{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lMc7BeUOWOM-"
      },
      "source": [
        "# Setup"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Ubn5V43WxgI"
      },
      "source": [
        "## Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WqeraxZdWRzi"
      },
      "outputs": [],
      "source": [
        "!pip install --upgrade scikit-learn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qzg2v8_5zGhq"
      },
      "outputs": [],
      "source": [
        "!pip install imbalanced-learn==0.8.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yVSIzGg-W7MJ"
      },
      "source": [
        "## Importation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "zjAThsYGWrvV"
      },
      "outputs": [],
      "source": [
        "# Data handlig & Exploratory Data Analysis (EDA):\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "import os\n",
        "\n",
        "# Data Visualization & Exploratory Data Analysis (EDA):\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Saving objects\n",
        "import joblib\n",
        "import pickle\n",
        "from joblib import dump, load\n",
        "\n",
        "# Machine learning\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.neural_network import MLPClassifier\n",
        "from xgboost import XGBClassifier\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.svm import LinearSVC\n",
        "from sklearn.model_selection import GridSearchCV, StratifiedKFold\n",
        "\n",
        "# Metrics\n",
        "from sklearn.metrics import confusion_matrix, accuracy_score, precision_score,\\\n",
        "                            recall_score, mean_squared_error,\\\n",
        "                            classification_report, confusion_matrix, f1_score, make_scorer\n",
        "from sklearn.metrics import roc_auc_score, log_loss\n",
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "\n",
        "from collections import Counter\n",
        "from imblearn.over_sampling import RandomOverSampler\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mo7D6NA_t41n"
      },
      "source": [
        "# Data Loading\n",
        "## Mount the drive and load the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tGcm2HPofSWP",
        "outputId": "26e33fa4-4f08-408f-f4f1-5251811df99a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "jGtDelSktqvt"
      },
      "outputs": [],
      "source": [
        "#load data\n",
        "train_df=pd.read_csv('/content/drive/MyDrive/Sepsis_Data/Paitients_Files_Train.csv')\n",
        "test_df=pd.read_csv('/content/drive/MyDrive/Sepsis_Data/Paitients_Files_Test.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWL9J4ft1Cyo"
      },
      "source": [
        "# View the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "883DdpRc08Zn",
        "outputId": "6fe6b050-e26e-4767-9e87-06b8dd3c5bed"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ID  PRG   PL  PR  SK   TS   M11    BD2  Age  Insurance   Sepssis\n",
              "0    ICU200010    6  148  72  35    0  33.6  0.627   50          0  Positive\n",
              "1    ICU200011    1   85  66  29    0  26.6  0.351   31          0  Negative\n",
              "2    ICU200012    8  183  64   0    0  23.3  0.672   32          1  Positive\n",
              "3    ICU200013    1   89  66  23   94  28.1  0.167   21          1  Negative\n",
              "4    ICU200014    0  137  40  35  168  43.1  2.288   33          1  Positive\n",
              "..         ...  ...  ...  ..  ..  ...   ...    ...  ...        ...       ...\n",
              "594  ICU200604    6  123  72  45  230  33.6  0.733   34          0  Negative\n",
              "595  ICU200605    0  188  82  14  185  32.0  0.682   22          1  Positive\n",
              "596  ICU200606    0   67  76   0    0  45.3  0.194   46          1  Negative\n",
              "597  ICU200607    1   89  24  19   25  27.8  0.559   21          0  Negative\n",
              "598  ICU200608    1  173  74   0    0  36.8  0.088   38          1  Positive\n",
              "\n",
              "[599 rows x 11 columns]"
            ],
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>PRG</th>\n",
              "      <th>PL</th>\n",
              "      <th>PR</th>\n",
              "      <th>SK</th>\n",
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              "      <th>0</th>\n",
              "      <td>ICU200010</td>\n",
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              "      <td>35</td>\n",
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              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
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              "      <td>Positive</td>\n",
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              "      <th>1</th>\n",
              "      <td>ICU200011</td>\n",
              "      <td>1</td>\n",
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              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ICU200012</td>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ICU200013</td>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>1</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ICU200014</td>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>ICU200604</td>\n",
              "      <td>6</td>\n",
              "      <td>123</td>\n",
              "      <td>72</td>\n",
              "      <td>45</td>\n",
              "      <td>230</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.733</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>ICU200605</td>\n",
              "      <td>0</td>\n",
              "      <td>188</td>\n",
              "      <td>82</td>\n",
              "      <td>14</td>\n",
              "      <td>185</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.682</td>\n",
              "      <td>22</td>\n",
              "      <td>1</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>ICU200606</td>\n",
              "      <td>0</td>\n",
              "      <td>67</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45.3</td>\n",
              "      <td>0.194</td>\n",
              "      <td>46</td>\n",
              "      <td>1</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>ICU200607</td>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>24</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.559</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>ICU200608</td>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.088</td>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>599 rows × 11 columns</p>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
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              "\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
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              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
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              "      <style>\n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\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",
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              "\n",
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              "          document.querySelector('#df-508ede99-113a-43d1-8e02-a9e69563fa6d 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-508ede99-113a-43d1-8e02-a9e69563fa6d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "yir_fIEj1hBN",
        "outputId": "4c8282bd-9393-4762-b155-a4da17ec9ada"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            ID  PRG   PL  PR  SK   TS   M11    BD2  Age  Insurance\n",
              "0    ICU200609    1  109  38  18  120  23.1  0.407   26          1\n",
              "1    ICU200610    1  108  88  19    0  27.1  0.400   24          1\n",
              "2    ICU200611    6   96   0   0    0  23.7  0.190   28          1\n",
              "3    ICU200612    1  124  74  36    0  27.8  0.100   30          1\n",
              "4    ICU200613    7  150  78  29  126  35.2  0.692   54          0\n",
              "..         ...  ...  ...  ..  ..  ...   ...    ...  ...        ...\n",
              "164  ICU200773   10  101  76  48  180  32.9  0.171   63          1\n",
              "165  ICU200774    2  122  70  27    0  36.8  0.340   27          1\n",
              "166  ICU200775    5  121  72  23  112  26.2  0.245   30          1\n",
              "167  ICU200776    1  126  60   0    0  30.1  0.349   47          1\n",
              "168  ICU200777    1   93  70  31    0  30.4  0.315   23          1\n",
              "\n",
              "[169 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-57229a5b-0451-4234-b86c-5628f0edfa04\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>PRG</th>\n",
              "      <th>PL</th>\n",
              "      <th>PR</th>\n",
              "      <th>SK</th>\n",
              "      <th>TS</th>\n",
              "      <th>M11</th>\n",
              "      <th>BD2</th>\n",
              "      <th>Age</th>\n",
              "      <th>Insurance</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ICU200609</td>\n",
              "      <td>1</td>\n",
              "      <td>109</td>\n",
              "      <td>38</td>\n",
              "      <td>18</td>\n",
              "      <td>120</td>\n",
              "      <td>23.1</td>\n",
              "      <td>0.407</td>\n",
              "      <td>26</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ICU200610</td>\n",
              "      <td>1</td>\n",
              "      <td>108</td>\n",
              "      <td>88</td>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>27.1</td>\n",
              "      <td>0.400</td>\n",
              "      <td>24</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ICU200611</td>\n",
              "      <td>6</td>\n",
              "      <td>96</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.7</td>\n",
              "      <td>0.190</td>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ICU200612</td>\n",
              "      <td>1</td>\n",
              "      <td>124</td>\n",
              "      <td>74</td>\n",
              "      <td>36</td>\n",
              "      <td>0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.100</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ICU200613</td>\n",
              "      <td>7</td>\n",
              "      <td>150</td>\n",
              "      <td>78</td>\n",
              "      <td>29</td>\n",
              "      <td>126</td>\n",
              "      <td>35.2</td>\n",
              "      <td>0.692</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>ICU200773</td>\n",
              "      <td>10</td>\n",
              "      <td>101</td>\n",
              "      <td>76</td>\n",
              "      <td>48</td>\n",
              "      <td>180</td>\n",
              "      <td>32.9</td>\n",
              "      <td>0.171</td>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>165</th>\n",
              "      <td>ICU200774</td>\n",
              "      <td>2</td>\n",
              "      <td>122</td>\n",
              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>ICU200775</td>\n",
              "      <td>5</td>\n",
              "      <td>121</td>\n",
              "      <td>72</td>\n",
              "      <td>23</td>\n",
              "      <td>112</td>\n",
              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>ICU200776</td>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>ICU200777</td>\n",
              "      <td>1</td>\n",
              "      <td>93</td>\n",
              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>169 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-57229a5b-0451-4234-b86c-5628f0edfa04')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-87a558a3-7254-4f49-8c5a-9cd355841bac')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-87a558a3-7254-4f49-8c5a-9cd355841bac button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
              "    [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-57229a5b-0451-4234-b86c-5628f0edfa04 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-57229a5b-0451-4234-b86c-5628f0edfa04');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "test_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fbdvtAnX1lRJ",
        "outputId": "0e587671-a625-43d8-f73e-571f78f1e026"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age', 'Insurance',\n",
              "       'Sepssis'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# View the dataset columns\n",
        "train_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tRio0QKb3-O9"
      },
      "source": [
        "## Columns metadata [Details about the columns]\n",
        "1. ID : Unique number to represent patient ID\n",
        "2. PRG : Plasma glucose\n",
        "3.  PL : Blood Work Result-1 (mu U/ml)\n",
        "4. PR : Blood Pressure (mm Hg)\n",
        "5. SK : Blood Work Result-2 (mm)\n",
        "6. TS : Blood Work Result-3 (mu U/ml)\n",
        "7. M11 : Body mass index (weight in kg/(height in m)^2\n",
        "8. BD2 : Blood Work Result-4 (mu U/ml)\n",
        "9. Age : Patients age (years)\n",
        "10. Insurance : If a patient holds a valid insurance card\n",
        "11. Sepssis : Positive: if a patient in ICU will develop a sepsis , and Negative: otherwise"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbAT-s0P9_JD",
        "outputId": "bae2e7ed-0952-47c4-8dde-8e7372f38d72"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 599 entries, 0 to 598\n",
            "Data columns (total 11 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   ID         599 non-null    object \n",
            " 1   PRG        599 non-null    int64  \n",
            " 2   PL         599 non-null    int64  \n",
            " 3   PR         599 non-null    int64  \n",
            " 4   SK         599 non-null    int64  \n",
            " 5   TS         599 non-null    int64  \n",
            " 6   M11        599 non-null    float64\n",
            " 7   BD2        599 non-null    float64\n",
            " 8   Age        599 non-null    int64  \n",
            " 9   Insurance  599 non-null    int64  \n",
            " 10  Sepssis    599 non-null    object \n",
            "dtypes: float64(2), int64(7), object(2)\n",
            "memory usage: 51.6+ KB\n"
          ]
        }
      ],
      "source": [
        "# General information about the train_df\n",
        "train_df.info()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values\n",
        "# Find percentage missing values in both datasets\n",
        "percent_missing_train = (train_df.isnull().sum()/train_df.shape[0])*100\n",
        "percent_missing_test = (test_df.isnull().sum()/test_df.shape[0])*100"
      ],
      "metadata": {
        "id": "IgPdnDPf_9Tv"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Percent missing train')\n",
        "percent_missing_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdGeXEpUAEw-",
        "outputId": "b5dcc406-8a71-49ab-ad7d-62ba68b439cc"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percent missing train\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID           0.0\n",
              "PRG          0.0\n",
              "PL           0.0\n",
              "PR           0.0\n",
              "SK           0.0\n",
              "TS           0.0\n",
              "M11          0.0\n",
              "BD2          0.0\n",
              "Age          0.0\n",
              "Insurance    0.0\n",
              "Sepssis      0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Finding duplicate values in both datasets:"
      ],
      "metadata": {
        "id": "5OC6bkUAAYJu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Find duplicate values in train_df\n",
        "print(f'Duplicate entries in train_df:',(train_df.duplicated().sum()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzKBESmfAN0W",
        "outputId": "61b147ea-6bff-41dc-dce3-de5d5f3cdfc3"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate entries in train_df: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Find duplicate values in test_df\n",
        "print(f'Duplicate entries in test_df:',(train_df.duplicated().sum()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3MVMqvQyAmY4",
        "outputId": "2b25de19-b7e8-49c0-c148-2505df31fbb4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Duplicate entries in test_df: 0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRQh8mf06SCb"
      },
      "source": [
        "# Explorative Data Analysis (EDA) of sepssis Dataset\n",
        "\n",
        "Exploratory Data Analysis (EDA) is a crucial step in any data analysis or machine learning project. It involves the initial investigation of the dataset to gain insights, discover patterns, and identify trends or anomalies. EDA plays a significant role in understanding the data, refining data preprocessing steps, and guiding the selection of appropriate machine learning algorithms."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qM_aUfbF_RoU"
      },
      "source": [
        "##  Univariate Analysis:\n",
        "Univariate analysis focuses on examining individual variables in the dataset. It helps us understand the distribution, central tendency, and spread of each variable. We will perform univariate analysis for numerical and categorical variables separately."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "mY-nRQgh4oYM"
      },
      "outputs": [],
      "source": [
        "# Univariate Analysis for Numerical Variables\n",
        "numerical_cols = ['PRG', 'PL', 'PR', 'SK', 'TS', 'M11', 'BD2', 'Age']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "LbyXSAzE_rYH",
        "outputId": "877c17b7-f652-468b-b6d1-5f8689e0d7ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              PRG          PL          PR          SK          TS         M11  \\\n",
              "count  599.000000  599.000000  599.000000  599.000000  599.000000  599.000000   \n",
              "mean     3.824708  120.153589   68.732888   20.562604   79.460768   31.920033   \n",
              "std      3.362839   32.682364   19.335675   16.017622  116.576176    8.008227   \n",
              "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      1.000000   99.000000   64.000000    0.000000    0.000000   27.100000   \n",
              "50%      3.000000  116.000000   70.000000   23.000000   36.000000   32.000000   \n",
              "75%      6.000000  140.000000   80.000000   32.000000  123.500000   36.550000   \n",
              "max     17.000000  198.000000  122.000000   99.000000  846.000000   67.100000   \n",
              "\n",
              "              BD2         Age  \n",
              "count  599.000000  599.000000  \n",
              "mean     0.481187   33.290484  \n",
              "std      0.337552   11.828446  \n",
              "min      0.078000   21.000000  \n",
              "25%      0.248000   24.000000  \n",
              "50%      0.383000   29.000000  \n",
              "75%      0.647000   40.000000  \n",
              "max      2.420000   81.000000  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-4373c8ec-779f-4a0d-9c5c-98e244c91b2d\">\n",
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              "      <th></th>\n",
              "      <th>PRG</th>\n",
              "      <th>PL</th>\n",
              "      <th>PR</th>\n",
              "      <th>SK</th>\n",
              "      <th>TS</th>\n",
              "      <th>M11</th>\n",
              "      <th>BD2</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>599.000000</td>\n",
              "      <td>599.000000</td>\n",
              "      <td>599.000000</td>\n",
              "      <td>599.000000</td>\n",
              "      <td>599.000000</td>\n",
              "      <td>599.000000</td>\n",
              "      <td>599.000000</td>\n",
              "      <td>599.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.824708</td>\n",
              "      <td>120.153589</td>\n",
              "      <td>68.732888</td>\n",
              "      <td>20.562604</td>\n",
              "      <td>79.460768</td>\n",
              "      <td>31.920033</td>\n",
              "      <td>0.481187</td>\n",
              "      <td>33.290484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.362839</td>\n",
              "      <td>32.682364</td>\n",
              "      <td>19.335675</td>\n",
              "      <td>16.017622</td>\n",
              "      <td>116.576176</td>\n",
              "      <td>8.008227</td>\n",
              "      <td>0.337552</td>\n",
              "      <td>11.828446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.078000</td>\n",
              "      <td>21.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>64.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>27.100000</td>\n",
              "      <td>0.248000</td>\n",
              "      <td>24.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>116.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>0.383000</td>\n",
              "      <td>29.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>123.500000</td>\n",
              "      <td>36.550000</td>\n",
              "      <td>0.647000</td>\n",
              "      <td>40.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>198.000000</td>\n",
              "      <td>122.000000</td>\n",
              "      <td>99.000000</td>\n",
              "      <td>846.000000</td>\n",
              "      <td>67.100000</td>\n",
              "      <td>2.420000</td>\n",
              "      <td>81.000000</td>\n",
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              "  </tbody>\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "      }\n",
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              "\n",
              "      <script>\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4373c8ec-779f-4a0d-9c5c-98e244c91b2d');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Summary Statistics\n",
        "train_df[numerical_cols].describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hV66LzShAS-3"
      },
      "source": [
        "### Visualize the Summary statistics\n",
        "Numerical columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "aWz89fBJARQf",
        "outputId": "dfc3c85a-2166-43a0-ba37-8edf3ce5a6f0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Histograms\n",
        "plt.figure(figsize=(12, 8))\n",
        "for i, col in enumerate(numerical_cols):\n",
        "    plt.subplot(3, 3, i + 1)\n",
        "    sns.histplot(train_df[col], kde=True)\n",
        "    plt.title(col)\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0j3grThZzc8"
      },
      "source": [
        "### Observations from the summary statistics and histograms:\n",
        "\n",
        "  1. Plasma glucose (PRG) has a mean of approximately 3.82, with values ranging from 0 to 17. The distribution of PRG is right-skewed, with more values concentrated towards lower values.\n",
        "\n",
        "  2. Blood Work Result-1 (PL) has a mean of approximately 120.15, with values ranging from 0 to 198. The distribution of PL appears to be somewhat normally distributed, with a few outliers towards higher values.\n",
        "\n",
        "  3. Blood Pressure (PR) has a mean of approximately 68.73, with values ranging from 0 to 122. The distribution of PR seems to be slightly right-skewed, with more values towards lower blood pressure levels.\n",
        "\n",
        "  4. Blood Work Result-2 (SK) has a mean of approximately 20.56, with values ranging from 0 to 99. The distribution of SK is heavily right-skewed, with a concentration of values towards zero.\n",
        "\n",
        "  5. Blood Work Result-3 (TS) has a mean of approximately 79.46, with values ranging from 0 to 846. The distribution of TS is highly right-skewed, with a few extreme values leading to a long tail.\n",
        "\n",
        "  6. Body mass index (M11) has a mean of approximately 31.92, with values ranging from 0 to 67.1. The distribution of M11 appears to be approximately normally distributed.\n",
        "\n",
        "  7. Blood Work Result-4 (BD2) has a mean of approximately 0.48, with values ranging from 0.078 to 2.42. The distribution of BD2 is right-skewed, with more values towards lower levels.\n",
        "\n",
        "  8. Age has a mean of approximately 33.29, with values ranging from 21 to 81. The distribution of Age is right-skewed, with more values towards younger ages."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lxeBagULFPud"
      },
      "source": [
        "## Univariate Analysis for Categorical Variable\n",
        "### Frequency count of 'Sepssis'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6K_ubdzM1LQ",
        "outputId": "0acf805d-3aea-4257-b5c3-b059d1da0b42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Positive Count: 208\n",
            "Negative Count: 391\n"
          ]
        }
      ],
      "source": [
        "# Count the occurrences of 'Positive' and 'Negative'\n",
        "value_counts = train_df['Sepssis'].value_counts()\n",
        "\n",
        "positive_count = value_counts['Positive']\n",
        "negative_count = value_counts['Negative']\n",
        "\n",
        "print(\"Positive Count:\", positive_count)\n",
        "print(\"Negative Count:\", negative_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "jc0R2l9aFQsH",
        "outputId": "3cc723e8-7cd6-454f-89cf-6f85867dcb4c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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Ko1GjRlqxYoVWrlypDRs26H//+59mzZolLy8vHTt2TGfOnFGtWrXybVenTh3l5uZa41jHjh2rkydP6vbbb1dERISGDx+u7du3F7mugnTr1k2HDx9WYmKiJGnfvn1KSkpSt27drD579uyRMUY1a9a0Qnve8ssvvyg1NfWyxzh79qxGjhxpjaEuX768KlSooJMnTyotLS1f/4sf07wAk/eY5j02Fz9HK1So4BR2rlazZs3UsmXLS4693bt3r4wxeuWVV/Kd/6hRoyTJug8OHjyoGjVq5NtHQW07d+7UQw89JH9/f/n5+alChQrWBxELul+uxiOPPKISJUpYQziMMVqwYIE1vluS9cdS7969853Phx9+qMzMzMse38/P76qnMMx7rAp6vteuXfuaXmdXep5czlNPPaXbb79dMTExqly5sv72t79d9Vh9gDG3wBX89ttvSktLK/CXXx4vLy998803WrNmjb766istXbpU8+bNU6tWrbR8+fKr+gR8YcbJXq1LfXlATk7OTftU/qWOYy768NnNVL58eUVFRV3zfpo1a6Z9+/bp888/1/Lly/Xhhx9q0qRJmj59uvr3738dKpU6duwob29vzZ8/X02aNNH8+fNVokQJPfLII1af3NxcORwOLVmypMD7+1JjQPMMGTJEM2bM0LPPPqvIyEj5+/vL4XCoe/fuBf6B5orHdNSoUWrRooXef//9fGNA82p8/vnnFR0dXeD2l3v9FuTkyZNq3ry5/Pz8NHbsWFWvXl2lSpXSjz/+qBEjRhT5PzQhISG6//77NX/+fL344ovauHGjDh065DR7Qd6+33zzTacrzBe63GNau3ZtbdmyRYcPH1ZoaGiR6iyIw+Eo8DG+8AN2F7qW50lgYKC2bt2qZcuWacmSJVqyZIlmzJihXr16OX2oFCgI4Ra4grwPOlzql2aeEiVKqHXr1mrdurUmTpyo119/XS+99JLWrFmjqKio6/6NZhf/K9wYo71796pevXpWW9myZXXy5Ml82x48eNDpX+6FqS0sLEwrV67UqVOnnK7e5v2bNiws7Kr3daXjbN++Xbm5uU5Xb6/3cS5WoUIFeXt7a/fu3fnW7dq1SyVKlHAKDAEBAerbt6/69u2rjIwMNWvWTKNHj75suC3M/e3j46MOHTpowYIFmjhxoubNm6f7779fISEhVp/q1avLGKNq1arp9ttvv+p951m4cKF69+7t9IUJ586dK/C5czXyHps9e/Y4Pc+OHTt2VVftCtK8eXO1aNFC48eP18iRI53W5R3D3d39in+0hIWFae/evfnaL25bu3atjh8/rk8//VTNmjWz2i+cUSNPYV/b3bp101NPPaXdu3dr3rx58vb2VseOHa31ecN4/Pz8ivRHWMeOHfXvf/9bc+bMUXx8/GX75j1Wu3fvVqtWrZzW7d692+l1VrZs2QKHFFzL1d3L3XceHh7q2LGjOnbsqNzcXD311FN6//339corrxT6jxX8tTAsAbiM1atX69VXX1W1atUKHJOX58SJE/na8q64ZGZmSvozpEgqcmC42KxZs5z+9bhw4UIdPXpUMTExVlv16tW1ceNGp0+oL168ON/0UIWprX379srJydHf//53p/ZJkybJ4XA4Hf9atG/fXsnJyU6fwM/OztaUKVPk6+ur5s2bX5fjXMzNzU1t27bV559/7jTEIyUlRXPnzlXTpk2tfx8fP37caVtfX1/VqFHDeswvxcfHp1DPg27duunIkSP68MMPtW3bNqchCZLUuXNnubm5acyYMfmuihlj8tV5MTc3t3zbTZky5ZJX5K4kKipK7u7umjJlitN+r/VbvPLG3n7wwQdO7YGBgdZV3aNHj+bb7sJpyqKjo5WYmOj0bXwnTpzQxx9/7LRN3lXHC+vPysrSe++9l2//Pj4+hRqm0KVLF7m5uenf//63FixYoA4dOlivQUlq2LChqlevrrfeeksZGRmXPZ+CPPzww4qIiNC4ceOs4SwXOnXqlF566SVJ0t13363AwEBNnz7d6Xm7ZMkS/fLLL4qNjbXaqlevrl27djkdf9u2bVc1PdmlXOq95+LnbIkSJaw/3K/0+gK4cgv8f0uWLNGuXbuUnZ2tlJQUrV69WitWrFBYWJi++OKLy07GPnbsWH3zzTeKjY1VWFiYUlNT9d5776ly5crWPJPVq1dXmTJlNH36dJUuXVo+Pj5q1KiRqlWrVqR6AwIC1LRpU/Xt21cpKSmaPHmyatSo4TRdWf/+/bVw4UK1a9dOXbt21b59+zRnzpx8U08VpraOHTuqZcuWeumll3TgwAHVr19fy5cv1+eff65nn332ktNaFdbAgQP1/vvvq0+fPkpKSlLVqlW1cOFCrV+/XpMnT77qD8wUxWuvvWbNW/zUU0+pZMmSev/995WZmakJEyZY/cLDw9WiRQs1bNhQAQEB2rx5sxYuXKjBgwdfdv8NGzbUypUrNXHiRIWEhKhatWrW9GIFad++vUqXLq3nn39ebm5u6tKli9P66tWr67XXXlN8fLwOHDigTp06qXTp0tq/f78+++wzDRw4UM8///wl99+hQwfNnj1b/v7+Cg8PV2JiolauXHnZqdoup0KFCnr++eeVkJCgDh06qH379tqyZYuWLFmi8uXLF2mf0p9Xb5s3b17ghwmnTp2qpk2bKiIiQgMGDNBtt92mlJQUJSYm6rffftO2bdskSS+88ILmzJmjNm3aaMiQIdZUYFWqVNGJEyesK4lNmjRR2bJl1bt3bz399NNyOByaPXt2gf9Sb9iwoebNm6dhw4bpnnvuka+vr9OV2IsFBgaqZcuWmjhxok6dOpXvj5USJUroww8/VExMjO644w717dtXlSpV0u+//641a9bIz89PX3755SX37+7urk8//VRRUVFq1qyZunbtqvvuu0/u7u7auXOn5s6dq7Jly2rcuHFyd3fX+PHj1bdvXzVv3lyPPvqoNRVY1apVNXToUGu/f/vb3zRx4kRFR0erX79+Sk1N1fTp03XHHXdYH7IsrIYNG0qSnn76aUVHR8vNzU3du3dX//79deLECbVq1UqVK1fWwYMHNWXKFDVo0MAadw9c0k2fnwEoZvKmFcpbPDw8THBwsGnTpo155513nKacynPx9E6rVq0yDz74oAkJCTEeHh4mJCTEPProo+bXX3912u7zzz834eHhpmTJkk5TBDVv3tzccccdBdZ3qanA/v3vf5v4+HgTGBhovLy8TGxsrDU11IXefvttU6lSJePp6Wnuu+8+s3nz5gKn9LlUbRdP82OMMadOnTJDhw41ISEhxt3d3dSsWdO8+eab+b42VhdM53Shq/0K1pSUFNO3b19Tvnx54+HhYSIiIgqcrqywU4FdTd8ff/zRREdHG19fX+Pt7W1atmxpNmzY4NTntddeM/fee68pU6aM8fLyMrVr1zbjxo0zWVlZVp+CpgLbtWuXadasmfHy8nKaGuviqcAu1KNHD2sarkv5z3/+Y5o2bWp8fHyMj4+PqV27tomLizO7d+++7Ln+8ccf1v3s6+troqOjza5du/I9TpeaNi/vOblmzRqrLScnx4wZM8ZUrFjReHl5mRYtWpiffvqpUF+/W9BzJ+9YBdWxb98+06tXLxMcHGzc3d1NpUqVTIcOHczChQud+m3ZssXcf//9xtPT01SuXNkkJCSYd99910gyycnJVr/169ebxo0bGy8vLxMSEmJeeOEFs2zZsnznmpGRYR577DFTpkwZI8l6vRQ0FVief/zjH0aSKV269CW/ynvLli2mc+fOply5csbT09OEhYWZrl27mlWrVl3x/jPmz8d15MiRJiIiwnh7e5tSpUqZunXrmvj4eHP06FGnvvPmzTN33nmn8fT0NAEBAaZHjx7mt99+y7fPOXPmmNtuu814eHiYBg0amGXLll1yKrA333wz3/aSzKhRo6zb2dnZZsiQIaZChQrG4XBYr5WFCxeatm3bmsDAQOPh4WGqVKlinnjiiXx1AwVxGOPCT3UAAFAMPPvss3r//feVkZHBVyADtzjG3AIA/lIunk7s+PHjmj17tpo2bUqwBWyAMbcAgL+UyMhItWjRQnXq1FFKSor++c9/Kj09Xa+88oqrSwNwHRBuAQB/Ke3bt9fChQv1wQcfyOFw6K677tI///lPpym/ANy6GHMLAAAA22DMLQAAAGyDcAsAAADbYMyt/vwe7yNHjqh06dLX/StSAQAAcO2MMTp16pRCQkKcvpb9YoRbSUeOHHH6rngAAAAUT4cPH1blypUvuZ5wK1lf43n48GHrO+MBAABQfKSnpys0NPSKX79OuJWsoQh+fn6EWwAAgGLsSkNI+UAZAAAAbINwCwAAANsg3AIAAMA2CLcAAACwDcItAAAAbKPYhNs33nhDDodDzz77rNV27tw5xcXFqVy5cvL19VWXLl2UkpLitN2hQ4cUGxsrb29vBQYGavjw4crOzr7J1QMAAKA4KBbhdtOmTXr//fdVr149p/ahQ4fqyy+/1IIFC7Ru3TodOXJEnTt3ttbn5OQoNjZWWVlZ2rBhgz766CPNnDlTI0eOvNmnAAAAgGLA5eE2IyNDPXr00D/+8Q+VLVvWak9LS9M///lPTZw4Ua1atVLDhg01Y8YMbdiwQRs3bpQkLV++XD///LPmzJmjBg0aKCYmRq+++qqmTp2qrKwsV50SAAAAXMTl4TYuLk6xsbGKiopyak9KStL58+ed2mvXrq0qVaooMTFRkpSYmKiIiAgFBQVZfaKjo5Wenq6dO3de8piZmZlKT093WgAAAHDrc+k3lH3yySf68ccftWnTpnzrkpOT5eHhoTJlyji1BwUFKTk52epzYbDNW5+37lISEhI0ZsyYa6weAAAAxY3LrtwePnxYzzzzjD7++GOVKlXqph47Pj5eaWlp1nL48OGbenwAAADcGC4Lt0lJSUpNTdVdd92lkiVLqmTJklq3bp3effddlSxZUkFBQcrKytLJkyedtktJSVFwcLAkKTg4ON/sCXm38/oUxNPTU35+fk4LAAAAbn0uC7etW7fWjh07tHXrVmu5++671aNHD+tnd3d3rVq1ytpm9+7dOnTokCIjIyVJkZGR2rFjh1JTU60+K1askJ+fn8LDw2/6OQEAAMC1XDbmtnTp0qpbt65Tm4+Pj8qVK2e19+vXT8OGDVNAQID8/Pw0ZMgQRUZGqnHjxpKktm3bKjw8XD179tSECROUnJysl19+WXFxcfL09Lzp5wQAAADXcukHyq5k0qRJKlGihLp06aLMzExFR0frvffes9a7ublp8eLFGjRokCIjI+Xj46PevXtr7NixLqwaAAAAruIwxhhXF+Fq6enp8vf3V1paGuNvAQAAiqGrzWsun+cWAAAAuF4ItwAAALANwi0AAABso1h/oAwAcGs6NDbC1SUAuEGqjNzh6hIuiyu3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA2Xhttp06apXr168vPzk5+fnyIjI7VkyRJrfYsWLeRwOJyWJ5980mkfhw4dUmxsrLy9vRUYGKjhw4crOzv7Zp8KAAAAioGSrjx45cqV9cYbb6hmzZoyxuijjz7Sgw8+qC1btuiOO+6QJA0YMEBjx461tvH29rZ+zsnJUWxsrIKDg7VhwwYdPXpUvXr1kru7u15//fWbfj4AAABwLZeG244dOzrdHjdunKZNm6aNGzda4dbb21vBwcEFbr98+XL9/PPPWrlypYKCgtSgQQO9+uqrGjFihEaPHi0PD48bfg4AAAAoPorNmNucnBx98sknOn36tCIjI632jz/+WOXLl1fdunUVHx+vM2fOWOsSExMVERGhoKAgqy06Olrp6enauXPnJY+VmZmp9PR0pwUAAAC3PpdeuZWkHTt2KDIyUufOnZOvr68+++wzhYeHS5Iee+wxhYWFKSQkRNu3b9eIESO0e/duffrpp5Kk5ORkp2ArybqdnJx8yWMmJCRozJgxN+iMAAAA4CouD7e1atXS1q1blZaWpoULF6p3795at26dwsPDNXDgQKtfRESEKlasqNatW2vfvn2qXr16kY8ZHx+vYcOGWbfT09MVGhp6TecBAAAA13P5sAQPDw/VqFFDDRs2VEJCgurXr6933nmnwL6NGjWSJO3du1eSFBwcrJSUFKc+ebcvNU5Xkjw9Pa0ZGvIWAAAA3PpcHm4vlpubq8zMzALXbd26VZJUsWJFSVJkZKR27Nih1NRUq8+KFSvk5+dnDW0AAADAX4dLhyXEx8crJiZGVapU0alTpzR37lytXbtWy5Yt0759+zR37ly1b99e5cqV0/bt2zV06FA1a9ZM9erVkyS1bdtW4eHh6tmzpyZMmKDk5GS9/PLLiouLk6enpytPDQAAAC7g0nCbmpqqXr166ejRo/L391e9evW0bNkytWnTRocPH9bKlSs1efJknT59WqGhoerSpYtefvlla3s3NzctXrxYgwYNUmRkpHx8fNS7d2+neXEBAADw1+EwxhhXF+Fq6enp8vf3V1paGuNvAeA6ODQ2wtUlALhBqozc4ZLjXm1eK3ZjbgEAAICiItwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbcGm4nTZtmurVqyc/Pz/5+fkpMjJSS5YssdafO3dOcXFxKleunHx9fdWlSxelpKQ47ePQoUOKjY2Vt7e3AgMDNXz4cGVnZ9/sUwEAAEAx4NJwW7lyZb3xxhtKSkrS5s2b1apVKz344IPauXOnJGno0KH68ssvtWDBAq1bt05HjhxR586dre1zcnIUGxurrKwsbdiwQR999JFmzpypkSNHuuqUAAAA4EIOY4xxdREXCggI0JtvvqmHH35YFSpU0Ny5c/Xwww9Lknbt2qU6deooMTFRjRs31pIlS9ShQwcdOXJEQUFBkqTp06drxIgROnbsmDw8PK7qmOnp6fL391daWpr8/Pxu2LkBwF/FobERri4BwA1SZeQOlxz3avNasRlzm5OTo08++USnT59WZGSkkpKSdP78eUVFRVl9ateurSpVqigxMVGSlJiYqIiICCvYSlJ0dLTS09Otq78FyczMVHp6utMCAACAW5/Lw+2OHTvk6+srT09PPfnkk/rss88UHh6u5ORkeXh4qEyZMk79g4KClJycLElKTk52CrZ56/PWXUpCQoL8/f2tJTQ09PqeFAAAAFzC5eG2Vq1a2rp1q77//nsNGjRIvXv31s8//3xDjxkfH6+0tDRrOXz48A09HgAAAG6Okq4uwMPDQzVq1JAkNWzYUJs2bdI777yjbt26KSsrSydPnnS6epuSkqLg4GBJUnBwsH744Qen/eXNppDXpyCenp7y9PS8zmcCAAAAV3P5lduL5ebmKjMzUw0bNpS7u7tWrVplrdu9e7cOHTqkyMhISVJkZKR27Nih1NRUq8+KFSvk5+en8PDwm147AAAAXMulV27j4+MVExOjKlWq6NSpU5o7d67Wrl2rZcuWyd/fX/369dOwYcMUEBAgPz8/DRkyRJGRkWrcuLEkqW3btgoPD1fPnj01YcIEJScn6+WXX1ZcXBxXZgEAAP6CXBpuU1NT1atXLx09elT+/v6qV6+eli1bpjZt2kiSJk2apBIlSqhLly7KzMxUdHS03nvvPWt7Nzc3LV68WIMGDVJkZKR8fHzUu3dvjR071lWnBAAAABcqdvPcugLz3ALA9cU8t4B9Mc8tAAAAcJMQbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbhFsAAADYBuEWAAAAtkG4BQAAgG0QbgEAAGAbLg23CQkJuueee1S6dGkFBgaqU6dO2r17t1OfFi1ayOFwOC1PPvmkU59Dhw4pNjZW3t7eCgwM1PDhw5WdnX0zTwUAAADFQElXHnzdunWKi4vTPffco+zsbL344otq27atfv75Z/n4+Fj9BgwYoLFjx1q3vb29rZ9zcnIUGxur4OBgbdiwQUePHlWvXr3k7u6u119//aaeDwAAAFzLpeF26dKlTrdnzpypwMBAJSUlqVmzZla7t7e3goODC9zH8uXL9fPPP2vlypUKCgpSgwYN9Oqrr2rEiBEaPXq0PDw88m2TmZmpzMxM63Z6evp1OiMAAAC4UrEac5uWliZJCggIcGr/+OOPVb58edWtW1fx8fE6c+aMtS4xMVEREREKCgqy2qKjo5Wenq6dO3cWeJyEhAT5+/tbS2ho6A04GwAAANxsLr1ye6Hc3Fw9++yzuu+++1S3bl2r/bHHHlNYWJhCQkK0fft2jRgxQrt379ann34qSUpOTnYKtpKs28nJyQUeKz4+XsOGDbNup6enE3ABAABsoNiE27i4OP3000/67rvvnNoHDhxo/RwREaGKFSuqdevW2rdvn6pXr16kY3l6esrT0/Oa6gUAAEDxUyyGJQwePFiLFy/WmjVrVLly5cv2bdSokSRp7969kqTg4GClpKQ49cm7falxugAAALAnl4ZbY4wGDx6szz77TKtXr1a1atWuuM3WrVslSRUrVpQkRUZGaseOHUpNTbX6rFixQn5+fgoPD78hdQMAAKB4cumwhLi4OM2dO1eff/65SpcubY2R9ff3l5eXl/bt26e5c+eqffv2KleunLZv366hQ4eqWbNmqlevniSpbdu2Cg8PV8+ePTVhwgQlJyfr5ZdfVlxcHEMPAAAA/mJceuV22rRpSktLU4sWLVSxYkVrmTdvniTJw8NDK1euVNu2bVW7dm0999xz6tKli7788ktrH25ublq8eLHc3NwUGRmpxx9/XL169XKaFxcAAAB/DS69cmuMuez60NBQrVu37or7CQsL09dff329ygIAAMAtqlh8oAwAAAC4Hgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANooUbm+77TYdP348X/vJkyd12223XXNRAAAAQFEUKdweOHBAOTk5+dozMzP1+++/X3NRAAAAQFGULEznL774wvp52bJl8vf3t27n5ORo1apVqlq16nUrDgAAACiMQoXbTp06SZIcDod69+7ttM7d3V1Vq1bV22+/fd2KAwAAAAqjUOE2NzdXklStWjVt2rRJ5cuXvyFFAQAAAEVRqHCbZ//+/de7DgAAAOCaFSncStKqVau0atUqpaamWld08/zrX/+65sIAAACAwipSuB0zZozGjh2ru+++WxUrVpTD4bjedQEAAACFVqRwO336dM2cOVM9e/a83vUAAAAARVakeW6zsrLUpEmT610LAAAAcE2KFG779++vuXPnXu9aAAAAgGtSpGEJ586d0wcffKCVK1eqXr16cnd3d1o/ceLE61IcAAAAUBhFCrfbt29XgwYNJEk//fST0zo+XAYAAABXKVK4XbNmzfWuAwAAALhmRRpzCwAAABRHRbpy27Jly8sOP1i9enWRC/orajh8lqtLAHCDJL3Zy9UlAMBfSpHCbd542zznz5/X1q1b9dNPP6l3797Xoy4AAACg0IoUbidNmlRg++jRo5WRkXFNBQEAAABFdV3H3D7++OP617/+dT13CQAAAFy16xpuExMTVapUqeu5SwAAAOCqFSncdu7c2Wl56KGH1LhxY/Xt21dPPPHEVe8nISFB99xzj0qXLq3AwEB16tRJu3fvdupz7tw5xcXFqVy5cvL19VWXLl2UkpLi1OfQoUOKjY2Vt7e3AgMDNXz4cGVnZxfl1AAAAHALK1K49ff3d1oCAgLUokULff311xo1atRV72fdunWKi4vTxo0btWLFCp0/f15t27bV6dOnrT5Dhw7Vl19+qQULFmjdunU6cuSIOnfubK3PyclRbGyssrKytGHDBn300UeaOXOmRo4cWZRTAwAAwC3MYYwxri4iz7FjxxQYGKh169apWbNmSktLU4UKFTR37lw9/PDDkqRdu3apTp06SkxMVOPGjbVkyRJ16NBBR44cUVBQkCRp+vTpGjFihI4dOyYPD48rHjc9PV3+/v5KS0uTn5/fDT3HgjAVGGBff9WpwA6NjXB1CQBukCojd7jkuFeb165pzG1SUpLmzJmjOXPmaMuWLdeyK0lSWlqaJCkgIMDa//nz5xUVFWX1qV27tqpUqaLExERJf47zjYiIsIKtJEVHRys9PV07d+4s8DiZmZlKT093WgAAAHDrK9JUYKmpqerevbvWrl2rMmXKSJJOnjypli1b6pNPPlGFChUKvc/c3Fw9++yzuu+++1S3bl1JUnJysjw8PKxj5AkKClJycrLV58Jgm7c+b11BEhISNGbMmELXCAAAgOKtSFduhwwZolOnTmnnzp06ceKETpw4oZ9++knp6el6+umni1RIXFycfvrpJ33yySdF2r4w4uPjlZaWZi2HDx++4ccEAADAjVekK7dLly7VypUrVadOHastPDxcU6dOVdu2bQu9v8GDB2vx4sX65ptvVLlyZas9ODhYWVlZOnnypNPV25SUFAUHB1t9fvjhB6f95c2mkNfnYp6envL09Cx0nQAAACjeinTlNjc3V+7u7vna3d3dlZube9X7McZo8ODB+uyzz7R69WpVq1bNaX3Dhg3l7u6uVatWWW27d+/WoUOHFBkZKUmKjIzUjh07lJqaavVZsWKF/Pz8FB4eXthTAwAAwC2sSOG2VatWeuaZZ3TkyBGr7ffff9fQoUPVunXrq95PXFyc5syZo7lz56p06dJKTk5WcnKyzp49K+nPKcf69eunYcOGac2aNUpKSlLfvn0VGRmpxo0bS5Latm2r8PBw9ezZU9u2bdOyZcv08ssvKy4ujquzAAAAfzFFCrd///vflZ6erqpVq6p69eqqXr26qlWrpvT0dE2ZMuWq9zNt2jSlpaWpRYsWqlixorXMmzfP6jNp0iR16NBBXbp0UbNmzRQcHKxPP/3UWu/m5qbFixfLzc1NkZGRevzxx9WrVy+NHTu2KKcGAACAW1iRxtyGhobqxx9/1MqVK7Vr1y5JUp06dZym7LoaVzPFbqlSpTR16lRNnTr1kn3CwsL09ddfF+rYAAAAsJ9CXbldvXq1wsPDlZ6eLofDoTZt2mjIkCEaMmSI7rnnHt1xxx369ttvb1StAAAAwGUVKtxOnjxZAwYMKPBbIfz9/fXEE09o4sSJ1604AAAAoDAKFW63bdumdu3aXXJ927ZtlZSUdM1FAQAAAEVRqHCbkpJS4BRgeUqWLKljx45dc1EAAABAURQq3FaqVEk//fTTJddv375dFStWvOaiAAAAgKIoVLht3769XnnlFZ07dy7furNnz2rUqFHq0KHDdSsOAAAAKIxCTQX28ssv69NPP9Xtt9+uwYMHq1atWpKkXbt2aerUqcrJydFLL710QwoFAAAArqRQ4TYoKEgbNmzQoEGDFB8fb81T63A4FB0dralTpyooKOiGFAoAAABcSaG/xCHvCxP++OMP7d27V8YY1axZU2XLlr0R9QEAAABXrUjfUCZJZcuW1T333HM9awEAAACuSaE+UAYAAAAUZ4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALbh0nD7zTffqGPHjgoJCZHD4dCiRYuc1vfp00cOh8NpadeunVOfEydOqEePHvLz81OZMmXUr18/ZWRk3MSzAAAAQHHh0nB7+vRp1a9fX1OnTr1kn3bt2uno0aPW8u9//9tpfY8ePbRz506tWLFCixcv1jfffKOBAwfe6NIBAABQDJV05cFjYmIUExNz2T6enp4KDg4ucN0vv/yipUuXatOmTbr77rslSVOmTFH79u311ltvKSQk5LrXDAAAgOKr2I+5Xbt2rQIDA1WrVi0NGjRIx48ft9YlJiaqTJkyVrCVpKioKJUoUULff//9JfeZmZmp9PR0pwUAAAC3vmIdbtu1a6dZs2Zp1apVGj9+vNatW6eYmBjl5ORIkpKTkxUYGOi0TcmSJRUQEKDk5ORL7jchIUH+/v7WEhoaekPPAwAAADeHS4clXEn37t2tnyMiIlSvXj1Vr15da9euVevWrYu83/j4eA0bNsy6nZ6eTsAFAACwgWJ95fZit912m8qXL6+9e/dKkoKDg5WamurUJzs7WydOnLjkOF3pz3G8fn5+TgsAAABufbdUuP3tt990/PhxVaxYUZIUGRmpkydPKikpyeqzevVq5ebmqlGjRq4qEwAAAC7i0mEJGRkZ1lVYSdq/f7+2bt2qgIAABQQEaMyYMerSpYuCg4O1b98+vfDCC6pRo4aio6MlSXXq1FG7du00YMAATZ8+XefPn9fgwYPVvXt3ZkoAAAD4C3LpldvNmzfrzjvv1J133ilJGjZsmO68806NHDlSbm5u2r59ux544AHdfvvt6tevnxo2bKhvv/1Wnp6e1j4+/vhj1a5dW61bt1b79u3VtGlTffDBB646JQAAALiQS6/ctmjRQsaYS65ftmzZFfcREBCguXPnXs+yAAAAcIu6pcbcAgAAAJdDuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALbh0nD7zTffqGPHjgoJCZHD4dCiRYuc1htjNHLkSFWsWFFeXl6KiorSnj17nPqcOHFCPXr0kJ+fn8qUKaN+/fopIyPjJp4FAAAAiguXhtvTp0+rfv36mjp1aoHrJ0yYoHfffVfTp0/X999/Lx8fH0VHR+vcuXNWnx49emjnzp1asWKFFi9erG+++UYDBw68WacAAACAYqSkKw8eExOjmJiYAtcZYzR58mS9/PLLevDBByVJs2bNUlBQkBYtWqTu3bvrl19+0dKlS7Vp0ybdfffdkqQpU6aoffv2euuttxQSEnLTzgUAAACuV2zH3O7fv1/JycmKioqy2vz9/dWoUSMlJiZKkhITE1WmTBkr2EpSVFSUSpQooe+///6S+87MzFR6errTAgAAgFtfsQ23ycnJkqSgoCCn9qCgIGtdcnKyAgMDndaXLFlSAQEBVp+CJCQkyN/f31pCQ0Ovc/UAAABwhWIbbm+k+Ph4paWlWcvhw4ddXRIAAACug2IbboODgyVJKSkpTu0pKSnWuuDgYKWmpjqtz87O1okTJ6w+BfH09JSfn5/TAgAAgFtfsQ231apVU3BwsFatWmW1paen6/vvv1dkZKQkKTIyUidPnlRSUpLVZ/Xq1crNzVWjRo1ues0AAABwLZfOlpCRkaG9e/dat/fv36+tW7cqICBAVapU0bPPPqvXXntNNWvWVLVq1fTKK68oJCREnTp1kiTVqVNH7dq104ABAzR9+nSdP39egwcPVvfu3ZkpAQAA4C/IpeF28+bNatmypXV72LBhkqTevXtr5syZeuGFF3T69GkNHDhQJ0+eVNOmTbV06VKVKlXK2ubjjz/W4MGD1bp1a5UoUUJdunTRu+++e9PPBQAAAK7nMMYYVxfhaunp6fL391daWppLxt82HD7rph8TwM2R9GYvV5fgEofGRri6BAA3SJWRO1xy3KvNa8V2zC0AAABQWIRbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUAAIBtEG4BAABgG4RbAAAA2EaxDrejR4+Ww+FwWmrXrm2tP3funOLi4lSuXDn5+vqqS5cuSklJcWHFAAAAcKViHW4l6Y477tDRo0et5bvvvrPWDR06VF9++aUWLFigdevW6ciRI+rcubMLqwUAAIArlXR1AVdSsmRJBQcH52tPS0vTP//5T82dO1etWrWSJM2YMUN16tTRxo0b1bhx40vuMzMzU5mZmdbt9PT06184AAAAbrpif+V2z549CgkJ0W233aYePXro0KFDkqSkpCSdP39eUVFRVt/atWurSpUqSkxMvOw+ExIS5O/vby2hoaE39BwAAABwcxTrcNuoUSPNnDlTS5cu1bRp07R//37df//9OnXqlJKTk+Xh4aEyZco4bRMUFKTk5OTL7jc+Pl5paWnWcvjw4Rt4FgAAALhZivWwhJiYGOvnevXqqVGjRgoLC9P8+fPl5eVV5P16enrK09PzepQIAACAYqRYX7m9WJkyZXT77bdr7969Cg4OVlZWlk6ePOnUJyUlpcAxugAAALC/WyrcZmRkaN++fapYsaIaNmwod3d3rVq1ylq/e/duHTp0SJGRkS6sEgAAAK5SrIclPP/88+rYsaPCwsJ05MgRjRo1Sm5ubnr00Ufl7++vfv36adiwYQoICJCfn5+GDBmiyMjIy86UAAAAAPsq1uH2t99+06OPPqrjx4+rQoUKatq0qTZu3KgKFSpIkiZNmqQSJUqoS5cuyszMVHR0tN577z0XVw0AAABXKdbh9pNPPrns+lKlSmnq1KmaOnXqTaoIAAAAxdktNeYWAAAAuBzCLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3CLQAAAGyDcAsAAADbINwCAADANgi3AAAAsA3bhNupU6eqatWqKlWqlBo1aqQffvjB1SUBAADgJrNFuJ03b56GDRumUaNG6ccff1T9+vUVHR2t1NRUV5cGAACAm8gW4XbixIkaMGCA+vbtq/DwcE2fPl3e3t7617/+5erSAAAAcBOVdHUB1yorK0tJSUmKj4+32kqUKKGoqCglJiYWuE1mZqYyMzOt22lpaZKk9PT0G1vsJeRknnXJcQHceK56X3G1U+dyXF0CgBvEVe9recc1xly23y0fbv/3v/8pJydHQUFBTu1BQUHatWtXgdskJCRozJgx+dpDQ0NvSI0A/rr8pzzp6hIA4PpK8Hfp4U+dOiV//0vXcMuH26KIj4/XsGHDrNu5ubk6ceKEypUrJ4fD4cLKYHfp6ekKDQ3V4cOH5efn5+pyAOCa8b6Gm8UYo1OnTikkJOSy/W75cFu+fHm5ubkpJSXFqT0lJUXBwcEFbuPp6SlPT0+ntjJlytyoEoF8/Pz8+CUAwFZ4X8PNcLkrtnlu+Q+UeXh4qGHDhlq1apXVlpubq1WrVikyMtKFlQEAAOBmu+Wv3ErSsGHD1Lt3b91999269957NXnyZJ0+fVp9+/Z1dWkAAAC4iWwRbrt166Zjx45p5MiRSk5OVoMGDbR06dJ8HzIDXM3T01OjRo3KNywGAG5VvK+huHGYK82nAAAAANwibvkxtwAAAEAewi0AAABsg3ALAAAA2yDcAjfY2rVr5XA4dPLkycv2q1q1qiZPnnxTagIAV+G9Djca4Rb4//r06SOHwyGHwyEPDw/VqFFDY8eOVXZ29jXtt0mTJjp69Kg18fTMmTML/NKQTZs2aeDAgdd0LAB/bXnvY2+88YZT+6JFi276N3DyXgdXIdwCF2jXrp2OHj2qPXv26LnnntPo0aP15ptvXtM+PTw8FBwcfMVfLBUqVJC3t/c1HQsASpUqpfHjx+uPP/5wdSkF4r0ONxrhFriAp6engoODFRYWpkGDBikqKkpffPGF/vjjD/Xq1Utly5aVt7e3YmJitGfPHmu7gwcPqmPHjipbtqx8fHx0xx136Ouvv5bkPCxh7dq16tu3r9LS0qyrxKNHj5bk/K+6xx57TN26dXOq7fz58ypfvrxmzZol6c9v4ktISFC1atXk5eWl+vXra+HChTf+TgJQrEVFRSk4OFgJCQmX7PPdd9/p/vvvl5eXl0JDQ/X000/r9OnT1vqjR48qNjZWXl5eqlatmubOnZtvOMHEiRMVEREhHx8fhYaG6qmnnlJGRoYk8V4HlyLcApfh5eWlrKws9enTR5s3b9YXX3yhxMREGWPUvn17nT9/XpIUFxenzMxMffPNN9qxY4fGjx8vX1/ffPtr0qSJJk+eLD8/Px09elRHjx7V888/n69fjx499OWXX1q/KCRp2bJlOnPmjB566CFJUkJCgmbNmqXp06dr586dGjp0qB5//HGtW7fuBt0bAG4Fbm5uev311zVlyhT99ttv+dbv27dP7dq1U5cuXbR9+3bNmzdP3333nQYPHmz16dWrl44cOaK1a9fqP//5jz744AOlpqY67adEiRJ69913tXPnTn300UdavXq1XnjhBUm818HFDABjjDG9e/c2Dz74oDHGmNzcXLNixQrj6elpOnXqZCSZ9evXW33/97//GS8vLzN//nxjjDERERFm9OjRBe53zZo1RpL5448/jDHGzJgxw/j7++frFxYWZiZNmmSMMeb8+fOmfPnyZtasWdb6Rx991HTr1s0YY8y5c+eMt7e32bBhg9M++vXrZx599NGinD4AG7jwfaxx48bmb3/7mzHGmM8++8zk/crv16+fGThwoNN23377rSlRooQ5e/as+eWXX4wks2nTJmv9nj17jCTrPaogCxYsMOXKlbNu814HV7HF1+8C18vixYvl6+ur8+fPKzc3V4899pg6d+6sxYsXq1GjRla/cuXKqVatWvrll18kSU8//bQGDRqk5cuXKyoqSl26dFG9evWKXEfJkiXVtWtXffzxx+rZs6dOnz6tzz//XJ988okkae/evTpz5ozatGnjtF1WVpbuvPPOIh8XgH2MHz9erVq1ynfFdNu2bdq+fbs+/vhjq80Yo9zcXO3fv1+//vqrSpYsqbvuustaX6NGDZUtW9ZpPytXrlRCQoJ27dql9PR0ZWdn69y5czpz5sxVj6nlvQ43AuEWuEDLli01bdo0eXh4KCQkRCVLltQXX3xxxe369++v6OhoffXVV1q+fLkSEhL09ttva8iQIUWupUePHmrevLlSU1O1YsUKeXl5qV27dpJk/Qvvq6++UqVKlZy24/vdAUhSs2bNFB0drfj4ePXp08dqz8jI0BNPPKGnn3463zZVqlTRr7/+esV9HzhwQB06dNCgQYM0btw4BQQE6LvvvlO/fv2UlZVVqA+M8V6H641wC1zAx8dHNWrUcGqrU6eOsrOz9f3336tJkyaSpOPHj2v37t0KDw+3+oWGhurJJ5/Uk08+qfj4eP3jH/8oMNx6eHgoJyfnirU0adJEoaGhmjdvnpYsWaJHHnlE7u7ukqTw8HB5enrq0KFDat68+bWcMgAbe+ONN9SgQQPVqlXLarvrrrv0888/53uvy1OrVi1lZ2dry5YtatiwoaQ/r6BeOPtCUlKScnNz9fbbb6tEiT8/vjN//nyn/fBeB1ch3AJXULNmTT344IMaMGCA3n//fZUuXVr/93//p0qVKunBBx+UJD377LOKiYnR7bffrj/++ENr1qxRnTp1Ctxf1apVlZGRoVWrVql+/fry9va+5FWOxx57TNOnT9evv/6qNWvWWO2lS5fW888/r6FDhyo3N1dNmzZVWlqa1q9fLz8/P/Xu3fv63xEAbjkRERHq0aOH3n33XattxIgRaty4sQYPHqz+/fvLx8dHP//8s1asWKG///3vql27tqKiojRw4EBNmzZN7u7ueu655+Tl5WVNaVijRg2dP39eU6ZMUceOHbV+/XpNnz7d6di818FlXD3oFyguLvwgxsVOnDhhevbsafz9/Y2Xl5eJjo42v/76q7V+8ODBpnr16sbT09NUqFDB9OzZ0/zvf/8zxuT/QJkxxjz55JOmXLlyRpIZNWqUMcb5QxZ5fv75ZyPJhIWFmdzcXKd1ubm5ZvLkyaZWrVrG3d3dVKhQwURHR5t169Zd830B4NZU0PvY/v37jYeHh7nwV/4PP/xg2rRpY3x9fY2Pj4+pV6+eGTdunLX+yJEjJiYmxnh6epqwsDAzd+5cExgYaKZPn271mThxoqlYsaL1njhr1ize61AsOIwxxoXZGgAAFHO//fabQkNDtXLlSrVu3drV5QCXRbgFAABOVq9erYyMDEVEROjo0aN64YUX9Pvvv+vXX3+1xsMCxRVjbgEAgJPz58/rxRdf1H//+1+VLl1aTZo00ccff0ywxS2BK7cAAACwDb5+FwAAALZBuAUAAIBtEG4BAABgG4RbAAAA2AbhFgAAALZBuAUA5DNz5kyVKVPG1WUAQKERbgHARY4dO6ZBgwapSpUq8vT0VHBwsKKjo7V+/XpXl6Zu3brp119/dXUZAFBofIkDALhIly5dlJWVpY8++ki33XabUlJStGrVKh0/ftzVpcnLy0teXl6uLgMACo0rtwDgAidPntS3336r8ePHq2XLlgoLC9O9996r+Ph4PfDAA1af/v37q0KFCvLz81OrVq20bds2ax+jR49WgwYN9P777ys0NFTe3t7q2rWr0tLSrD5r167VvffeKx8fH5UpU0b33XefDh48KEnatm2bWrZsqdKlS8vPz08NGzbU5s2bJeUflnC5vgBQnBBuAcAFfH195evrq0WLFikzM7PAPo888ohSU1O1ZMkSJSUl6a677lLr1q114sQJq8/evXs1f/58ffnll1q6dKm2bNmip556SpKUnZ2tTp06qXnz5tq+fbsSExM1cOBAORwOSVKPHj1UuXJlbdq0SUlJSfq///u/S369amH6AoArMSwBAFygZMmSmjlzpgYMGKDp06frrrvuUvPmzdW9e3fVq1dP3333nX744QelpqbK09NTkvTWW29p0aJFWrhwoQYOHChJOnfunGbNmqVKlSpJkqZMmaLY2Fi9/fbb8vDwUFpamjp06KDq1atLkurUqWPVcOjQIQ0fPly1a9eWJNWsWfOS9RamLwC4ElduAcBFunTpoiNHjuiLL75Qu3bttHbtWt11112aOXOmtm3bpoyMDJUrV866yuvr66v9+/dr37591j6qVKliBVtJioyMVG5urnbv3q2AgAD16dNH0dHR6tixo9555x0dPXrU6jts2DD1799fUVFReuONN5z2e7HC9AUAVyLcAoALlSpVSm3atNErr7yiDRs2qE+fPho1apQyMjJUsWJFbd261WnZvXu3hg8fftX7nzFjhhITE9WkSRPNmzdPt99+uzZu3CjpzzG7O3fuVGxsrFavXq3w8HB99tlnBe6nMH0BwJUItwBQjISHh+v06dO66667lJycrJIlS6pGjRpOS/ny5a3+hw4d0pEjR6zbGzduVIkSJVSrVi2r7c4771R8fLw2bNigunXrau7cuda622+/XUOHDtXy5cvVuXNnzZgx45K1FaYvALgK4RYAXOD48eNq1aqV5syZo+3bt2v//v1asGCBJkyYoAcffFBRUVGKjIxUp06dtHz5ch04cEAbNmzQSy+95DRLQalSpdS7d29t27ZN3377rZ5++ml17dpVwcHB2r9/v+Lj45WYmKiDBw9q+fLl2rNnj+rUqaOzZ89q8ODBWrt2rQ4ePKj169dr06ZNTmNy8xSmLwC4Gh8oAwAX8PX1VaNGjTRp0iTt27dP58+fV2hoqAYMGKAXX3xRDodDX3/9tV566SX17dtXx44dU3BwsJo1a6agoCBrPzVq1FDnzp3Vvn17nThxQh06dNB7770nSfL29tauXbv00Ucf6fjx46pYsaLi4uL0xBNPKDs7W8ePH1evXr2UkpKi8uXLq3PnzhozZky+Wt3c3K66LwC4msMYY1xdBACg8EaPHq1FixZp69atri4FAIoNhiUAAADANgi3AAAAsA2GJQAAAMA2uHILAAAA2yDcAgAAwDYItwAAALANwi0AAABsg3ALAAAA2yDcAgAAwDYItwAAALANwi0AAABs4/8BPC0yx6ZFYKkAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Create a bar plot\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.barplot(x=['Positive', 'Negative'], y=[positive_count, negative_count])\n",
        "plt.title('Distribution of Positive and Negative Counts')\n",
        "plt.xlabel('Sepssis')\n",
        "plt.ylabel('Count')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "WVVr42P6RWwx",
        "outputId": "cd768aae-59e0-4fe2-dd4b-61ea14cb3adc"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Data\n",
        "positive_count = 208\n",
        "negative_count = 391\n",
        "\n",
        "# Create a list of values and corresponding labels\n",
        "counts = [positive_count, negative_count]\n",
        "labels = ['Positive', 'Negative']\n",
        "colors = ['lightcoral', 'lightskyblue']\n",
        "\n",
        "# Create an exploded pie chart\n",
        "plt.figure(figsize=(6, 6))\n",
        "plt.pie(counts, labels=labels, colors=colors, autopct='%1.1f%%', startangle=90, explode=(0.1, 0), shadow=True)\n",
        "plt.title(\"Distribution of Sepssis in ICU Patients\")\n",
        "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CHcvLVQaPKeu"
      },
      "source": [
        "From the figures, we can observe that there are 208 patients in the ICU who have been diagnosed with Sepssis (Positive Count), and 391 patients in the ICU who have not been diagnosed with Sepssis (Negative Count). This indicates that Sepssis is present in a smaller proportion of ICU patients compared to those who do not have Sepssis.\n",
        "\n",
        "The distribution shows that the number of patients without Sepssis is higher than the number of patients with Sepssis. This suggests that Sepssis is not a very common condition among patients in the ICU based on the available dataset."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yhe1prjcbIg1"
      },
      "source": [
        "##  Bivariate and Multivariate Analysis:\n",
        "\n",
        "Bivariate analysis involves exploring the relationship between two variables, while multivariate analysis examines the relationships among multiple variables simultaneously. We will visualize the relationships between features and the target variable \"Sepssis.\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iqa7lBCzbl09"
      },
      "source": [
        "### Visualize the Numerical variables against 'Sepssis'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "1ncPXFTuQfF1",
        "outputId": "d9f71423-5cea-4d7e-cf12-e8866bc34ee2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Box plots for numerical variables against 'Sepssis'\n",
        "plt.figure(figsize=(12, 6))\n",
        "for i, col in enumerate(numerical_cols):\n",
        "    plt.subplot(2, 4, i + 1)\n",
        "    sns.boxplot(x='Sepssis', y=col, data=train_df)\n",
        "    plt.title(f\"{col} vs Sepssis\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9sRx3Om_czK9"
      },
      "source": [
        "### Observations from the box plots of numerical variables against 'Sepssis':\n",
        "\n",
        "  1. Plasma glucose (PRG) vs Sepssis: The median value of PRG for patients with Sepsis (Positive) appears slightly higher than for those without Sepsis (Negative). The box plot shows that the interquartile range (IQR) for the Positive group is slightly larger, indicating higher variability in PRG values among patients with Sepsis.\n",
        "\n",
        "  2. Blood Work Result-1 (PL) vs Sepssis: The median PL value for patients with Sepsis is comparable to that of patients without Sepsis. However, the IQR for the Positive group is narrower, suggesting less variability in PL values for patients with Sepsis.\n",
        "\n",
        "  3. Blood Pressure (PR) vs Sepssis: The median PR value for both groups is almost the same. The box plots show similar IQRs for patients with and without Sepsis, indicating similar variability in blood pressure values.\n",
        "\n",
        "  4. Blood Work Result-2 (SK) vs Sepssis: The median SK value for patients with Sepsis is slightly higher than for patients without Sepsis. The box plot shows a wider IQR for the Positive group, suggesting higher variability in SK values among patients with Sepsis.\n",
        "\n",
        "  5. Blood Work Result-3 (TS) vs Sepssis: The median TS value for patients with Sepsis is notably higher than for patients without Sepsis. The box plot indicates that patients with Sepsis generally have higher TS values, and the IQR for the Positive group is wider, showing greater variability.\n",
        "\n",
        "  6. Body mass index (M11) vs Sepssis: The median M11 value for both groups is almost the same. The box plots show similar IQRs for patients with and without Sepsis, indicating similar variability in BMI values.\n",
        "\n",
        "  7. Blood Work Result-4 (BD2) vs Sepssis: The median BD2 value for patients with Sepsis is comparable to that of patients without Sepsis. The box plots indicate similar IQRs for both groups, suggesting similar variability in BD2 values.\n",
        "\n",
        "  8. Age vs Sepssis: The median age for patients with Sepsis is slightly higher than for patients without Sepsis. The box plot shows that the IQR for the Positive group is slightly larger, indicating higher variability in ages among patients with Sepsis."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFt75dVIc9bt"
      },
      "source": [
        "### Visualize categorical variables against 'Sepssis'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "JBIjdcpKc-CK",
        "outputId": "13056bc4-8700-426c-f7d5-5db2f469c543"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Bar plots for categorical variables against 'Sepssis'\n",
        "categorical_cols = ['Insurance']\n",
        "\n",
        "plt.figure(figsize=(6, 4))\n",
        "for i, col in enumerate(categorical_cols):\n",
        "    plt.subplot(1, 1, i + 1)\n",
        "    sns.countplot(x=col, hue='Sepssis', data=train_df)\n",
        "    plt.title(f\"{col} vs Sepssis\")\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xSNHqdmKdSJa"
      },
      "source": [
        "### Observations from the bar plots of the categorical variable 'Insurance' against 'Sepssis':\n",
        "\n",
        "The bar plot shows the distribution of patients with Positive (Sepsis) and Negative (Non-Sepsis) values for the 'Insurance' category.\n",
        "\n",
        "  - Insurance vs Sepssis: The 'Insurance' category has two levels, and the plot displays the count of patients with each insurance type for both groups.\n",
        "For patients with Sepsis (Positive group), there are two bars representing the count of patients with each insurance type. The height of each bar corresponds to the number of patients in the Positive group with that specific insurance type.\n",
        "  - For patients without Sepsis (Negative group), there are also two bars representing the count of patients with each insurance type. The height of each bar corresponds to the number of patients in the Negative group with that specific insurance type."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OgU9QxtldSNa"
      },
      "source": [
        "### Correlation Heatmap for numerical variables\n",
        "1. Creating a mask for the lower triangle:\n",
        "  The mask is a boolean matrix with the same shape as the correlation matrix. The np.triu() function sets the upper triangle of the matrix to True (1) and the lower triangle to False (0). By using np.ones_like(), we create a matrix of ones with the same shape as the correlation matrix. Finally, by specifying dtype=bool, we convert all the ones in the matrix to True and all other values to False.\n",
        "\n",
        "2. We set the figure size for the heatmap and then use Seaborn's heatmap() function to plot the correlation matrix. The annot=True argument adds the numerical values to the heatmap cells. The cmap='coolwarm' argument sets the color map for the heatmap, where cooler colors represent negative correlation, and warmer colors represent positive correlation. The center=0 argument sets the center of the color map to 0, making 0 correlation appear as a white cell. The mask=mask argument specifies the mask created earlier to display only the lower triangle of the heatmap."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "apTK7OrUgtUn",
        "outputId": "e8e7ecb0-2757-4515-8cd6-e95dd6824c12"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Correlation Heatmap for numerical variables\n",
        "correlation_matrix = train_df[numerical_cols].corr()\n",
        "\n",
        "# Mask to display only the lower triangle of the heatmap\n",
        "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', center=0, mask=mask)\n",
        "plt.title(\"Correlation Heatmap\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qTFuYAWzdb-h"
      },
      "source": [
        "### From the output of the correlation heatmap, we can make the following observations:\n",
        "\n",
        "  1. Most of the correlations are relatively weak: The majority of the numerical variables have correlations close to 0. This suggests that there is no strong linear relationship between these variables.\n",
        "\n",
        "  2. Positive correlations: Some variables have positive correlations, which means that as one variable increases, the other tends to increase as well. For example, there is a positive correlation between \"SK\" and \"M11.\"\n",
        "\n",
        "  3. Negative correlations: There are also negative correlations between certain variables, indicating that as one variable increases, the other tends to decrease.\n",
        "\n",
        "  4. Strong correlation between \"Age\" and \"PRG\": \"Age\" and \"PRG\" have a relatively stronger positive correlation compared to other pairs of variables, suggesting that there might be some relationship between these two variables."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2wZQgVSbnuOm"
      },
      "source": [
        "# Feature Processing & Engineering"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5WBAm3Q9oEAL"
      },
      "source": [
        "## Data Cleaning\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMwZ_bxF35J5"
      },
      "source": [
        "## Drop ID and Insurance features from both datasets since they have no significace in our analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "_xU8uThk4NXb"
      },
      "outputs": [],
      "source": [
        "# Drop 'ID' and 'Insurance' columns from both datasets\n",
        "train_df.drop(columns=['ID', 'Insurance'], inplace=True)\n",
        "test_df.drop(columns=['ID', 'Insurance'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "6LlwP80x6AgJ",
        "outputId": "d5539c6d-67e2-47f6-bf8f-aa06b799c74e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PRG   PL  PR  SK   TS   M11    BD2  Age   Sepssis\n",
              "0      6  148  72  35    0  33.6  0.627   50  Positive\n",
              "1      1   85  66  29    0  26.6  0.351   31  Negative\n",
              "2      8  183  64   0    0  23.3  0.672   32  Positive\n",
              "3      1   89  66  23   94  28.1  0.167   21  Negative\n",
              "4      0  137  40  35  168  43.1  2.288   33  Positive\n",
              "..   ...  ...  ..  ..  ...   ...    ...  ...       ...\n",
              "594    6  123  72  45  230  33.6  0.733   34  Negative\n",
              "595    0  188  82  14  185  32.0  0.682   22  Positive\n",
              "596    0   67  76   0    0  45.3  0.194   46  Negative\n",
              "597    1   89  24  19   25  27.8  0.559   21  Negative\n",
              "598    1  173  74   0    0  36.8  0.088   38  Positive\n",
              "\n",
              "[599 rows x 9 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-62f85c9d-ae3e-4e96-b179-bdf6ca24b342\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRG</th>\n",
              "      <th>PL</th>\n",
              "      <th>PR</th>\n",
              "      <th>SK</th>\n",
              "      <th>TS</th>\n",
              "      <th>M11</th>\n",
              "      <th>BD2</th>\n",
              "      <th>Age</th>\n",
              "      <th>Sepssis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6</td>\n",
              "      <td>148</td>\n",
              "      <td>72</td>\n",
              "      <td>35</td>\n",
              "      <td>0</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.627</td>\n",
              "      <td>50</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>85</td>\n",
              "      <td>66</td>\n",
              "      <td>29</td>\n",
              "      <td>0</td>\n",
              "      <td>26.6</td>\n",
              "      <td>0.351</td>\n",
              "      <td>31</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>8</td>\n",
              "      <td>183</td>\n",
              "      <td>64</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>23.3</td>\n",
              "      <td>0.672</td>\n",
              "      <td>32</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>66</td>\n",
              "      <td>23</td>\n",
              "      <td>94</td>\n",
              "      <td>28.1</td>\n",
              "      <td>0.167</td>\n",
              "      <td>21</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>137</td>\n",
              "      <td>40</td>\n",
              "      <td>35</td>\n",
              "      <td>168</td>\n",
              "      <td>43.1</td>\n",
              "      <td>2.288</td>\n",
              "      <td>33</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>594</th>\n",
              "      <td>6</td>\n",
              "      <td>123</td>\n",
              "      <td>72</td>\n",
              "      <td>45</td>\n",
              "      <td>230</td>\n",
              "      <td>33.6</td>\n",
              "      <td>0.733</td>\n",
              "      <td>34</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>595</th>\n",
              "      <td>0</td>\n",
              "      <td>188</td>\n",
              "      <td>82</td>\n",
              "      <td>14</td>\n",
              "      <td>185</td>\n",
              "      <td>32.0</td>\n",
              "      <td>0.682</td>\n",
              "      <td>22</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>596</th>\n",
              "      <td>0</td>\n",
              "      <td>67</td>\n",
              "      <td>76</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>45.3</td>\n",
              "      <td>0.194</td>\n",
              "      <td>46</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>597</th>\n",
              "      <td>1</td>\n",
              "      <td>89</td>\n",
              "      <td>24</td>\n",
              "      <td>19</td>\n",
              "      <td>25</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.559</td>\n",
              "      <td>21</td>\n",
              "      <td>Negative</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>74</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.088</td>\n",
              "      <td>38</td>\n",
              "      <td>Positive</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>599 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-62f85c9d-ae3e-4e96-b179-bdf6ca24b342')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-3812aa6a-9043-449c-8fae-cf89bc3cbdf6\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3812aa6a-9043-449c-8fae-cf89bc3cbdf6')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-3812aa6a-9043-449c-8fae-cf89bc3cbdf6 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-62f85c9d-ae3e-4e96-b179-bdf6ca24b342 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-62f85c9d-ae3e-4e96-b179-bdf6ca24b342');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "66eWzAo46Ixp",
        "outputId": "c6877ce6-944a-46f7-ce53-90099dbdb021"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PRG   PL  PR  SK   TS   M11    BD2  Age\n",
              "0      1  109  38  18  120  23.1  0.407   26\n",
              "1      1  108  88  19    0  27.1  0.400   24\n",
              "2      6   96   0   0    0  23.7  0.190   28\n",
              "3      1  124  74  36    0  27.8  0.100   30\n",
              "4      7  150  78  29  126  35.2  0.692   54\n",
              "..   ...  ...  ..  ..  ...   ...    ...  ...\n",
              "164   10  101  76  48  180  32.9  0.171   63\n",
              "165    2  122  70  27    0  36.8  0.340   27\n",
              "166    5  121  72  23  112  26.2  0.245   30\n",
              "167    1  126  60   0    0  30.1  0.349   47\n",
              "168    1   93  70  31    0  30.4  0.315   23\n",
              "\n",
              "[169 rows x 8 columns]"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-d57cf0d0-b216-464c-82af-5b8dd3da1163\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PRG</th>\n",
              "      <th>PL</th>\n",
              "      <th>PR</th>\n",
              "      <th>SK</th>\n",
              "      <th>TS</th>\n",
              "      <th>M11</th>\n",
              "      <th>BD2</th>\n",
              "      <th>Age</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>23.1</td>\n",
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              "      <td>26</td>\n",
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              "      <td>1</td>\n",
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              "      <td>27.1</td>\n",
              "      <td>0.400</td>\n",
              "      <td>24</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6</td>\n",
              "      <td>96</td>\n",
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              "      <td>0</td>\n",
              "      <td>23.7</td>\n",
              "      <td>0.190</td>\n",
              "      <td>28</td>\n",
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              "      <td>0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>0.100</td>\n",
              "      <td>30</td>\n",
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              "    <tr>\n",
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              "      <td>35.2</td>\n",
              "      <td>0.692</td>\n",
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              "      <td>32.9</td>\n",
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              "      <td>63</td>\n",
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              "      <td>70</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>36.8</td>\n",
              "      <td>0.340</td>\n",
              "      <td>27</td>\n",
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              "      <th>166</th>\n",
              "      <td>5</td>\n",
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              "      <td>26.2</td>\n",
              "      <td>0.245</td>\n",
              "      <td>30</td>\n",
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              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>1</td>\n",
              "      <td>126</td>\n",
              "      <td>60</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>30.1</td>\n",
              "      <td>0.349</td>\n",
              "      <td>47</td>\n",
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              "    <tr>\n",
              "      <th>168</th>\n",
              "      <td>1</td>\n",
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              "      <td>70</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "      <td>30.4</td>\n",
              "      <td>0.315</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>169 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d57cf0d0-b216-464c-82af-5b8dd3da1163')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-1717602b-b68f-4994-a095-27037bdeaea8\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1717602b-b68f-4994-a095-27037bdeaea8')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1717602b-b68f-4994-a095-27037bdeaea8 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-d57cf0d0-b216-464c-82af-5b8dd3da1163 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-d57cf0d0-b216-464c-82af-5b8dd3da1163');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "test_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyS2Zg5z84mI"
      },
      "source": [
        "## Separate the target label 'Sepssis' from the features in train_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "CJpsI66-845d"
      },
      "outputs": [],
      "source": [
        "# Separate the target label 'Sepssis' from the features in train_df\n",
        "X = train_df.drop(columns=['Sepssis'])\n",
        "y = train_df['Sepssis']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cdYddDZJ9FC_",
        "outputId": "85bd5122-fe22-42a5-c52b-70bffce1d570"
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      "outputs": [
        {
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              "   PRG   PL  PR  SK   TS   M11    BD2  Age\n",
              "0    6  148  72  35    0  33.6  0.627   50\n",
              "1    1   85  66  29    0  26.6  0.351   31\n",
              "2    8  183  64   0    0  23.3  0.672   32\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "X.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXVD-zIp9Li4",
        "outputId": "2afcfd5f-8514-4608-d04c-554f1a55ebf4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    Positive\n",
              "1    Negative\n",
              "2    Positive\n",
              "3    Negative\n",
              "4    Positive\n",
              "Name: Sepssis, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "y.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKPaw-6RHkzL"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Tg_ZOjJHlTI"
      },
      "source": [
        "# Splitting the data into training and validation sets (80% train, 20% validation)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "1_SONtudIP2x"
      },
      "outputs": [],
      "source": [
        "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
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        },
        "id": "XtjfkVRNKFWk",
        "outputId": "1392ebd7-9594-4b2f-96fd-2cd13bce1ad3"
      },
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        {
          "output_type": "execute_result",
          "data": {
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              "     PRG   PL  PR  SK   TS   M11    BD2  Age\n",
              "161    7  102  74  40  105  37.2  0.204   45\n",
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              "..   ...  ...  ..  ..  ...   ...    ...  ...\n",
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              "107    4  144  58  28  140  29.5  0.287   37\n",
              "395    2  127  58  24  275  27.7  1.600   25\n",
              "\n",
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              "      <th>230</th>\n",
              "      <td>4</td>\n",
              "      <td>142</td>\n",
              "      <td>86</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.645</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>131</th>\n",
              "      <td>9</td>\n",
              "      <td>122</td>\n",
              "      <td>56</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>33.3</td>\n",
              "      <td>1.114</td>\n",
              "      <td>33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>107</th>\n",
              "      <td>4</td>\n",
              "      <td>144</td>\n",
              "      <td>58</td>\n",
              "      <td>28</td>\n",
              "      <td>140</td>\n",
              "      <td>29.5</td>\n",
              "      <td>0.287</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>395</th>\n",
              "      <td>2</td>\n",
              "      <td>127</td>\n",
              "      <td>58</td>\n",
              "      <td>24</td>\n",
              "      <td>275</td>\n",
              "      <td>27.7</td>\n",
              "      <td>1.600</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>479 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdc4c3d1-01d3-46b8-94a6-d2259c45c0e0')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-69e4ffcd-6190-4659-ad54-5a1bf3f59112\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69e4ffcd-6190-4659-ad54-5a1bf3f59112')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-69e4ffcd-6190-4659-ad54-5a1bf3f59112 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-fdc4c3d1-01d3-46b8-94a6-d2259c45c0e0 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-fdc4c3d1-01d3-46b8-94a6-d2259c45c0e0');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "X_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8Giznf92KLns",
        "outputId": "846345b3-4570-4b01-f48e-7d8dc0a0a95e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     PRG   PL  PR  SK   TS   M11    BD2  Age\n",
              "32     3   88  58  11   54  24.8  0.267   22\n",
              "152    9  156  86  28  155  34.3  1.189   42\n",
              "407    0  101  62   0    0  21.9  0.336   25\n",
              "244    2  146  76  35  194  38.2  0.329   29\n",
              "162    0  114  80  34  285  44.2  0.167   27\n",
              "..   ...  ...  ..  ..  ...   ...    ...  ...\n",
              "517    7  125  86   0    0  37.6  0.304   51\n",
              "94     2  142  82  18   64  24.7  0.761   21\n",
              "496    5  110  68   0    0  26.0  0.292   30\n",
              "582   12  121  78  17    0  26.5  0.259   62\n",
              "436   12  140  85  33    0  37.4  0.244   41\n",
              "\n",
              "[120 rows x 8 columns]"
            ],
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              "      <th>PRG</th>\n",
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              "      <th>SK</th>\n",
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              "      <td>0.761</td>\n",
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              "      <td>0.244</td>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>120 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-188e9ac1-89ad-4f31-9c0b-8ced48db9592')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: #1967D2;\n",
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              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
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              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "\n",
              "        displayQuickchartButton(document);\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-188e9ac1-89ad-4f31-9c0b-8ced48db9592 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-188e9ac1-89ad-4f31-9c0b-8ced48db9592');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "X_val"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7VKCggMtEvNA"
      },
      "source": [
        "## Feature Scaling\n",
        "Perform feature scaling on X_train dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "uieuc-2dIOnH"
      },
      "outputs": [],
      "source": [
        "# Create the preprocessor\n",
        "preprocessor = StandardScaler()\n",
        "\n",
        "# Fit and transform the training data\n",
        "X_train_scaled = preprocessor.fit_transform(X_train)\n",
        "\n",
        "# Transform the validation data using the preprocessor fitted on the training data\n",
        "X_val_scaled = preprocessor.transform(X_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "5cbMySUhc54i"
      },
      "outputs": [],
      "source": [
        "# Initialize the LabelEncoder\n",
        "label_encoder = LabelEncoder()\n",
        "\n",
        "# Fit and transform the training labels\n",
        "y_train_encoded = label_encoder.fit_transform(y_train)\n",
        "\n",
        "# Transform the validation labels using the encoder fitted on the training labels\n",
        "y_val_encoded = label_encoder.transform(y_val)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RO6U5YKoto71"
      },
      "source": [
        "## Handling Imbalanced Data\n",
        "\n",
        "- By using the RandomOverSampler, we can generate synthetic samples for the minority class, resulting in a more balanced dataset for training.\n",
        "- This will help the model learn from both classes more effectively and potentially improve its performance on the test data.\n",
        "\n",
        "*We must remember to use the resampled data for model training and validation to avoid data leakage.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCWRGUX8tmVd",
        "outputId": "a41baa18-c819-45fe-d203-797984c5dd99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Class Distribution before oversampling: Counter({'Negative': 313, 'Positive': 166})\n",
            "Class Distribution after oversampling: Counter({0: 313, 1: 313})\n"
          ]
        }
      ],
      "source": [
        "# Check the class distribution before oversampling\n",
        "print(\"Class Distribution before oversampling:\", Counter(y_train))\n",
        "\n",
        "# Create an instance of RandomOverSampler\n",
        "oversampler = RandomOverSampler(random_state=42)\n",
        "\n",
        "# Fit and resample the training data\n",
        "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train_scaled, y_train_encoded)\n",
        "\n",
        "# Check the class distribution after oversampling\n",
        "print(\"Class Distribution after oversampling:\", Counter(y_train_resampled))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SnSgOE2oKaj7"
      },
      "source": [
        "# [12] Machine Learning Models to train:\n",
        "We shall train 9 Classification Models below and pick the 4 best performing models for further tuning:\n",
        "1. Logistic Regression\n",
        "2. K-Nearest Neighbors\n",
        "3. Decision Tree\n",
        "4. Support Vector Machine (Linear Kernel)\n",
        "5. Support Vector Machine (RBF Kernel)\n",
        "6. Neural Network\n",
        "7. Random Forest\n",
        "8. Gradient Boosting\n",
        "9. XGBoost\n",
        "\n",
        "We will create a function that will be used to evaluate the models Afterwards we will train all 8 models append the results to a dictionary and finally put it in a dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "9wke_ZDtQgKy"
      },
      "outputs": [],
      "source": [
        "#Create A function to Evaluate the models\n",
        "def evaluate(actual,predicted,model_name):\n",
        "    PrecisionScore = precision_score(actual,predicted,pos_label=1)\n",
        "    RecallScore =   recall_score(actual,predicted,pos_label=1)\n",
        "    F1_score =     f1_score(actual,predicted,pos_label=1)\n",
        "    Accuracy =    accuracy_score(actual,predicted)\n",
        "\n",
        "    result={'Model':model_name, 'Precision_Score':PrecisionScore,'Recall_Score':RecallScore,'F1_Score':F1_score,'Accuracy':Accuracy}\n",
        "\n",
        "    return result"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IxUY-dlSlIm"
      },
      "source": [
        "- First we set an empty list\n",
        "- we then put all the models in a dictionary\n",
        "- We loop through the models in the dictionary and fit them using the pipeline\n",
        "- The result is stored in the list and then converted to a dataframe"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdoVyJOHSmfn",
        "outputId": "e35da357-0fbb-4796-9c73-cb18540010fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_classes.py:32: FutureWarning: The default value of `dual` will change from `True` to `'auto'` in 1.5. Set the value of `dual` explicitly to suppress the warning.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/svm/_base.py:1242: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/neural_network/_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# This variable will hold the list of dictionaries of the results of the different models\n",
        "dict_list = []\n",
        "\n",
        "models = {'Logistic Regression': LogisticRegression(),\n",
        "         'K-Nearest Neighbors': KNeighborsClassifier(),\n",
        "         'Decision Tree': DecisionTreeClassifier(),\n",
        "         'Support Vector Machine (Linear Kernel)': LinearSVC(),\n",
        "         'Support Vector Machine (RBF Kernel)': SVC(),\n",
        "         'Neural Network': MLPClassifier(),\n",
        "         'Random Forest': RandomForestClassifier(),\n",
        "         'Gradient Boosting': GradientBoostingClassifier(),\n",
        "         'XGBoost': XGBClassifier()\n",
        "         }\n",
        "\n",
        "# Train all the models using a for loop\n",
        "\n",
        "for model_name, model in models.items():\n",
        "    # Create a pipeline with the model and the scaler\n",
        "    pipeline = Pipeline([\n",
        "        ('scaler', StandardScaler()),\n",
        "        ('model', model)\n",
        "    ])\n",
        "\n",
        "    # Fit the model using the pipeline on the scaled training data\n",
        "    pipeline.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "    # Make predictions on the scaled validation data\n",
        "    y_pred = pipeline.predict(X_val_scaled)\n",
        "\n",
        "    # Evaluate the model using the evaluation function\n",
        "    evaluation_result = evaluate(y_val_encoded, y_pred, model_name)\n",
        "\n",
        "    # Append the evaluation result to the list of dictionaries\n",
        "    dict_list.append(evaluation_result)\n",
        "\n",
        "# Convert the list of dictionaries to a DataFrame\n",
        "results_df = pd.DataFrame(dict_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umEs9x2yooVo"
      },
      "source": [
        "## Sorting Results by F1-score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "7SciQbH-oPbp",
        "outputId": "8b831b75-df68-4f79-c93d-57271e66b4c2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                    Model  Precision_Score  Recall_Score  \\\n",
              "0                     K-Nearest Neighbors         0.564516      0.833333   \n",
              "1     Support Vector Machine (RBF Kernel)         0.596154      0.738095   \n",
              "2                       Gradient Boosting         0.604167      0.690476   \n",
              "3                           Random Forest         0.608696      0.666667   \n",
              "4                     Logistic Regression         0.553571      0.738095   \n",
              "5  Support Vector Machine (Linear Kernel)         0.555556      0.714286   \n",
              "6                           Decision Tree         0.609756      0.595238   \n",
              "7                          Neural Network         0.549020      0.666667   \n",
              "8                                 XGBoost         0.581395      0.595238   \n",
              "\n",
              "   F1_Score  Accuracy  \n",
              "0  0.673077  0.716667  \n",
              "1  0.659574  0.733333  \n",
              "2  0.644444  0.733333  \n",
              "3  0.636364  0.733333  \n",
              "4  0.632653  0.700000  \n",
              "5  0.625000  0.700000  \n",
              "6  0.602410  0.725000  \n",
              "7  0.602151  0.691667  \n",
              "8  0.588235  0.708333  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-89cbeb6a-f885-4212-98e5-3b8805221cc2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Model</th>\n",
              "      <th>Precision_Score</th>\n",
              "      <th>Recall_Score</th>\n",
              "      <th>F1_Score</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K-Nearest Neighbors</td>\n",
              "      <td>0.564516</td>\n",
              "      <td>0.833333</td>\n",
              "      <td>0.673077</td>\n",
              "      <td>0.716667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Support Vector Machine (RBF Kernel)</td>\n",
              "      <td>0.596154</td>\n",
              "      <td>0.738095</td>\n",
              "      <td>0.659574</td>\n",
              "      <td>0.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.604167</td>\n",
              "      <td>0.690476</td>\n",
              "      <td>0.644444</td>\n",
              "      <td>0.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Random Forest</td>\n",
              "      <td>0.608696</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>0.733333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.553571</td>\n",
              "      <td>0.738095</td>\n",
              "      <td>0.632653</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Support Vector Machine (Linear Kernel)</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>0.714286</td>\n",
              "      <td>0.625000</td>\n",
              "      <td>0.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Decision Tree</td>\n",
              "      <td>0.609756</td>\n",
              "      <td>0.595238</td>\n",
              "      <td>0.602410</td>\n",
              "      <td>0.725000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Neural Network</td>\n",
              "      <td>0.549020</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.602151</td>\n",
              "      <td>0.691667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>XGBoost</td>\n",
              "      <td>0.581395</td>\n",
              "      <td>0.595238</td>\n",
              "      <td>0.588235</td>\n",
              "      <td>0.708333</td>\n",
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              "              title=\"Suggest charts.\"\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
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              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
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              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-89cbeb6a-f885-4212-98e5-3b8805221cc2');\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",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "#sort the results by F1 score\n",
        "results_df.sort_values(by='F1_Score',ascending=False,inplace=True,ignore_index=True)\n",
        "#display results\n",
        "results_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4S31a6bfqwob"
      },
      "source": [
        "## Visualize the Models F1-score performance"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "GMy9Rn0xq5UK",
        "outputId": "dc1f74e8-42c9-4c84-bec1-780be5d95fe5"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Sort the DataFrame based on the F1-Score in descending order\n",
        "results_df = results_df.sort_values(by='F1_Score', ascending=False)\n",
        "\n",
        "# Plot the models according to their F1-Score\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.barh(results_df['Model'], results_df['F1_Score'])\n",
        "plt.xlabel('F1-Score')\n",
        "plt.ylabel('Model')\n",
        "plt.title('Model F1-Scores')\n",
        "\n",
        "# Invert the y-axis to display highest value at the top\n",
        "plt.gca().invert_yaxis()\n",
        "\n",
        "# Add text annotations for each bar\n",
        "for bar in bars:\n",
        "    plt.text(bar.get_width(), bar.get_y() + bar.get_height() / 2, f\"{bar.get_width():.3f}\", ha='left', va='center')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zc_L2Q64w8S7"
      },
      "source": [
        "From the bar plot of the F1-Score, we can observe the performance of each model. The F1-Score is a metric that considers both precision and recall, making it a good overall performance measure, especially in cases where there is an imbalance between classes.\n",
        "\n",
        "Observations:\n",
        "\n",
        "- The \"K-Nearest-Neighbors\" model has the highest F1-Score, indicating that it performs the best among all the models evaluated.\n",
        "- The \"Support Vector Machine (RBF Kernel)\" model follows closely in performance with a slightly lower F1-Score.\n",
        "- The \"Logistic Regression\" model comes third, with relatively high F1-Scores.\n",
        "- In position 4 is \"Gradient Boosting\" model.\n",
        "- The other remaining models have lower F1-Scores compared to the top-performing models.\n",
        "\n",
        "Based on the F1-Scores and their relative performance, the four models that seem promising for further fine-tuning using hyperparameters are:\n",
        "\n",
        "1. K-Nearest Neighbors\n",
        "2. Support Vector Machine (RBF Kernel)\n",
        "3. Logistic Regression\n",
        "4. Gradient Boosting\n",
        "\n",
        "These models have demonstrated better overall performance and could potentially benefit from hyperparameter tuning to achieve even better results. Hyperparameter tuning involves selecting the best combination of hyperparameters for a model to optimize its performance on the given dataset. Grid search or random search can be used to find the best hyperparameter values for these models."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZIcSUYjK04JP"
      },
      "source": [
        "# Hyperparameter Tuning for Four chosen Models using GridSearchCV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "U-8kRLR4yZ9U",
        "outputId": "1ae3533f-e377-4cf3-e1e9-840e952788e7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best parameters for K-Nearest Neighbors:\n",
            "{'model__algorithm': 'auto', 'model__n_neighbors': 7, 'model__p': 2, 'model__weights': 'distance'}\n",
            "Best parameters for Support Vector Machine (RBF Kernel):\n",
            "{'model__C': 10, 'model__gamma': 'scale', 'model__kernel': 'rbf'}\n",
            "Best parameters for Gradient Boosting:\n",
            "{'model__learning_rate': 0.1, 'model__max_depth': 5, 'model__n_estimators': 200}\n",
            "Best parameters for Logistic Regression:\n",
            "{'model__C': 0.1, 'model__solver': 'liblinear'}\n",
            "=========================================\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 model  F1_score  Accuracy\n",
              "0                  K-Nearest Neighbors  0.810567  0.813067\n",
              "1  Support Vector Machine (RBF Kernel)  0.782353  0.782781\n",
              "2                    Gradient Boosting  0.852433  0.853067\n",
              "3                  Logistic Regression  0.740706  0.741232"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-b37af911-f146-4810-a805-37da5b0280d8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>F1_score</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>K-Nearest Neighbors</td>\n",
              "      <td>0.810567</td>\n",
              "      <td>0.813067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Support Vector Machine (RBF Kernel)</td>\n",
              "      <td>0.782353</td>\n",
              "      <td>0.782781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.852433</td>\n",
              "      <td>0.853067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.740706</td>\n",
              "      <td>0.741232</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b37af911-f146-4810-a805-37da5b0280d8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-ba5d5f98-a500-4576-9b96-b551361b68de\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ba5d5f98-a500-4576-9b96-b551361b68de')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-ba5d5f98-a500-4576-9b96-b551361b68de button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-b37af911-f146-4810-a805-37da5b0280d8 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-b37af911-f146-4810-a805-37da5b0280d8');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ],
      "source": [
        "# Set up the parameter grid for each model\n",
        "knn_params = {\n",
        "    'model__n_neighbors': [3, 5, 7],\n",
        "    'model__weights': ['uniform', 'distance'],\n",
        "    'model__algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
        "    'model__p': [1, 2],\n",
        "}\n",
        "\n",
        "svm_rbf_params = {\n",
        "    'model__C': [0.1, 1, 10],\n",
        "    'model__gamma': ['scale', 'auto'],\n",
        "    'model__kernel': ['rbf'],\n",
        "}\n",
        "\n",
        "gradient_boosting_params = {\n",
        "    'model__learning_rate': [0.1, 0.01],\n",
        "    'model__n_estimators': [100, 200, 300],\n",
        "    'model__max_depth': [3, 4, 5]\n",
        "}\n",
        "\n",
        "logistic_regression_params = {\n",
        "    'model__C': [0.1, 1, 10],\n",
        "    'model__solver': ['liblinear', 'saga']\n",
        "}\n",
        "\n",
        "# Perform hyperparameter tuning with cross-validation for each model\n",
        "models = {\n",
        "    'K-Nearest Neighbors': (KNeighborsClassifier(), knn_params),\n",
        "    'Support Vector Machine (RBF Kernel)': (SVC(), svm_rbf_params),\n",
        "    'Gradient Boosting': (GradientBoostingClassifier(), gradient_boosting_params),\n",
        "    'Logistic Regression': (LogisticRegression(), logistic_regression_params),\n",
        "}\n",
        "\n",
        "\n",
        "best_score = []\n",
        "for model_name, (model, params) in models.items():\n",
        "    # Make a pipeline\n",
        "    pipe = Pipeline([('Preprocessor', preprocessor), ('model', model)])\n",
        "    grid_search = GridSearchCV(pipe, params, cv=5, scoring=['accuracy', 'f1_macro'], refit='f1_macro', n_jobs=-1)\n",
        "    # Fit gridsearch\n",
        "    grid_search.fit(X_train_resampled, y_train_resampled)\n",
        "\n",
        "    # Print Best parameters\n",
        "    print(f\"Best parameters for {model_name}:\")\n",
        "    print(grid_search.best_params_)\n",
        "\n",
        "    # Put scores in a dict\n",
        "    scores = {'model': model_name, 'F1_score': grid_search.cv_results_['mean_test_f1_macro'][grid_search.best_index_], 'Accuracy': grid_search.cv_results_['mean_test_accuracy'][grid_search.best_index_]}\n",
        "    best_score.append(scores)\n",
        "\n",
        "# Put scores in a dataframe\n",
        "print('=========================================')\n",
        "scores_df = pd.DataFrame(best_score)\n",
        "scores_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "sfdcm6jMBhK2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "24c62080-65f0-42d6-e7a8-e822ca22c01e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 model  F1_score  Accuracy\n",
              "0                    Gradient Boosting  0.852433  0.853067\n",
              "1                  K-Nearest Neighbors  0.810567  0.813067\n",
              "2  Support Vector Machine (RBF Kernel)  0.782353  0.782781\n",
              "3                  Logistic Regression  0.740706  0.741232"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-3c747c6c-a049-49dd-a313-f35cdcb55734\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>F1_score</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Gradient Boosting</td>\n",
              "      <td>0.852433</td>\n",
              "      <td>0.853067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>K-Nearest Neighbors</td>\n",
              "      <td>0.810567</td>\n",
              "      <td>0.813067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Support Vector Machine (RBF Kernel)</td>\n",
              "      <td>0.782353</td>\n",
              "      <td>0.782781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Logistic Regression</td>\n",
              "      <td>0.740706</td>\n",
              "      <td>0.741232</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c747c6c-a049-49dd-a313-f35cdcb55734')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-13aa8960-5cba-4596-853b-1871a1824a8b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-13aa8960-5cba-4596-853b-1871a1824a8b')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-13aa8960-5cba-4596-853b-1871a1824a8b button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-3c747c6c-a049-49dd-a313-f35cdcb55734 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-3c747c6c-a049-49dd-a313-f35cdcb55734');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "#sort Dataframe\n",
        "scores_df.sort_values(by='F1_score',ignore_index=True,ascending=False, inplace=True)\n",
        "scores_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzlnCjpVCgit"
      },
      "source": [
        "\n",
        "Based on the F1-scores, we can observe the following:\n",
        "\n",
        "- Gradient Boosting has the highest F1-score of 0.852433, indicating that it performs the best among the four models in terms of balancing precision and recall.\n",
        "- K-Nearest Neighbors comes second with an F1-score of 0.810567.\n",
        "- Support Vector Machine (RBF Kernel) follows with an F1-score of 0.782353.\n",
        "- Logistic Regression has the lowest F1-score of 0.740706.\n",
        "\n",
        "All the models seem to have relatively similar performance, but the Gradient Boosting has a slight edge with the highest F1-score."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEL3TxqEC-c6"
      },
      "source": [
        "## Visualize by using a bar plot to compare the performance metrics of different models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "Yxyy1W2HCjkO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "outputId": "c4aa614c-66e3-46b5-9c3b-e36e54ee1038"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Data\n",
        "model_names = ['Gradient Boosting', 'K-Nearest Neighbors', 'Support Vector Machine (RBF Kernel)','Logistic Regression']\n",
        "f1_scores = [0.852433, 0.810567, 0.782353, 0.740706]\n",
        "\n",
        "# Plot the models according to their F1-Score\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.barh(model_names, f1_scores)\n",
        "plt.xlabel('F1-Score')\n",
        "plt.ylabel('Model')\n",
        "plt.title('Model F1-Scores')\n",
        "plt.gca().invert_yaxis()  # Invert the y-axis to display highest value at the top\n",
        "\n",
        "# Add the F1-score values on the bars\n",
        "for bar, f1_score in zip(bars, f1_scores):\n",
        "    plt.text(bar.get_width(), bar.get_y() + bar.get_height() / 2, f'{f1_score:.4f}', ha='left', va='center')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tsCh09I8GECt"
      },
      "source": [
        "## Gradient Boosting as the Model of choice:\n",
        "\n",
        "Based on the provided performance metrics, the best performing model among the four is the \"Gradient Boosting\" model.\n",
        "\n",
        "Here are the reasons:\n",
        "\n",
        "F1-score: The F1-score of the Gradient Boosting model was 0.8509, which considers both precision and recall. The F1-score is a harmonic mean of precision and recall, providing a balanced evaluation of the model's performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 126
        },
        "id": "sD3DiY0-Iv_T",
        "outputId": "2845082c-b5f5-4766-d64f-37d8e5fc2b0e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor', StandardScaler()),\n",
              "                ('model',\n",
              "                 GradientBoostingClassifier(max_depth=5, n_estimators=300))])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#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>Pipeline(steps=[(&#x27;preprocessor&#x27;, StandardScaler()),\n",
              "                (&#x27;model&#x27;,\n",
              "                 GradientBoostingClassifier(max_depth=5, n_estimators=300))])</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\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;, StandardScaler()),\n",
              "                (&#x27;model&#x27;,\n",
              "                 GradientBoostingClassifier(max_depth=5, n_estimators=300))])</pre></div></div></div><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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" 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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GradientBoostingClassifier</label><div class=\"sk-toggleable__content\"><pre>GradientBoostingClassifier(max_depth=5, n_estimators=300)</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "# Create the Gradient Boosting Classifier with the best hyperparameters\n",
        "gradient_boosting_classifier = GradientBoostingClassifier(learning_rate=0.1, max_depth=5, n_estimators=300)\n",
        "\n",
        "# Create the pipeline with the preprocessor and the MLPClassifier\n",
        "pipe = Pipeline([('preprocessor', preprocessor), ('model', gradient_boosting_classifier)])\n",
        "\n",
        "# Fit the pipeline with the training data\n",
        "pipe.fit(X_train_resampled, y_train_resampled)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Getting feature importance"
      ],
      "metadata": {
        "id": "Xsnjj-obQPqm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the Gradient Boosting model from the pipeline\n",
        "gradient_boosting_model = pipe.named_steps['model']\n",
        "\n",
        "# Get the feature importances\n",
        "feature_importances = gradient_boosting_model.feature_importances_\n",
        "\n",
        "# Sort the feature importances in descending order\n",
        "sorted_indices = feature_importances.argsort()[::-1]\n",
        "\n",
        "# Get the names of the features in the original order\n",
        "feature_names = X_train.columns\n",
        "\n",
        "# Print the top 10 most important features and their importances\n",
        "print(\"Top 8 Most Important Features:\")\n",
        "for i in range(8):\n",
        "    print(f\"{feature_names[sorted_indices[i]]}: {feature_importances[sorted_indices[i]]}\")\n",
        "\n",
        "# Plot the feature importances\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(range(X_train.shape[1]), feature_importances[sorted_indices])\n",
        "plt.xticks(range(X_train.shape[1]), feature_names[sorted_indices], rotation=45, ha=\"right\")\n",
        "plt.xlabel(\"Feature\")\n",
        "plt.ylabel(\"Importance\")\n",
        "plt.title(\"Feature Importances\")\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "id": "_crTbZXlQOJH",
        "outputId": "74277745-b324-43ee-b4a9-210f30c351c0"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 8 Most Important Features:\n",
            "PL: 0.3719398046515228\n",
            "M11: 0.17426120445824284\n",
            "BD2: 0.10825028646386531\n",
            "Age: 0.08805868314962724\n",
            "TS: 0.07396202840901399\n",
            "PR: 0.07262181983896926\n",
            "SK: 0.05805706798812822\n",
            "PRG: 0.052849105040630256\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bClz9_cnViSI"
      },
      "source": [
        "## Visualize ROC and AUC of the Gradient Boosting Model\n",
        "\n",
        "1. y_pred_proba = pipe.predict_proba(X_val_scaled): This line uses the trained gradient boosting (stored in the pipe pipeline) to make predictions on the validation data (X_val_scaled). Instead of predicting the class labels directly, predict_proba returns the predicted probabilities for each class. In this case, since we are interested in the positive class ('Positive'), we extract the probabilities for class index 1, which represents the positive class.\n",
        "\n",
        "2. positive_class_probs = y_pred_proba[:, 1]: This line extracts the probabilities for the positive class ('Positive') from the y_pred_proba array. The variable positive_class_probs will now contain the predicted probabilities of the positive class for each sample in the validation set.\n",
        "\n",
        "3. fpr, tpr, thresholds = roc_curve(y_val_encoded, positive_class_probs): This line calculates the False Positive Rate (FPR), True Positive Rate (TPR), and corresponding thresholds for different classification thresholds. The function roc_curve takes the true labels of the validation set (y_val_encoded) and the predicted probabilities of the positive class (positive_class_probs) as input and returns the FPR, TPR, and thresholds.\n",
        "\n",
        "4. auc_score = roc_auc_score(y_val_encoded, positive_class_probs): This line calculates the Area Under the Curve (AUC) score. The roc_auc_score function takes the true labels of the validation set (y_val_encoded) and the predicted probabilities of the positive class (positive_class_probs) as input and computes the AUC score.\n",
        "\n",
        "5. Plot the ROC Curve: This part of the code creates a plot using matplotlib to visualize the ROC curve. The ROC curve is plotted using the FPR on the x-axis and the TPR on the y-axis. The plt.plot function is used to draw the ROC curve, and the plt.plot([0, 1], [0, 1], color='gray', linestyle='--') line adds a diagonal line representing a random classifier (AUC = 0.5). The plt.xlabel, plt.ylabel, plt.title, and plt.legend functions set labels and a legend for the plot. Finally, plt.grid(True) adds gridlines to the plot, and plt.show() displays the plot.\n",
        "\n",
        "The ROC curve shows the model's performance at different classification thresholds, and the AUC score represents the overall performance of the model in distinguishing between the positive and negative classes. A model with a higher AUC score is generally better at correctly classifying positive and negative samples, with an AUC score of 1.0 representing a perfect classifier."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "IrnfDERFVtAm",
        "outputId": "518ed805-75e1-47ba-d5de-eed457f6b688"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Make predictions using the trained neural network\n",
        "y_pred_proba = pipe.predict_proba(X_val_scaled)\n",
        "\n",
        "# Extract the probabilities for the positive class (class index 1)\n",
        "positive_class_probs = y_pred_proba[:, 1]\n",
        "\n",
        "# Calculate the false positive rate (FPR) and true positive rate (TPR) for different thresholds\n",
        "fpr, tpr, thresholds = roc_curve(y_val_encoded, positive_class_probs)\n",
        "\n",
        "# Calculate the AUC (Area Under the Curve)\n",
        "auc_score = roc_auc_score(y_val_encoded, positive_class_probs)\n",
        "\n",
        "# Plot the ROC curve\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.plot(fpr, tpr, color='blue', lw=2, label=f'ROC Curve (AUC = {auc_score:.3f})')\n",
        "plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
        "plt.xlabel('False Positive Rate (FPR)')\n",
        "plt.ylabel('True Positive Rate (TPR)')\n",
        "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
        "plt.legend(loc='lower right')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "okSCPE0CYghg"
      },
      "source": [
        "- The value of 0.785 for the AUC (Area Under the Curve) score indicates the performance of the Gradient Boosting model in distinguishing between the positive and negative classes. The AUC score ranges from 0 to 1, where a score of 0.5 represents a random classifier, and a score of 1.0 represents a perfect classifier.\n",
        "\n",
        "- In this case, the AUC score of 0.783 suggests that the Random Forest model performs reasonably well in distinguishing between the positive and negative samples in the validation set. The closer the AUC score is to 1.0, the better the model's ability to correctly rank positive instances higher than negative instances.\n",
        "\n",
        "- An AUC score of 0.768 indicates that the model is better than a random classifier but may not be perfect in its predictions. Depending on the specific problem and domain, an AUC score of 0.783 can be considered as a good result, particularly if the data is imbalanced or the task is challenging."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JoloAfkZY0Hd"
      },
      "source": [
        "## Confusion Matrix\n",
        "\n",
        "The code below will plot a heatmap representing the confusion matrix, where each cell represents the count of true positives, false positives, true negatives, and false negatives. The diagonal elements represent correct predictions (true positives and true negatives), while off-diagonal elements represent incorrect predictions (false positives and false negatives). The darker shades in the heatmap indicate higher counts, indicating stronger predictive performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "imqhIhDbYmye",
        "outputId": "5bf705e2-50da-4db6-cbf0-59ce2dd094b0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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at/X++CcUWj7YxVMD14c1a35Vv36JuvPOWEVFtdP33yfl2Wb37gPq1+9l3XLLw4qOflCdOg3WwYOHXTAtioOXqwcAJCko0E9Lvk7QD0lb1KHH/+nI8VOqHlFBJ9LSJUklfb0VfVNVvT5pjjZt3afgQD+9MSpWX3wwVHe2fcHF0wP/fBkZZxQVVVWdOrXSgAGv5Vm/f3+KunV7Vp06tdJTT3WTv39J7dy5X3a7twumRXEgIOAWhjzRTr+nHNPjQ991LNt34Ijjv0/9mam2jzj/0Ro84kP9+N2rqhxWRgcOHiu2WYHrUUzMrYqJuTXf9ePHz1LTprfomWd6OpZVqRJaHKPBRVwaEEePHtX06dOVlJSk1NRUSVKFChXUqFEjxcXFKSQkxJXjoRjd1+oWff/DJs1+Z5DubFBLB1NP6L1Zi/ThJ0vyvU9AQEnl5OTo5KmMYpwUwIVycnK0bNla9e7dUb16vaStW/eoUqXyevzxB9WyZUNXj4ci4rJrINasWaOaNWtq0qRJCgwMVNOmTdW0aVMFBgZq0qRJuuGGG7R27drL7icrK0unTp1y+rGs7GJ4BChMVSuXU5/uLbUrOVX3P/q6pn20SOMSYvXIg00vur3dXkKvDO+qz79ZqT/TM4t5WgB/d+xYmjIyMjVt2pdq0qS+pk9PVKtWd2jAgNFavXqzq8dDEXHZGYiBAwfqoYce0tSpU2Wz2ZzWWZalfv36aeDAgUpKynuhzt+NHj1aCQkJTss8A25UicA6hT4zio6Hh4fWb9qjkWM+kyRt3LJXN0ZVVp9HWmj2l8udtvXy8tRHbw+STTY99cJ0V4wL4G9ycnIkSS1aNFBcXAdJUq1a1bR+/W/69NP/6vbb+Xv8T+SyMxAbN27U4MGD88SDJNlsNg0ePFgbNmy47H6GDx+utLQ0px+vgNpFMDGKUurhE9q283enZb/t/EOVK5Z1Wubl5anZbw9SlYpl1faR1zj7ALiB4OAAeXl5KjKyitPyyMjKOnjwSD73wrXOZWcgKlSooNWrV+uGG2646PrVq1erfPnyl92P3W6X3W53WmazeRbKjCg+SWt3qGZkmNOyGtVCtf/3o47bufEQWbWC7n74ZR0/mV7cYwK4CG/vEqpTp4aSk53/J2Dv3j9UsSLXsv1TuSwghg4dqr59+2rdunVq0aKFIxYOHTqkxYsXa9q0aXrjjTdcNR6K2eT352vpnAQN699eX333s26LjtRj3e7SgOfel3Q+Hj6e+rTq3VRVHXuOkaenh8qHBEqSjp9M17lzXPcCFKXTpzO1f3+K4/bvvx/Stm17FBjor7CwcurVq6MGDx6j2267SQ0a1NGKFeu1dOlq/etfed/yiX8Gm2VZlqsO/tlnn2n8+PFat26dsrPP/wPg6empW265RfHx8ercufMV7de3StfCHBPF5J4W9ZT4bBdVj6igvQeOaNL78x3vwqhSqay2r5x80fu17pyoFT9vK85RcZUy9ydcfiO4lVWrNqtHj+fzLH/ggbv0+uuDJUlffrlI7733hVJTj6lq1YoaOLCbWra8o7hHxVWrWaCtXBoQuc6dO6ejR8+fqi5btqxKlChxVfsjIAD3RkAA7qxgAeEWHyRVokQJhYbygSMAAFwr+C4MAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxggIAABgjIAAAADGCAgAAGCMgAAAAMYICAAAYIyAAAAAxowDYubMmZo3b57j9jPPPKOgoCA1atRI+/btK9ThAACAezIOiNdee02+vr6SpKSkJE2ZMkVjxoxR2bJlNXjw4EIfEAAAuB8v0zscOHBA1atXlyTNnTtXnTp1Ut++fdW4cWM1a9assOcDAABuyPgMhL+/v44dOyZJWrhwoVq1aiVJ8vHxUWZmZuFOBwAA3JLxGYhWrVqpd+/eqlevnnbs2KF7771XkrRlyxZFREQU9nwAAMANGZ+BmDJliho2bKgjR47oq6++UpkyZSRJ69atU9euXQt9QAAA4H5slmVZrh6isPlWIWQAd5a5P8HVIwDIV80CbVWglzA2bdpU4MPefPPNBd4WAABcmwoUENHR0bLZbMrvZEXuOpvNpuzs7EIdEAAAuJ8CBURycnJRzwEAAK4hBQqI8PDwop4DAABcQ67ouzBmzZqlxo0bKywszPHx1RMmTNA333xTqMMBAAD3ZBwQ77zzjuLj43Xvvffq5MmTjmsegoKCNGHChMKeDwAAuCHjgJg8ebKmTZumF154QZ6eno7lt956qzZv3lyowwEAAPdkHBDJycmqV69enuV2u12nT58ulKEAAIB7Mw6IqlWrasOGDXmW//e//1WtWrUKYyYAAODmjL8LIz4+Xv3799eZM2dkWZZWr16tTz75RKNHj9b7779fFDMCAAA3YxwQvXv3lq+vr1588UVlZGSoW7duCgsL08SJE9WlS5eimBEAALiZq/oujIyMDKWnp6tcuXKFOdNV47swAPfGd2EA7qwQvwvjYg4fPqzt27dLOv9R1iEhIVe6KwAAcI0xvojyzz//1KOPPqqwsDDFxMQoJiZGYWFh6t69u9LS0opiRgAA4GaMA6J3795atWqV5s2bp5MnT+rkyZP67rvvtHbtWj3++ONFMSMAAHAzxtdA+Pn5acGCBbrzzjudlq9YsUJ33323W3wWBNdAAO6NayAAd1awayCMz0CUKVNGgYGBeZYHBgYqODjYdHcAAOAaZBwQL774ouLj45WamupYlpqaqmHDhmnEiBGFOhwAAHBPBXoXRr169WSz2Ry3d+7cqSpVqqhKlSqSpP3798tut+vIkSNcBwEAwHWgQAHRoUOHIh4DAABcS67qg6TcFRdRAu6NiygBd1ZEF1ECAAAYfxJldna2xo8fr88//1z79+/X2bNnndYfP3680IYDAADuyfgMREJCgt588009/PDDSktLU3x8vDp27CgPDw+NGjWqCEYEAADuxjggZs+erWnTpmnIkCHy8vJS165d9f777+ull17Szz//XBQzAgAAN2McEKmpqapTp44kyd/f3/H9F23bttW8efMKdzoAAOCWjAOiUqVKSklJkSRFRkZq4cKFkqQ1a9bIbrcX7nQAAMAtGQfEAw88oMWLF0uSBg4cqBEjRqhGjRrq0aOHHnvssUIfEAAAuJ+r/hyIn3/+WStXrlSNGjXUrl27wprrqvA5EIB743MgAHdWTJ8Dcccddyg+Pl4NGjTQa6+9drW7AwAA14BC+yTKjRs3qn79+srOzi6M3V2lHa4eAMAlbDrO7yjgrm4u3bZA2/FJlAAAwBgBAQAAjBEQAADAWIG/CyM+Pv6S648cOXLVwwAAgGtDgQPil19+uew2TZs2vaphAADAtaHAAbF06dKinAMAAFxDuAYCAAAYIyAAAIAxAgIAABgjIAAAgDECAgAAGLuigFixYoW6d++uhg0b6o8//pAkzZo1Sz/++GOhDgcAANyTcUB89dVXatOmjXx9ffXLL78oKytLkpSWlsa3cQIAcJ0wDohXXnlFU6dO1bRp01SiRAnH8saNG2v9+vWFOhwAAHBPxgGxffv2i37iZGBgoE6ePFkYMwEAADdnHBAVKlTQrl278iz/8ccfVa1atUIZCgAAuDfjgOjTp48GDRqkVatWyWaz6eDBg5o9e7aGDh2qJ554oihmBAAAbqbA34WR67nnnlNOTo5atGihjIwMNW3aVHa7XUOHDtXAgQOLYkYAAOBmbJZlWVdyx7Nnz2rXrl1KT09X7dq15e/vX9izXYUdrh4AwCVsOs7vKOCubi7dtkDbGZ+ByOXt7a3atWtf6d0BAMA1zDggmjdvLpvNlu/6JUuWXNVAAADA/RkHRHR0tNPtc+fOacOGDfr1118VGxtbWHMBAAA3ZhwQ48ePv+jyUaNGKT09/aoHAgAA7q/Qvkyre/fumj59emHtDgAAuLFCC4ikpCT5+PgU1u4AAIAbM34Jo2PHjk63LctSSkqK1q5dqxEjRhTaYAAAwH0ZB0RgYKDTbQ8PD0VFRSkxMVGtW7cutMEAAID7MgqI7Oxs9ezZU3Xq1FFwcHBRzQQAANyc0TUQnp6eat26Nd+6CQDAdc74IsqbbrpJe/bsKYpZAADANcI4IF555RUNHTpU3333nVJSUnTq1CmnHwAA8M9X4C/TSkxM1JAhQ1SqVKn/3flvH2ltWZZsNpuys7MLf0pjfFEP4M74Mi3AfRX0y7QKHBCenp5KSUnRtm3bLrldTExMgQ5ctPjjBLgzAgJwX4X+bZy5neEegQAAAFzJ6BqIS30LJwAAuH4YfQ5EzZo1LxsRx48fv6qBAACA+zMKiISEhDyfRAkAAK4/RgHRpUsXlStXrqhmAQAA14gCXwPB9Q8AACBXgQOigO/2BAAA14ECv4SRk5NTlHMAAIBriPFHWQMAABAQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAmJerBwAkac2aX/XBB1/r119368iR45oy5Xm1bNnQaZvduw9o7NgZWrPmV2VnZysysrImTx6usLByLpoauD7MmblYq37YrD/2HZa3vYSi6oTrkSfbqmK48+/e9s179cm7/9GuLfvl4WFTRM2KemF8X9l9SrhochQlAgJuISPjjKKiqqpTp1YaMOC1POv3709Rt27PqlOnVnrqqW7y9y+pnTv3y273dsG0wPVlyy+71aZTI1WvVUXZ2Tn6eOp8vfL0exr/8TD5+NolnY+HVwdP0wM97lKv+Afk4emhfTsPysPD5uLpUVRslmVZrh6i8O1w9QC4ClFR7fKcgRg8eIy8vDw1duwQF06GwrLpOL+j17K0E+nqfe9IJbz9pGrXi5QkPd97om6+raa6PH6Pi6fD1bq5dNsCbcc1EHB7OTk5WrZsrSIiKqpXr5fUsGF3PfTQEH3/fZKrRwOuSxnpZyRJ/gElJUlpx//Uzi37FVjaXy/0maTe947US09M0baNe1w5JoqYWwfEgQMH9Nhjj11ym6ysLJ06dcrpJyvrbDFNiOJw7FiaMjIyNW3al2rSpL6mT09Uq1Z3aMCA0Vq9erOrxwOuKzk5OZoxYa6ibo5QlchQSdKhg8clSZ+/v1At29+hF8b3UbWoSkocOFUpB464clwUIbcOiOPHj2vmzJmX3Gb06NEKDAx0+hk9+t1imhDFIScnR5LUokUDxcV1UK1a1dS370Nq1uw2ffrpf108HXB9ef+Nr3VgT6oGv/yoY5n1/39HW3VoqOZtb1fVqEqKe7q9wqqU05JvV7tqVBQxl15E+e9///uS6/fsufzpr+HDhys+Pt5pmd2+/6rmgnsJDg6Ql5enIiOrOC2PjKysdeu2umgq4Prz/htfa/1PW5XwTn+VKRfkWB5UNkCSVKlqeaftK0aU09FDJ4pzRBQjlwZEhw4dZLPZdKnrOG22S1/Ba7fbZbfbL1jKlfn/JN7eJVSnTg0lJ//utHzv3j9UsWKIi6YCrh+WZemDcXO0+ofNSnj7SZUPK+O0vlxoaQWXDdDBfYedlqfsP6J6DWsV56goRi59CSM0NFRff/21cnJyLvqzfv16V46HYnT6dKa2bdujbdvOn3X6/fdD2rZtjw4ePP8HqVevjvrPf37U558v0L59B/XRR99p6dLV6tr1XleODVwX3n/ja61YsE6DErrLp6RdJ46d0oljp5R15pyk8/+j1/6R5pr/xY9KWrJRKQeO6tN3/6M/9h3WXe1ud/H0KCoufRvn/fffr+joaCUmJl50/caNG1WvXj3Ha+AFx1vErjWrVm1Wjx7P51n+wAN36fXXB0uSvvxykd577wulph5T1aoVNXBgN7VseUdxj4pCwNs4ry0PNbz426effPFhNb/vf4Ew51+LteCrn5R+KlPh1UPVfUBb1apbrbjGRCEp6Ns4XRoQK1as0OnTp3X33XdfdP3p06e1du1axcTEGO6ZP06AOyMgAPd1TQRE0eGPE+DOCAjAffFBUgAAoMgQEAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIwREAAAwBgBAQAAjBEQAADAGAEBAACMERAAAMAYAQEAAIzZLMuyXD0EcClZWVkaPXq0hg8fLrvd7upxAPwNv5/XLwICbu/UqVMKDAxUWlqaAgICXD0OgL/h9/P6xUsYAADAGAEBAACMERAAAMAYAQG3Z7fbNXLkSC7QAtwQv5/XLy6iBAAAxjgDAQAAjBEQAADAGAEBAACMERAAAMAYAQG3NmXKFEVERMjHx0cNGjTQ6tWrXT0SAEnLly9Xu3btFBYWJpvNprlz57p6JBQzAgJu67PPPlN8fLxGjhyp9evXq27dumrTpo0OHz7s6tGA697p06dVt25dTZkyxdWjwEV4GyfcVoMGDXTbbbfprbfekiTl5OSocuXKGjhwoJ577jkXTwcgl81m05w5c9ShQwdXj4JixBkIuKWzZ89q3bp1atmypWOZh4eHWrZsqaSkJBdOBgCQCAi4qaNHjyo7O1vly5d3Wl6+fHmlpqa6aCoAQC4CAgAAGCMg4JbKli0rT09PHTp0yGn5oUOHVKFCBRdNBQDIRUDALXl7e+uWW27R4sWLHctycnK0ePFiNWzY0IWTAQAkycvVAwD5iY+PV2xsrG699VbdfvvtmjBhgk6fPq2ePXu6ejTgupeenq5du3Y5bicnJ2vDhg0qXbq0qlSp4sLJUFx4Gyfc2ltvvaWxY8cqNTVV0dHRmjRpkho0aODqsYDr3rJly9S8efM8y2NjYzVjxoziHwjFjoAAAADGuAYCAAAYIyAAAIAxAgIAABgjIAAAgDECAgAAGCMgAACAMQICAAAYIyAAAIAxAgK4zsXFxalDhw6O282aNdPTTz9d7HMsW7ZMNptNJ0+eLLJjXPhYr0RxzAlcCwgIwA3FxcXJZrPJZrPJ29tb1atXV2Jiov76668iP/bXX3+tl19+uUDbFvc/phEREZowYUKxHAvApfFlWoCbuvvuu/Xhhx8qKytL8+fPV//+/VWiRAkNHz48z7Znz56Vt7d3oRy3dOnShbIfAP9snIEA3JTdbleFChUUHh6uJ554Qi1bttS///1vSf87Ff/qq68qLCxMUVFRkqQDBw6oc+fOCgoKUunSpdW+fXvt3bvXsc/s7GzFx8crKChIZcqU0TPPPKMLvw7nwpcwsrKy9Oyzz6py5cqy2+2qXr26PvjgA+3du9fxZUrBwcGy2WyKi4uTdP6r10ePHq2qVavK19dXdevW1Zdfful0nPnz56tmzZry9fVV8+bNnea8EtnZ2erVq5fjmFFRUZo4ceJFt01ISFBISIgCAgLUr18/nT171rGuILP/3b59+9SuXTsFBwfLz89PN954o+bPn39VjwW4FnAGArhG+Pr66tixY47bixcvVkBAgBYtWiRJOnfunNq0aaOGDRtqxYoV8vLy0iuvvKK7775bmzZtkre3t8aNG6cZM2Zo+vTpqlWrlsaNG6c5c+borrvuyve4PXr0UFJSkiZNmqS6desqOTlZR48eVeXKlfXVV1+pU6dO2r59uwICAuTr6ytJGj16tD766CNNnTpVNWrU0PLly9W9e3eFhIQoJiZGBw4cUMeOHdW/f3/17dtXa9eu1ZAhQ67q+cnJyVGlSpX0xRdfqEyZMlq5cqX69u2r0NBQde7c2el58/Hx0bJly7R371717NlTZcqU0auvvlqg2S/Uv39/nT17VsuXL5efn5+2bt0qf3//q3oswDXBAuB2YmNjrfbt21uWZVk5OTnWokWLLLvdbg0dOtSxvnz58lZWVpbjPrNmzbKioqKsnJwcx7KsrCzL19fXWrBggWVZlhUaGmqNGTPGsf7cuXNWpUqVHMeyLMuKiYmxBg0aZFmWZW3fvt2SZC1atOiicy5dutSSZJ04ccKx7MyZM1bJkiWtlStXOm3bq1cvq2vXrpZlWdbw4cOt2rVrO61/9tln8+zrQuHh4db48ePzXX+h/v37W506dXLcjo2NtUqXLm2dPn3aseydd96x/P39rezs7ALNfuFjrlOnjjVq1KgCzwT8U3AGAnBT3333nfz9/XXu3Dnl5OSoW7duGjVqlGN9nTp1nK572Lhxo3bt2qVSpUo57efMmTPavXu30tLSlJKSogYNGjjWeXl56dZbb83zMkauDRs2yNPT86L/552fXbt2KSMjQ61atXJafvbsWdWrV0+StG3bNqc5JKlhw4YFPkZ+pkyZounTp2v//v3KzMzU2bNnFR0d7bRN3bp1VbJkSafjpqen68CBA0pPT7/s7Bd66qmn9MQTT2jhwoVq2bKlOnXqpJtvvvmqHwvg7ggIwE01b95c77zzjry9vRUWFiYvL+dfVz8/P6fb6enpuuWWWzR79uw8+woJCbmiGXJfkjCRnp4uSZo3b54qVqzotM5ut1/RHAXx6aefaujQoRo3bpwaNmyoUqVKaezYsVq1alWB93Els/fu3Vtt2rTRvHnztHDhQo0ePVrjxo3TwIEDr/zBANcAAgJwU35+fqpevXqBt69fv74+++wzlStXTgEBARfdJjQ0VKtWrVLTpk0lSX/99ZfWrVun+vXrX3T7OnXqKCcnRz/88INatmyZZ33uGZDs7GzHstq1a8tut2v//v35nrmoVauW44LQXD///PPlH+Ql/PTTT2rUqJGefPJJx7Ldu3fn2W7jxo3KzMx0xNHPP/8sf39/Va5cWaVLl77s7BdTuXJl9evXT/369dPw4cM1bdo0AgL/eLwLA/iHeOSRR1S2bFm1b99eK1asUHJyspYtW6annnpKv//+uyRp0KBBev311zV37lz99ttvevLJJy/5GQ4RERGKjY3VY489prlz5zr2+fnnn0uSwsPDZbPZ9N133+nIkSNKT09XqVKlNHToUA0ePFgzZ87U7t27tX79ek2ePFkzZ86UJPXr1087d+7UsGHDtH37dn388ceaMWNGgR7nH3/8oQ0bNjj9nDhxQjVq1NDatWu1YMEC7dixQyNGjNCaNWvy3P/s2bPq1auXtm7dqvnz52vkyJEaMGCAPDw8CjT7hZ5++mktWLBAycnJWr9+vZYuXapatWoV6LEA1zRXX4QBIK+/X0Rpsj4lJcXq0aOHVbZsWctut1vVqlWz+vTpY6WlpVmWdf6iyUGDBlkBAQFWUFCQFR8fb/Xo0SPfiygty7IyMzOtwYMHW6GhoZa3t7dVvXp1a/r06Y71iYmJVoUKFSybzWbFxsZalnX+ws8JEyZYUVFRVokSJayQkBCrTZs21g8//OC437fffmtVr17dstvtVpMmTazp06cX6CJKSXl+Zs2aZZ05c8aKi4uzAgMDraCgIOuJJ56wnnvuOatu3bp5nreXXnrJKlOmjOXv72/16dPHOnPmjGOby81+4UWUAwYMsCIjIy273W6FhIRYjz76qHX06NF8HwPwT2GzrHyungIAAMgHL2EAAABjBAQAADBGQAAAAGMEBAAAMEZAAAAAYwQEAAAwRkAAAABjBAQAADBGQAAAAGMEBAAAMEZAAAAAY/8PBTLhdA/n9EEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Make predictions using the trained neural network\n",
        "y_pred = pipe.predict(X_val_scaled)\n",
        "\n",
        "# Compute the confusion matrix\n",
        "cm = confusion_matrix(y_val_encoded, y_pred)\n",
        "\n",
        "# Visualize the confusion matrix as a heatmap\n",
        "plt.figure(figsize=(6, 6))\n",
        "sns.heatmap(cm, annot=True, cmap='YlGnBu', fmt='d', cbar=False)\n",
        "plt.xlabel('Predicted Labels')\n",
        "plt.ylabel('True Labels')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vgkZERY0cF_M",
        "outputId": "3fb5d05c-a2ad-44b9-8a52-2e806d8f23af"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[62, 16],\n",
              "       [16, 26]])"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ],
      "source": [
        "cm"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zVmQKL35cojW"
      },
      "source": [
        "The confusion matrix for the Neural Network model indicates the following:\n",
        "\n",
        "True Positive (TP): There are 26 instances that are correctly predicted as positive (actual positive and predicted positive).\n",
        "\n",
        "True Negative (TN): There are 62 instances that are correctly predicted as negative (actual negative and predicted negative).\n",
        "\n",
        "False Positive (FP): There are 16 instances that are incorrectly predicted as positive (actual negative but predicted positive).\n",
        "\n",
        "False Negative (FN): There are 16 instances that are incorrectly predicted as negative (actual positive but predicted negative).\n",
        "\n",
        "These results show that the Neural Network model correctly predicted 26 positive cases and 62 negative cases. However, it misclassified 16 instances as false positives and 16 instances as false negatives. The model's performance is not perfect, but it is capturing both positive and negative cases to some extent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DtQbqqobdPfH"
      },
      "source": [
        "## Predictions from the Gradient Boosting Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DVJza49ycvs2",
        "outputId": "fea1ebca-bac8-499a-a183-437a19a2c9d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:457: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Make predictions using the trained pipeline\n",
        "y_pred = pipe.predict(test_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "LJIsd932fRAp"
      },
      "outputs": [],
      "source": [
        "# Create a DataFrame of  y_pred\n",
        "df_results = pd.DataFrame( y_pred)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "V8ZFega5-94g"
      },
      "outputs": [],
      "source": [
        "y_train_resampled_df = pd.DataFrame(y_train_resampled)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bnqFXVypE-Qu"
      },
      "source": [
        "## Compare the results of y and predicted_y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "uB5Qzaz5E8wd",
        "outputId": "096f4eca-5dcc-4b51-c886-3d032b376955"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Predicted_y_value  True_y_value\n",
              "0                  0             0\n",
              "1                  1             1\n",
              "2                  0             0\n",
              "3                  1             0\n",
              "4                  0             0\n",
              "5                  1             1\n",
              "6                  1             1\n",
              "7                  0             0\n",
              "8                  0             0\n",
              "9                  0             0"
            ],
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "# Get the first 10 rows of each dataframe\n",
        "df_results_first_10 = df_results.head(10)\n",
        "y_train_resampled_df_first_10 = y_train_resampled_df.head(10)\n",
        "\n",
        "# Concatenate the two dataframes side by side with appropriate column names\n",
        "result_side_by_side = pd.concat([df_results_first_10, y_train_resampled_df_first_10], axis=1)\n",
        "\n",
        "# Set new column names\n",
        "result_side_by_side.columns = ['Predicted_y_value', 'True_y_value']\n",
        "\n",
        "# Print the result\n",
        "result_side_by_side"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation of dataset X_val and y_val after and before prediction"
      ],
      "metadata": {
        "id": "S5Jwh9LWolZ9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Predictions of Sepssis from X_val dataset\n",
        "eval_pred= pipe.predict(X_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J0ntm1lIKnmO",
        "outputId": "ba614c07-44ae-4e6d-e674-7b2377a01ee7"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:457: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## X_val dataset predictions"
      ],
      "metadata": {
        "id": "Q7Wmc3b3n-Dx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sepssis predictions where Negative=0 and Positive=1\n",
        "eval_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDRBkH05Llzp",
        "outputId": "32aa7d11-b784-4264-8091-7e1d1d8ea16d"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1,\n",
              "       0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
              "       0, 0, 1, 0, 0, 0, 0, 0, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Original values of y_val\n",
        "y_val"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aWJCAOJKL0KW",
        "outputId": "d616a59e-bc0f-4d51-9c76-ea1c945efe1c"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "32     Negative\n",
              "152    Positive\n",
              "407    Negative\n",
              "244    Negative\n",
              "162    Negative\n",
              "         ...   \n",
              "517    Negative\n",
              "94     Negative\n",
              "496    Negative\n",
              "582    Negative\n",
              "436    Negative\n",
              "Name: Sepssis, Length: 120, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_val.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WYFbbOaKq2yP",
        "outputId": "95b3573e-7dcc-4d69-93b3-e27a7a6d8664"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Negative    78\n",
              "Positive    42\n",
              "Name: Sepssis, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Save the Gradient Boosting Model"
      ],
      "metadata": {
        "id": "lMFvZsg8zRB8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Save the model using pickle\n",
        "with open('gradient_boosting_model.pkl', 'wb') as f:\n",
        "    pickle.dump(pipe, f)"
      ],
      "metadata": {
        "id": "GcbS7VW5zY2g"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load and test the model on test_df once again"
      ],
      "metadata": {
        "id": "fJ-fIgx3zcj3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the saved model using pickle\n",
        "with open('gradient_boosting_model.pkl', 'rb') as f:\n",
        "    loaded_model = pickle.load(f)"
      ],
      "metadata": {
        "id": "sWsR0NmrzmpW"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Predict values of the test_df dataset\n",
        "predictions = loaded_model.predict(test_df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsaKwS_DzwfV",
        "outputId": "ceefc870-0d24-4787-d3a7-35bee2fea523"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:457: UserWarning: X has feature names, but StandardScaler was fitted without feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predictions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9z5sgfnF0Hus",
        "outputId": "5949fbbc-e5a6-4b0b-b599-ff43bab1eec5"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0,\n",
              "       1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1,\n",
              "       0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1,\n",
              "       0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0,\n",
              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Check the current working directory\n",
        "# print(\"Current working directory:\", os.getcwd())\n",
        "\n",
        "# # List all files in the current working directory\n",
        "# print(\"Files in the current working directory:\", os.listdir())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V76EjdSx_Krl",
        "outputId": "a56662a9-8c64-4785-bf0e-9ec37c2f11ce"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Current working directory: /content\n",
            "Files in the current working directory: ['.config', 'drive', 'gradient_boosting_model.pkl', 'sample_data']\n"
          ]
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}