2/H448/jqlTp6KlpQU9e/bEtGnToGkaAgICsGXLFrz++uuora1Fz549sXjxYkyfPt1u369LwWkCoovQbG5DamUBDlbk40hVIZoNsuDPoacJLoanN0SvK4D4IRBxA9v3SCCbssc0AZ0bpwmI7KDJ3IYD5bk4UJ6L9OoSmC/3nH6ynZYmyGN7gWN7ITUT0C0Rov9IiKSreAoj0TmwDBCdRUZNKbYXZ+FAeS5adYvqOHSxdAuQlw6Zlw656TOIPiMgBoxpX4hIRJ2wDBD9SHVLI3aWZmNHyQmUNtWpjkPW0toMmboFMnULEBrTXgr6j4Lw4c6IRADLABEsuo6UygJsL8nCkcoingVgdBWFkFv+A7ltORA/GNoVY4BeAyE0XsSVXBfLALmsmtYmbCrMwLbiLNS1NauOQ/amW4DjB6AfPwD4BbevLRg0HiIgTHUyIrtjGSCXU9xYg/UF6dhVks3FgNSuvgpyz3eQ+9a2ry0YMQMiNEZ1KiK7YRkgl3G8phTr8tOQUlnAiQDqmm6BTNsJmbYLSBgMbcT1ENHx5/88IifHMkCGpkuJ5Ip8rMs/iuy6CtVxyGlIICsZelYyENsX2ogZED0HqA5FZDMsA2RIFl3H9pITWF+QxrMC6PLkpUPPSwcie0G7ajqQeCWE4GJDMhaWATIUXUrsLs3GqtxUlDc3qI5DRlKSA33V2+2XWB4+DaLfNdz6+Bzu3fqpXZ/v72N/brXHysnJQVxcHA4ePIghQ4Zc8Of9+GJJzoY/yWQIUkocrMjDNydTUdRYozoOGVllEeS69yF3r4I29haIpOGqExFdNpYBcnoZ1SVYnn0QOfWVqqOQK6kpax8p6JYEbcJtEJE9VSciumSc+CKnVdhQgzeO/IDFqRtZBEidggzoy/4Efc17kPVVqtPQRdB1Ha+88gp69+4NT09P9OjRAy+88MIZx1ksFixYsAB9+/ZFbm4uACAzMxPjxo2Dl5cX+vfvj/Xr19s7vlVxZICcTn1bM1bkHMKO4hPcLZAchIQ8ugMyc3/7eoLh1/HCSE7giSeewLvvvoslS5ZgzJgxKCoqQnp6eqdjWlpaMG/ePOTk5GDr1q0IDw+HruuYPXs2IiMjsXv3btTU1GDhwoVqvggrYRkgpyGlxPaSLHyVnYwGc6vqOERnamuB3LkSMnULxJg57YsMhVCdirpQV1eHpUuX4o033sAdd9wBAEhISMCYMWOQk5MDAKivr8f111+PlpYWbNq0CYGBgQCADRs2ID09HWvXrkVMTPvmVC+++CKmT5+u5GuxBpYBcgoFDdVYdnwPsmrLVUchOr/6Ksg1/4Q8uBHahLkQ3RJVJ6KfSEtLQ0tLCyZPnnzWY+bNm4fu3bvj+++/h7e3d6fPjY2N7SgCADBy5Eib5rU1rhkgh9ZiMePLEwfx/MHVLALkfEqyof/7pfb1BE31qtPQj/z4zf1sZsyYgZSUFOzcudMOidRiGSCHlVyRj2f3r8L6gjTokmsDyHnJozugf/gUZMZe1VHolMTERHh7e2Pjxo1nPebXv/41XnrpJcycORObN2/uuL1fv37Iy8tDUVFRx227du2yaV5b4zQBOZzK5gZ8nrUPhyoLVEchsp7GWuir3gF674E2+RcQvoGqE7k0Ly8vPP7443jsscfg4eGB0aNHo6ysDEeOHOk0dfDggw/CYrHghhtuwOrVqzFmzBhMmTIFSUlJuOOOO/Dqq6+itrYWTz75pMKv5vKxDJBD2VlyAp9n7UOzxaw6CpFtHD8APS8dYsJt0AaMVp3GZqy5I6CtPPXUU3Bzc8PTTz+NwsJCREdH47777jvjuIULF0LXdcyYMQNr1qzBqFGjsGLFCtx9990YMWIEevXqhb/+9a+YNm2agq/COoSUHH8l9RraWrDs+F7sL89VHcUlvbX37EOlZEMJQ6BdeweET4DqJJesubkZ2dnZiIuLg5eXl+o4LskafwccGSDl0qqK8UHGTlS3NqmOQmRfWcnQC7OgXXs7RO8rVachF8YyQMq06RasyEnG9wXHuHUQua6mOujfvAnRfxTExHkQnj6qE5ELYhkgJQoaqvFe+g4UNFarjkLkEOTRHZAFGdBuvB8igtc5IPviqYVkV1JKbChIx4sH17AIEP1UTTn0z/4M/cg21UnIxXBkgOym2dKGD47twsGKPNVRiByXpQ1y7fvQC09ATPo5hIkv02R7/CkjuyhpqsXbR7eiqLFGdRQipyBTN0OW5bZPG/iHqI5DBsdpArK51MoCvJS8lkWA6GIVZ0P/ZBFk7lHVScjgWAbIZqSU+C73MN48sgWN5jbVcYicU1M99OVLoO/5FtwWhmyF0wRkE1wfQGRFUofc9hVk0Qlo0+7m6YdkdSwDZHWlTXV4++gWFHJagMi6spKhL3se2k2/gQiNOf/xClleu9uuz2d6+D27Pt+l+OCDD7Bw4UJUV1df8Of88MMPmDhxIqqqqhAUFGSzbJwmIKs6Vl2CPyevYREgspXqEuif/xmy8LjqJC7t2WefxZAhQ1THsBqWAbKaA+W5+OvhTVwfQGRrLY3Qv1wMeeKQ6iRkECwDZBWbCzPxj7TtMEtddRQi12Buhf7Nm9APc4OiSzFhwgQ89NBDeOyxxxASEoKoqCg8++yzHffn5ubipptugp+fHwICAnDrrbeipKQEQPtw/6JFi3Do0CEIISCEwAcffAAAqK6uxr333ovIyEh4eXnhiiuuwKpVq7rMUFZWhuHDh2PWrFloaWkBAHz33XdISkqCt7c3Jk6ciJycHFt+GzpwzQBdtm9OpuDb3MOqYxC5Ht0Cue596I210EbMUJ3G6Xz44Yd4+OGHsXv3buzcuRN33nknRo8ejcmTJ3cUgc2bN8NsNuOBBx7A3Llz8cMPP2Du3Lk4fPgw1qxZgw0bNgAAAgMDoes6pk+fjrq6OnzyySdISEjA0aNHYTKZznjuvLw8XHvttbjmmmvw3nvvwWQyIS8vD7Nnz8YDDzyAX/3qV9i3bx8eeeQRu3wvWAbokulSx2fH92FLMecuiVSS25ZDb6iBmHAbhBCq4ziNQYMG4ZlnngEAJCYm4o033sDGje2X805NTUV2djZiY2MBAB999BEGDBiAvXv34qqrroKfnx/c3NwQFRXV8Xjr1q3Dnj17kJaWhqSkJABAfHz8Gc977NgxXHvttZg1axZef/31jr+zt99+GwkJCVi8eDEAoE+fPkhNTcXLL79su2/CKZwmoEvSplvw97RtLAJEDkIe3AD53buQFrPqKE5j0KBBnT6Ojo5GaWkp0tLSEBsb21EEAKB///4ICgpCWlraWR8vOTkZ3bt37ygCXWlqasLYsWMxe/ZsLF26tFN5S0tLw9VXX93p+JEjR17sl3VJWAboojWZW7E0dROSK/JVRyGiH5HHdkP/eilka7PqKE7B3d2908dCCOj6pa978vb2Pu8xnp6emDJlClatWoWCgoJLfi5rYxmgi9LQ1orFKRuRWVuqOgoRdeXkUehfvArZWKc6idPq168f8vLykJf3v03Tjh49iurqavTv3x8A4OHhAYvF0unzBg0ahPz8fGRkZJz1sTVNw8cff4xhw4Zh4sSJKCws7PS8e/bs6XT8rl27rPElnRfLAF2wJnMrXj/8PfIaqlRHIaJzKclpLwRN9aqTOKUpU6Zg4MCBmD9/Pg4cOIA9e/bg9ttvx/jx4zF8+HAAQK9evZCdnY3k5GSUl5ejpaUF48ePx7hx4zBnzhysX78e2dnZWL16NdasWdPp8U0mE5YtW4bBgwdj0qRJKC4uBgDcd999yMzMxKOPPopjx47h008/7ThLwda4gJAuSLO5DUsPb0JufaXqKHSZNCFgEhpMQkA79Se8/QGpA7oF0E//aTn/g5HjqiiA/tUSaLf8DsLz/MPX1uYMOwKejRACK1euxIMPPohx48ZB0zRMmzYNf/vb3zqOmTNnDr766itMnDgR1dXVeP/993HnnXdi+fLl+N3vfod58+ahoaEBvXv3xksvvXTGc7i5ueGzzz7D3LlzMWnSJPzwww/o0aMHli9fjt/+9rf429/+hhEjRuDFF1/EggULbP81S175gs6jxWLGXw9vwvHaMtVR6Cw8NBOCPHwQ6OGFQA/vjv8FeXojyMMHwZ4+CPDwgpfJ/fwP9iOytRlorAXqKiHrq4D6aqChBqivhmyoARqq228zt9riyyJr6JYIbfZvIdw9bfLwzc3NyM7ORlxcHLy8vGzyHHRu1vg74MgAnVOrxYw3jvzAIuAgvExuiPULQU+/EPTwC0EvvxAEefrA09T5n7IudehSQpwaBbhUwsML8PACgiLaRwqkBIQGoXV+TNnWAtRXQRbnACUnIUtygNJcoI0L2ZQryIT+zZvQbnoQwu3iyiC5DpYBOqs23YK3jm5BRg0XC6rw0zf+eP9QhHn5ta94lhJSSpi0rt/oNaFBs/Lp5kI7c+OUjvvcPYHgKCAwHOgzApqmtV9ut6YMsuhEe0EozWkvCFzpbn8nj0D/9u/Qbvz1Of8eyXWxDFCXzLoF7xzdirTqYtVRXIa/uycGhnRD36AoxPuHIdzbDwC6fOPXhAAccHOZH7/RCCHaRxQCQn9SEMohi7KA3KOQJ1KAJq56t4usg5Br3gOm3wNxGaNFZEwsA3QGXep4N307DlcVnv9guixR3gEYHNoNQ0JjEecfCqD9zd8Z3vgv1JkFIRwICAH6Xg0BAMUnII8fhMxKBiqLVMV0CTJ9N+DuCXHtHaqjkINhGaAzfJ61nxsK2YgGgd6B4Rgc0h1DwrojzMsPutQBiI6dyExO/MZ/oX5cEGRUHERkHLSxt0DWlENm7m+/Gl9BZvsZDmRVMnULdHdPaBNus+7jci26MpezUdJpPJuAOtlQkI4vThxQHcNQ3DUTBoV0w+DQbhgU0h3ebu6w6PpZ5/tdnbRYIEwmyJYmyBPJQFZy+3QCz1iwKnH1jdBG33zZj2OxWJCZmQkfHx+Eh4fz2gh2JKVEa2srysrKYLFYkJiYCO0SX1dYBqhDcnke3knbBgn+SFhDhJc/xkX3xpio3iwAl6hTMUjdApnyA1DNBa3WIqbeCe2KsZf9OPX19cjPz+fogCI+Pj6Ijo6Gh4fHJT8GywABAHLqKrA4ZQNaudHMZdEgMDAkBhNjktAvOBoWqV/WqX30P1K3QGgmyJNHoCd/D5xI4TTC5TK5QfvZoxAxvS/7oSwWC9ra2qwQii6GyWSCm5vbZY/IsAwQypvr8XLyOtTynPBL5u/uhTFRCZgQnYQgT2+OAthQRymor4ZM/h7y8Nb2jZHo0vgEQJv/FIR/iOokpBDLgItrNLfilUPrUdRYozqKU+odEI4J0Um4MjwWAgIC4JypnUgp2zdBgoTM2Ad5aFP7okO6eBE9oM39vc12KSTHxzLgwiy6jqWHN+FYTYnqKE5nYEgMZvUagm6+QRwFcAAdowVl+dC3LQeyU1RHcjoiaTi0G36tOgYpwjLgwj7J3IOtxcdVx3AqCQHhuCVuCOIDwrkewAF1lILC49C3fAEU8uf7YohRN0O75kbVMUgBlgEXtbPkBD7IsM91so0gxicQs+OGYGBIN5YAJ9BRCk4cah8pKC9QHclJCGgz74fofaXqIGRnLAMuqKChGi8lr+WZAxcg1NMXM3sOwtURvc7YGZAcn9QtgNAgj+6E3PE1UFehOpLjc/eEdtsfIMK7q05CdsQy4GKaLW3488G1KG7i6utz8XP3xIzYAZgQnQQALAFOTuoWQALy0PeQu1cBTfWqIzm2gDBo8/8I4e2vOgnZCcuAi/ln+nbsLTupOobDchMarovtj2nd+8OkaZwOMBipWwCLGXLvasi9qwGLWXUkxxXbF9otj/CiRi6CZcCF/FCYgc+y9qmO4bB6+YViQZ+RCPf2b784EBmWlDpQVQp9zT+B4mzVcRyWGDMH2ogZqmOQHbAMuIicugq8emg9zNyx7QxuQsONPQdiavf+Z1wqmIyrfT2BaB8l2PkNRwm6opmgzXsSIrKn6iRkYywDLqChrRUvHFyNipYG1VEcDkcDiKME5xESDW3+0xDul77vPTk+lgEX8NbRLTjESxJ3wtEA+jGOEpybGDwR2uRfqI5BNsQyYHDbirPwceZu1TEcCkcD6Gw4SnB22s3/BxE/SHUMshGWAQMrb67Hnw58h2b+lgOAowF0YThKcBY+AdBufw7Ch6cbGhHLgEFJKfFa6kZk1PDa7wAQ7OmD3wyYgBifQI4G0AWRUgfKC6F/vRSoq1QdxzHED4bp5odUpyAb4K9GBrWx8BiLwCkJAeF4auh0RHsHsAjQBRNCA0Kjof3iGSCmt+o4juHEIegpP6hOQTbAMmBApU11+DrnkOoYDmF0ZAIeGTQZ3m7unBagiyY0E+DpA+3WxyCuGKM6jkOQP/wbsqpYdQyyMr46GoyUEh9l7Eabi193QBMCc+OH4fakq6FBQOMuanSJhKYBQoM29S6IifMAV/9ZMrdCX/3P9mkUMgwX/6k2ni1Fx5FZ69rTA75uHlh4xSRMjGm/roDg1ABdptM/Q2LIJGhzHgG8fBUnUqw4GzJls+oUZEUsAwZS2dyAr3IOqo6hVLRPIJ4cOh29A8NZAsjqhNCA7onQ5j8NhMaojqOU3PYVZFOd6hhkJSwDBvJ51j6XPo1wUEg3/GHIdQjy8OYFhshmhGYC/IOh/fyPQPxg1XHUaWmE3LpcdQqyEr5iGsSRqkIcqixQHUOZKd364v7+4+CmmbhQkGxOaCbA5A7tpgchhk1VHUcZeXgbZNEJ1THICviqaQAWXcd/sg6ojqHMDT2uwM/ir4QQgqcOkt0ITWv/mRs/F+KaG1XHUURC//4TLiY0AJYBA9hUlIHiplrVMZS4uddg3NiTW6SSWtqomyFGz1YdQ42Sk1xMaAAsA06utrUZq06mqo6hxK3xV2J67ADVMYgAANrV10OMn6s6hhJcTOj8WAac3MqTh9BkaVMdw+7mJQzH5G59Vccg6kQbNhVi0nzVMeyPiwmdHsuAE8utr8T2YtdbvHNr/DBMOLWHAJGj0YZMgphwm+oYdsfFhM6NZcCJfZ61HxKudZ2p2b2GYHK3PqpjEJ2TduW1EGPmqI5hZ1xM6MxYBpzUntIcZNWWqY5hVzf0GIjrYvurjkF0QbQRMyCumak6hn2VnIRM26U6BV0ClgEnZNYtWJGTrDqGXV3XvR9u7DlQdQyii6KNugniqumqY9iV3PVfSBe/NoozYhlwQtuLT6CypVF1DLsZFtYDs+OGqo5BdEm0sbcAicNVx7Cf6lLItJ2qU9BFYhlwMmbdgtX5R1THsJtY32Dc1WckdOlaayPIOKSuQ5t+NxAeqzqK3chdqzg64GRYBpzMtuIsVLnIqIC/uxd+M2D8qUsQc2dBck5C0wDNBG3W/wHe/qrj2EdNGeTRHapTWNUHH3yAoKCgi/qcH374AUIIVFdX2ySTNbEMOJE23YLVea4xKmASGn7dfyz83b14rQFyekIzAT4B0G76DaCZVMexC7l7FaSDXjjt2WefxZAhQ1THcCh8lXUi24qPo7q1SXUMu/h5wnDE+YexCJBhCM0ERMe7zqZENeWGGx0wMr7SOok23YI1eUdVx7CLCdFJGBPdm1MDZDhCaNAGjYcYPFF1FLuw1ejAhAkT8NBDD+Gxxx5DSEgIoqKi8Oyzz3bcn5ubi5tuugl+fn4ICAjArbfeipKSEgDtw/2LFi3CoUOHIISAEAIffPABAKC6uhr33nsvIiMj4eXlhSuuuAKrVq3qMkNZWRmGDx+OWbNmoaWlBQDw3XffISkpCd7e3pg4cSJycnKs/rXbCsuAk9ha5BqjAn2DIjE3YZjqGEQ2JSb+HIh1ge20aysgj2y3yUN/+OGH8PX1xe7du/HKK6/gueeew/r166HrOm666SZUVlZi8+bNWL9+PU6cOIG5c9uvGzF37lw88sgjGDBgAIqKilBUVIS5c+dC13VMnz4d27dvxyeffIKjR4/ipZdegsl05rROXl4exo4diyuuuAJffvklPD09kZeXh9mzZ+PGG29EcnIy7rnnHvz+97+3ydduC26qA9D5tekWrMk3/qhAmJcf7us3FoAEwFEBMjZt5gPQP1kE1JSrjmJTcvcqyAGjIUzWfbsZNGgQnnnmGQBAYmIi3njjDWzcuBEAkJqaiuzsbMTGtp/B8dFHH2HAgAHYu3cvrrrqKvj5+cHNzQ1RUVEdj7du3Trs2bMHaWlpSEpq3+48Pj7+jOc9duwYrr32WsyaNQuvv/46xKkRzLfffhsJCQlYvHgxAKBPnz5ITU3Fyy+/bNWv21Y4MuAEthdnocbgowKeJjc8OGA8PDQ3aII/lmRsQtMAd09osxYC7l6q49hWXSXk4W1Wf9hBgzpfujw6OhqlpaVIS0tDbGxsRxEAgP79+yMoKAhpaWlnfbzk5GR07969owh0pampCWPHjsXs2bOxdOnSjiIAAGlpabj66qs7HT9y5MiL/bKU4auug5NS4vvCDNUxbG5O3FBEePtzwSC5DKGZgKBIiHG3qI5ic3Lvd5C6da9Z4O7u3uljIQT0y3gOb2/v8x7j6emJKVOmYNWqVSgoKLjk53JEfOV1cEeri1DSVKs6hk31DYrE+OhEjgiQyxGaBm3wRCC2n+ootlVbAWQl2+Wp+vXrh7y8POTl5XXcdvToUVRXV6N///Zrm3h4eMBi6bwp0qBBg5Cfn4+MjLP/8qVpGj7++GMMGzYMEydORGFhYafn3bNnT6fjd+1ynus08NXXwX1fYOxRAU+TG+5MGgkLr3RGLkrqFmjTFhh+ukA/uMEuzzNlyhQMHDgQ8+fPx4EDB7Bnzx7cfvvtGD9+PIYPb98WulevXsjOzkZycjLKy8vR0tKC8ePHY9y4cZgzZw7Wr1+P7OxsrF69GmvWrOn0+CaTCcuWLcPgwYMxadIkFBcXAwDuu+8+ZGZm4tFHH8WxY8fw6aefdpyl4AxYBhxYaVMdjlQVnv9AJzYnbigCPbxg4qgAuSihmQDfIONPF+QfgyzLt/nTCCGwcuVKBAcHY9y4cZgyZQri4+Px73//u+OYOXPmYNq0aZg4cSLCw8Px2WefAQCWL1+Oq666CvPmzUP//v3x2GOPnTGCAABubm747LPPMGDAAEyaNAmlpaXo0aMHli9fjq+//hqDBw/GO++8gxdffNHmX6+1CCm56buj+nfWfnxfeEx1DJvpGxSJ3w6crDoGkcOwfPEXIO/si9ycnbhiLLSpd6qOQV3gr2MOqtnShh0lJ1THsBkvTg8QdeIK0wUyfTdkU73qGNQFlgEHtbPkBJotbapj2AynB4g6+990wc9UR7EdcyvkUdtsQkSXh6/EDkhKiU2Fmapj2EzfoEiM49kDRGdoP7tgAtDDuGcXyNStqiNQF/hq7ICMfDrh6ekBndMDRF2SugXadQaeLqgsgswz7looZ8Uy4IB2FBt3rcDNvQYj0MOLowJEZ9ExXTBmtuooNiNTN6uOQD/BaxM4mGZzGw5VGmtnq9MivPwxPjqJVyMkOg+hacDgCZAHNwDVpec89p0dafj7jnTkVLYvzOsfFYQ/XjsE0/vFIqeyDr1f+KLLz/v89om4ZXBcl/ct+GwLPtp3vNNtU/t0w3e/ug4A0GK24Ff/2YZvDuciyt8bf5szElOSunUc+5dNqcirqsfS2V1vxysz90M21UN4+53zayP7YRlwMAcr8tCmn3leqxHc1GsQpJQAywDRBRGjZkF+9/dzHtMt0BcvXD8ciWEBkAA+2puJ2e9vxL6Hb0LfiEDkP3Nbp+Pf3XUMi39IxbS+3c/5uNf17Yb35o7t+NjT7X9X73t35zEcyK/AtoduwJq0fPxy2WYUPjsPQghkV9ThvV3HsPu3M8/+4BYz5NEdEMOmnjMD2Q/Hah3M7tIc1RFsoodfMIaH9+S1B4gukNBM0PqOACJ6nPO4Gwf0wIx+sUgMD0RSeCCenzEcfh5u2H2yDCZNQ1SAT6f/rUw9iZ8NjoOfp/s5H9fTZOr0ecE+nh33pZdW44b+sRgQFYz7x/RDWX0zyhuaAQAPLN+BP98wHAFeHud8fJm+55z3k33xldmB1LQ2Ib26RHUMm5jdawgsVr5QCZHRSd0CbeyF70xo0XX8++AJNLSacU3P8DPu359XjuTCStw14uxX5jttc1Yxop/5FP1f+hIPfLkDFafe7AFgUEwItmeXoKnNjHXpBYgO8EaYrxc+3Z8FLzcTbh7Y6/xhS7Iha419+WZnwmkCB7KnNAcSxtsQsk9gJPoFR6uOQeR0hGYCeg4AYvsA51iBn1pUiTF/XYVmswV+Hu748q7J6B8VfMZx7+/JQL/IIIyKizzn817XtztmDeyFXqF+OFFehz+u3o/r312H7Q/dAJOm4a4RSUgtrMTAV75CmK8XPvvlRFQ1teLZtQew8dfT8dTq/fjPwROIDw3AP28bg26Bvl0+j8zYDzH8uov7ppBNcDtiB/L8gdXIa6hSHcPq/jB0Grr7BHGKgOgSSN0ClOVBX/ansx7TarYgt7oBNU2tWJ6Sg3/tzsD390/vVAia2szo/uznePLawXh4wsCLynCiohZJL36JtfdOw+SkmC6PufvzrRgcE4K4EH/8cfU+7HjoRry6KRVHiqvwxZ1n2XY8Oh6meU9eVBayDb46O4jChhpDFoGhobHo6RfCIkB0iYRmgojsBfS+8qzHeLiZ0DssAMNiw/Di9cMxKCYYf9t6tNMxyw/loLHNjF8O733RGeJDAxDm64Wsiq73P9l0vAhHiqvwwJh++CGrCNP6xsLX0x0/GxKHzVnFZ3/gomzIusqLzkPWx1doB7GnLEd1BKvTIDA7bgg3GCK6TFLXoY39GXCB+3Posv30vx/7154M3DigB8L9vC/6+fOrG1DR2Ixof58z7mtuM+Ohr3bi7VtGw6Rp0KVE26n1QW0WHRb9XIPPEjJz/0XnIetjGXAQew1YBkZFxSPC258bDBFdJqFpEMEREANGn3HfH77dhy1ZxciprENqUSX+8O0+bM4qwrwrEzqOOV5ei60nirHg6q4XDg54aTm+Ts0BANS3tOGx/+7BrpOlyKmsw8aMQsx+fwN6hwZgat9uZ3zu8+uTMa1vdwztHgoAGNUrAl+n5iClsBJvbUvDqLiIc35tMmPfhX4byIa4gNABFDZUo7y5QXUMq3ITGmb2HARdSm4yRGQFUtchRs+CTNsJWMwdt5fVN+Guz7agqLYRgd4eGBgdjO/+33W4ts//3rjf35OB7oG+mJp05ps5ABwrq0FNU/uF0UyaQGphFT7edxzVTa2ICfDBtX1isGjasE57DQDA4aIqfHkoB/sfvqnjtjmD2qcGJrz5LZLCA/HJLyac+wsrzIKsr4LwO3PBI9kPFxA6gLX5R/FVdrLqGFY1IrwX7u47SnUMIsPRv/sHZPpu1TGsSkz8ObShZ1lkSHbB8VsHcLiyUHUEq5sYk8S1AkRWJnULxBDjvWlyqkA9lgHFmsxtyDLYxhvdfYMQHxDGtQJEViY0E0RMAhB27q2EnU5hJmRDjeoULo2v1oodrSqCxWC/QY+PTuRug0Q2InULxOCJqmNYl5SQWQdVp3BpLAOKHa4y1hSBl8kdIyPjua8AkY0IzQQxYBTgcfGnCDq03DTVCVwaX7EVklIabr3AyMg4uHF6gMi2TO4Q/bu+PLCzkvkZqiO4NL5qK5RbX4XatubzH+hEJsX0MeDVFYgcjYQw2ur7xlrICmP9cuRMWAYUOlxVoDqCVSUFRpzaZIj7ChDZkhAaRHAU0L2P6ihWJfPSVUdwWSwDCh2tOsee3U5oQnQSFw4S2Un7aYaTVMewrvyzX5mRbItlQBGzbsHJeuNcoCPQwxtDw2K5cJDIToRmgug9FPANUh3FarhuQB2+ciuSV1+FNt1y/gOdxDURcQBXCxDZmTDWQkKuG1CGZUCRrDpjbTR0ZVgsBLhWgMiuBCDOcWljZ8R1A2qwDChywkC7Dga4e6GnXwgEFw4S2ZUQGhAVB/gEqI5iPVw3oATLgCJZtWWqI1jNwJAY1RGIXJqIG6Q6gtVw3YAaLAMKVDY3oLq1SXUMqxkc2h061wsQqSH19oWERsF1A0qwDChgpPUC7poJ/YOjYeKug0RKCM0E9BwAuLmrjmI1soCjA/bGV3AFThhoiqBfUBTcNZPqGEQuTbi5A7H9VMewnrI81QlcDsuAAkZaPDgopBs3GiJSTFrMEAlDVMewGllurN1ZnQHLgJ2ZdQvyGqpVx7AKAWBIWHduNESkmDC5nVo3YJAzelgG7I6v4nZW2lQHizTGb9I9/ULh7+6lOgYRARA+AUBkT9UxrKOlEbKuSnUKl8IyYGdFjbWqI1jN4NBuhik2RM5O6hZDTRWggqMD9sQyYGfFTTWqI1jNwJBu0IwyLEnk7IQGET9YdQqrkeX5qiO4FJYBOzPKyICb0BDjG8hdB4kchBACCOsGmNxUR7EOrhuwK5YBOytqNMbIQDffIO4tQORghGZqLwQGIDlNYFd8NbcjXUqUNNWpjmEVPf1CICV3HSRyJFJKiIheqmNYR0URJNck2Q3LgB1VNDcY5rLFPfxCYGEZIHIsusU4ZxSYW4Fq42zQ5uhYBuzISIsH4wLC4Mb9BYgcijC5QUTHq45hPVw3YDd8NbcjIy0ejDbSJVOJjCQ0xjCLCLluwH5YBuyo2CBlgIsHiRyXkRYRghsP2Q1f0e2osqVBdQSr4OJBIsdlpEWE0iBbtzsDlgE7qmppVB3BKrh4kMiBGWkRYYNx1lk5OpYBO6pqNUYZ4OJBIsdlqEWELAN2w1d0O2kyt6LFYlYd47KZuHiQyPGFxgCaSXWKy9dYy70G7IRlwE6qWppUR7CKIA9vLh4kcnBCMwF+QapjXD7dAjQaY6M2R8dXdTupbTNGGQj08FYdgYguhG+Q6gTWwakCu2AZsJPa1mbVEayCZYDISfgGqk5gHSwDdsEyYCd1bcYoA0Ge3tB5JgGRQ5NShzDCNAF4eqG9sAzYSa1BykCguzd0Lughcmy6zpEBuigsA3ZS19qiOoJVBHp4QUCojkFE5ySMs2agnmXAHlgG7KTJ0qo6glUEefrAxD0GiBybpnGagC4KX9XtxCiXLg729FEdgYjOQwgB+IeqjmEdBll87ehYBuyk1WKMMsCzCYichK9BNgeztKlO4BJYBuykVTfG7oM+bh6qYxDRBRBevsbYhdAgv0g5OpYBOzHCNEGAh5fqCER0MYxwRgFHBuyCZcBOWg1QBvzcPFVHIKKL4e2vOsHlM8BrpzNgGbCTNgMMdfFKhUROxgj/Zg1wgTdnYICfFOdghDUDGi9QRORcDLFmwPlfO50BX93txAjTBCbBzYaInArLAF0glgE7kFLCbIAyoLEMEDkXThPQBTLAT4rjk5AwwqV9TJwmIHIuHBmgC8RXdzvQhGaI/fyN8DUQuRQjjOYZYFTVGbAM2IkR5tstvFohkXMxwFlM0C2QfO2xOZYBOzHCaXm6NMJkB5ELkQYoAwAMMc/q4Jz/HcpJuAnnn7vT2c6JnItugH+zbh4QBvhlytHxO2wnRhgZsHBkgMi5GKEMuHPnU3tw/ncoJ2GElfhcM0DkZIyw+I7XRLEL53+HchJuBjjFp5mn+BA5l9Zm1QkuH0cG7IJlwE7cDDAyUNPapDoCEV2MhhrVCS4fRwbswvnfoZyEyQBrBpotbYa4FDORK5DmVsAIBd6dZcAenP8dykl4GGCaAABqjTDsSOQKGutUJ7AOD04T2APLgJ34GWTeq7q1UXUEIroQdVWqE1iF4DSBXbAM2Im/QYa6qloaud8AkYOTug5ZX6k6hnUY5LXT0bEM2Im/QUYGalqbuBMhkaOTOlBfrTqFdXCawC5YBuzEKCMDNa1NEAa4zgKRoQlhjDMJAJ5NYCcsA3bib5Af6OrWJkNsoERkZEIzGagMeKtO4BL4qm4nAQYaGSAixycNMk0g/ENUR3AJLAN2Ypw1Azy1kMgpNFSrTmAdAaGqE7gElgE7CTDKNEELTy0kcgosA3QRWAbsxNfNExqcf+Fdk6WNUwVEDk7WVwMtBvh36ukN4emjOoVLYBmwEyGEYTYeyq6t4F4DRA5K6jpQfEJ1DOsICFOdwGWwDNhRmJef6ghWcbK+AtxpgMhRSciSk6pDWAenCOyGZcCOIr39VUewipP1lTy9kMhBCc0EWZKjOoZVCJYBu+Eruh1FeAeojmAVuUbZ5pTIqAxSBjgyYD8sA3ZklJGBurYWLiIkclCyvhpoqlcdwyoE1wzYDcuAHUUYpAwAXERI5IgMtXgQ4MiAHbEM2FGkt78BTi5sx0WERI7IQIsHAZ5NYEcsA3bkYXJDkIcxzpnlIkIix2OkxYNw94TwNsYZWM6Ar+Z2ZpSpAi4iJHJQRikDITGqE7gUlgE74yJCIrIVQy0ejIhVHcGlsAzYWaSPMU4vBIC0qmJYdC4iJHIE0mKBzE1THcN6wlkG7IllwM66+wapjmA1hyrzYdL4I0TkCITJBJl1UHUMqxEsA3bFV3I76+UXCmGQcwqOVhVxZIDIQUiLGcg5ojqGlQiODNgZy4Cdebm5I8ogUwXNFjMyakq53wCRYlLXgfxjQFuz6ijWERgOYZDLvjsLlgEFevkbZyON5Ip8wCAjHUROSwjI4waaIojsqTqCy2EZUCDOzzhlIKUyH5pgGSBSSQgBeeKQ6hjWExWnOoHLYRlQwEgjA5UtjShoqIaU3I+QSAUpJWRZPlBnnL0/BMuA3bEMKNDdNwjumkl1DKs5WJEHnZsTE6khdcjjB1SnsB7NBHCawO5YBhQwaRpifYNVx7CalIoCbk1MpIjQTJBZyapjWE9oDIS7p+oULoev4IoYaaogt74Sta0GWcVM5GRkYy1QapyLE3GKQA2WAUXiDFQGJNqnCrjnAJF9SYsFMnO/6hjW1S1JdQKXxDKgSGJghOoIVrW37CR3IySyM2EyQR7bozqGFQmIXgNUh3BJfPVWJNjTBzE+gapjWE1mTSmKG2t5VgGRnUipQ1YWAfkZqqNYT0QshEE2ZXM2LAMK9QuOUh3BqjYVHlMdgciFCMiDG1WHsCrR8wrVEVwWy4BCA4KjVUewql2l2WjTLapjELkGcxtk2k7VKayKUwTqsAwolBgQYaj9BpotZuwszeZCQiIbkxYL5JHtgJHO4nH3BGJ6q07hslgGFPIwuSExIFx1DKvaXJjJhYRENiZMJsiUTapjWFdsXwiTm+oULouv2or1N9hUQUFjNU7UlsHCKxkS2YTULZCFx4HyAtVRrEr04noBlVgGFDNaGQCA7wszuCMhkY0IzWS4hYMAIHpyvYBKfMVWrJtvEII8vFXHsKqD5XloaGtRHYPIkGRTvbGuRQAAgeEQwZGqU7g0lgEHYLTRAbPUsaXoOKcKiKxM6hbIlB8Ai1l1FKviWQTqcbWGAxgS2h07Sk6c85iGskrsfudz5O0+BHNzCwK6RWLCE/civG88dLMZe9/9Arm7klFXVAYPX290G34FRtx7G3zDzn5BpH3/Wo4DH3zV6bbAHtGY+8lfOj7e+cYnyFi9BW5enhhx721InDq6474Tm3YjY+1WTHvpd2c89pbiTFwX2/9CvwVEdEEEZMoW1SGsjvsLqMcy4AAGBEfDx80djea2Lu9vqWvAygcWIWZof0x/5TF4BfmjNr8Ynv6+AABzcyvKM3Nw5R2zENq7B1rqGrDjrx9j7ROLMfvd58/53MFx3XH9a090fKyZ/neq48ntB3B8ww7MWPx71OQXY/NL/0DsiEHwCvJHa30j9r77H1y/5ImuHhaVLY3YXpyFUVHxXD9AZAVSt0Ae3gbUVaiOYl3unkBP/uKgGsuAA3DTTBgSGnvW0YHkZf+FX0QoJjxxb8dtATH/u7aBh59Ppzd0ABi98A58fe/TqC8ph19k2FmfWzNp8AkN6vK+qpMFiB7SD+F94xHeNx47//YxaotK4RXkj11vf4Z+N08552Ovyk3FyMg4QJz1ECK6UFJC7vpGdQqrE72H8pLFDoBlwEFcFd7zrGXg5Pb96D5iENY/vRRFyenwDQ9G/5unoN+Nk876eK0NTYAQ8PDzOefz1uSX4JNZD8Dk4Y7IAYkYce/cjjf40N49kf7fTWipa0BtYSnMLa0I7B6F4pRjqMjMwZiH7zrnY1e3NmFjwTFc270vNI4OEF0yqeuQB9YD9dWqo1id6DdSdQQCy4DD6BsUCX93T9R1sQq/rqgMaSs3YuCt0zH0FzehLP0Ediz9CCY3NyRNH3fG8eaWVux55zP0njwSHr5nLwMR/RMw4Yl7EdgjGo0V1Tjw/lf45jfP4ZYPX4aHjzdiRwxC72tHY8WvnoLJwx0T/nAf3Lw8sXXxvzDhD/fh6NcbcOSrdfAK9MPYR+9BSFz3M55jTf5RjI9JhJeJZYDokplbIPd8pzqF9fkEAD04ReAIWAYchCY0XBnWA5uLMs+4T+o6wvvEY8Sv5gIAwpJ6oTI7D0e/2XhGGdDNZmx45m+QEhjzyLl/c+9xzZCO/w5N6IGIfgn49Nb/w4nvd6PvDRMAAMMXzMHwBXM6jtv//nJ0G34FNDcTDn78NW754CXk7jiIH154G7P/+cIZz9FobsXq3CO4qddgaILzBUQXS0odcve3QEuj6ihWJ/qMgOCOpQ6BfwsOZHh4zy5v9wkNQlCvbp1uC+7ZDfUlnRcSnS4C9SXluP61359zVKArnv6+CIqNRm1BcZf3V58sROa67bjq7p+h6OBRRA/uC++gAMRPvBrlGTlobWzq8vM2Fh5DfVsLdF7emOiiSKkDjfWG3GQI4BSBI2EZcCCJAeFdbkAUOTAJNXlFnW6rziuC/48W750uAjX5xbh+yRPwCvS/6Odva2xGbUFJlwsKpZTY+pf3MPI3v4C7jxd0XUI3W049d/uf0tL1vgJtugXfnEzhOkKiiyYgd6wAzK2qg1hfcBREVC/VKegUlgEHIoTAsPAeZ9w+8GfTUXLkOA5+vBI1+cU4vn470v+7Cf1nXQugvQisf2opytJPYNJT90NadDRWVKOxohqWtv9tTrJq4Ys4vHxdx8e73lyGwuQ01BWVoTg1A+v+uARC05AwZdQZGdJXbYJXkD96jr4SABB1RRIKDhxByZFMpH6xGsG9unWc6tiV7SVZKG9ugM6NiIguiNR1oKa8/eqEBiT6XaM6Av0I1ww4mBHhvbCx4Fin2yL6JWDqCwux5+//xoEPV8A/KhwjH/xFxwZADWVVOLm9fXvS5Qv+0Olzb1j6JGKGti/QqS0sQXNNXcd99WWV+H7RG2iurYd3kD8iB/bBze8sgndQQKfHaKyswcGPV+Kmt579X6b+CRg0dwbWPP4XeAcFYMIf7jvn16VLiRU5yfhVvzEX9w0hclFC02DZ+iWgW1RHsQmWAccipORErqP504HvkN9QrTqG1QkATw6dhhjfIG5ERHQOUrcA5QXQP1mkOoptxPSG6bauNywjNfiK7IDGRvVWHcEmJIBPMvdCcPUA0XkI6Os/VB3CZkTfq1VHoJ9gGXBAV0fEwVMz5gxOTn0F1uYf5ZkFRGchpQ65dzVQkqM6im2Y3CD6jFCdgn6CZcABebu5n/U0QyNYdTIVpU11sOhcTEj0Y1K3AFWlhtx2+DTRZwSEt5/qGPQTLAMOaly0MacKgPZLHL9/bCcENyEi+gkBffW7hrtE8Y+JYVNVR6AusAycRU5ODoQQSE5OvqjPE0Lg66+/vuzn7+Ufijj/0Mt+HEfF6QKizgw/PQAAPfpBhMeqTkFdYBlwYBNjklRHsClOFxC1c4XpAQDQrrxWdQQ6C5YBBzYsrAcC3L1Ux7AZThcQnWb86QGERANxg1SnoLNw+TKg6zpeeeUV9O7dG56enujRowdeeOHMC+5YLBYsWLAAffv2RW5uLgAgMzMT48aNg5eXF/r374/169dbNZubZjL02gGA0wVELjE9AEBcOYXF34EZ8/y1i/DEE0/g3XffxZIlSzBmzBgUFRUhPT290zEtLS2YN28ecnJysHXrVoSHh0PXdcyePRuRkZHYvXs3ampqsHDhQqvnGx+diLX5aWgz6C5kQPt0wdDQWIR7+cHEK5iRC5G6BaguM/z0ALz8IPqduc05OQ6XLgN1dXVYunQp3njjDdxxxx0AgISEBIwZMwY5OTkAgPr6elx//fVoaWnBpk2bEBgYCADYsGED0tPTsXbtWsTExAAAXnzxRUyfPt2qGQM8vDEmKgGbCjOs+riOxCx1/OvYDjw+ZCqklPztgVzC6c1fDT89AEAMHg/h7qE6Bp2DS/8alpaWhpaWFkyePPmsx8ybNw8NDQ1Yt25dRxE4/bmxsbEdRQAARo60zeU4r+veH24G3773ZH0llmXuZREglyGEgNzwkeGnB2Bygxg8SXUKOg9jv8Och7f3mZcL/qkZM2YgJSUFO3futEOirgV7+mBkZLyy57eX7SVZ+L7gGNcPkOFJqUM/sAHy8DbVUWxO9BkB4RekOgadh0uXgcTERHh7e2Pjxo1nPebXv/41XnrpJcycORObN2/uuL1fv37Iy8tDUVFRx227du2yWdZpsf2hucBvzV9kH0BmTSksvNQxGZTULUBeBuTmf6uOYhfcZMg5uHQZ8PLywuOPP47HHnsMH330EbKysrBr1y689957nY578MEH8fzzz+OGG27Atm3tTX7KlClISkrCHXfcgUOHDmHr1q148sknbZY1zMsPV0fE2ezxHYUuJf6ethXVLU3cf4AMR+oWoK4K+qq3AFcovAlDuMmQk3DpMgAATz31FB555BE8/fTT6NevH+bOnYvS0tIzjlu4cCEWLVqEGTNmYMeOHdA0DStWrEBTUxNGjBiBe+65p8tTEq1pemx/aC5wxb8Gcyv+duQHWKTOKQMyDKnrgMUM/eulQHOD6ji2JzRoY+aoTkEXSEjJV1tn8l76duwpO6k6hl0MCumG+/uP46JCMgQpJfSVfwNOHFIdxS7EgNHQrlugOgZdIJcfGXA202OvcIGxgXYplQVYedI1XjjJ+OS2r1ymCMDkBjHqZtUp6CKwDDiZGN9ADAvroTqG3azOO4p9ZSehu8L8KhmS1HXo6Xsg936nOordiCGTIPxDVMegi8Ay4IRmxQ0x/L4DP/ZBxi7kNVTzDANyOlK3AGV5kOveVx3Ffjy8IUZcrzoFXSTXeUcxkDAvP0zq1kd1DLtp0y1Ymvo9ShprWQjIaUjdAlQWQ1++GDC3qo5jN+KqaRDefqpj0EViGXBSM2KvgL+Br2j4Uw3mVryWuhHlTfU85ZAc3ulrDuhfvOIaZw6c5hsIwcsUOyWWASfl7eaOmT0Hqo5hV3VtLVicuhFVrY0sBOSw2vcSqGwvAk31quPYlbj6Bgh3T9Ux6BKwDDixMVEJiPEJPP+BBlLT2oS/pGxATRs3JSLHY9F16M0N0P/9MtBQozqOfQVGQAwcpzoFXSKWASemCQ23xg9THcPuqloa8cqh9RwhIIdi0XVUtTbi2dTvURMSqTqO3YnRN0OYXPpCuE6NZcDJ9QuOwsCQmPMfaDCnC0F5Sz0XFZJyFqmjvKUerxxaj9LWRjwd0Q01PfqqjmU/sX2h9b1adQq6DCwDBnBL3JUwudCphqfVtDbh1UMbUNJYx0JAylikjpLGOrx6aD1qWpsAAG0CrlMITO7QptyuOgVdJtd7BzGgKJ8ATIhJVB1Dibq2ZvwlZT0KG2q4MRHZnS51FDZU4y8p61HX1tLpPlcpBOKaGyGCXW9axGhYBgzipp6DEeblqzqGEg3mVryash4plYXgpTbIXqSUSKksxKspG9Bwln0EDF8IwrpDDL9OdQqyApYBg/A0ueEXvV13zq7FYsY7R7fg27zDAMCrHZLNnP7Z+jb3MN45ugUtFvM5jzdsIRAC2pTbuWjQIFgGDKRfcBRGR8arjqGMBPDfk6n4R9o2WKTOMw3I6ixSh0Xq+HvaVvw3NxUXWjmNWAjEoIkQMQmqY5CVsAwYzC3xVyLQw1t1DKX2l+fi5eR1qGtrZiEgq7HoOupam/Fy8jocKM+76M83VCHwC4YYO1t1CrIilgGD8XHzwM8ThquOoVxeQxWeP7gGOfUVnDKgy6ZLiZz6Cjx/cA3yGqou+XGMUgi0SfMhXPyXDqNhGTCgIWGxLnWZ47Opa2vGaykbsaMkS3UUcnI7SrKwOGUj6tqaL/uxnL4Q9L4SovdQ1SnIylgGDOq2hOHwdfNQHUM5s9TxceYefJ61D7qUPP2QLpgudehS4vOsffg4c49V97Jw2kLg4Q1t0s9VpyAbYBkwqAAPL5fcqvhsNhVmYEnqRtS0NrMQ0HnpUkdNazOWpG7EpsIMmzyHMxYCMeE2CL9g1THIBoTkidmG9s7RrThYcfGLnYzKy+SGOXFDMS46ERapu+TOjXR2p38mNhdlYnn2wfOeNmgN7hJ4rrQAgbnpNn+uyyH6XgNtxv9THYNshGXA4BrNrXj+wGpUtLjQNdUvQN+gSNyZNBKBHl7QWAgI/xsN+CBjJ9KrS+z63A5fCIIiof3iaQgPL9VJyEZYBlxAdm05Xk3ZwP37f8LL5IbZcUMxnqMELk3FaEBXHLYQmNygzXsSIoKLko2MZcBFrMtPw/Lsg6pjOCSOErgulaMBXXHEQiAmzYc2ZJLqGGRjLAMuQkqJN49uRmploeooDsnz1FoCjhK4BkcZDeiKQxWC3lfCNPMB1SnIDlgGXEh9Wwv+dOA7VJ+6zCqdqW9QJO5IvAbBnj4AACGE4kRkTadf7qpaGvFh5i6HGA3oikMUgoAwaL94BsLLR10GshuWAReTWVOK11I2Qr/gXdVdj5vQMDa6N27sMRDebh7QWAgMQZcSTeZW/Dc3FVuKjjv8GhqlhUAzQZv7e4ho173WiathGXBB3+YexjcnU1THcHieJjdc260vruveHyZN49SBkzp90ao1+UexoSDdoaYEzkdVIRBjfwbtqml2fU5Si2XABelS4s0jP+BwVZHqKE7Bz90TM2IHYEJ0EgDApLEUOIPTF6naVJSB1XlHUN/WojjRpbF7IYgbCO3m/+MUmYthGXBRTeY2vHJoHQoba1RHcRqhnr64sedAXBMRB11KlgIHZdF1aEJgV2k2/nsy1RB7bNitEARFtJ9G6O1n2+chh8My4MLKm+vxUvJa1Dnpb0yqxPgEYlavIRgU2g0WXWcpcBCn/y5SKgqwIifZcEXX5oXAyw/avD9ABEfa5vHJobEMuLjjNWVYkroRZgdfTOWIevmHYmJ0Eq4K7wkhBAR49oG9SSkhT/25t+wkNhVlIKeuQnUsm7FZITC5QbvldxDdEq37uOQ0WAYIu0qy8X7GTtUxnJafuydGR8ZjQkwSQjx9OVpgB6f3CahsacCmwgzsKDnhtGsCLpb1C4GAmPH/oPW92kqPR86IZYAAACtykrEm76jqGE5NQGBAcDQmxiRhQHA0dEiegWBlFqlDg8CRqiJsKszAkaoiSBc8TdaahUCMuhnaNTdaIRU5M5YBAtA+zPr3tG28wqGVhHn5YlxUIsZG94aPmwd0qXOr40t0+nvXaG7F1qLj2FKcifJm518UeLmsUQjEgNHQrltgxVTkrFgGqEOrxYxXU9Yjt75KdRTDcBMahoX3wOjIBPQODIdJaJxGuACnv0cWqeN4TRm2l2Rhf1ku17b8xGUVgh79oM1aCGFys34wcjosA9RJdUsjXk1Zz9+8bMDb5I4BITEYHNINg0K6wcvNncXgR05/L5rNbUipLMChinwcqSpCk6VNdTSHdkmFIDQG2m1PQHhyq2FqxzJAZyhvrserh9bzGgY2pAmB3gHhGBzaHUNDYxHq5Qtd6hAQLnNGQvuZABKa0FDR3ICD5Xk4VJmP47Vl0PmydFEuqhD4BED7+ZMQAWG2D0ZOg2WAulTcWIu/pGxAXVuz6iguIdonAINCumNoaHf08g+FEAK6lJAG2tzIousQQkATAlJK5NRV4GBFPlIq81HUWKs6ntO7oELg7gntZ49CRMXZLxg5BZYBOqu8+iq8lroRjeZW1VFciqfJDT18g9HDLwQ9/UMQ7x+GMC8/pyoIP33jL2uuR3ZdOU7WVSK3vhK5DVVOdY0AZ3HOQuDmAW32byG6J9k/GDk8lgE6p+zaciw5/D1fuBU7V0EA2lfc61JCCGG30xktUoeUEpoQHWdK8I1fvS4LgZsHtJsfgujRT10wcmgsA3ReGdUl+OuRH9CmW1RHoR/xNLmhu28Qgj19EejuhSBPbwR6eCPIwwfBnj4I8PCCl8m90+ec3rEPp8/Ol+g4S1+c+j9x6qOudlRstrShtrUZVS2NqG5tRHVLE2pam1DT1oyqlgbkN1Tzjd8BdCoEJjdoNz0I0esK1bHIgbEM0AU5XFmIt49u4aldTsZdM50qCO1FwdfNA9qp0YPTv9GfHkmwSL1jhMFy6s8GcytqWptQ3dr+ps9C6DzcJbCovBghQyZDxA9WHYccHMsAXbAD5bl4N307V3oTOQE3oeFX/cZgcGh31VHICbAM0EVJqSjAP9K38TdEIgfmrplwX7+xuCIkRnUUchIsA3TRMmpK8eaRzWjmZjBEDsddM+H+/uPQPzhadRRyIiwDdEly6yvx18ObUOciV4ojcgaeJjfc338c+gZFqY5CToZlgC5ZcWMtXj/8PapaGlVHIXJ5Ae5eePCKCejhF6I6CjkhlgG6LJUtDXg9dRNKmriDHJEqUd4BeOiKiQj18lUdhZwUywBdtrrWZvz1yCZe7ZBIgd4B4bi//3j4unuojkJOjGWArKLJ3Ia3jm5GRk2p6ihELuPKsFgs6DMK7ppJdRRyciwDZDVm3YJlx/diR8kJ1VGIDG9yTB/cEn8lNBe5yiXZFssAWd3a/KNYkX3o9Ia3RGRFAsAt8VdiSre+qqOQgbAMkE0cqsjHe8d2cJ96Iivy1NxwR9I1GBbeQ3UUMhiWAbKZgoZqvHV0C8qb61VHIXJ6kd7+uK/fWMT4BqmOQgbEMkA21dDWin+mb8PR6mLVUYic1pDQ7rgzaSS83dzPfzDRJWAZIJvTpY4VOYewLj9NdRQip6JB4KZegzEttr/qKGRwLANkN/vLcvFx5m408ZoGROfl7+6Je/qO5tbCZBcsA2RXFc0NeO/YdmTVlquOQuSw4vxDcW+/sQj29FEdhVwEywDZnS51rMo9jNW5R6Dz9EOiTsZHJ+LW+Cvhxo2EyI5YBkiZzJpSvHdsBy90RIT2Cw39MvFqDArtpjoKuSCWAVKqoa0Vn2TuxoGKPNVRiJQZFtYDP+99FfzcPVVHIRfFMkAOYWvRcfznxH606hbVUYjsxsfNA/MShmNERC/VUcjFsQyQwyhurMFHmbu5uJBcQv/gaNyeeDUXCZJDYBkghyKlxOaiTKzIOYRmnoJIBuSpuWFO/FCMj05UHYWoA8sAOaTqlkZ8lrUPyRX5qqMQWU1iQATuSLoa4d7+qqMQdcIyQA4tuTwPn2XtQ3Vrk+ooRJcswN0Lc+KH4pqIONVRiLrEMkAOr8nchq9zkrG56Dgvi0xORRMCE6OTcGPPQbyuADk0lgFyGidqy/FJ5h4UNFarjkJ0XokBEZjXezi68SqD5ARYBsip6FLHtuIT+O/JFNS2NauOQ3SGQA9vzIkbgqs5JUBOhGWAnFKLxYx1+WlYn5+GFt2sOg5R+5RATBJu7MEpAXI+LAPk1Gpam/Dfk6nYXpzF6xyQEgICIyJ64sYeA3mWADktlgEyhKLGGnyVnYyUygLVUciFDA2NxcyeAxHDdQHk5FgGyFAyqkuwPCcZOXUVqqOQgQ0IjsZNPQejp3+I6ihEVsEyQIZ0tKoIq/OOIKOmVHUUMpCkwAjc1HMQegdGqI5CZFUsA2RoJ2rLsSbvCFIqC7iigC5ZQkA4buhxBfoHR6uOQmQTLAPkEgoaqrEm7yj2lZ3kQkO6IJoQGBbWA5O79UGcf5jqOEQ2xTJALqW8uR7r8tOwo+QE2ni5ZOqCj5s7xkT1xsSYJIR4+qqOQ2QXLAPkkmpbm7C1OAvbi7NQ0dKgOg45gHAvP0yK6YNRUfHwMnGfAHItLAPk0nQpcbSqCFuLjyOlsgA6/zm4nKTACEyO6YNBod2hCaE6DpESLANEp1S3NGJ7yQmOFriAUE9fXBMRh2si4xDBjYKIWAaIfoqjBcbkqbnhyrBYjIyMR1JgBARHAYg6sAwQnUNtazMOVuRhf1kuMmtKeSaCkxEAkgIjMTIyDkPDYrkWgOgsWAaILlBdazMOVuTjQHkujtWUcMTAQQkAPf1CMCQsFiPCeyHUi2cEEJ0PywDRJahva0FyRR72l+chvbqYxUAxTQgkBUZgSGgshoR2R7Cnj+pIRE6FZYDoMjW0tSC1qhDpVcVIry5BVWuj6kguwd/dC1cER2NgSDf0D46Ct5uH6khETotlgMjKihtrkV5djLTqYmTUlKDR3KY6kiF4mdwQ7x+G3oER6B8chV5+oVwESGQlLANENqRLidz6yo5ykFVbzp0PL1CAuxd6B4Sjd2A4EgMj0N03CJrQVMciMiSWASI7skgdhQ01yK6rQE5dBU7WV6Cwocblz1LQIBDh7Y+4gDAknioAkd4BqmMRuQyWASLFWi1mFDRWI6++GvkNVcirr0JBYzVaLGbV0WwixNMHMT5BiPENRDffIHTzCUKUTwDcNZPqaEQui2WAyEHVtjajrLkOZc31KGuqR3lz/an/rkNtW7PqeGclIBDg4YVgD28EefogxNMX0T6B6OYbiBifIHi78Vx/IkfDMkDkhFotZpQ1txeE+rYW1Jtb0NDWigZzC+rb/vffDeZWNLS1wCz1S34uTQh4mdzhZXKD56k/vUzu8Da5I8jTB8GePgj28Eawpy+CPX0Q5OENk8a5fSJnwjJA5AKaLW0w6xboUv7vf5CdPpanPtaEgKfJrePNn8P3RMbHMkBEROTiOJZHRETk4lgGiIiIXBzLABERkYtjGSAiInJxLANEREQujmWAiIjIxbEMEBERuTiWASIiIhfHMkBEROTiWAaIiIhcHMsAERGRi2MZICIicnEsA0RERC6OZYCIiMjFsQwQERG5OJYBIiIiF8cyQERE5OJYBoiIiFwcywAREZGLYxkgIiJycSwDRERELo5lgIiIyMWxDBAREbk4lgEiIiIXxzJARETk4v4/BV4JuN2JTxsAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Encoding categorical features and checking Label Encoder's mapping"
      ],
      "metadata": {
        "id": "Amzyfevv8GRd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "le = LabelEncoder()\n",
        "\n",
        "for col in cat_cols:\n",
        "    df[col] = le.fit_transform(df[col])"
      ],
      "metadata": {
        "id": "ra8zrzszHMSk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category_mapping = dict(zip(le.classes_, le.transform(le.classes_)))\n",
        "print(category_mapping)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VE1nbsT2GcUI",
        "outputId": "9bdf2938-4058-4ae2-d63e-579b842d3743"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'ckd': 0, 'notckd': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "CiM1wmtOHp2e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e51b9302-043e-414b-9d29-df2fcaf26396"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 400 entries, 0 to 399\n",
            "Data columns (total 15 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   age             400 non-null    float64\n",
            " 1   bp              400 non-null    float64\n",
            " 2   al              400 non-null    float64\n",
            " 3   pcc             400 non-null    int64  \n",
            " 4   ba              400 non-null    int64  \n",
            " 5   bgr             400 non-null    float64\n",
            " 6   bu              400 non-null    float64\n",
            " 7   sc              400 non-null    float64\n",
            " 8   htn             400 non-null    int64  \n",
            " 9   dm              400 non-null    int64  \n",
            " 10  cad             400 non-null    int64  \n",
            " 11  appet           400 non-null    int64  \n",
            " 12  pe              400 non-null    int64  \n",
            " 13  ane             400 non-null    int64  \n",
            " 14  classification  400 non-null    int64  \n",
            "dtypes: float64(6), int64(9)\n",
            "memory usage: 47.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Model training and evaluation"
      ],
      "metadata": {
        "id": "PXCLgcRJ8Ulw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting dataset to train and test sets"
      ],
      "metadata": {
        "id": "r71Bj5pe8pDt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.drop('classification', axis=1)\n",
        "y = df['classification']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n"
      ],
      "metadata": {
        "id": "P07UR08FHzVZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "models = {\n",
        "    \"Random Forest\": RandomForestClassifier(),\n",
        "    \"Logistic Regression\": LogisticRegression(solver='lbfgs', max_iter=1000)\n",
        "}\n",
        "\n",
        "for model_name, model in models.items():\n",
        "    model.fit(X_train, y_train)\n",
        "\n",
        "    # Make predictions on the test set\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    # Evaluate the model\n",
        "    accuracy = accuracy_score(y_test, y_pred)\n",
        "    print(f'{model_name} Accuracy: {accuracy:.2f}')\n",
        "    print(f'{model_name} Classification Report:')\n",
        "    print(classification_report(y_test, y_pred))\n",
        "\n",
        "    cm = confusion_matrix(y_test, y_pred)\n",
        "    print(f'{model_name} Confusion Matrix:')\n",
        "    print(cm)\n",
        "    cm1 = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "    # Plot confusion matrix using seaborn heatmap\n",
        "    plt.figure(figsize=(8, 6))\n",
        "    sns.heatmap(cm1, annot=True, fmt='d', cmap='Oranges', xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['Actual 0', 'Actual 1'])\n",
        "    plt.title('Confusion Matrix')\n",
        "    plt.show()\n",
        "\n",
        "\n",
        "    print('-' * 55)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AMWeXeaabN-l",
        "outputId": "5025e4bd-5471-49e3-f805-0cd6a6e4033e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Accuracy: 0.97\n",
            "Random Forest Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.95      0.97        76\n",
            "           1       0.92      1.00      0.96        44\n",
            "\n",
            "    accuracy                           0.97       120\n",
            "   macro avg       0.96      0.97      0.96       120\n",
            "weighted avg       0.97      0.97      0.97       120\n",
            "\n",
            "Random Forest Confusion Matrix:\n",
            "[[72  4]\n",
            " [ 0 44]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------------------------------------\n",
            "Logistic Regression Accuracy: 0.94\n",
            "Logistic Regression Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.92      0.95        76\n",
            "           1       0.88      0.98      0.92        44\n",
            "\n",
            "    accuracy                           0.94       120\n",
            "   macro avg       0.93      0.95      0.94       120\n",
            "weighted avg       0.95      0.94      0.94       120\n",
            "\n",
            "Logistic Regression Confusion Matrix:\n",
            "[[70  6]\n",
            " [ 1 43]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-------------------------------------------------------\n",
            "CPU times: user 1.14 s, sys: 81.7 ms, total: 1.22 s\n",
            "Wall time: 1.81 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "pipe_rf =  Pipeline([\n",
        "    (\"scaler\", MinMaxScaler()),\n",
        "    (\"clf\", RandomForestClassifier())\n",
        "])\n",
        "param_grid = {\n",
        "    \"clf__n_estimators\": [100, 500, 1000],\n",
        "    \"clf__max_depth\": [1, 5, 10, None],\n",
        "    \"clf__max_features\": [\"sqrt\", \"log2\", None],\n",
        "}\n",
        "\n",
        "rf_gs = GridSearchCV(pipe_rf, param_grid=param_grid, cv=3, n_jobs=-1, verbose=1000)\n",
        "rf_gs.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "DQb6jAavnDhz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "1e8d8342-53d7-41c9-a076-a470730fd28a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 36 candidates, totalling 108 fits\n",
            "CPU times: user 1.67 s, sys: 336 ms, total: 2 s\n",
            "Wall time: 1min 45s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[('scaler', MinMaxScaler()),\n",
              "                                       ('clf', RandomForestClassifier())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'clf__max_depth': [1, 5, 10, None],\n",
              "                         'clf__max_features': ['sqrt', 'log2', None],\n",
              "                         'clf__n_estimators': [100, 500, 1000]},\n",
              "             verbose=1000)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()),\n",
              "                                       (&#x27;clf&#x27;, RandomForestClassifier())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;clf__max_depth&#x27;: [1, 5, 10, None],\n",
              "                         &#x27;clf__max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;, None],\n",
              "                         &#x27;clf__n_estimators&#x27;: [100, 500, 1000]},\n",
              "             verbose=1000)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()),\n",
              "                                       (&#x27;clf&#x27;, RandomForestClassifier())]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;clf__max_depth&#x27;: [1, 5, 10, None],\n",
              "                         &#x27;clf__max_features&#x27;: [&#x27;sqrt&#x27;, &#x27;log2&#x27;, None],\n",
              "                         &#x27;clf__n_estimators&#x27;: [100, 500, 1000]},\n",
              "             verbose=1000)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, MinMaxScaler()), (&#x27;clf&#x27;, RandomForestClassifier())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier()</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_gs.score(X_train, y_train)\n",
        "y_pred_rf = rf_gs.predict(X_test)"
      ],
      "metadata": {
        "id": "YiE0o0ceqvlb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(rf_gs, \"Random_Forest_model.joblib\")"
      ],
      "metadata": {
        "id": "r-QtVe8r_vwk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08fa762b-c8a5-43ce-f044-235bfc531c79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Random_Forest_model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_gs.best_params_"
      ],
      "metadata": {
        "id": "FwLl-f2-rVHS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "76dbab52-aaf9-4ad0-b3c9-a5bb09367d60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'clf__max_depth': 5, 'clf__max_features': 'sqrt', 'clf__n_estimators': 100}"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_model(y_pred, y_test):\n",
        "  accuracy = accuracy_score(y_test, y_pred)\n",
        "  print(classification_report(y_test, y_pred))\n",
        "  print(confusion_matrix(y_test, y_pred))\n",
        "  print(f\"Model accuracy: {accuracy:.4f}\")\n",
        "  # print(classification_report)"
      ],
      "metadata": {
        "id": "tE6A59bu9VeN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(y_pred_rf, y_test)"
      ],
      "metadata": {
        "id": "H_490KRvA2Oi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e124ee16-e2dc-468f-9527-e36ff77f0e91"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.93      0.97        76\n",
            "           1       0.90      1.00      0.95        44\n",
            "\n",
            "    accuracy                           0.96       120\n",
            "   macro avg       0.95      0.97      0.96       120\n",
            "weighted avg       0.96      0.96      0.96       120\n",
            "\n",
            "[[71  5]\n",
            " [ 0 44]]\n",
            "Model accuracy: 0.9583\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "pipe_lr =  Pipeline([\n",
        "    (\"scaler\", StandardScaler()),\n",
        "    (\"logistic\", LogisticRegression(solver='lbfgs', max_iter=1000))\n",
        "])\n",
        "param_grid_lr = {\"logistic__C\":np.logspace(-2,2,5)}\n",
        "\n",
        "lr_gs = GridSearchCV(pipe_lr, param_grid=param_grid_lr, cv=3, n_jobs=-1)\n",
        "lr_gs.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "_0TQ-BIC5k5E",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 186
        },
        "outputId": "0699f0c3-eafb-4caa-af68-210f40228ba8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 63.6 ms, sys: 2.03 ms, total: 65.7 ms\n",
            "Wall time: 228 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[('scaler', StandardScaler()),\n",
              "                                       ('logistic',\n",
              "                                        LogisticRegression(max_iter=1000))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={'logistic__C': array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])})"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;logistic&#x27;,\n",
              "                                        LogisticRegression(max_iter=1000))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;logistic__C&#x27;: array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
              "             estimator=Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                                       (&#x27;logistic&#x27;,\n",
              "                                        LogisticRegression(max_iter=1000))]),\n",
              "             n_jobs=-1,\n",
              "             param_grid={&#x27;logistic__C&#x27;: array([1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02])})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler()),\n",
              "                (&#x27;logistic&#x27;, LogisticRegression(max_iter=1000))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=1000)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(lr_gs, \"Logistic_Regression_model.joblib\")"
      ],
      "metadata": {
        "id": "YJR_iy4L_XsI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cc63517-123d-46a7-f6cc-7dd5c078ab34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Logistic_Regression_model.joblib']"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_lr = lr_gs.predict(X_test)"
      ],
      "metadata": {
        "id": "BFheoXoM9QLH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate_model(y_pred_lr, y_test)"
      ],
      "metadata": {
        "id": "4LyGqPUK9chv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "03c169f1-0c21-453e-cc38-eb9b666a1354"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.95      0.97        76\n",
            "           1       0.92      1.00      0.96        44\n",
            "\n",
            "    accuracy                           0.97       120\n",
            "   macro avg       0.96      0.97      0.96       120\n",
            "weighted avg       0.97      0.97      0.97       120\n",
            "\n",
            "[[72  4]\n",
            " [ 0 44]]\n",
            "Model accuracy: 0.9667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fpr1, tpr1, _ = roc_curve(y_test, lr_gs.predict_proba(X_test)[:, 1])\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr1, tpr1, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc1:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve - Logistic Regression')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "yj5j4hXDaxFS",
        "outputId": "da968888-7fdd-4ecd-cda4-a8bc281112b4"
      },
      "execution_count": null,
      "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": [
        "fpr1, tpr1, _ = roc_curve(y_test, rf_gs.predict_proba(X_test)[:, 1])\n",
        "roc_auc1 = auc(fpr1, tpr1)\n",
        "\n",
        "# Plot ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr1, tpr1, color='darkorange', lw=2, label=f'ROC curve (AUC = {roc_auc1:.2f})')\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('ROC Curve - Random Forest')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 527
        },
        "id": "FvPAjJXLbyX2",
        "outputId": "f9d035a4-6b99-4da8-f038-431ab15204be"
      },
      "execution_count": null,
      "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": [
        "cm1 = confusion_matrix(y_test, y_pred_lr)\n",
        "\n",
        "ğplt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm1, annot=True, fmt='d', cmap='Blues', xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['Actual 0', 'Actual 1'])\n",
        "plt.title('Confusion Matrix - Logistic Regression')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "-hBIWPJL9izG",
        "outputId": "45f2087e-c81c-4e47-dedf-fc9b38931565"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm1 = confusion_matrix(y_test, y_pred_rf)\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm1, annot=True, fmt='d', cmap='Oranges', xticklabels=['Predicted 0', 'Predicted 1'], yticklabels=['Actual 0', 'Actual 1'])\n",
        "plt.title('Confusion Matrix - Random Forest')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "okOV8Jg0H8nR",
        "outputId": "0f4ca36e-4306-4474-c0fe-daa3651a617b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Getting user input for web"
      ],
      "metadata": {
        "id": "xq4ZT3lLZpEd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_user_input(feature_names):\n",
        "    features = []\n",
        "\n",
        "    for feature_name in feature_names:\n",
        "        while True:\n",
        "            try:\n",
        "                value = float(input(f'Enter value for {feature_name}: '))\n",
        "                break\n",
        "            except ValueError:\n",
        "                print('Invalid input. Please enter a numeric value.')\n",
        "\n",
        "        features.append(value)\n",
        "\n",
        "    return np.array(features).reshape(1, -1)\n",
        "\n",
        "\n",
        "\n",
        "def make_prediction(model_filename, user_input):\n",
        "    model = joblib.load(model_filename)\n",
        "    prediction = model.predict(user_input)[0]\n",
        "    proba = model.predict_proba(user_input).max()\n",
        "    return prediction, proba\n",
        "\n",
        "def main():\n",
        "    features = ['age','blood pressure', 'albumin',\n",
        "                 'pus cellc cumps', 'bacteria', 'blood glucose rand',\n",
        "                'blood urea','serum creatinine', 'hypertension', 'diabetes mellitus',\n",
        "                'caronory artery disease', 'appetite', 'pedal edema','anemia' ]\n",
        "\n",
        "    user_input = get_user_input(features)\n",
        "    prediction, proba = make_prediction(\"Logistic_Regression_model.joblib\", user_input)\n",
        "    print(f\"Your test result is {'positive' if prediction == 0 else 'negative'}.\")\n",
        "    print(f\"The certanity of prediction: {proba*100:.2f}%\")\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "  main()\n"
      ],
      "metadata": {
        "id": "OxUMZQrF0cdl"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}