CS512_Project_Final_Code.ipynb

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      "name": "CS512_Project_Final_Code.ipynb",
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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mugekuskon/HeartDiseasePrediction/blob/main/CS512_Project_Final_Code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qzrVti6xdr1"
      },
      "source": [
        "# **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "0zWMR3fJ5f8-",
        "outputId": "4ced2385-be09-47f0-c9b8-a86725c0362f"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from time import time\n",
        "\n",
        "import seaborn as sns\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.inspection import permutation_importance\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
        "from sklearn.metrics import plot_confusion_matrix\n",
        "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score, f1_score, precision_score, recall_score, accuracy_score, auc, roc_curve, accuracy_score\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder  \n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "plt.style.use('default')\n",
        "\n",
        "data = pd.read_csv('/content/heart.csv')\n",
        "data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>172</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>49</td>\n",
              "      <td>F</td>\n",
              "      <td>NAP</td>\n",
              "      <td>160</td>\n",
              "      <td>180</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>156</td>\n",
              "      <td>N</td>\n",
              "      <td>1.0</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>37</td>\n",
              "      <td>M</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>283</td>\n",
              "      <td>0</td>\n",
              "      <td>ST</td>\n",
              "      <td>98</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>48</td>\n",
              "      <td>F</td>\n",
              "      <td>ASY</td>\n",
              "      <td>138</td>\n",
              "      <td>214</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>108</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>54</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>150</td>\n",
              "      <td>195</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>122</td>\n",
              "      <td>N</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>913</th>\n",
              "      <td>45</td>\n",
              "      <td>M</td>\n",
              "      <td>TA</td>\n",
              "      <td>110</td>\n",
              "      <td>264</td>\n",
              "      <td>0</td>\n",
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              "      <td>132</td>\n",
              "      <td>N</td>\n",
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              "      <th>914</th>\n",
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              "      <td>144</td>\n",
              "      <td>193</td>\n",
              "      <td>1</td>\n",
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              "      <td>141</td>\n",
              "      <td>N</td>\n",
              "      <td>3.4</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>915</th>\n",
              "      <td>57</td>\n",
              "      <td>M</td>\n",
              "      <td>ASY</td>\n",
              "      <td>130</td>\n",
              "      <td>131</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>115</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
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              "      <th>916</th>\n",
              "      <td>57</td>\n",
              "      <td>F</td>\n",
              "      <td>ATA</td>\n",
              "      <td>130</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>LVH</td>\n",
              "      <td>174</td>\n",
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              "      <th>917</th>\n",
              "      <td>38</td>\n",
              "      <td>M</td>\n",
              "      <td>NAP</td>\n",
              "      <td>138</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
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              "      <td>173</td>\n",
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              "      <td>Up</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
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              "<p>918 rows × 12 columns</p>\n",
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              "                                                     [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",
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            "text/plain": [
              "     Age Sex ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40   M           ATA  ...      0.0        Up             0\n",
              "1     49   F           NAP  ...      1.0      Flat             1\n",
              "2     37   M           ATA  ...      0.0        Up             0\n",
              "3     48   F           ASY  ...      1.5      Flat             1\n",
              "4     54   M           NAP  ...      0.0        Up             0\n",
              "..   ...  ..           ...  ...      ...       ...           ...\n",
              "913   45   M            TA  ...      1.2      Flat             1\n",
              "914   68   M           ASY  ...      3.4      Flat             1\n",
              "915   57   M           ASY  ...      1.2      Flat             1\n",
              "916   57   F           ATA  ...      0.0      Flat             1\n",
              "917   38   M           NAP  ...      0.0        Up             0\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MV9ns7to7lUQ"
      },
      "source": [
        "sorted_data = data.sort_values(by=[\"HeartDisease\"])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mDvqccKq_wlJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ee11e82-accd-4955-fd34-78b7261e7c52"
      },
      "source": [
        "print(\"Shape is: \", sorted_data.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape is:  (918, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "75o-Feku-Vqq",
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          "height": 300
        },
        "outputId": "be1a2efe-0f1c-4c88-abfa-8c8805ef81f3"
      },
      "source": [
        "sorted_data.describe()"
      ],
      "execution_count": null,
      "outputs": [
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          "data": {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
              "      <td>132.396514</td>\n",
              "      <td>198.799564</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>0.553377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>18.514154</td>\n",
              "      <td>109.384145</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.497414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>173.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
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              "  </tbody>\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",
              "  <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-f0666289-a3f8-4d60-bed8-f70b05cfd609 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-f0666289-a3f8-4d60-bed8-f70b05cfd609');\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",
              "  "
            ],
            "text/plain": [
              "              Age   RestingBP  ...     Oldpeak  HeartDisease\n",
              "count  918.000000  918.000000  ...  918.000000    918.000000\n",
              "mean    53.510893  132.396514  ...    0.887364      0.553377\n",
              "std      9.432617   18.514154  ...    1.066570      0.497414\n",
              "min     28.000000    0.000000  ...   -2.600000      0.000000\n",
              "25%     47.000000  120.000000  ...    0.000000      0.000000\n",
              "50%     54.000000  130.000000  ...    0.600000      1.000000\n",
              "75%     60.000000  140.000000  ...    1.500000      1.000000\n",
              "max     77.000000  200.000000  ...    6.200000      1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rackYocsafyf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a7c8a5b-57d2-423b-f5a1-6d948d3e5893"
      },
      "source": [
        "sorted_data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                 int64\n",
              "Sex                object\n",
              "ChestPainType      object\n",
              "RestingBP           int64\n",
              "Cholesterol         int64\n",
              "FastingBS           int64\n",
              "RestingECG         object\n",
              "MaxHR               int64\n",
              "ExerciseAngina     object\n",
              "Oldpeak           float64\n",
              "ST_Slope           object\n",
              "HeartDisease        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HM_BB7bfw_tP"
      },
      "source": [
        "## **Handling Missing Values.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hoN7sZ0kal1Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "41251804-bc77-40f9-a95e-74e65a6be3d8"
      },
      "source": [
        "sorted_data.isna().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               0\n",
              "Sex               0\n",
              "ChestPainType     0\n",
              "RestingBP         0\n",
              "Cholesterol       0\n",
              "FastingBS         0\n",
              "RestingECG        0\n",
              "MaxHR             0\n",
              "ExerciseAngina    0\n",
              "Oldpeak           0\n",
              "ST_Slope          0\n",
              "HeartDisease      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3DVK7YZaniD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3a46f70b-7893-47e2-e03d-550a4e3040c3"
      },
      "source": [
        "#checking the missing values again.\n",
        "sorted_data.isna().any()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age               False\n",
              "Sex               False\n",
              "ChestPainType     False\n",
              "RestingBP         False\n",
              "Cholesterol       False\n",
              "FastingBS         False\n",
              "RestingECG        False\n",
              "MaxHR             False\n",
              "ExerciseAngina    False\n",
              "Oldpeak           False\n",
              "ST_Slope          False\n",
              "HeartDisease      False\n",
              "dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_data['Cholesterol']=sorted_data['Cholesterol'].replace(0,sorted_data['Cholesterol'].median())\n",
        "sorted_data['RestingBP']=sorted_data['RestingBP'].replace(0,sorted_data['RestingBP'].median())\n",
        "print(sorted_data[\"Cholesterol\"].median() )\n",
        "print(sorted_data[\"RestingBP\"].median() )"
      ],
      "metadata": {
        "id": "ItRaehjT-v4L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ae44f8c3-d217-4bfd-8371-1a6e9762132d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "223.0\n",
            "130.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sorted_data.describe()"
      ],
      "metadata": {
        "id": "uVal5SVf_Qn5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "b5b998a3-fad2-4b9e-d427-4a1a8bb16bc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-97b8f83e-48db-4e82-8d9e-4aa39113457c\">\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>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "      <td>918.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>53.510893</td>\n",
              "      <td>132.538126</td>\n",
              "      <td>240.581699</td>\n",
              "      <td>0.233115</td>\n",
              "      <td>136.809368</td>\n",
              "      <td>0.887364</td>\n",
              "      <td>0.553377</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.432617</td>\n",
              "      <td>17.990127</td>\n",
              "      <td>53.982967</td>\n",
              "      <td>0.423046</td>\n",
              "      <td>25.460334</td>\n",
              "      <td>1.066570</td>\n",
              "      <td>0.497414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>85.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>-2.600000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>214.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>54.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>223.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>138.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>60.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>267.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>603.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97b8f83e-48db-4e82-8d9e-4aa39113457c')\"\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",
              "  <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-97b8f83e-48db-4e82-8d9e-4aa39113457c 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-97b8f83e-48db-4e82-8d9e-4aa39113457c');\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",
              "  "
            ],
            "text/plain": [
              "              Age   RestingBP  ...     Oldpeak  HeartDisease\n",
              "count  918.000000  918.000000  ...  918.000000    918.000000\n",
              "mean    53.510893  132.538126  ...    0.887364      0.553377\n",
              "std      9.432617   17.990127  ...    1.066570      0.497414\n",
              "min     28.000000   80.000000  ...   -2.600000      0.000000\n",
              "25%     47.000000  120.000000  ...    0.000000      0.000000\n",
              "50%     54.000000  130.000000  ...    0.600000      1.000000\n",
              "75%     60.000000  140.000000  ...    1.500000      1.000000\n",
              "max     77.000000  200.000000  ...    6.200000      1.000000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ANDopYtf9uqR"
      },
      "source": [
        "## **Checking Imbalance / Counting Values.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ouwQlQY8ylI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9a94b38b-45a9-4fc9-e9e5-883669ca3308"
      },
      "source": [
        "sorted_data.value_counts([\"HeartDisease\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "HeartDisease\n",
              "1               508\n",
              "0               410\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AuKf4dYn86EI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "897967ad-921a-4399-edc1-6cf1b9269590"
      },
      "source": [
        "sorted_data.value_counts([\"ChestPainType\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ChestPainType\n",
              "ASY              496\n",
              "NAP              203\n",
              "ATA              173\n",
              "TA                46\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "14i6uQHy9JiL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fa6db8a-8160-43e9-8528-7ff7f036d8be"
      },
      "source": [
        "sorted_data.value_counts([\"FastingBS\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "FastingBS\n",
              "0            704\n",
              "1            214\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "buigQ_eW9RC6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c46ca510-cafa-4565-85fe-652b30cea435"
      },
      "source": [
        "sorted_data.value_counts([\"RestingECG\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RestingECG\n",
              "Normal        552\n",
              "LVH           188\n",
              "ST            178\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m_3RCYn59dW4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bad916a2-48df-4f6c-b7d3-2d052056d34b"
      },
      "source": [
        "sorted_data.value_counts([\"ExerciseAngina\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ExerciseAngina\n",
              "N                 547\n",
              "Y                 371\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-7KXm8i9oeg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de07eccc-1dbb-4986-c66a-7312224bf8c1"
      },
      "source": [
        "sorted_data.value_counts([\"ST_Slope\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ST_Slope\n",
              "Flat        460\n",
              "Up          395\n",
              "Down         63\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vih_g7mvyj15"
      },
      "source": [
        "## **Encoding the categorical data.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tNIc5tkRyi7H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "ae931a5d-47b4-4fd1-9eca-3090d1ccd57f"
      },
      "source": [
        "\n",
        "\n",
        "labelencoder = LabelEncoder()\n",
        "# Assigning numerical values and storing in another column\n",
        "sorted_data['ChestPainType'] = labelencoder.fit_transform(sorted_data['ChestPainType'])\n",
        "sorted_data\n",
        "\n",
        "#ChestPainType:\n",
        "#ASY:0\n",
        "#ATA:1\n",
        "#NAP:2\n",
        "#TA:3\n",
        "\n",
        "#Deleting a column : sorted_data.drop('ChestPainTypeLabeled', axis=1, inplace=True)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>Age</th>\n",
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              "      <td>105</td>\n",
              "      <td>Y</td>\n",
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              "      <td>67</td>\n",
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              "      <th>...</th>\n",
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              "      <td>127</td>\n",
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              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
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              "      <td>216</td>\n",
              "      <td>0</td>\n",
              "      <td>Normal</td>\n",
              "      <td>110</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>63</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>139</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>ST</td>\n",
              "      <td>128</td>\n",
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              "      <td>1.2</td>\n",
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              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>458</th>\n",
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              "      <td>1</td>\n",
              "      <td>ST</td>\n",
              "      <td>86</td>\n",
              "      <td>N</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>918 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33cbb92c-1e5a-4d68-8dc8-897e1da4c9a9')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \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",
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              "      padding: 0 0 0 0;\n",
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              "    }\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",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-33cbb92c-1e5a-4d68-8dc8-897e1da4c9a9 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-33cbb92c-1e5a-4d68-8dc8-897e1da4c9a9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Age Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40   M              1  ...      0.0        Up             0\n",
              "620   65   M              0  ...      0.4        Up             0\n",
              "619   74   F              1  ...      0.2        Up             0\n",
              "618   64   M              0  ...      0.2      Flat             0\n",
              "616   67   F              2  ...      1.6      Flat             0\n",
              "..   ...  ..            ...  ...      ...       ...           ...\n",
              "492   49   M              3  ...      3.0      Flat             1\n",
              "493   51   M              2  ...      1.7      Flat             1\n",
              "494   60   M              0  ...      2.5      Flat             1\n",
              "485   63   M              1  ...      1.2      Flat             1\n",
              "458   61   M              0  ...      1.5      Flat             1\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eBPzEitE1EMe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "47520c2f-8d58-47e5-9045-e92bee5372bf"
      },
      "source": [
        "labelencoder = LabelEncoder()\n",
        "# Assigning numerical values and storing in another column.\n",
        "sorted_data['RestingECG'] = labelencoder.fit_transform(sorted_data['RestingECG'])\n",
        "sorted_data\n",
        "\n",
        "#RestingECG:\n",
        "#Normal:1\n",
        "#LVH:0\n",
        "#ST:2\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<style scoped>\n",
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              "      <td>0</td>\n",
              "      <td>142</td>\n",
              "      <td>216</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>110</td>\n",
              "      <td>Y</td>\n",
              "      <td>2.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>63</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>139</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>128</td>\n",
              "      <td>Y</td>\n",
              "      <td>1.2</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458</th>\n",
              "      <td>61</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>134</td>\n",
              "      <td>223</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>86</td>\n",
              "      <td>N</td>\n",
              "      <td>1.5</td>\n",
              "      <td>Flat</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>918 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-306e22a4-91b9-448c-b1ba-188d5a093fe9')\"\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",
              "  <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-306e22a4-91b9-448c-b1ba-188d5a093fe9 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-306e22a4-91b9-448c-b1ba-188d5a093fe9');\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",
              "  "
            ],
            "text/plain": [
              "     Age Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40   M              1  ...      0.0        Up             0\n",
              "620   65   M              0  ...      0.4        Up             0\n",
              "619   74   F              1  ...      0.2        Up             0\n",
              "618   64   M              0  ...      0.2      Flat             0\n",
              "616   67   F              2  ...      1.6      Flat             0\n",
              "..   ...  ..            ...  ...      ...       ...           ...\n",
              "492   49   M              3  ...      3.0      Flat             1\n",
              "493   51   M              2  ...      1.7      Flat             1\n",
              "494   60   M              0  ...      2.5      Flat             1\n",
              "485   63   M              1  ...      1.2      Flat             1\n",
              "458   61   M              0  ...      1.5      Flat             1\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UkRxiq7l1401",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "ce548bed-1445-42db-9d0a-7328f4490e1e"
      },
      "source": [
        "labelencoder = LabelEncoder()\n",
        "# Assigning numerical values and storing in another column\n",
        "sorted_data['ExerciseAngina'] = labelencoder.fit_transform(sorted_data['ExerciseAngina'])\n",
        "sorted_data\n",
        "\n",
        "#ExerciseAngina:\n",
        "#N:0\n",
        "#Y:1\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<p>918 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bd10bbc-806b-4763-b101-312514d47760')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \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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              "      height: 32px;\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",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3bd10bbc-806b-4763-b101-312514d47760 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-3bd10bbc-806b-4763-b101-312514d47760');\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",
              "  "
            ],
            "text/plain": [
              "     Age Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40   M              1  ...      0.0        Up             0\n",
              "620   65   M              0  ...      0.4        Up             0\n",
              "619   74   F              1  ...      0.2        Up             0\n",
              "618   64   M              0  ...      0.2      Flat             0\n",
              "616   67   F              2  ...      1.6      Flat             0\n",
              "..   ...  ..            ...  ...      ...       ...           ...\n",
              "492   49   M              3  ...      3.0      Flat             1\n",
              "493   51   M              2  ...      1.7      Flat             1\n",
              "494   60   M              0  ...      2.5      Flat             1\n",
              "485   63   M              1  ...      1.2      Flat             1\n",
              "458   61   M              0  ...      1.5      Flat             1\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AqRIklwy3YjU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "85c9ed98-2584-4aa2-fb52-b06741244d19"
      },
      "source": [
        "labelencoder = LabelEncoder()\n",
        "# Assigning numerical values and storing in another column\n",
        "sorted_data['ST_Slope'] = labelencoder.fit_transform(sorted_data['ST_Slope'])\n",
        "sorted_data\n",
        "\n",
        "#ST_Slope:\n",
        "#Up:2\n",
        "#Flat:1\n",
        "#Down:0 ??? check et ? \n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "      <th>HeartDisease</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>40</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>289</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>620</th>\n",
              "      <td>65</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>177</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>619</th>\n",
              "      <td>74</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>269</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>121</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>618</th>\n",
              "      <td>64</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>128</td>\n",
              "      <td>263</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>105</td>\n",
              "      <td>1</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>616</th>\n",
              "      <td>67</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "      <td>115</td>\n",
              "      <td>564</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>1</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>492</th>\n",
              "      <td>49</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "      <td>130</td>\n",
              "      <td>223</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>145</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>493</th>\n",
              "      <td>51</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>137</td>\n",
              "      <td>339</td>\n",
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              "      <td>127</td>\n",
              "      <td>1</td>\n",
              "      <td>1.7</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>60</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>142</td>\n",
              "      <td>216</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>110</td>\n",
              "      <td>1</td>\n",
              "      <td>2.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>485</th>\n",
              "      <td>63</td>\n",
              "      <td>M</td>\n",
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              "      <td>139</td>\n",
              "      <td>217</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>128</td>\n",
              "      <td>1</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>458</th>\n",
              "      <td>61</td>\n",
              "      <td>M</td>\n",
              "      <td>0</td>\n",
              "      <td>134</td>\n",
              "      <td>223</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>86</td>\n",
              "      <td>0</td>\n",
              "      <td>1.5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>918 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ee97497-01b6-4725-9cf5-f74ca64838a8')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \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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              "      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",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [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-2ee97497-01b6-4725-9cf5-f74ca64838a8 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-2ee97497-01b6-4725-9cf5-f74ca64838a8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "     Age Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40   M              1  ...      0.0         2             0\n",
              "620   65   M              0  ...      0.4         2             0\n",
              "619   74   F              1  ...      0.2         2             0\n",
              "618   64   M              0  ...      0.2         1             0\n",
              "616   67   F              2  ...      1.6         1             0\n",
              "..   ...  ..            ...  ...      ...       ...           ...\n",
              "492   49   M              3  ...      3.0         1             1\n",
              "493   51   M              2  ...      1.7         1             1\n",
              "494   60   M              0  ...      2.5         1             1\n",
              "485   63   M              1  ...      1.2         1             1\n",
              "458   61   M              0  ...      1.5         1             1\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nRdrBUak3FXT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "ce44e85c-441a-4283-a704-ea49d3c0ca11"
      },
      "source": [
        "labelencoder = LabelEncoder()\n",
        "# Assigning numerical values and storing in another column\n",
        "sorted_data['Sex'] = labelencoder.fit_transform(sorted_data['Sex'])\n",
        "sorted_data\n",
        "\n",
        "#Sex:\n",
        "#M:1\n",
        "#F:0\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-dca48aa9-b63f-4efa-89b2-ce78ceacba43\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Sex</th>\n",
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              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
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              "      <th>618</th>\n",
              "      <td>64</td>\n",
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              "      <td>128</td>\n",
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              "      <td>1</td>\n",
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              "      <th>616</th>\n",
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              "      <td>1</td>\n",
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              "<p>918 rows × 12 columns</p>\n",
              "</div>\n",
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              "      \n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      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-dca48aa9-b63f-4efa-89b2-ce78ceacba43 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-dca48aa9-b63f-4efa-89b2-ce78ceacba43');\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",
              "  "
            ],
            "text/plain": [
              "     Age  Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "0     40    1              1  ...      0.0         2             0\n",
              "620   65    1              0  ...      0.4         2             0\n",
              "619   74    0              1  ...      0.2         2             0\n",
              "618   64    1              0  ...      0.2         1             0\n",
              "616   67    0              2  ...      1.6         1             0\n",
              "..   ...  ...            ...  ...      ...       ...           ...\n",
              "492   49    1              3  ...      3.0         1             1\n",
              "493   51    1              2  ...      1.7         1             1\n",
              "494   60    1              0  ...      2.5         1             1\n",
              "485   63    1              1  ...      1.2         1             1\n",
              "458   61    1              0  ...      1.5         1             1\n",
              "\n",
              "[918 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "sns.heatmap(sorted_data.corr(),  cmap=cmap)\n"
      ],
      "metadata": {
        "id": "_Hl1OKaIR6Uy",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        },
        "outputId": "81783332-ca8f-41ed-9c4e-58e1abff4a25"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fcfd32f8a10>"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# sorted_data.plot(kind=\"box\",subplots=True,layout=(7,2),figsize=(15,20))  #Outlier Detection"
      ],
      "metadata": {
        "id": "wbRgiKSuTjPL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ma_Zk7XG-M2j"
      },
      "source": [
        "## **Splitting the data**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AlgorithmsScores = {} #will record all useful scores for all used algorithms."
      ],
      "metadata": {
        "id": "3DTses4Pfpal"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEJIaHsi7GkG"
      },
      "source": [
        "features_sorted_data = sorted_data.drop([\"HeartDisease\"],axis='columns')\n",
        "\n",
        "X = features_sorted_data #Features\n",
        "y = sorted_data['HeartDisease'] #Target\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H3G0ZTExAlA9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "56983402-6e50-4a61-fc2f-b431aab33210"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, stratify = y, random_state = 7)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c4bf92c5-9ed8-41af-8bf1-7775a769aac4\">\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>Age</th>\n",
              "      <th>Sex</th>\n",
              "      <th>ChestPainType</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>RestingECG</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>378</th>\n",
              "      <td>70</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>140</td>\n",
              "      <td>223</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>157</td>\n",
              "      <td>1</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>555</th>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>118</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>58</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>146</td>\n",
              "      <td>218</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>105</td>\n",
              "      <td>0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>110</td>\n",
              "      <td>202</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>364</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>120</td>\n",
              "      <td>223</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>97</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</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>765</th>\n",
              "      <td>41</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>112</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>179</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>514</th>\n",
              "      <td>43</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>223</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>296</th>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>145</td>\n",
              "      <td>223</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>139</td>\n",
              "      <td>1</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>846</th>\n",
              "      <td>39</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>118</td>\n",
              "      <td>219</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>140</td>\n",
              "      <td>0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>535</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>223</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>122</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>642 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c4bf92c5-9ed8-41af-8bf1-7775a769aac4')\"\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",
              "  <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-c4bf92c5-9ed8-41af-8bf1-7775a769aac4 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-c4bf92c5-9ed8-41af-8bf1-7775a769aac4');\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",
              "  "
            ],
            "text/plain": [
              "     Age  Sex  ChestPainType  ...  ExerciseAngina  Oldpeak  ST_Slope\n",
              "378   70    1              0  ...               1      2.0         1\n",
              "555   58    1              2  ...               1      0.0         1\n",
              "695   58    1              0  ...               0      2.0         1\n",
              "64    50    0              1  ...               0      0.0         2\n",
              "364   56    1              2  ...               0      0.0         1\n",
              "..   ...  ...            ...  ...             ...      ...       ...\n",
              "765   41    1              2  ...               0      0.0         2\n",
              "514   43    1              0  ...               0      0.5         2\n",
              "296   50    1              0  ...               1      0.7         1\n",
              "846   39    1              0  ...               0      1.2         1\n",
              "535   56    1              0  ...               1      1.0         1\n",
              "\n",
              "[642 rows x 11 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmiNv2XZ9Kl7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d860f34d-694c-4356-f56d-03acfbb411d4"
      },
      "source": [
        "# Shape of Train Data\n",
        "print(\"Shape of the training data: \", X_train.shape[0])\n",
        "\n",
        "# Class distribution in Train Data\n",
        "print(\"Number of negative samples in training: \", y_train[y_train == 0].shape[0])\n",
        "print(\"Number of positive samples in training: \", y_train[y_train == 1].shape[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of the training data:  642\n",
            "Number of negative samples in training:  287\n",
            "Number of positive samples in training:  355\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = pd.DataFrame(y_train)\n",
        "t = pd.concat([X_train,y], axis=1)\n",
        "cmap = sns.diverging_palette(230, 20, as_cmap=True)\n",
        "sns.heatmap(t.corr(),  cmap=cmap)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 933
        },
        "id": "JBs2rJLVTGX4",
        "outputId": "7b067eb6-90da-4e3c-973c-cccd3e55834a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>Age</th>\n",
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              "      <th>RestingBP</th>\n",
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              "      <th>ExerciseAngina</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>ST_Slope</th>\n",
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              "      <th>64</th>\n",
              "      <td>50</td>\n",
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              "      <th>765</th>\n",
              "      <td>41</td>\n",
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              "      <td>50</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>642 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1c67be1c-b0ef-492b-b5ba-a7afe6fe56eb')\"\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",
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              "  </svg>\n",
              "      </button>\n",
              "      \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",
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              "      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-1c67be1c-b0ef-492b-b5ba-a7afe6fe56eb 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-1c67be1c-b0ef-492b-b5ba-a7afe6fe56eb');\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",
              "  "
            ],
            "text/plain": [
              "     Age  Sex  ChestPainType  ...  Oldpeak  ST_Slope  HeartDisease\n",
              "378   70    1              0  ...      2.0         1             1\n",
              "555   58    1              2  ...      0.0         1             1\n",
              "695   58    1              0  ...      2.0         1             1\n",
              "64    50    0              1  ...      0.0         2             0\n",
              "364   56    1              2  ...      0.0         1             0\n",
              "..   ...  ...            ...  ...      ...       ...           ...\n",
              "765   41    1              2  ...      0.0         2             0\n",
              "514   43    1              0  ...      0.5         2             1\n",
              "296   50    1              0  ...      0.7         1             1\n",
              "846   39    1              0  ...      1.2         1             1\n",
              "535   56    1              0  ...      1.0         1             1\n",
              "\n",
              "[642 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Models**"
      ],
      "metadata": {
        "id": "MPWknAYrVQiT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import CategoricalNB\n",
        "from sklearn.naive_bayes import ComplementNB\n",
        "\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "import lightgbm as lgb\n",
        "from sklearn.neural_network import MLPClassifier"
      ],
      "metadata": {
        "id": "IsdcnlBvVsGd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yBSmar-n-2dg"
      },
      "source": [
        "## **Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6gtdINSKAnTU"
      },
      "source": [
        "#Standardizing the data\n",
        "\n",
        "#Training data\n",
        "X_train_log = X_train.copy()\n",
        "scaler = StandardScaler()\n",
        "X_train_log[X_train_log.columns] = scaler.fit_transform(X_train_log[X_train_log.columns].to_numpy())\n",
        "\n",
        "#Testing data\n",
        "X_test_log = X_test.copy()\n",
        "scaler = StandardScaler()\n",
        "X_test_log[X_test_log.columns] = scaler.fit_transform(X_test_log[X_test_log.columns].to_numpy())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "clf = lr.fit(X_train_log, y_train)\n",
        "y_pred = clf.predict(X_test_log)\n",
        "\n",
        "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(clf.score(X_test_log, y_test)))\n",
        "print('')\n",
        "print(\"Accuracy: {:.3f}\".format(metrics.accuracy_score(y_test, y_pred)))\n",
        "print(\"Precision:{:.3f}\".format(metrics.precision_score(y_test, y_pred)))\n",
        "print(\"Recall: {:.3f}\".format(metrics.recall_score(y_test, y_pred)))\n",
        "print('')\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "id": "iidmDJUv1Wd-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c3038497-30a9-48ec-e9de-f2acf8e9649f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of logistic regression classifier on test set: 0.86\n",
            "\n",
            "Accuracy: 0.859\n",
            "Precision:0.896\n",
            "Recall: 0.843\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.88      0.85       123\n",
            "           1       0.90      0.84      0.87       153\n",
            "\n",
            "    accuracy                           0.86       276\n",
            "   macro avg       0.86      0.86      0.86       276\n",
            "weighted avg       0.86      0.86      0.86       276\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Inspecting the confusion matrix.\n",
        "\n",
        "initial_logreg_cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "class_names=[0,1]\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "#Creating heatmap.\n",
        "\n",
        "sns.heatmap(pd.DataFrame(initial_logreg_cnf_matrix), annot=True, cmap=\"Greens\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Logistic Regression', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "#71 - 11\n",
        "#16 - 86"
      ],
      "metadata": {
        "id": "FVtUfTPJUcyV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "2e53e766-f35a-46d2-e5d1-faf08d9728fe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 427.9555555555555, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 95
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC curve and AUC.\n",
        "\n",
        "y_pred_prob_lr = clf.predict_proba(X_test_log)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_prob_lr)\n",
        "logistic_auc = metrics.roc_auc_score(y_test, y_pred_prob_lr)\n",
        "plt.plot(fpr,tpr,label=\"Logistic Regression, auc=\"+str( \"{:.3f}\".format(logistic_auc)), color='green')\n",
        "plt.legend(loc=4)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "2sUyrqoA2Qmv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "39998abd-3cd4-4cd4-fe4f-cfa6e3d17152"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#HyperParameter Tuning: Maximum iteration, C value, the penalty (regularization) and the solver are being tested.\n",
        "                        #For C values:  smaller values specify stronger regularization.\n",
        "                        #For small datasets, ‘liblinear’ is a good choice, whereas ‘sag’ and ‘saga’ are faster for large ones.\n",
        "                        #Not used all solver since not compatible with other penalty etc.\n",
        "\n",
        "parameters ={\"max_iter\": [20,40,100, 200, 500,1000], \"C\":[0.01, 0.1,0.5,0.8,0.9, 1,1.1,1.2,1.5,2],'penalty':['l2'], 'solver':[ 'lbfgs','liblinear']}\n",
        "lr = LogisticRegression()\n",
        "logreg_cv = GridSearchCV(lr,parameters,cv=5, verbose = True) #5 fold cross validation.\n",
        "logreg_cv.fit(X_train_log,y_train)"
      ],
      "metadata": {
        "id": "5n9o7bmO1dMj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1492c810-dba7-45e8-fcd3-994a5ba67bce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 120 candidates, totalling 600 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=LogisticRegression(),\n",
              "             param_grid={'C': [0.01, 0.1, 0.5, 0.8, 0.9, 1, 1.1, 1.2, 1.5, 2],\n",
              "                         'max_iter': [20, 40, 100, 200, 500, 1000],\n",
              "                         'penalty': ['l2'], 'solver': ['lbfgs', 'liblinear']},\n",
              "             verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best: %f using %s\" % (logreg_cv.best_score_, logreg_cv.best_params_))\n",
        "means = logreg_cv.cv_results_['mean_test_score']\n",
        "stds = logreg_cv.cv_results_['std_test_score']\n",
        "params = logreg_cv.cv_results_['params']\n",
        "\n",
        "#Debugging purposes\n",
        "\n",
        "# for mean, stdev, param in zip(means, stds, params):\n",
        "#     print(\"%f (%f) with: %r\" % (mean, stdev,  param))"
      ],
      "metadata": {
        "id": "Esc5Oz48ZkQz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6061f748-db46-4456-9144-309158679165"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best: 0.839656 using {'C': 0.8, 'max_iter': 20, 'penalty': 'l2', 'solver': 'liblinear'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing Phase:\n",
        "\n",
        "# print(logreg_cv.best_estimator_)\n",
        "\n",
        "logreg_cv = logreg_cv.best_estimator_\n",
        "logreg_cv = logreg_cv.fit(X_train_log, y_train)\n",
        "y_pred = logreg_cv.predict(X_test_log)\n",
        "\n",
        "\n",
        "grid = plt.GridSpec(2, 3, wspace=0.4, hspace=0.3)\n",
        "\n",
        "\n",
        "#Calculating various scores.\n",
        "logisticRegression_f1 = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "logisticRegression_accuracy = logreg_cv.score(X_test_log,y_test)\n",
        "logisticRegression_recall = recall_score(y_test,y_pred,average=\"binary\")\n",
        "logisticRegression_precision = precision_score(y_test,y_pred,average=\"binary\")\n",
        "\n",
        "print(\"F1 score: \", logisticRegression_f1)\n",
        "print(\"Accuracy: \",logisticRegression_accuracy)\n",
        "print(\"Recall: \",logisticRegression_recall )\n",
        "print(\"Precision: \",logisticRegression_precision)\n",
        "print(classification_report(y_test,y_pred))\n",
        "\n",
        "print('')\n",
        "\n",
        "#Investigate Confusion Matrix.\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "class_names=[0,1] #name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "# Creating heatmap\n",
        "print('')\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"Greens\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Logistic Regression', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label') #decrease error from 11 to 10, 16 to 15.\n",
        "\n",
        "\n",
        "\n",
        "# Old cm: 71-11\n",
        "          #16-86"
      ],
      "metadata": {
        "id": "GzaBQJOfcag9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 802
        },
        "outputId": "88f483e7-985b-4e39-8523-51bf76433fb3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 score:  0.8590482833449586\n",
            "Accuracy:  0.8586956521739131\n",
            "Recall:  0.8431372549019608\n",
            "Precision:  0.8958333333333334\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.88      0.85       123\n",
            "           1       0.90      0.84      0.87       153\n",
            "\n",
            "    accuracy                           0.86       276\n",
            "   macro avg       0.86      0.86      0.86       276\n",
            "weighted avg       0.86      0.86      0.86       276\n",
            "\n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 427.9555555555555, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC curve and ROC area for each class\n",
        "\n",
        "#After tuning:\n",
        "y_pred_prob_lr_cv = logreg_cv.predict_proba(X_test_log)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_prob_lr_cv)\n",
        "logisticRegression_auc = metrics.roc_auc_score(y_test, y_pred_prob_lr_cv)\n",
        "\n",
        "plt.plot(fpr,tpr,label=\"After Tuning: Logistic Regression, auc=\"+str( \"{:.3f}\".format(logisticRegression_auc)), color='green')\n",
        "plt.legend(loc=4)\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "n7uS4VCVWTFV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "deddcf33-693c-4d6f-d250-49548a795b8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#F1, Acc, Recall, Precision, AUC.\n",
        "AlgorithmsScores[\"LR\"] = [format(logisticRegression_f1, '.3f'), \"{:.3f}\".format(logisticRegression_accuracy), \"{:.3f}\".format(logisticRegression_recall), \"{:.3f}\".format(logisticRegression_precision), \"{:.3f}\".format(logisticRegression_auc)]\n",
        "AlgorithmsScores"
      ],
      "metadata": {
        "id": "1rUb2_8Mdq8G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6344f0ca-f968-43e1-c2ba-ea0cf276fbd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Gaussian NB': ['0.877', '0.877', '0.856', '0.916', '0.928'],\n",
              " 'LR': ['0.859', '0.859', '0.843', '0.896', '0.927'],\n",
              " 'LightGBM': ['0.906', '0.899', '0.882', '0.931', '0.942'],\n",
              " 'MLP': ['0.846', '0.841', '0.791', '0.910', '0.923'],\n",
              " 'Random Forest': ['0.877', '0.877', '0.856', '0.916', '0.932'],\n",
              " 'SVM': ['0.873', '0.862', '0.856', '0.891', '0.921'],\n",
              " 'XGBoost': ['0.895', '0.895', '0.876', '0.931', '0.944']}"
            ]
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## **Naive Bayes**"
      ],
      "metadata": {
        "id": "TqoLVaD2jWhx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler() # define scaler\n",
        "training_time = np.zeros(5) # traning time array\n",
        "accuracy = np.zeros(5) # accuracy array \n",
        "prec = np.zeros(5) # precision array\n",
        "recall = np.zeros(5) # recall array\n",
        "######################################################## Gaussian NB\n",
        "x_nb_train = X_train\n",
        "y_nb_train = y_train\n",
        "\n",
        "model_gaussian = GaussianNB() # defining Gaussian NB\n",
        "t0 = time()\n",
        "model_gaussian.fit(x_nb_train, y_nb_train) # train the model with train data\n",
        "training_time[0] = time() - t0\n",
        "\n",
        "x_nb_test = X_test\n",
        "x_features = model_gaussian.feature_names_in_\n",
        "y_nb_test = y_test\n",
        "\n",
        "y_pred = model_gaussian.predict(x_nb_test)\n",
        "# get feature importances\n",
        "imps_gaus = permutation_importance(model_gaussian, x_nb_test, y_nb_test)\n",
        "importances_gaus = imps_gaus.importances_mean\n",
        "std_gaus = imps_gaus.importances_std\n",
        "indices_gaus = np.argsort(importances_gaus)[::-1]\n",
        "\n",
        "y_pred_prob = model_gaussian.predict_proba(x_nb_test)[::,1] \n",
        "fpr_gaus, tpr_gaus, _ = metrics.roc_curve(y_test,  y_pred_prob) # roc curve\n",
        "f1_gaus = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "auc_gaus = metrics.roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "prec[0] = metrics.precision_score(y_test, y_pred)\n",
        "recall[0] = metrics.recall_score(y_test, y_pred)\n",
        "cnf_matrix_gaus = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "accuracy[0] = round(100*model_gaussian.score(x_nb_test, y_nb_test),2)\n",
        "\n",
        "print(\"The accuracy of the trained Gaussian NB model: %\",accuracy[0])\n",
        "print(\"The precision of the trained Gaussian NB model: %\",round(prec[0]*100,2))\n",
        "print(\"The recall of the trained Gaussian NB model: %\",round(recall[0]*100,2))\n",
        "print(\"\")\n",
        "######################################################## Multinomial NB\n",
        "x_nb_train = X_train\n",
        "y_nb_train = y_train\n",
        "x_nb_train = scaler.fit_transform(x_nb_train)\n",
        "\n",
        "model_no_laplace = MultinomialNB(alpha=0.001) # defining multinominal NB with no Laplacian smoothing\n",
        "t0 = time()\n",
        "model_no_laplace.fit(x_nb_train, y_nb_train) # train the model with train data\n",
        "training_time[1] = time() - t0\n",
        "\n",
        "x_nb_test = X_test\n",
        "y_nb_test = y_test\n",
        "x_nb_test = scaler.fit_transform(x_nb_test)\n",
        "\n",
        "y_pred = model_no_laplace.predict(x_nb_test)\n",
        "# get feature importances\n",
        "imps_mnb_nl = permutation_importance(model_no_laplace, x_nb_test, y_nb_test)\n",
        "importances_mnb_nl = imps_mnb_nl.importances_mean\n",
        "std_mnb_nl = imps_mnb_nl.importances_std\n",
        "indices_mnb_nl = np.argsort(importances_mnb_nl)[::-1]\n",
        "\n",
        "y_pred_prob = model_no_laplace.predict_proba(x_nb_test)[::,1] \n",
        "fpr_mnb_nl, tpr_mnb_nl, _ = metrics.roc_curve(y_test,  y_pred_prob) # roc curve\n",
        "f1_mnb_nl = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "auc_mnb_nl = metrics.roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "prec[1] = metrics.precision_score(y_test, y_pred)\n",
        "recall[1] = metrics.recall_score(y_test, y_pred)\n",
        "cnf_matrix_mnb_nl = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "accuracy[1] = round(100*model_no_laplace.score(x_nb_test, y_nb_test),2)\n",
        "print(\"The accuracy of the trained Multinomial NB model with no smoothing: %\",accuracy[1])\n",
        "print(\"The precision of the trained Multinomial NB model with no smoothing: %\",round(prec[1]*100,2))\n",
        "print(\"The recall of the trained Multinomial NB model with no smoothing: %\",round(recall[1]*100,2))\n",
        "print(\"\")\n",
        "x_nb_train = X_train\n",
        "y_nb_train = y_train\n",
        "x_nb_train = scaler.fit_transform(x_nb_train)\n",
        "\n",
        "model = MultinomialNB() # defining multinominal NB\n",
        "t0 = time()\n",
        "model.fit(x_nb_train, y_nb_train) # train the model with train data\n",
        "training_time[2] = time() - t0\n",
        "\n",
        "x_nb_test = X_test\n",
        "y_nb_test = y_test\n",
        "x_nb_test = scaler.fit_transform(x_nb_test)\n",
        "\n",
        "y_pred = model.predict(x_nb_test)\n",
        "# get feature importances\n",
        "imps_mnb = permutation_importance(model, x_nb_test, y_nb_test)\n",
        "importances_mnb = imps_mnb.importances_mean\n",
        "std_mnb = imps_mnb.importances_std\n",
        "indices_mnb = np.argsort(importances_mnb)[::-1]\n",
        "\n",
        "y_pred_prob = model.predict_proba(x_nb_test)[::,1] \n",
        "fpr_mnb, tpr_mnb, _ = metrics.roc_curve(y_test,  y_pred_prob) # roc curve\n",
        "f1_mnb = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "auc_mnb = metrics.roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "prec[2] = metrics.precision_score(y_test, y_pred)\n",
        "recall[2] = metrics.recall_score(y_test, y_pred)\n",
        "cnf_matrix_mnb = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "accuracy[2] = round(100*model.score(x_nb_test, y_nb_test),2)\n",
        "print(\"The accuracy of the trained Multinomial NB model with smoothing: %\",accuracy[2])\n",
        "print(\"The precision of the trained Multinomial NB model with smoothing: %\",round(prec[2]*100,2))\n",
        "print(\"The recall of the trained Multinomial NB model with smoothing: %\",round(recall[2]*100,2))\n",
        "print(\"\")\n",
        "######################################################## Categorical NB\n",
        "x_nb_train = X_train\n",
        "y_nb_train = y_train\n",
        "x_nb_train = scaler.fit_transform(x_nb_train)\n",
        "\n",
        "model_cb = CategoricalNB() # defining Categorical NB\n",
        "t0 = time()\n",
        "model_cb.fit(x_nb_train, y_nb_train) # train the model with train data\n",
        "training_time[3] = time() - t0\n",
        "\n",
        "x_nb_test = X_test\n",
        "y_nb_test = y_test\n",
        "x_nb_test = scaler.fit_transform(x_nb_test)\n",
        "\n",
        "y_pred = model_cb.predict(x_nb_test)\n",
        "# get feature importances\n",
        "imps_cb = permutation_importance(model_cb, x_nb_test, y_nb_test)\n",
        "importances_cb = imps_cb.importances_mean\n",
        "std_cb = imps_cb.importances_std\n",
        "indices_cb = np.argsort(importances_cb)[::-1]\n",
        "\n",
        "y_pred_prob = model_cb.predict_proba(x_nb_test)[::,1] \n",
        "fpr_cb, tpr_cb, _ = metrics.roc_curve(y_test,  y_pred_prob) # roc curve\n",
        "f1_cb = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "auc_cb = metrics.roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "prec[3] = metrics.precision_score(y_test, y_pred)\n",
        "recall[3] = metrics.recall_score(y_test, y_pred)\n",
        "cnf_matrix_cb = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "accuracy[3] = round(100*model_cb.score(x_nb_test, y_nb_test),2)\n",
        "print(\"The accuracy of the trained Categorical NB model: %\",accuracy[3])\n",
        "print(\"The precision of the trained Categorical NB model: %\",round(prec[3]*100,2))\n",
        "print(\"The recall of the trained Categorical NB model: %\",round(recall[0]*100,2))\n",
        "print(\"\")\n",
        "######################################################## Complement NB\n",
        "x_nb_train = X_train\n",
        "y_nb_train = y_train\n",
        "x_nb_train = scaler.fit_transform(x_nb_train)\n",
        "\n",
        "model_comp = ComplementNB() # defining Complement NB\n",
        "t0 = time()\n",
        "model_comp.fit(x_nb_train, y_nb_train) # train the model with train data\n",
        "training_time[4] = time() - t0\n",
        "\n",
        "x_nb_test = X_test\n",
        "y_nb_test = y_test\n",
        "x_nb_test = scaler.fit_transform(x_nb_test)\n",
        "\n",
        "y_pred = model_comp.predict(x_nb_test)\n",
        "\n",
        "y_pred_prob = model_comp.predict_proba(x_nb_test)[::,1] \n",
        "fpr_cm, tpr_cm, _ = metrics.roc_curve(y_test,  y_pred_prob) # roc curve\n",
        "f1_cm = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "auc_cm = metrics.roc_auc_score(y_test, y_pred_prob)\n",
        "\n",
        "prec[4] = metrics.precision_score(y_test, y_pred)\n",
        "recall[4] = metrics.recall_score(y_test, y_pred)\n",
        "cnf_matrix_cm = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "accuracy[4] = round(100*model_comp.score(x_nb_test, y_nb_test),2)\n",
        "print(\"The accuracy of the trained Complement NB model: %\",accuracy[4] )\n",
        "print(\"The precsion of the trained Complement NB model: %\",round(prec[4]*100,2))\n",
        "print(\"The recall of the trained Complement NB model: %\",round(recall[4]*100,2) )"
      ],
      "metadata": {
        "id": "-3gW82jnjndp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1f67562e-a882-4aa6-bb19-b62e1ff3876e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy of the trained Gaussian NB model: % 87.68\n",
            "The precision of the trained Gaussian NB model: % 91.61\n",
            "The recall of the trained Gaussian NB model: % 85.62\n",
            "\n",
            "The accuracy of the trained Multinomial NB model with no smoothing: % 84.06\n",
            "The precision of the trained Multinomial NB model with no smoothing: % 87.07\n",
            "The recall of the trained Multinomial NB model with no smoothing: % 83.66\n",
            "\n",
            "The accuracy of the trained Multinomial NB model with smoothing: % 84.06\n",
            "The precision of the trained Multinomial NB model with smoothing: % 87.07\n",
            "The recall of the trained Multinomial NB model with smoothing: % 83.66\n",
            "\n",
            "The accuracy of the trained Categorical NB model: % 82.61\n",
            "The precision of the trained Categorical NB model: % 88.32\n",
            "The recall of the trained Categorical NB model: % 85.62\n",
            "\n",
            "The accuracy of the trained Complement NB model: % 81.88\n",
            "The precsion of the trained Complement NB model: % 88.15\n",
            "The recall of the trained Complement NB model: % 77.78\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#F1, Acc, Recall, Precision, AUC.\n",
        "AlgorithmsScores[\"Gaussian NB\"] = [format(f1_gaus, '.3f'), \"{:.3f}\".format(accuracy[0]/100), \"{:.3f}\".format(recall[0]), \"{:.3f}\".format(prec[0]), \"{:.3f}\".format(auc_gaus)]\n",
        "# AlgorithmsScores[\"Multinomial NB\"] = [format(f1_mnb, '.2f'), \"{:.2f}\".format(accuracy[2]/100), \"{:.2f}\".format(recall[2]), \"{:.2f}\".format(prec[2]), \"{:.2f}\".format(auc_mnb)]\n",
        "# AlgorithmsScores[\"Categorical NB\"] = [format(f1_cb, '.2f'), \"{:.2f}\".format(accuracy[3]/100), \"{:.2f}\".format(recall[3]), \"{:.2f}\".format(prec[3]), \"{:.2f}\".format(auc_cb)]\n",
        "# AlgorithmsScores[\"Complement NB\"] = [format(f1_cm, '.2f'), \"{:.2f}\".format(accuracy[4]/100), \"{:.2f}\".format(recall[4]), \"{:.2f}\".format(prec[4]), \"{:.2f}\".format(auc_cm)]\n",
        "print(f1_gaus)\n",
        "print(f1_mnb)\n",
        "print(f1_mnb_nl)\n",
        "print(f1_cb)\n",
        "print(f1_cm)"
      ],
      "metadata": {
        "id": "Fwp2QYiYGEKs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1e88af0-1c50-416c-f971-b575731c8228"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.877136732520564\n",
            "0.8408833678398896\n",
            "0.8408833678398896\n",
            "0.8265996772606063\n",
            "0.8193552371541502\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix_gaus), annot=True, cmap=\"Blues\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix Gaussian NB', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "Jo3m1aRZ-J42",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "360c54de-23a6-478b-9250-e30c71d73f12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix_mnb_nl), annot=True, cmap=\"Blues\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix Multinomial NB model with no smoothing', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "1ZBVNq4wV4gX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f3b2e9fa-5f53-4563-e36f-cb31fef1713f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix_mnb), annot=True, cmap=\"Blues\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix Multinomial NB model', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n"
      ],
      "metadata": {
        "id": "uIY5Emq3V5SZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21372f7b-e784-441a-ccd1-afb526146fba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 42
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix_cb), annot=True, cmap=\"Blues\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix Categorical NB model', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "tYq8uY0DV9pC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5cd42b42-ba73-4ece-bbc6-69f3f01eeb99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix_cm), annot=True, cmap=\"Blues\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix Complement NB model', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "JQLmpQBiV_9L",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "016fa2a2-29db-492b-c83a-b768f9680982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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F39+OVZZdnBdff5t9f30zM96dzdG7rs83t1odgP79WlhrxaVY6Tt/Zfo7H+T5FKzAfvLjH3Lbv8cxZMhQrrz6unnz/3bRBVwy5iJaWvqx1VZf5Ojjjs+xyr6riEHkIX4K6IKxzzD85H/MN++4b3yWcY9OZr1DL2bco5M57hsbAHDaVY+w+dFXsPnRV/CTC+7j9glTHEK2QIbvshtnnXPufPPuu/cext0ylsuuvIarrrme7xwwIqfqrAHXI2o4B1EB3fnEFN585/355u282apceMszAFx4yzN8bfNVP/a4Pbf8FJfeNrE3SrQS22jjTVhiySXnm3fZJWM48KCRDBgwAIChQ4fmUZpRzCBq2KE5SWuRXSSp7VoUk4FrIuLJRu2zL1t2yUFMnf4eAFOnv8eySw6ab/mgAf3ZfsOVOHrUnXmUZyX34qRJPPjAeM44/TQWXnhhjjnueNZd7zN5l9U3NUe21KQhLSJJJwAXk/1K7kuTgDGSftDF4+ZdoGnOpNsbUVqf0f7iIF/ddBXufvI1H5azhpjT2srMmTO5cMylHH3s8Xz/2KPIhh2z3uYW0UdGAOtExIeVMyWdCkwgG7n1Yyov0DRo+Dl+F9fg9Zmz+MTgRZg6/T0+MXgRps2cNd/yPbb8FJfd7sNy1hjDhg1j2+22RxLrfeYztLS0MH36dIYMGZJ3aX1Os4RLLRrVRzQXWL6D+culZdbDrr/vRfbdZk0A9t1mTa67d9K8ZUssMoAvrLMc11bMM+tJX9p2O+6/714AJk16gQ8//JDBgwfnXFXfJNU2NYNGtYiOAsZKepaPrl++MrA6cHiD9tlnjD52W7ZcdzmWXmIgE8/bh5+NGc9vr3iIC7+/PftttxYvTXubfX/9r3nrf33zVRn78Cu898GcHKu2sjjhuGMYf/99zJgxne232YpDDzuCXXf9Bj/57x+x2/CdWWihhfjZL04p5P/My6CIv3c16jiupBZgU+Y/WeH+iGit5vE+NGe9bfoVB+ddgvVBA/v37OkFax5/Y02fnc/8eofck6thZ81FxFzgnkZt38zMPq6ILSKPrGBmViIFzCEHkZlZmbT4ekRmZpYnt4jMzCxX7iMyM7NcFTCHHERmZmXiFpGZmeXKQWRmZrkqYA45iMzMysQtIjMzy1UBc8hBZGZWJm4RmZlZrgqYQw4iM7MycYvIzMxyVcAcchCZmZWJW0RmZparAuaQg8jMrEzcIjIzs1wVMIccRGZmZeIWkZmZ5aqAOeQgMjMrE7eIzMwsVw4iMzPLVQFziJa8CzAzs57T0qKapmpIOlrSBEmPSxojaaCk1STdK2mipEskDai75nofaGZmzUdSTVMV21sB+B6wcUSsC/QD9gJ+BZwWEasD04ER9dbsIDIzKxGptqlK/YFBkvoDiwBTgG2Ay9Py0cAu9dbsIDIzK5EWqaZJ0khJ4yumkZXbi4jJwG+Bl8gCaCbwADAjIuak1V4BVqi3Zp+sYGZWIrWerBARo4BRnW9Pg4HhwGrADOAyYIf6K/w4B5GZWYk04PTt7YAXImJa2v6VwBbAUpL6p1bRisDkenfQaRBJehuItrvpZ6TbERFL1LtTMzNrjCpPhKvFS8DmkhYBZgHbAuOBW4HdgYuB/YCr691Bp0EUEYvXu1EzM8tHT7eIIuJeSZcDDwJzgIfIDuVdD1ws6edp3nn17qOqQ3OSvgCsERHnS1oaWDwiXqh3p2Zm1hiN+EJrRJwInNhu9vPApj2x/W6DSNKJwMbAp4HzgQHAhWTHCM3MrImI4g2tUE2LaFdgA7JmGRHxqiQftjMza0IN6CNquGqCaHZEhKQAkLRog2syM7M6FXHQ02q+0HqppHPITtX7LvAv4E+NLcvMzOrRoJEVGqrbFlFE/FbS9sBbwJrATyLi5oZXZmZmNWtplnSpQbVfaH0MGET2PaLHGleOmZktiALmUPeH5iQdBNwH7Eb25aV7JB3Y6MLMzKx2PT36dm+opkX0fWCDiHgDQNJQ4C7gz40szMzMatck2VKTaoLoDeDtivtvp3lmZtZkStVHJOmYdHMicK+kq8n6iIYDj/ZCbWZmVqPixVDXLaK2L60+l6Y2dQ9sZ2ZmjdUs/T616GrQ05N7sxAzM1twpRxZQdIywPHAOsDAtvkRsU0D6zIzszoUsUVUzcgKFwFPkV2d72RgEnB/A2syM7M6FXFkhWqCaGhEnAd8GBH/jogDAbeGzMyaUFm/R/Rh+jlF0leBV4EhjSvJzMzqVco+IuDnkpYEjgXOAJYAjm5oVWZmVpdmaeXUoppBT69LN2cCX2psOWZmtiCKF0Ndf6H1DLIvsHYoIr7XkIrMzKxupRpZARjfa1WYmVmPKGAOdfmF1tG9WYiZmS24UvYRmZlZcRQwhxxEZmZlUrY+IjMzK5gC5lDznjX3wFn7NnLzZh8zeJPD8y7B+qBZD53Zo9srWx+Rz5ozMyuYasZtazY+a87MrETK1iIC5l0G4gRgbXwZCDOzplbEseaqvQzEk/gyEGZmTa9fi2qamoEvA2FmViItqm1qBr4MhJlZiRSwi8iXgTAzK5NSfqHVl4EwMyuOUp2+3UbS+XTwxdbUV2RmZk2kgA2iqg7NXVdxeyCwK1k/kZmZNZmyHpq7ovK+pDHAHQ2ryMzM6lbAHKpr0NM1gGV7uhAzM1twzXJKdi2q6SN6m/n7iKaSjbRgZmZNpqyH5hbvjULMzGzBFTCHuj/TT9LYauaZmVn+GjGygqSlJF0u6SlJT0r6nKQhkm6W9Gz6ObjumrvY8UBJQ4ClJQ1OOx0iaVVghXp3aGZmjaMa/1XpdODGiFgLWJ9s/NEfAGMjYg1gbLpfl64OzR0MHAUsDzwA8yp+C+jZKzmZmVmP6OmTFdLIOlsB+wNExGxgtqThwNZptdHAOOo8f6Cr6xGdDpwu6YiIOKOejZuZWe+qNYgkjQRGVswaFRGjKu6vBkwDzpe0PlnD5EhgWERMSetMBYbVXXMV68yVtFRF0YMl/We9OzQzs8aRVNMUEaMiYuOKaVS7TfYHNgTOiogNgHdpdxguIoIORuCpVjVB9N2ImFGxw+nAd+vdoZmZNU4DTlZ4BXglIu5N9y8nC6bXJC0HkH6+XnfNVazTTxXXnpXUDxhQ7w7NzKxxpNqm7kTEVOBlSZ9Os7YFngCuAfZL8/YDrq635mpGVrgRuETSOen+wWmemZk1mQZ9ofUI4CJJA4DngQPIGjKXShoBvAjsWe/GqwmiE8g6sg5N928G/lTvDs3MrHEaMcRPRDwMbNzBom17YvvdHpqLiLkRcXZE7B4Ru5M1yXwWnZlZE+rpQ3O9oapBTyVtAOxN1vR6AbiykUWZmVl9Wqr/kmrT6DSIJK1JFj57A/8HXAIoInyVVjOzJtUsrZxadNUiegq4Hdg5IiYCSDq6V6oyM7O6FPEyEF31Ee0GTAFulfQnSdtCAdt8ZmZ9SItU09QMOg2iiPh7ROwFrAXcSjbu3LKSzpL05d4q0MzMqlfEkxWqOWvu3Yj4W0R8DVgReAhfGM/MrCmVqkXUkYiYnsYl6pFzx83MrGcVsUVU1enbZmZWDDW1LpqEg8jMrETULM2cGjiIzMxKpHgx5CAyMyuVZjkBoRYOIjOzEileDDmIzMxKpYANIgeRmVmZ+GQFMzPLlU/fNjOzXLlFZGZmuSpeDDmIzMxKpZ9bRGZmlicfmjMzs1wVL4YcRGZmpVLABpGDyMysTFoK2CZyEJmZlYhbRGZmliu5RWRmZnlyi8jMzHLlPiIzM8uVW0RmZpYrB5GZmeXKJyuYmVmuWoqXQw4iM7MycYvIzMxy5T4iMzPLlVtE1qtmz/6A/zryIObMnk1rayuf++K27H3AofOWn/uHXzP2hqsZc8OdOVZpZXD2ifuw41brMu3Nt9l4j18CsNt2G/Bfh+zEWqsNY8tv/5YHn3gJgIX69+PMH+/NhmuvzNyYy3G/voLbH3g2z/L7lCL2ERXx8uaWLLTQAH566jmcdt4lnHruGB66726efuJRACY+/QTvvPNWzhVaWVxw7T0MP+yP882b8Nyr7HXsn7jjwefmm3/gblsAsMmev2TnQ87klGN2LeQ1copKNf5rBg6iApPEoEGLANA6Zw6trXMQorW1ldFn/57vHHxkzhVaWdz54HO8OfO9+eY9/cJrPPvi6x9bd61PfoJx9z8NwLTp7zDz7VlstPbKvVKnZX1EtUzNwEFUcK2trRx90F7sv+t2rL/RZqy59nr846pL2OTzWzFk6DJ5l2d90GPPTGbnL65Hv34trLL8UDZYeyVW/MTgvMvqM1Tj1Ax6PYgkHdDFspGSxksaf+mFf+7NsgqrX79+nHbuxZx72Y08+9QEJjzyAHf9+198dbe98i7N+qjRV9/N5NdmcOdFx/Ob73+Dex55gdbWuXmX1We0SDVNzSCPkxVOBs7vaEFEjAJGATzx6rvRm0UV3aKLLc66n92Yxx8ez9TJL3PoPsMB+OCD9zl0n69z1kXX5Fyh9RWtrXM5/ndXzrt/61+O4dmXPn4IzxqjEdEiqR8wHpgcETtLWg24GBgKPAB8OyJm17v9hgSRpEc7WwQMa8Q++6KZM6bTv39/Fl1scT744H0eeeAedt17f86/8uZ56+y94xYOIetVgwYuhBDvvT+bbTZbizmtc3nq+al5l9V3NKaRcyTwJLBEuv8r4LSIuFjS2cAI4Kx6N96oFtEw4CvA9HbzBdzVoH32OdPfmMYfTjmRuXNbmTs32GLr7dnkc1vlXZaV0Oj/2Z8tN1qDpZdajIk3/oyfnf0Pps98l1NP2IOlBy/GlX84hEefnszXD/sjywxenGv/9zDmzg1enTaDET8enXf5fUpPnwknaUXgq8AvgGOUnQK5DfCttMpo4CQWIIgU0fNHwCSdB5wfEXd0sOxvEfGtDh42Hx+as9620VdPyLsE64NmPXRmjybHfc/PrOmzc9NPLtnl/iVdDvwPsDhwHLA/cE9ErJ6WrwTcEBHr1lUwDTpZISJGdBRCaVm3IWRmZvWp9ay5ypPE0jRy3raknYHXI+KBRtbskRXMzMqkxvZV5UliHdgC+LqknYCBZH1EpwNLSeofEXOAFYHJddeLv0dkZlYqPTmyQkT8MCJWjIhVgb2AWyJiH+BWYPe02n7A1QtSs4PIzKxEemlkhRPITlyYSHYK93kLUrMPzZmZlUijvqIaEeOAcen288CmPbVtB5GZWZk0x2AJNXEQmZmVSLOMqF0LB5GZWYk0yfBxNXEQmZmVSAFzyEFkZlYqBUwiB5GZWYm4j8jMzHLlPiIzM8tVAXPIQWRmViYqYJPIQWRmViIFzCEHkZlZmRQwhxxEZmalUsAkchCZmZWIT982M7NcuY/IzMxyVcAcchCZmZVKAZPIQWRmViLuIzIzs1y5j8jMzHJVwBxyEJmZlUoBk8hBZGZWIu4jMjOzXLmPyMzMclXAHHIQmZmVSgGTyEFkZlYi7iMyM7NcuY/IzMxyVcAcchCZmZWJW0RmZpaz4iWRg8jMrETcIjIzs1wVMIccRGZmZeIWkZmZ5crfIzIzs3wVL4ccRGZmZVLAHHIQmZmVifuIzMwsV+4jMjOzfBUvhxxEZmZlUsAcchCZmZVJEfuIWvIuwMzMeo5q/Nft9qSVJN0q6QlJEyQdmeYPkXSzpGfTz8H11uwgMjMrEam2qQpzgGMjYm1gc+AwSWsDPwDGRsQawNh0vy4OIjMz61RETImIB9Ptt4EngRWA4cDotNpoYJd69+EgMjMrkVpbRJJGShpfMY3sfNtaFdgAuBcYFhFT0qKpwLB6a/bJCmZmJVLr94giYhQwqtvtSosBVwBHRcRbqjiuFxEhKWosdR63iMzMSqQBfURIWogshC6KiCvT7NckLZeWLwe8Xm/NDiIzsxLp6SBS1vQ5D3gyIk6tWHQNsF+6vR9wdb01+9CcmVmJNGCIny2AbwOPSXo4zfsRcApwqaQRwIvAnvXuwEFkZlYiPf2F1oi4g84HbNi2J/bhIDIzK5ECDqzgIDIzK5UCJpGDyMysRHwZCDMzy1URBz1VRN3fQbImJWlk+pKaWa/we84WhL9HVE6dDtFh1iB+z1ndHERmZpYrB5GZmeXKQVROPlZvvc3vOaubT1YwMwC54GwAAAD5SURBVLNcuUVkZma5chCZmVmuHEQlImkHSU9Lmiip7uvHm1VL0p8lvS7p8bxrseJyEJWEpH7AH4EdgbWBvSWtnW9V1gf8Bdgh7yKs2BxE5bEpMDEino+I2cDFwPCca7KSi4jbgDfzrsOKzUFUHisAL1fcfyXNMzNrag4iMzPLlYOoPCYDK1XcXzHNMzNrag6i8rgfWEPSapIGAHsB1+Rck5lZtxxEJRERc4DDgZuAJ4FLI2JCvlVZ2UkaA9wNfFrSK5JG5F2TFY+H+DEzs1y5RWRmZrlyEJmZWa4cRGZmlisHkZmZ5cpBZGZmuXIQmZlZrhxEZmaWq/8HV06BGZGjEY4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.legend import Legend\n",
        "# Compute ROC curve and ROC area for each class\n",
        "def ROC(fpr_par,tpr_par,legend,auc):\n",
        "  plt.plot(fpr_par,tpr_par,label=legend+str(auc))\n",
        "  \n",
        "  plt.legend(loc=4)\n",
        "  plt.show()\n",
        "\n",
        "ROC(fpr_gaus,tpr_gaus,\"Gaussian NB\",auc_gaus)\n",
        "ROC(fpr_mnb_nl,tpr_mnb_nl,\"Multinomial NB no smoothing\",auc_mnb_nl)\n",
        "ROC(fpr_mnb,tpr_mnb,\"Multinomial NB smoothing\",auc_mnb)\n",
        "ROC(fpr_cb,tpr_cb,\"Categorical NB\",auc_cb)\n",
        "ROC(fpr_cm,tpr_cm,\"Complement NB\",auc_cm)\n",
        "\n",
        "plt.figure(figsize=(9, 5))\n",
        "plt.plot(fpr_gaus,tpr_gaus,label=\"Gaussian NB: \"+str(auc_gaus))\n",
        "plt.plot(fpr_mnb,tpr_mnb,label=\"Multinomial NB: \"+str(auc_mnb))\n",
        "plt.plot(fpr_cb,tpr_cb,label=\"Categorical NB: \"+str(auc_cb))\n",
        "plt.plot(fpr_cm,tpr_cm,label=\"Complement NB: \"+str(auc_cm))\n",
        "plt.legend(loc=4)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "mr-yrH3hAw1y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93489e06-9a43-487c-d89b-4d4cf93eec99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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+ZMgQdw/9HHz55Zfu7n748GHv37+/5+fn1/rMdddd53/+85+Puy1391WrVvl7771Xoyb30M/7Rx995IMHD/b169eHl//1r3/1G264wd3dDx486N26dfNPP/3US0pKPD093cvKytzd/frrr/dnnnmm1v4j/R4BhV5HrrbIs1wyUjrx0u2XxLsMidLDr29g487Y/jWVcW4nHrwms871y5cvp127dvzkJz8JL+vWrRtTpkwBYNu2bfzwhz/k4MGDAMyaNYtLL72UlStX8uSTT/LGG28AMHnyZLKzsxk/fjxTp04lLy+PxMREhg8fzpNPPskrr7zCww8/TEJCAklJSaxevbrGNtatW8ddd91FeXk5p556Ks888wznn38+zz77LHl5eZSVlbF161auvfZaHn/88Yh9ufvuu5k+fTojRoyos7/uzpdffkmPHj0AWLRoEbfccgtmxsCBA9m3bx+7du0iJSUl/JlPPvmEnj17kpycDMCwYcN47bXXGDp0KEOGDAm3GzhwIM899xwQmtctLy/n8OHDuDsVFRWcc8457Nq1iwMHDjBw4EAAbrnlFhYuXHjcmjt06MBll11GcXFxrXVfffUVM2bMYN68eYwdOza8vFOnTuHXBw8ejDjP/MILL5Cbmxuut2PHjkBotFxRUVHrMwcOHGD58uU888wzx90WwHe+8x22bdtWq12vXr0i9tHMOHjwIEeOHOHQoUO0a9eOTp06cejQofCytm3bUlZWxrnnnhtxGw3RIgNdpD4bNmzg4osvrnN9586deeutt2jfvj1btmwhNze3xp/Kx9q7dy8LFizgo48+wszYt28fANOmTWPJkiWkpqaGl1V3wQUXsGbNGhITE1m6dCm//vWvee211wAoKiri/fff55RTTuH8889nypQpdOnSpdY2qqaKVqxYwemnn15j3Zo1a8jKymLv3r106NCB3/zmNwDs2LGjxrbS0tLYsWNHjUDv0aMHmzdvZtu2baSlpbFw4UIOHz5ca/9//OMfw8F8ySWXMGTIEFJSUnB3Jk+eTK9evSgsLCQtLa3W/qrMmjWL+fPnk52dzW9/+1vOPPPMOv+tITS18otf/ILTTjut1rp7772X+fPnk5SUxIoVK2qsKysr480332TWrFnhZV9//TX9+vWjuLiYSZMmMWDAgBqfWbhwIUOHDq3xn0Vd22qoMWPGsGjRIlJSUigrK2PmzJl84xvfAOCXv/wlXbt25dRTT2X48OEMHz78hPdTRYEuTe54I+nmMmnSJN5++23atWvH+vXrqaioYPLkyRQVFZGQkMDHH3983M8nJSXRvn17JkyYwNVXX83VV18NwKBBgxg/fjxjx47luuuuq/W5/fv3M27cOLZs2YKZUVFREV43dOhQkpKSAMjIyOCzzz6LGOgA9913H4888giPPfZYjeWXX355+K+Jxx57jHvuuYe5c+dG9W9y5plnMmfOHG644QbatGnDpZdeytatW2u0ee655ygsLGTVqlUAFBcXs2nTJkpKSgD47ne/y5o1azj11FPr3M8dd9zB/fffj5mFg/pPf/pTne2LiorYunUrM2fOjDganj59OtOnT+fRRx9l1qxZPPzww+F1r7/+OoMGDQqHJkBCQgJFRUXs27ePa6+9lg8//LDGHPgLL7zAj3/841r7ibSthlq3bh0JCQns3LmTL774gssvv5xhw4Zx5plnsmjRIj799FPOOOMMrr/+ep577jluvvnmE94XRHlQ1MxyzGyzmRWb2dQI608xs5cq1681s/RGVSXSSJmZmTUOBM6ePZtly5ZRWloKhA5InXPOOXzwwQcUFhaGR6aJiYkcPXo0/LmqA1WJiYmsW7eOMWPG8MYbb5CTkwPA3LlzeeSRR9i+fTv9+vVj7969Neq4//77GTJkCB9++CGvv/56jQNfp5xySvh1QkICR44cqbM/V155JYcOHaKgoKDONqNGjWL16tUApKamsn379vC6kpISUlNTa33mmmuuYe3ateTn53P++edz3nnnhdctXbqU6dOnk5eXF651wYIFDBw4kI4dO9KxY0dGjBhBfn4+qamp4ZA/dn/nnHMOCQkJtGnThttuu41169bV2QeA/Px8CgsLSU9P57LLLuPjjz8OHwiu7gc/+EH4r50qL774Yo0pkurOOOMMhgwZwptvvhletmfPHtatW8dVV11Vq/3xthWt559/npycHNq2bUvnzp0ZNGgQhYWFLF26lO7du5OcnEzbtm257rrrePfddxu1L4gi0M0sAZgNjAAygFwzyzim2QTgC3fvAcwEHkMkjq688krKy8uZM2dOeFlZWVn49f79+0lJSaFNmzb85S9/4euvvwZC8+wbN27kP//5D/v27WPZsmVAaE53//79jBw5kpkzZ/LBBx8AsHXrVgYMGMC0adNITk6uEaJV+6kKtmeffbZRfbrvvvvqnGcHePvtt/nWt74FhMJ9/vz5uDsFBQUkJSXVmG6pUnVWzBdffMFTTz0VHqm+//773H777eTl5dG5c+dw+65du7Jq1SqOHDlCRUUFq1atolevXqSkpNCpUycKCgpwd+bPn8/o0aMB2LVrV/jzCxYsqHWGyLHuuOMOdu7cybZt23j77bc577zzWLlyJQBbtmwJt1u0aBEXXHBB+P3+/ftZtWpVeL8ApaWl4amwQ4cO8dZbb9X4zKuvvsrVV19N+/bta9QQaVsnomvXrixfvhwIzfkXFBRwwQUX0LVrVwoKCigrK8PdWbZsWZ3z8A0RzZRLf6DY3T8BMLMXgdHAxmptRgMPVb5+FZhlZlZ5RFak2ZkZCxcu5Gc/+xmPP/44ycnJdOjQITxl8dOf/pTvf//7zJ8/n5ycHDp06ABAly5dGDt2LL1796Z79+5cdNFFAHz55ZeMHj2a8vJy3J0ZM2YAoQOWW7Zswd0ZOnQoF154YXh6AkKn+I0bN45HHnkk4iiwIUaOHBk+gFmlag7d3UlKSuIPf/hDuO3ixYvp0aMHp512Wo0DfllZWRQVFQFw1113hf9zeuCBB8Ij9LvvvpuvvvqK66+/HggFU15eHmPGjGH58uX06dMHMyMnJ4drrrkGgKeeeorx48dz6NAhRowYEZ53v+eeeygqKsLMSE9P5+mnnw7Xkp6ezoEDBzh8+DALFy7k73//OxkZx44X/9/UqVPZvHkzbdq0oVu3bjWmlxYsWMDw4cPD30sI/Wcybtw4vv76a44ePcrYsWPD02UQGoVPnVpr0iHitgByc3NZuXIle/bsIS0tjYcffpgJEyawYMECpkyZQmlpKVdddRVZWVksWbKESZMmceutt5KZmYm7c+utt9K3b18gNL9+8cUXk5iYyEUXXcTEiRPr7He0rL7MNbMxQI67/7jy/Q+BAe4+uVqbDyvblFS+31rZZs8x25oITATo2rVrv+rntkbr4dc3ACfHvKzUbdOmTTEZcYi0ZpF+j8zsPXfPjtS+WQ+Kuvs8YB5Adnb2CY3eFeQiIpFFc1B0B1D90Hta5bKIbcwsEUgC9iIiIs0mmkBfD/Q0s+5m1g64Ecg7pk0eMK7y9RhguebPRT8CIifuRH5/6g10dz8CTAaWAJuAl919g5lNM7NRlc3+CJxlZsXAz4HaRxmkVWnfvj179+5VqIucAK+8H/qxZ9/Up96Dok0lOzvbj3dlnrRsemKRSOPU9cSik+agqLQebdu2bdCTVkSk8Vrc7XNFRCQyBbqISEAo0EVEAiJuB0XNrBRo+KWiIWcDe+ptFSzqc+ugPrcOjelzN3dPjrQiboHeGGZWWNdR3qBSn1sH9bl1aKo+a8pFRCQgFOgiIgHRUgN9XrwLiAP1uXVQn1uHJulzi5xDFxGR2lrqCF1ERI6hQBcRCYiTOtBb48Opo+jzz81so5n908yWmVm3eNQZS/X1uVq775uZm1mLP8Utmj6b2djK7/UGM3u+uWuMtSh+trua2Qoze7/y53tkPOqMFTP7k5ntrnyiW6T1Zma/r/z3+KeZXdzonbr7SfkFJABbgW8C7YAPgIxj2vwUmFv5+kbgpXjX3Qx9HgKcVvn6jtbQ58p2pwOrgQIgO951N8P3uSfwPnBm5fvO8a67Gfo8D7ij8nUGsC3edTeyz98BLgY+rGP9SOB/AAMGAmsbu8+TeYQefji1ux8Gqh5OXd1o4M+Vr18FhpqZNWONsVZvn919hbtXPb6+gNATpFqyaL7PAP8NPAYE4X680fT5NmC2u38B4O67m7nGWIumzw50qnydBOxsxvpizt1XA/8+TpPRwHwPKQDOMLOUxuzzZA70VGB7tfcllcsitvHQgzj2A2c1S3VNI5o+VzeB0P/wLVm9fa78U7SLu/+tOQtrQtF8n88DzjOzd8yswMxymq26phFNnx8CbjazEmAxMKV5Soubhv6+10v3Q2+hzOxmIBsYHO9ampKZtQFmAOPjXEpzSyQ07XIFob/CVptZH3ffF9eqmlYu8Ky7/9bMLgH+Yma93f1ovAtrKU7mEXprfDh1NH3GzIYB9wKj3P0/zVRbU6mvz6cDvYGVZraN0FxjXgs/MBrN97kEyHP3Cnf/FPiYUMC3VNH0eQLwMoC75wPtCd3EKqii+n1viJM50Fvjw6nr7bOZXQQ8TSjMW/q8KtTTZ3ff7+5nu3u6u6cTOm4wyt1b8vMLo/nZXkhodI6ZnU1oCuaT5iwyxqLp8+fAUAAz60Uo0EubtcrmlQfcUnm2y0Bgv7vvatQW430kuJ6jxCMJjUy2AvdWLptG6BcaQt/wV4BiYB3wzXjX3Ax9Xgr8Cyiq/MqLd81N3edj2q6khZ/lEuX32QhNNW0E/he4Md41N0OfM4B3CJ0BUwQMj3fNjezvC8AuoILQX1wTgJ8AP6n2PZ5d+e/xv7H4udal/yIiAXEyT7mIiEgDKNBFRAJCgS4iEhAKdBGRgFCgi4gEhAJdRCQgFOgiIgHxf5QUtMjJ7bdBAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def F_Importance(importances,test,features,indices,std,title):\n",
        "  plt.figure(figsize=(20, 7))\n",
        "  plt.title(title)\n",
        "  plt.bar(range(test.shape[1]), importances[indices], color=\"b\", yerr=std[indices], align=\"center\")\n",
        "  plt.xticks(range(test.shape[1]), [features[indices[i]] for i in range(11)])\n",
        "  plt.xlim([-1,test.shape[1]])\n",
        "  plt.show()\n",
        "\n",
        "F_Importance(importances_gaus,x_nb_test,x_features,indices_gaus,std_gaus,\"Gaussian NB Feature Importances\")"
      ],
      "metadata": {
        "id": "fSUrtfpHRl8o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba5c3c3a-a359-4687-c7e1-72f9aa234e71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "F_Importance(importances_mnb,x_nb_test,x_features,indices_mnb,std_mnb,\"Multinomial NB Feature Importances\")"
      ],
      "metadata": {
        "id": "rS1a1XuIbYef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "762e2f87-4176-4d11-8392-21ee83847871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "training_time = np.array(training_time) / np.max(training_time)\n",
        "training_time = training_time\n",
        "\n",
        "barWidth = 0.2 # bar width \n",
        "fig = plt.subplots(figsize =(16, 12))\n",
        " \n",
        "br1 = np.arange(len(training_time))\n",
        "br2 = [x + barWidth for x in br1]\n",
        "br3 = [x + barWidth for x in br2]\n",
        "br4 = [x + barWidth for x in br3]\n",
        " \n",
        "# Make the plot\n",
        "plt.bar(br1, training_time, color ='r', width = barWidth,\n",
        "        edgecolor ='grey', label ='Training Time Normalized')\n",
        "plt.bar(br2, accuracy/100, color ='b', width = barWidth,\n",
        "        edgecolor ='grey', label ='Accuracy')\n",
        "plt.bar(br3, prec, color ='g', width = barWidth,\n",
        "        edgecolor ='grey', label ='Precision')\n",
        "plt.bar(br4, recall, color ='yellow', width = barWidth,\n",
        "        edgecolor ='grey', label ='Recall')\n",
        "\n",
        " \n",
        "# Adding Xticks\n",
        "plt.xlabel('Model', fontweight ='bold', fontsize = 15)\n",
        "plt.ylabel('Score', fontweight ='bold', fontsize = 15)\n",
        "plt.xticks([r + barWidth for r in range(len(training_time))],\n",
        "        ['Gaussian NB', 'Multinomial NB', 'Multinomial NB with smoothing', 'Categorical NB', 'Complement NB'])\n",
        " \n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "7MV4fh7a_UGU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d975e323-2ade-4f08-9b80-c6180381aa12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **SVM**"
      ],
      "metadata": {
        "id": "zYU0ZGMmSRF7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import unravel_index\n",
        "\n",
        "x_SVM_train = X_train\n",
        "y_SVM_train = y_train\n",
        "x_SVM_test = X_test\n",
        "y_SVM_test = y_test\n",
        "\n",
        "\n",
        "SVM_classifier = SVC(probability=True)\n",
        "SVM_classifier.fit(x_SVM_train,y_SVM_train)\n",
        "y_default_predict = SVM_classifier.predict(x_SVM_test)\n",
        "accuracy_default_SVM = metrics.accuracy_score(y_SVM_test, y_default_predict)\n",
        "print('accuracy of default SVM: %',100*accuracy_default_SVM)\n",
        "prec_default_SVM = metrics.precision_score(y_SVM_test, y_default_predict)\n",
        "print('precision of default SVM: %',100*prec_default_SVM)\n",
        "recall_default_SVM = metrics.recall_score(y_SVM_test, y_default_predict)\n",
        "print('recall of default SVM: %',100*recall_default_SVM)\n",
        "f1_default_SVM = metrics.f1_score(y_SVM_test, y_default_predict)\n",
        "print('f1 of default SVM: %',100*f1_default_SVM)\n",
        "\n"
      ],
      "metadata": {
        "id": "TvGjBdj3bhlx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60340f50-bd9e-4247-ab06-b02eecbfe7de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy of default SVM: % 69.92753623188406\n",
            "precision of default SVM: % 70.83333333333334\n",
            "recall of default SVM: % 77.77777777777779\n",
            "f1 of default SVM: % 74.14330218068535\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_default_SVM_prob = SVM_classifier.predict_proba(x_SVM_test)[::,1] \n",
        "fpr_default_SVM, tpr_default_SVM, _ = metrics.roc_curve(y_SVM_test,  y_pred_default_SVM_prob)\n",
        "auc_SVM = metrics.roc_auc_score(y_SVM_test, y_pred_default_SVM_prob)\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(fpr_default_SVM,tpr_default_SVM,label=\"SVM default = \"+str(auc_SVM),color='darkorange')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TQ1ClPFgO6pP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0db17997-4b77-4c7e-90e7-6483ef5e60e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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igAK94nJnw7TLDk63PS+4WkREilCgV9T+faGPF0yF47vDsS2CrUdEpJACPVILHoFlL8B320PTxzSH+qnB1iQiUoQCPVIrX4cda6Fl39CraeegKxIROYQCvSKOz4QLpwZdhYhIiXTaoohIgtAeell2rINJp0LeTvhuB7TqF3RFIiKlUqCXZcca2LkW2l0A9VtB2yFBVyQiUioFeiSyboDWPwy6ChGRMinQi8rfe/A8cwhdDSoiEicU6Afs2gB/bgf5ew5fllQr9vWIiFSQAv2A3ZtCYd55ODTtenB+7WRo0TuwskREIqVAL67tEGh/QdBViIhUWETnoZvZQDNbbmYrzezWEpbfaGZLzey/ZvaOmbWOfqkiIlKWcgPdzJKAJ4FzgE5Atpl1KtbsQ6Cnu3cDJgMPRrtQEREpWyRDLqcAK939cwAzmwQMAZYeaODuM4u0nwtcHs0iq9Si/4X5D0JB4dktZsHWIyJyhCIJ9BRgXZHpXKBXGe2vBv5Z0gIzGwmMBEhLS4uwxCq2/j+wbwd0GAo1j4GU04KuSETkiET1oKiZXQ70BM4oabm7TwQmAvTs2dOjue1KSW4JA/4cdBUiIpUSSaCvB1oVmU4tnHcIMzsLuAM4w92/i055IiISqUjOcpkPtDezDDOrDVwKHHIPWTPrDvwfMNjdN0W/TBERKU+5e+junm9mNwDTgSTgaXdfYmb3AAvcfSrwEJAMvGKhg4pr3X1wFdZdOfvz4K2rQxcTbVoMdRsFXZGISKVFNIbu7tOAacXm3VXk87OiXFfV2rkWlj4PDdtAw3TIGBR0RSIilVa9rxTtOx46/SToKkREokJPLBIRSRDVaw992Qsw938OvUWuiEiCSPxAd4dFj8POXFgzPfQUojaDIeVUaHVm0NWJiERN4gf6nq0w60ZIqg01akP6QDj/paCrEhGJusQPdApCH/o9Blk/D7YUEZEqpIOiIiIJInH30DfkwMJHS36knIhIAkrcPfTlL8Nnr8L2NXB8dzjhlKArEhGpUom7hw5Quz4M/yToKkREYiJx99BFRKoZBbqISIJQoIuIJIjEG0PfsRa+3Rh6iYhUI4kV6Pvz4C/fO3iq4rEtgq1HRCSGEiPQV7wGM66DgvxQmHe5KvTQ54Ztgq5MRCRm4jvQ5z8EWz6BTR+Gnj6UdT3UqAk9fgENM4KuTkQkpuI70P9zO9Q8Buo2hjbnwg+fCLoiEZHAxHegA3S/AU79TdBViIgETqctiogkCAW6iEiCiP8hF4mZvLw8cnNz2bt3b9CliCS8unXrkpqaSq1atSJ+T/wFujts+yx0iqJ70NVUK7m5udSvX5/09HTMLOhyRBKWu7N161Zyc3PJyIj8jL34C/RPnoa3rjk4XbNecLVUM3v37lWYi8SAmdGkSRM2b95coffFX6Dv/Tr08ZznQqcstu4fbD3VjMJcJDaO5Hct/gL9gPY/glrHBl2FiMhRQ2e5SNz4zW9+Q+fOnenWrRtZWVnMmzePu+++m9tuu+2QdosXL6Zjx44ApKenc9pppx2yPCsriy5dupS7veHDhzN58uQy23z66adkZWXRvXt3Vq1aVcEewfjx43n44YcBeOaZZ9iwYUOF11GSZ599lvbt29O+fXueffbZEtsMGzaMrKwssrKySE9PJysr65Dla9euJTk5OVwfwFVXXcXxxx9f6tfvkUcewczYsmULALNmzaJhw4bh7dxzzz3hto899hidO3emS5cuZGdnhw+2P/HEE7Rr1+6Q9UBoXHn06NG0a9eObt26sWjRonL7++KLL9K1a1e6devGwIEDw+sbP348KSkp4bqmTZsGwJo1a6hXr154/rXXXhteV79+/TjxxBPDyzZt2gTAu+++S48ePahZs+ZhPy8333wznTt3pmPHjowePRovdtxv8ODBEf0sRszdA3mddNJJfkQ+eND9Ydz37Tqy98sRW7p0aWDbfv/99713796+d+9ed3ffvHmzr1+/3pcvX+4ZGRmHtL3lllv87rvvdnf31q1be2Zmpq9du9bdQ33IzMz0zp07l7vNK6+80l955ZUy29x3331+7733HkmX3N193Lhx/tBDD7m7+xlnnOHz588/4nUdsHXrVs/IyPCtW7f6119/7RkZGf7111+X+Z4bb7wx/DU74KKLLvKhQ4eG63N3nz17ti9cuLDEr9/atWv97LPP9rS0NN+8ebO7u8+cOdPPPffcw9rm5uZ6enq67969293dL774Yv/LX/7i7u6LFi3y1atXe+vWrcPrcXd/8803feDAgV5QUOA5OTl+yimnlNnfvLw8b9asWXgdY8eO9XHjxrn7oV/3olavXl3qz0Zp35/Vq1f7Rx995D/5yU8O+Xl57733vG/fvp6fn+/5+fneu3dvnzlzZnj5q6++6tnZ2WX+LJb0Owcs8FJyNX6HXCRYM38JmxZHd53HZ8GZj5e4aOPGjTRt2pQ6deoA0LRp0/CyRo0aMW/ePHr16gXAyy+/zPTp08PLL7nkEl566SVuuukmXnzxRbKzs3n++ecP24a7M2rUKN5++21atWpF7dq1w8sWLlzIjTfeyK5du2jatCnPPPMMH374IY8//jhJSUm88847zJw5kwsuuIB169axd+9efvGLXzBy5EgAkpOT2bVrFwCTJ0/mH//4B88880x4/ZMnT2bBggX8+Mc/pl69euTk5FCv3pEd8J8+fTr9+/encePGAPTv359//etfZGdnl9je3Xn55Zf597//HZ73+uuvk5GRwbHHHjqsefrpp7NmzZoS1zNmzBgefPBBhgwZElGd+fn57Nmzh1q1arF7925atmwJQPfu3Uts/8Ybb3DFFVdgZvTu3ZtvvvmGjRs3MmvWrBL7O3ToUNydb7/9liZNmrBjxw7atWsXUW0VkZ6eDkCNGocOeJgZe/fuZd++fbg7eXl5NG/eHIBdu3bx6KOPMnHiRC655JKo1aIhF4kLZ599NuvWraNDhw78/Oc/Z/bs2eFl2dnZTJo0CYC5c+fSuHFj2rdvH15+0UUX8dprrwHw97//nfPPP7/EbUyZMoXly5ezdOlSnnvuOd5//30gdP79qFGjmDx5MgsXLuSqq67ijjvuYNCgQVx77bWMGTOGmTNnAvD000+zcOFCFixYwIQJE9i6dWtE/Rs6dCg9e/bkhRdeYPHixYeF+UMPPRT+V7/oa/To0Yeta/369bRq1So8nZqayvr160vd9pw5c2jevHn4a7Zr1y4eeOABxo0bF1HtEArblJQUMjMzD1uWk5NDZmYm55xzDkuWLAEgJSWFm266ibS0NFq0aEHDhg05++yzy9xGaf0qbX6tWrV46qmn6Nq1Ky1btmTp0qVcffXV4XZPPPEE3bp146qrrmLbtm3h+atXr6Z79+6cccYZzJkz55AafvrTn5KVlcW999572PBJcX369OHMM8+kRYsWtGjRggEDBoSHAu+8805+9atfccwxx5S5jorSHrocmVL2pKtKcnIyCxcuZM6cOcycOZNhw4Zx//33M3z4cIYNG0bfvn155JFHmDRp0mF7ok2aNKFRo0ZMmjSJjh07lvpL9O6775KdnU1SUhItW7bkBz/4AQDLly/nk08+oX//0BlV+/fvp0WLku+1P2HCBKZMmQLAunXrWLFiBU2aNKl0/8eOHcvYsWMrvZ6SHPiv5YDx48czZswYkpOTI3r/7t27+e1vf8tbb7112LIePXrwxRdfkJyczLRp07jgggtYsWIF27Zt44033mD16tUcd9xxXHzxxfz1r3/l8ssvj1q/8vLyeOqpp/jwww9p06YNo0aN4r777uPXv/411113HXfeeSdmFg7Xp59+mhYtWrB27VqaNGnCwoULueCCC1iyZAkNGjTghRdeICUlhZ07d3LRRRfx/PPPc8UVV5S6/ZUrV7Js2TJyc3OB0H8Oc+bMoX79+qxatYrHHnus1P92jlREe+hmNtDMlpvZSjO7tYTldczspcLl88wsPapVigBJSUn069ePu+++myeeeIJXX30VgFatWpGRkcHs2bN59dVXGTZs2GHvHTZsGNdff32pww5lcXc6d+7M4sWLWbx4MR9//HGJ4TVr1ixmzJhBTk4OH330Ed27dw8f6Ct6CtqRXGlbkT30lJQU1q1bF57Ozc0lJSWlxPXm5+fz2muvHfI1mzdvHjfffDPp6ek8/vjj/Pa3v+WJJ0q/k+mqVatYvXo1mZmZpKenk5ubS48ePfjyyy9p0KBB+A/DoEGDyMvLY8uWLcyYMYOMjAyaNWtGrVq1+NGPfhT+j6g0pfWrtPmLF4eGBNu2bYuZcckll4S30bx5c5KSkqhRowYjRozggw8+AKBOnTrhP8AnnXQSbdu25bPPPgtvH6B+/fpcdtll4feUZsqUKfTu3Zvk5GSSk5M555xzyMnJIScnhwULFpCens6pp57KZ599Rr9+/cpcV6TKDXQzSwKeBM4BOgHZZtapWLOrgW3u3g54DHggKtWJFFq+fDkrVqwITy9evJjWrVuHp7OzsxkzZgxt2rQhNTX1sPdfeOGF3HzzzQwYMKDUbZx++um89NJL7N+/n40bN4aHUU488UQ2b95MTk4OENrzOzB0UNT27dtp1KgRxxxzDJ9++ilz584NL2vevDnLli2joKAgvAdfXP369dm5c2eJy8aOHRv+g1L0NWHChMPaDhgwgLfeeott27axbds23nrrrVL7PWPGDL73ve8d8jWbM2cOa9asYc2aNfzyl7/k9ttv54YbbijlqwZdu3Zl06ZN4fekpqayaNEiTjjhBL788svw0MQHH3xAQUEBTZo0IS0tjblz57J7927cnXfeeSc8HFGawYMH89xzz+HuzJ07l4YNG4aHMkrqb0pKCkuXLg1fnPP222+Ht7Fx48FHVE6ZMiV8psnmzZvZv38/AJ9//jkrVqygTZs25Ofnh8+QycvL4x//+Ee5Z6ekpaUxe/Zs8vPzycvLY/bs2XTs2JHrrruODRs2sGbNGv7zn//QoUMHZs2aVea6IhXJHvopwEp3/9zd9wGTgOJHPYYAB84Vmgz80HQFikTRrl27uPLKK+nUqRPdunVj6dKljB8/Prz84osvZsmSJaXugdevX59bbrnlkAOdxV144YW0b9+eTp06ccUVV9CnTx8AateuzeTJk7nlllvIzMwkKyurxL3JgQMHkp+fT8eOHbn11lvp3bt3eNn999/PeeedR9++fUsdrhk+fDjXXnstWVlZ7NmzJ5IvS4kaN27MnXfeycknn8zJJ5/MXXfdFT5geM0117BgwYJw25KGqMqSnZ1Nnz59WL58Oampqfz5z38us/3kyZPp0qULmZmZjB49mkmTJmFm9OrVi6FDh9KjRw+6du1KQUFB+ADyhAkTSE1NJTc3l27dunHNNaErwwcNGkSbNm1o164dI0aM4Pe//32Z/W3ZsiXjxo3j9NNPp1u3bixevJjbb78dCJ1OeOB0xpkzZ/LYY48BoWG3A6fFDh06lD/84Q80btyY7777jgEDBoSXpaSkMGLECADmz59Pamoqr7zyCj/72c/o3LkzEDou0rZtW7p27UpmZiaZmZmlHr+JFitvYN/MhgID3f2awumfAL3c/YYibT4pbJNbOL2qsM2WYusaCYwESEtLO+mLL76oeMUr34Blf4VznoeadSv+fjliy5YtK3cvSkSip6TfOTNb6O49S2of07Nc3H2iu/d0957NmjU7spW0GwLnv6IwFxEpJpJAXw+0KjKdWjivxDZmVhNoCER2vpaIiERFJIE+H2hvZhlmVhu4FJharM1U4MrCz4cC//byxnIkLunbKhIbR/K7Vm6gu3s+cAMwHVgGvOzuS8zsHjMbXNjsz0ATM1sJ3AgcdmqjxL+6deuydetWhbpIFfPC+6HXrVuxoeVyD4pWlZ49e3rRo+1y9NMTi0Rip7QnFpV1UFRXikrEatWqVaGnp4hIbOleLiIiCUKBLiKSIBToIiIJIrCDoma2GTiCS0UBaApsKbdVYlGfqwf1uXqoTJ9bu3uJV2YGFuiVYWYLSjvKm6jU5+pBfa4eqqrPGnIREUkQCnQRkQQRr4E+MegCAqA+Vw/qc/VQJX2OyzF0ERE5XLzuoYuISDEKdBGRBHFUB3p1fDh1BH2+0Y1razYAAAMDSURBVMyWmtl/zewdM2td0nriSXl9LtLuIjNzM4v7U9wi6bOZXVL4vV5iZn+LdY3RFsHPdpqZzTSzDwt/vgcFUWe0mNnTZrap8IluJS03M5tQ+PX4r5n1qPRG3f2ofAFJwCqgDVAb+AjoVKzNz4E/FH5+KfBS0HXHoM9nAscUfn5ddehzYbv6wLvAXKBn0HXH4PvcHvgQaFQ4fXzQdcegzxOB6wo/7wSsCbruSvb5dKAH8EkpywcB/wQM6A3Mq+w2j+Y99Or4cOpy++zuM919d+HkXEJPkIpnkXyfAe4FHgAS4d69kfR5BPCku28DcPdNMa4x2iLpswMNCj9vCGyIYX1R5+7vAl+X0WQI8JyHzAWOM7OSnyAeoaM50FOAdUWmcwvnldjGQw/i2A40iUl1VSOSPhd1NaG/8PGs3D4X/ivayt3fjGVhVSiS73MHoIOZvWdmc81sYMyqqxqR9Hk8cLmZ5QLTgFGxKS0wFf19L5fuhx6nzOxyoCdwRtC1VCUzqwE8CgwPuJRYq0lo2KUfof/C3jWzru7+TaBVVa1s4Bl3f8TM+gDPm1kXdy8IurB4cTTvoVfHh1NH0mfM7CzgDmCwu38Xo9qqSnl9rg90AWaZ2RpCY41T4/zAaCTf51xgqrvnuftq4DNCAR+vIunz1cDLAO6eA9QldBOrRBXR73tFHM2BXh0fTl1un82sO/B/hMI83sdVoZw+u/t2d2/q7ununk7ouMFgd4/n5xdG8rP9OqG9c8ysKaEhmM9jWWSURdLntcAPAcysI6FA3xzTKmNrKnBF4dkuvYHt7r6xUmsM+khwOUeJBxHaM1kF3FE47x5Cv9AQ+oa/AqwEPgDaBF1zDPo8A/gKWFz4mhp0zVXd52JtZxHnZ7lE+H02QkNNS4GPgUuDrjkGfe4EvEfoDJjFwNlB11zJ/r4IbATyCP3HdTVwLXBtke/xk4Vfj4+j8XOtS/9FRBLE0TzkIiIiFaBAFxFJEAp0EZEEoUAXEUkQCnQRkQShQBcRSRAKdBGRBPH/UeSpuEk4FFAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_default_SVM = confusion_matrix(y_SVM_test,y_default_predict)\n",
        "class_names=[0,1]\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "\n",
        "sns.heatmap(pd.DataFrame(cm_default_SVM), annot=True, cmap=\"Oranges\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix SVM', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "pX0eyQOObhFg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80fbbe02-e715-42ba-edd9-09071a088b72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parameters ={\"C\": [1,2,3,4,5,6,7], 'kernel':['rbf', 'linear','poly','sigmoid']} \n",
        "SVM_classifier = SVC(probability=True)\n",
        "SVM_cv = GridSearchCV(SVM_classifier,parameters,cv=3, verbose = True) \n",
        "SVM_cv.fit(x_SVM_train,y_SVM_train)\n",
        "print(\"Best Hyperparameters:\",SVM_cv.best_params_)\n",
        "print(\"The accuracy is:\",SVM_cv.best_score_)\n",
        "print('')\n",
        "SVM_cv = SVM_cv.best_estimator_\n",
        "SVM_cv = SVM_cv.fit(x_SVM_train, y_SVM_train)\n",
        "y_SVM_predict = SVM_cv.predict(X_test)"
      ],
      "metadata": {
        "id": "lQ6XBHC2PpOZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3b704f00-21db-4b71-fc6f-9022cbd11a88"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 28 candidates, totalling 84 fits\n",
            "Best Hyperparameters: {'C': 6, 'kernel': 'linear'}\n",
            "The accuracy is: 0.8457943925233645\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(\"Best Hyperparameters:\",SVM_cv.best_params_)\n",
        "# print(\"The accuracy is:\",SVM_cv.best_score_)\n",
        "# print('')\n",
        "# SVM_cv = SVM_cv.best_estimator_\n",
        "# SVM_cv = SVM_cv.fit(x_SVM_train, y_SVM_train)\n",
        "# y_SVM_predict = SVM_cv.predict(X_test)"
      ],
      "metadata": {
        "id": "MMSkjSCxPpEY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_SVM = metrics.accuracy_score(y_SVM_test, y_SVM_predict)\n",
        "print('accuracy of  SVM: %',100*accuracy_SVM)\n",
        "prec_SVM = metrics.precision_score(y_SVM_test, y_SVM_predict)\n",
        "print('precision of  SVM: %',100*prec_SVM)\n",
        "recall_SVM = metrics.recall_score(y_SVM_test, y_SVM_predict)\n",
        "print('recall of  SVM: %',100*recall_SVM)\n",
        "f1_SVM = metrics.f1_score(y_SVM_test, y_SVM_predict)\n",
        "print('f1 of  SVM: %',100*f1_SVM)\n",
        "\n",
        "y_pred_SVM_prob = SVM_cv.predict_proba(x_SVM_test)[::,1] \n",
        "fpr_SVM, tpr_SVM, _ = metrics.roc_curve(y_SVM_test,  y_pred_SVM_prob)\n",
        "auc_SVM = metrics.roc_auc_score(y_SVM_test, y_pred_SVM_prob)\n",
        "\n",
        "\n",
        "\n",
        "plt.plot(fpr_SVM,tpr_SVM,label=\"SVM = \"+str(auc_SVM),color='darkorange')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "0wUi9ImXUlf-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8ce643c9-79d5-4e36-bf6c-e94d7de9bc9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy of  SVM: % 86.23188405797102\n",
            "precision of  SVM: % 89.1156462585034\n",
            "recall of  SVM: % 85.62091503267973\n",
            "f1 of  SVM: % 87.33333333333333\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_SVM = confusion_matrix(y_SVM_test,y_SVM_predict)\n",
        "class_names=[0,1]\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "\n",
        "sns.heatmap(pd.DataFrame(cm_SVM), annot=True, cmap=\"Oranges\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix SVM', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "VogBbF5DZTnV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38ea3379-f2cf-49be-fddc-54aceaaf231a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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axKJFi+ntLroOpwr3t0m6EtgPmBcRw9JtvYEbgEHA68AhEbFIycUvAvYB3gOOjojH27pGte4RrQQ2bmZ7/3SfVdiih+6k755HAdB3z6NY9NAdq/Y1du9Bj613YdFDt2dVnuXcHiO+yMOPJr9vXpv5d5YvX0GvXj0zrmrtVIV7RFcDezXZdiYwNSIGA1PTdYC9gcHpMha4tD0XqFaL6BRgqqSXgDfSbZsCmwMnVumaa41Pf/86emyzK5026Mu2k2cya+K5zJ78Mzb/4WQ22vvYZPj2jw5bdXyvnQ9iyWP3sPIf72VYteXFaWeewyOPPcGixYvZ5csHctI3x/DVA/fje+N/yn4Hf53OnTtz/nk/qPi/zK2dKvznHhEPSBrUZPMoYET6eSJwPzAu3X5NJP2y0yX1lNQ/Ima3dg1Vqx9XUgOwA6sPVng0Ij5qz/fdNWcdbcc752Zdgq2NuvWtaHK8sn+nkn53fvrOFW1ePw2iKUVdc4sjomf6WcCiiOgpaQpwfkRMS/dNBcZFxIzWzl+1UXMRsRKYXq3zm5nZPyu1JSppLEk3WsGE9H59u0RESGs206pnVjAzy5FSg6h4kFgJ5ha63CT1Bwqjo94ENik6bmC6rVV+oNXMLE86ZmqFO4DR6efRwO1F249SYjiwpK37Q+AWkZlZrlRh+PYkkoEJfSXNAs4heQTnRkljgJnAIenhfyQZuv0yyfDtY9pzDQeRmVmOVHqwYkQc3sKukc0cG0DJs906iMzMcqQeh807iMzM8qT+cshBZGaWJ24RmZlZpuowhxxEZmZ54haRmZlly0FkZmZZqsMcchCZmeWJu+bMzCxTdZhDDiIzs1ypwyRyEJmZ5Ugd5pCDyMwsT3yPyMzMMuUgMjOzTNVhDjmIzMxypQ6TyEFkZpYjdZhDDiIzszzxPSIzM8tUHeaQg8jMLFfqMIkcRGZmOdLQ4CAyM7MM+R6RmZllq/5yyEFkZpYnamjIuoSSOYjMzPLEXXNmZpapPAWRpKVAFFbT/0b6OSKiR5VrMzOzEkk56pqLiPU7shAzM6uAOmwRtSs6Je0s6Zj0c19Jm1W3LDMzK4tU2lID2rxHJOkcYHtgS+AqoAtwLfCF6pZmZmalyutzRAcB2wKPA0TEW5LcbWdmVovydI+oyIcREZICQFL3KtdkZmZlUh1O8dOe6LxR0u+AnpK+AfwvcFl1yzIzs7Lk8R5RRPxC0peAd4AtgLMj4p6qV2ZmZqXLadccwNPAuiTPET1dvXLMzGxN1ONghTajU9JxwCPAV4CDgemSjq12YWZmVoY8ds0B3wW2jYgFAJL6AA8BV1azMDMzK0MVwkXSqcBxfNwrdgzQH5gM9AEeA46MiA/LOX97OhMXAEuL1pem28zMrMZIDSUtbZ9PA4CTge0jYhjQCBwG/Ay4ICI2BxYBY8qtubW55k5LP74MPCzpdpI0HAU8Ve4FzcysiqrT3dYJWFfScqAbMBvYHTgi3T8RGA9cWu7JW1J4aPWVdCm4vZwLmZlZ9ZX6HJGkscDYok0TImJCYSUi3pT0C+DvwPvA3SRdcYsjYkV62CxgQLk1tzbp6bnlntTMzDJS4vDtNHQmtLRfUi+SnrDNgMXATcBea1DhP2nPXHMbAmcAWwHrFLZHxO6VLMTMzCqg8l1zewCvRcTbyel1K8lcoz0ldUpbRQOBN8u9QHui8zrgBZI0PBd4HXi03AuamVn1SCppaYe/A8MldVPyhZHAc8B9JI/0AIxmDW7btCeI+kTEFcDyiPi/iDiW5CaVmZnVmgo/RxQRDwM3k0x8/TRJbkwAxgGnSXqZZAj3FeWW3J7niJan/50taV/gLaB3uRc0M7MqqsIUPxFxDnBOk82vAjtU4vztCaIfS9oAOB24GOgBnFqJi5uZWWXV4xQ/7Zn0dEr6cQmwW3XLMTOzNVKHr4Fo7YHWi0keYG1WRJxclYrMzKxs7Zktoda01iKa0WFVmJlZZeSpay4iJnZkIWZmVgF5CiIzM6s/uRysYGZmdSRn94jMzKze5KlFlPWouR1veaXtg8wqaPx2/bMuwdZC419Y3vZBJchb15xHzZmZ1ZuGHHXNedScmVkdylmLCFj1GohxwFD8Gggzs9pWh4MV2vsaiOfxayDMzGpfQ2NpSw3wayDMzPKkwq+B6Ah+DYSZWZ7UYdecXwNhZpYnNdLKKYVfA2Fmlid5bBFJuopmHmxN7xWZmVktyWOLCJhS9Hkd4CCS+0RmZlZr8hhEEXFL8bqkScC0qlVkZmbly2PXXDMGAxtVuhAzM6uAPLaIJC1l9XtEc0hmWjAzs1qTxxZRRKzfEYWYmVkF1GGLqM3olDS1PdvMzKwGqKG0pQa09j6idYBuQF9JvYBCzPYABnRAbWZmVqo6bBG11jV3PHAKsDHwGB8H0TvAr6tcl5mZlaNGWjmlaO19RBcBF0k6KSIu7sCazMysXHXYImpPdK6U1LOwIqmXpH+vYk1mZlauOrxH1J4qvhERiwsrEbEI+Eb1SjIzs7Ll9DUQjZIUEQEgqRHoUt2yzMysLDXSyilFe4LoT8ANkn6Xrh+fbjMzs1pTI62cUrQniMYBY4Fvpev3AJdVrSIzMytfHbaI2qw4IlZGxG8j4uCIOBh4juQFeWZmVmtyeo8ISdsChwOHAK8Bt1azKDMzK1Mdtoham1lhC5LwORyYD9wAKCL8llYzs1pVhVZO+gjP5cAwkkmwjwVeJMmFQcDrwCHpqOqStRadLwC7A/tFxM7pQ60flXMRMzPrINV5jugi4E8RMQTYBngeOBOYGhGDganpellaq+IrwGzgPkmXSRrJx9P8mJlZLWpQaUsbJG0A7AJcARARH6bPlo4CJqaHTQQOLLvklnZExG0RcRgwBLiPZN65jSRdKmnPci9oZmZVVOJgBUljJc0oWsY2OeNmwNvAVZKekHS5pO5Av4iYnR4zB+hXbsntGTW3LCKuj4j9gYHAE/jFeGZmtanErrmImBAR2xctE5qcsROwHXBpRGwLLKNJN1w64UFQppKGV0TEorTokeVe0MzMqqjyw7dnAbMi4uF0/WaSYJorqX9ySfUH5pVbcv2N8zMzs5ZVeLBCRMwB3pC0ZbppJMnzpHcAo9Nto4Hbyy25Xc8RmZlZnajOc0QnAddJ6gK8ChxD0pC5UdIYYCbJc6ZlcRCZmeVJFYIoIp4Etm9mV0Vu0ziIzMzypEam7SmFg8jMLE/yNMWPmZnVIQeRmZllyl1zZmaWKbeIzMwsUw4iMzPLlIPIzMwy1dCYdQUlcxCZmeWJW0RmZpapBgeRmZllycO3zcwsU+6aMzOzTDmIzMwsU+6aMzOzTLlFZGZmmXIQmZlZphxEZmaWKd8jMjOzTLlFZGZmmXIQmZlZptw1Z2ZmmXKLyMzMMuUgso40e+48zhj/cxYsXIwEhxy4D6MPO4if/ddl3DdtOp07d2bTAf35jx+eTo/118u6XKtjo35yGVuM2IdlC+bxmwO2BWC3k8czZOQBxMqVLFs4j9vOGsPSebPpu9mWjPqPy+k/dFvuvfCHPHTlBRlXv5apwyBSRGRdQ/MWv16jhdWOefMX8Pb8hWw1ZDDvLnuPr44+kUv+8xzmzJvP8O0/S6dOjfz815cD8N0Tj8u42to3fvjgrEuoWZ/cfmc+fG8ZB51/5aog6tp9fT5YthSAHY88kQ0//RmmjD+B7r03ZIONP8mQPQ7gH+8schC1YfwLyyt6U2fl41eU9LuzYbsxmd9Uqr/otFU26tuHrYYkvzzX696NTw3ahLlvz2fn4Z+jU6fkLY2fHfYZ5sybn2WZlgMzZ0zj/SULV9tWCCGAzut2o/CP2mUL3+atZ2awcsXyDq3RUmoobakB7prLiVlvzeH5v73CNlsNWW37LXfexd577JpRVZZ3u59yHtuM+jofLF3C1aO/lHU5BjUTLqXo8IolHdPKvrGSZkiaMeHq6zuyrLq27L33OfnMH/G9U7/Jeut1X7X90quup7GxkQP22j3D6izP7r3wbC7Y7VM8NWUSO3z937Mux6AuW0RZVHFuSzsiYkJEbB8R2489+oiOrKluLV+xgpPP/BH777U7e+6286rtt065m/unPcIvzhuH6vC5AqsvT985iaFfOijrMgyS54hKWWpAVbrmJD3V0i6gXzWuuTaKCL7/41/xqUGbcMwRX121/YG/PMrlv7+Ja3/7c9ZdZ50MK7Q86/3JzVk482UAthx5APNfezHjiixRG+FSiqqMmpM0F/gysKjpLuChiNi4zZN41FybZjz5DF87/nS22HwzGtJ/2Zz2rWP48a9+w4cfLqfnBj0A2GbYEM4789tZlloXPGquZV/95e8Z9Pld6darL8sWzOW+i89j8K570XfQFkQEi9+ayZRzTmDpvLdYr28/xt48na7r9SBWruTD997lkn23Xm1wg32s4qPmnp5U2qi5fzk88+SqVhBdAVwVEdOa2Xd9RLTd7+Ygsg7mILIsVDyInplcWhANOyzzIKpK11xEjGlln2/+mJlVTW0MQCiFh2+bmeVJjQxAKIWDyMwsT+owiOqvDWdmZq1oKHFpm6RGSU9ImpKubybpYUkvS7pBUpc1rdjMzPKiOs8RfRt4vmj9Z8AFEbE5yejoFscFtIeDyMwsTyocRJIGAvsCl6frAnYHbk4PmQgcuCYlO4jMzHKltK654qnV0mVskxNeCJwBrEzX+wCLI2JFuj4LGLAmFXuwgplZnpQ4WCEiJgATmj+V9gPmRcRjkkaseXHNcxCZmeVJZScy/QJwgKR9gHWAHsBFQE9JndJW0UDgzTW5iLvmzMxyRSUuLYuIsyJiYEQMAg4D7o2IrwH3AQenh40Gbl+Tih1EZmZ50jGzb48DTpP0Msk9oyvWpGR3zZmZ5UmV3jEUEfcD96efXwV2qNS5HURmZjlSj+8fcxCZmeWJGrOuoGQOIjOzPHGLyMzMslV/Y9AcRGZmeeIWkZmZZcpBZGZm2XLXnJmZZcktIjMzy1SVHmitJgeRmVmuuEVkZmZZctecmZllyl1zZmaWKbeIzMwsWw4iMzPLkrvmzMwsW24RmZlZlnyPyMzMsuUgMjOzLNVhi6j+7mqZmVmuuEVkZpYnddgichCZmeWKg8jMzLLkFpGZmWXLQWRmZllyi8jMzLLlIDIzsyy5RWRmZtlyEJmZWZbcIjIzs2zVXxB5ih8zM8uUW0RmZjkid82ZmVm2HERmZpalOmwR+R6RmVmuqMSljbNJm0i6T9Jzkp6V9O10e29J90h6Kf1vr3IrdhCZmeWJVNrSthXA6RExFBgOnCBpKHAmMDUiBgNT0/WyOIjMzHKlsi2iiJgdEY+nn5cCzwMDgFHAxPSwicCB5VbsIDIzy5MSW0SSxkqaUbSMbfnUGgRsCzwM9IuI2emuOUC/ckv2YAUzs1wpbbBCREwAJrR5Vmk94BbglIh4p3iYeESEpCix0FUcRGZmeaLKd3RJ6kwSQtdFxK3p5rmS+kfEbEn9gXnlnt9dc2ZmeVLZW0QoafpcATwfEb8q2nUHMDr9PBq4vdyS3SIyM8uVij9H9AXgSOBpSU+m274HnA/cKGkMMBM4pNwLOIjMzPKkwg+0RsQ0Wk63kZW4hoPIzCxX6m9mBQeRmVme1OEUPw4iM7Ncqb8gUkTZQ7+tRkkamz4bYNYh/HfO1oSHb+dTi09Gm1WJ/85Z2RxEZmaWKQeRmZllykGUT+6rt47mv3NWNg9WMDOzTLlFZGZmmXIQmZlZphxEOSJpL0kvSnpZUtmv7TVrL0lXSpon6Zmsa7H65SDKCUmNwCXA3sBQ4PD0vfJm1XQ1sFfWRVh9cxDlxw7AyxHxakR8CEwmeae8WdVExAPAwqzrsPrmIMqPAcAbReuz0m1mZjXNQWRmZplyEOXHm8AmResD021mZjXNQZQfjwKDJW0mqQtwGMk75c3MapqDKCciYgVwInAX8DxwY0Q8m21VlneSJgF/AbaUNEvSmKxrsvrjKX7MzCxTbhGZmVmmHERmZpYpB5GZmWXKQWRmZplyEJmZWaYcRGZmlikHkZmZZer/AeN9IiWAtugEAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AlgorithmsScores[\"SVM\"] = [format(f1_SVM, '.3f'), \"{:.3f}\".format(accuracy_SVM), \"{:.3f}\".format(recall_SVM), \"{:.3f}\".format(prec_SVM), \"{:.3f}\".format(auc_SVM)]\n",
        "AlgorithmsScores"
      ],
      "metadata": {
        "id": "3POMBJCVMsF5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "245b97b8-dbd8-4fe4-dfa6-709db0637187"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Gaussian NB': ['0.877', '0.877', '0.856', '0.916', '0.928'],\n",
              " 'LR': ['0.859', '0.859', '0.843', '0.896', '0.926'],\n",
              " 'SVM': ['0.873', '0.862', '0.856', '0.891', '0.921']}"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Random Forest**"
      ],
      "metadata": {
        "id": "zVJDBud1Y53N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Parameters are:\n",
        "rf=RandomForestClassifier()\n",
        "\n",
        "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
        "rf.fit(X_train,y_train)\n",
        "y_pred=rf.predict(X_test)\n",
        "\n",
        "# Model Accuracy, how often is the classifier correct?\n",
        "print(\"Accuracy:\",metrics.accuracy_score(y_test, y_pred))"
      ],
      "metadata": {
        "id": "6BRQfDGlZBy5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb96fe9d-3ece-4f6e-a18f-9dcbba8b1f8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.8840579710144928\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"Greys\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Random Forest', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "id": "ipzzg8z6Z4ZH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 698
        },
        "outputId": "134cbce1-c9e3-40fe-da25-5b0d5519826e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.92      0.88       123\n",
            "           1       0.93      0.86      0.89       153\n",
            "\n",
            "    accuracy                           0.88       276\n",
            "   macro avg       0.88      0.89      0.88       276\n",
            "weighted avg       0.89      0.88      0.88       276\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 427.9555555555555, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC curve and ROC area for each class\n",
        "y_pred_prob_rf = rf.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_prob_rf)\n",
        "rf_auc = metrics.roc_auc_score(y_test, y_pred_prob_rf)\n",
        "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(rf_auc), color = 'black')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "EDe8uumfaGDx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "42d9cfb6-c019-4150-bc90-6dd0d8e68eda"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#HyperParameter Tuning: \n",
        "\n",
        "parameters ={\"max_depth\": [2,6,8,10,None], \"n_estimators\":[20, 50, 70, 100, 150, 200],'criterion':['gini','entropy']} \n",
        "rf = RandomForestClassifier()\n",
        "rf_cv = GridSearchCV(rf,parameters,cv=5, verbose = True) \n",
        "rf_cv.fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "oIx38hRha4V8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f78b02d-32da-42e3-9e82-90ed6c06d308"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [2, 6, 8, 10, None],\n",
              "                         'n_estimators': [20, 50, 70, 100, 150, 200]},\n",
              "             verbose=True)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Best Hyperparameters:\",rf_cv.best_params_)\n",
        "print(\"The accuracy is:\",rf_cv.best_score_)\n",
        "print('')\n",
        "rf_cv = rf_cv.best_estimator_\n",
        "rf_cv = rf_cv.fit(X_train, y_train)\n",
        "y_pred = rf_cv.predict(X_test)\n",
        "\n",
        "rf_f1 = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "rf_accuracy = rf_cv.score(X_test,y_test)\n",
        "rf_recall = recall_score(y_test,y_pred,average=\"binary\")\n",
        "rf_precision = precision_score(y_test,y_pred,average=\"binary\")\n",
        "\n",
        "print(\"F1 score:  {:.3f}\".format(rf_f1))\n",
        "print(\"Accuracy: {:.3f}\".format(rf_accuracy))\n",
        "print(\"Recall:  {:.3f}\".format(rf_recall))\n",
        "print(\"Precision: {:.3f}\".format(rf_precision))\n",
        "print(classification_report(y_test,y_pred))\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix\n",
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"Greys\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Random Forest', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label') \n",
        "\n",
        "#Older CM:\n",
        "\n",
        "#75-7\n",
        "#13-89"
      ],
      "metadata": {
        "id": "TMS6vA1TgTce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 820
        },
        "outputId": "29e9f729-4fd5-40b2-ae3c-8e558f301b7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Hyperparameters: {'criterion': 'gini', 'max_depth': 10, 'n_estimators': 200}\n",
            "The accuracy is: 0.8615067829457363\n",
            "\n",
            "F1 score:  0.892\n",
            "Accuracy: 0.891\n",
            "Recall:  0.882\n",
            "Precision: 0.918\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.90      0.88       123\n",
            "           1       0.92      0.88      0.90       153\n",
            "\n",
            "    accuracy                           0.89       276\n",
            "   macro avg       0.89      0.89      0.89       276\n",
            "weighted avg       0.89      0.89      0.89       276\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 427.9555555555555, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC curve and ROC area for each class\n",
        "y_pred_prob_rf_tuned = rf_cv.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_prob_rf_tuned)\n",
        "rf_cv_auc = metrics.roc_auc_score(y_test, y_pred_prob_rf_tuned)\n",
        "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(rf_cv_auc), color = \"black\")\n",
        "plt.legend(loc=4)\n",
        "plt.show()\n",
        "\n"
      ],
      "metadata": {
        "id": "zyE1fwVEimVp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "764cd56e-065b-477e-f97d-df4c0aeca535"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#F1, Acc, Recall, Precision, AUC.\n",
        "AlgorithmsScores[\"Random Forest\"] = [format(rf_f1, '.3f'), \"{:.3f}\".format(rf_accuracy), \"{:.3f}\".format(rf_recall), \"{:.3f}\".format(rf_precision), \"{:.3f}\".format(rf_cv_auc)]\n",
        "AlgorithmsScores"
      ],
      "metadata": {
        "id": "1G5bpwGojBMg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22ed2dc3-bd78-476c-fd7a-c0ab4b7ac6cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Gaussian NB': ['0.877', '0.877', '0.856', '0.916', '0.928'],\n",
              " 'LR': ['0.859', '0.859', '0.843', '0.896', '0.927'],\n",
              " 'LightGBM': ['0.906', '0.899', '0.882', '0.931', '0.942'],\n",
              " 'MLP': ['0.846', '0.841', '0.791', '0.910', '0.923'],\n",
              " 'Random Forest': ['0.892', '0.891', '0.882', '0.918', '0.932'],\n",
              " 'SVM': ['0.873', '0.862', '0.856', '0.891', '0.921'],\n",
              " 'XGBoost': ['0.895', '0.895', '0.876', '0.931', '0.944']}"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **XGBoost**"
      ],
      "metadata": {
        "id": "fHyDSM_gK-pN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# fit model no training data\n",
        "xgb = XGBClassifier()\n",
        "xgb.fit(X_train, y_train)\n",
        "\n",
        "# make predictions for test data\n",
        "y_pred = xgb.predict(X_test)\n",
        "predictions = [round(value) for value in y_pred]\n",
        "# evaluate predictions\n",
        "accuracy = accuracy_score(y_test, predictions)\n",
        "print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbcmPwbxLHvI",
        "outputId": "c6d0f8ce-468b-4f04-8cf0-4b92d6630c60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 89.13%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))\n",
        "\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "\n",
        "\n",
        "\n",
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"Reds\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('XGBoost', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')\n",
        "\n",
        "xgb_f1 = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "xgb_recall = recall_score(y_test,y_pred,average=\"binary\")\n",
        "xgb_precision = precision_score(y_test,y_pred,average=\"binary\")\n",
        "\n",
        "print(\"F1 score:  {:.4f}\".format(xgb_f1))\n",
        "print(\"Recall:  {:.4f}\".format(xgb_recall))\n",
        "print(\"Precision: {:.4f}\".format(xgb_precision))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "L56GKEKGePgh",
        "outputId": "942d9278-a8dd-402a-b497-349bb601dafd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.90      0.88       123\n",
            "           1       0.92      0.88      0.90       153\n",
            "\n",
            "    accuracy                           0.89       276\n",
            "   macro avg       0.89      0.89      0.89       276\n",
            "weighted avg       0.89      0.89      0.89       276\n",
            "\n",
            "F1 score:  0.8915\n",
            "Recall:  0.8824\n",
            "Precision: 0.9184\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC curve and ROC area for each class\n",
        "y_pred_prob_xgb = xgb.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_prob_xgb)\n",
        "xgb_auc = metrics.roc_auc_score(y_test, y_pred_prob_xgb)\n",
        "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(xgb_auc), color = 'red')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9AJX5vevhNkm",
        "outputId": "c6f56955-c664-4bca-bfa2-60817a0222b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Hyperparameter tuning\n",
        "\n",
        "parameters ={\"max_depth\": [6,3,5,8,10], \"eta\":[0.3, 0.1, 0.2, 0.4, 0.01, 0.05],'gamma':[0,1], \"n_estimator\":[10,20,50,100,200]} \n",
        "xgb = XGBClassifier()\n",
        "xgb_cv = GridSearchCV(xgb,parameters,cv=3, verbose = True) \n",
        "xgb_cv.fit(X_train,y_train)\n",
        "\n",
        "\n",
        "print(\"Best Hyperparameters:\",xgb_cv.best_params_)\n",
        "print(\"The accuracy is:\",xgb_cv.best_score_)\n",
        "print('')\n",
        "xgb_cv = xgb_cv.best_estimator_\n",
        "xgb_cv = xgb_cv.fit(X_train, y_train)\n",
        "y_pred = xgb_cv.predict(X_test)\n",
        "\n",
        "xgb_f1 = f1_score(y_test,y_pred,average=\"weighted\")\n",
        "xgb_accuracy = xgb_cv.score(X_test,y_test)\n",
        "xgb_recall = recall_score(y_test,y_pred,average=\"binary\")\n",
        "xgb_precision = precision_score(y_test,y_pred,average=\"binary\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8eWTn7j4eEHN",
        "outputId": "1b2e1e10-29b7-4d23-baab-d3f1af78deaa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 300 candidates, totalling 900 fits\n",
            "Best Hyperparameters: {'eta': 0.3, 'gamma': 1, 'max_depth': 3, 'n_estimator': 10}\n",
            "The accuracy is: 0.8551401869158878\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(\"F1 score:  {:.4f}\".format(xgb_f1))\n",
        "print(\"Accuracy: {:.4f}\".format(xgb_accuracy))\n",
        "print(\"Recall:  {:.4f}\".format(xgb_recall))\n",
        "print(\"Precision: {:.4f}\".format(xgb_precision))\n",
        "print(classification_report(y_test,y_pred))\n",
        "cnf_matrix = metrics.confusion_matrix(y_test, y_pred)\n",
        "cnf_matrix\n",
        "class_names=[0,1] # name  of classes\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "# create heatmap\n",
        "sns.heatmap(pd.DataFrame(cnf_matrix), annot=True, cmap=\"Reds\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('XGBoost', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label') \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        },
        "id": "zxmEDn5oatU0",
        "outputId": "48a2a418-1360-48b8-d33d-3cf196d89544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F1 score:  0.8952\n",
            "Accuracy: 0.8949\n",
            "Recall:  0.8758\n",
            "Precision: 0.9306\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.90      0.88       123\n",
            "           1       0.92      0.88      0.90       153\n",
            "\n",
            "    accuracy                           0.89       276\n",
            "   macro avg       0.89      0.89      0.89       276\n",
            "weighted avg       0.89      0.89      0.89       276\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 427.9555555555555, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hwsLCdOXKFb3//vs5zmVmZio5OVmSFBwcLDc3Ny1cuNCm85S9DciN1K9fXzVq1ND8+fOt18v272tlr+i8fkxB1JiX3DpiCxYs0JUrV3Idv2TJEpsVr1FRUcrMzLQGuFatWqlMmTL68MMPc10Ze+7cObtrBYDr0YED7lDNmjVTnz599OGHH+rPP/9Uy5Yt5ebmpsOHD2vNmjV66aWX1LlzZ3l5eWnw4MH68MMPNWTIELVu3VqxsbHavHmzypcvf8PvcHFx0eTJkzV06FD17NlTvXr1UqVKlRQXF6eDBw9q3rx5kq4FPUmaNm2aQkJC5Orqqi5duhRIjXlp06aNVqxYoTJlyqh27dratWuXtm3bZrM9y79lZGRo0KBBCgsLU3x8vD7//HPdd999at++vaRr97hNnjxZL7zwgnr16qXw8HB5eXnp+PHjiomJUZMmTRQREWFXrQBwPQIccAebOnWqGjRooMWLF2vGjBlydXVV9erV1b17dzVp0sQ67tlnn1Xx4sW1ePFi7dixQw0bNtTHH3+sIUOGGH5Hq1at9Nlnn+m9997Txx9/rKysLHl7e+vhhx+2junYsaMee+wxrV69Wt98842ysrLUpUuXAqsxNy+99JJcXFy0cuVK/fPPP2rSpIk++eQTPfHEE7mOj4iI0MqVK/Xuu+8qIyNDXbp00YQJE2ymh7t166bKlStr7ty5mjdvntLT01WlShXdf//96tWrl111AkBuLFn23K0LAACAQsM9cAAAAE6GAAcAAOBkCHAAAABOhgAHAADgZIrsKtR1laoVdgkArtPxyJ+FXQKA65W273F1t+Npi4fxoFs0JyvZeJAToQMHAADgZIpsBw4AAJgT3SVj/EYAAABOhg4cAAAwFZd/PeEEuSPAAQAAU2F60Bi/EQAAgJOhAwcAAEzFhRlUQ3TgAAAAnAwdOAAAYCp0l4wR4AAAgKmwCtUYIRcAAMDJ0IEDAACmQnfJGAEOAACYCqtQjRFyAQAAnAwdOAAAYCp0l4zxGwEAADgZOnAAAMBULGwjYogABwAATIXpQWP8RgAAAE6GDhwAADAVthExRgcOAADAydCBAwAApkJ3yRgBDgAAmAoPszdGyAUAAHAydOAAAICp0F0yRoADAACmwipUY4RcAAAAJ0MHDgAAmArdJWP8RgAAAE6GDhwAADAVF3ETnBECHAAAMBUWMRhjChUAAMDJ0IEDAACmQnfJGL8RAACAk6EDBwAATIV74IwR4AAAgKmwCtUYU6gAAABOhg4cAAAwFaZQjRHgAACAqTA9aIzfCAAAwMnQgQMAAKbCFKoxOnAAAABOhg4cAAAwFbYRMUaAAwAApsIUqjGmUAEAAJwMHTgAAGAqZmvAHTlyRPPmzdPvv/+uAwcOyM/PT6tWrbKeT01N1SeffKKYmBgdPnxYxYsXV8OGDTV69GjVqVPH5lopKSmaPn261q9fr4yMDLVq1UoTJkxQ5cqVb6kmOnAAAAA3cODAAcXExKhmzZry9/fPcf748eNasmSJWrZsqZkzZ+rll19WSkqK+vTpo0OHDtmMffbZZ7V161ZNnjxZb775puLj4/Xkk08qMzPzlmqiAwcAAEzFbPfAtWvXTh06dJAkjR07Vnv37rU5X6NGDX333XcqVaqU9Vjz5s3Vrl07ff7555o4caIk6bffftMPP/ygefPmKSQkRJLk6+ur8PBwrVu3TuHh4TddEx04AABgKi6yOPx1W/W43DgulS5d2ia8SZK7u7t8fHx06tQp67HNmzfLw8NDLVu2tB7z8/PTvffeq82bN99STXTgAADAHa99+/Y3PL9hwwaHfl9ycrIOHDig4OBg67G4uDj5+vrKYrENlH5+foqLi7ul69OBAwAApuJicfyroL3xxhuyWCzq16+f9VhycrLKli2bY6ynp6eSkpJu6fp04AAAgKnkR3fJ0R22G/nqq6/0xRdf6NVXX1XVqlXz5TvowAEAADhITEyMIiIi9Mwzz+iBBx6wOefh4aHU1NQcn0lKSpKnp+ctfQ8BDgAAmIolH14FYdeuXRo1apR69uypUaNG5Tjv5+en+Ph4ZWVl2RyPj4+Xn5/fLX0XAQ4AAOA2HTx4UEOGDFHz5s01ZcqUXMeEhoYqKSlJ27dvtx6Lj49XbGysQkNDb+n7uAcOAACYioulEFYd3MClS5cUExMjSUpMTFRqaqrWrFkjSWrWrJmysrL0+OOPq0SJEho4cKDNPnFlypRR7dq1JUmBgYEKCQnR+PHj9eKLL6pEiRKaMWOG6tSpo44dO95STZas6/t4RcS6StUKuwQA1+l45M/CLgHA9Urf2r1ZjvBF+SoOv+bD50/a/dljx47luQ3J/PnzJUkDBgzI9XyzZs20YMEC6/vsR2l99913yszMVEhIiCZMmKAqVW7tbybAATANAhxgQgQ4U2IKFQAAmIq5JlDNiUUMAAAAToYOHAAAMBU6cMYIcAAAwFSuf1YocmIKFQAAwMnQgQMAAKZC/80YAQ4AAJgK04PG+I0AAACcDB04AABgKqxhMEYHDgAAwMnQgQMAAKZiYRmDIQIcAAAwFeKbMaZQAQAAnAwdOAAAYCp04IzRgQMAAHAydOAAAICpuNCCM0SAAwAApsIqVGNMoQIAADgZOnAAAMBU6L8ZI8ABAABT4VFaxphCBQAAcDJ04AAAgKnQgDNGBw4AAMDJ0IEDAACm4kIPzhABDgAAmArxzRhTqAAAAE6GDhwAADAVthExRgcOAADAydCBAwAApkIDzhgBDgAAmAoPszfGFCoAAICToQMHAABMxYUGnCECHAAAMBXymzGmUAEAAJwMHTgAAGAqdOCM0YEDAABwMnTgAACAqbCNiDECHAAAMBUepWWMAAfTcHUvrVrDnpHnfYHyDGwst/LltXfEszq++AubcR6BjVW978PyvK+JytS7Vy5ublpXqVqu16wxaIC8WrWUZ5MmKlWjuhIXL9EfI0YXxJ8D3LHSLl7UvM8W6ve9e7Vnb6ySkpM1fUqEenXvah1z9epVLV8VrXUbNurPv/5SUlKyalSvpvBO/9HjA/qrRIkShfgXAM6Pe+BgGm5eXvJ//jm53323Uv6IzXNcpQ7tVb3/I8rKytKlI0dveE3fEcPkFdJSaX/9pasZGY4uGSiSzl+4oPfmfqS4uMOqc8/duY65dPmyxk2aqvPnz6vvQ700/vnnFNCgvmbNidQTw59VVlZWAVcNZ+KSD687DR04mMY/J09pU/1GSj91Wh6NGqr5+jW5jkv49DPFz3pPVy9fVt1X/5/ca/vnec2fe/TS5WOJkqR2hw/kS91AUVO5YkX98F20KlWsqD1/xOqh/oNyjHFzc1PUJx+pSeOG1mMP9+qp6nfdpVlz5mr7jp8V3LxZAVYN3FnuxFAKJ5WVnq70U6cNx6WfPqOrly/f1DWzwxsAxylevLgqVax44zFubjbhLdt/2rWRJB2Kj8+HynCnsOTD605DBw4AUGDOnD0rSSpfrlwhVwIzs7CKwRAdOABAgfno0wUqU8ZdoSHBhV0K4NRM14E7ffq0tm7dqri4OF24cEGSVK5cOfn5+ally5aqVKlSIVcIALDHnHmfaNuOnzRp3AvyKFu2sMuBidF/M2aaAJeRkaHXXntNixcv1pUrV1SpUiV5enpKkpKSknT69Gm5urqqb9++Gjt2rIoVM03pAAAD0Wu/08z35uihnt31yMMPFXY5MDkCnDHTpKCZM2dqxYoVioiIUFhYmMpe96+z1NRUffvtt3rjjTdUsmRJjRkzppAqBQDciq0/7tALEyerTauWmvLS2MIuB7gjmCbArVixQuPGjVOvXr1yPV+mTBn17t1bLi4umjFjBgEOAJzA73v2avhzL6hBvXs187VXmD3BTWERgzHTLGJIS0tT1apVDcdVrVpVaWlpBVARAOB2HIqL11MjR6t6tbv04btvq2TJkoVdEnDHMM0/hRo3bqw5c+YoICAgx/RpttTUVM2ZM0eBgYEFXB0Kivfj/1UxTw+VrFJFklSp039UotpdkqSEyI+VmZKikjWq667/vYfGo9G1faZ8nxslSbqccEwnln5lvV6ljv9RmQb1JEkuxYqpbL161rGn16xTauyfBfOHAXeYhYu/UHJKik6dPiNJ2hizRX+fPClJeqxvH1lcLHp82EglJ6fo8QH9tWnLVpvP+9SorsBGOfeJAyTJhQacIUuWSZ5nEhcXp4EDByotLU3BwcHy8/OzBrnU1FTFxcVp27Ztcnd316effio/P7/b+r68np2JwtVq5w6V8vHO9dzmJs10OeGYyge3UNMVX+U65tzWbfql5//dIF1/1gxV79sn17G5PWcVhavjEQK1s2gX3kOJJ07kem7D6uWSpPZdeub5+Qe6ddGrUyflS21wsNKeBf6Vu7xrOfyajRMOO/yahck0AU6SkpOTFRUVpS1btiguLk7JycmSJA8PD/n5+Sk0NFR9+/aVh4fHbX8XAQ4wHwIcYEIEOFMyVYArSAQ4wHwIcIAJFUKA+92nlsOv2ejoYYdfszCZZhEDAAAAbg4BDgAAmIrF4vjX7Thy5IgiIiLUo0cP1atXT127ds113NKlS9WpUycFBASoe/fu2rhxY44xKSkpGj9+vJo1a6bAwECNHDlSp06duuWaCHAAAMBULBaLw1+348CBA4qJiVHNmjXl7++f65jVq1dr4sSJCgsLU2RkpBo3bqzhw4dr165dNuOeffZZbd26VZMnT9abb76p+Ph4Pfnkk8rMzLylmkyzjQgAAIAZtWvXTh06dJAkjR07Vnv37s0x5t1331WXLl307LPPSpKaN2+u/fv367333lNkZKQk6bffftMPP/ygefPmKSQkRJLk6+ur8PBwrVu3TuHh4TddEx04AABgKmabQnVxuXFcSkhI0OHDhxUWFmZzPDw8XNu3b1d6erokafPmzfLw8FDLli2tY/z8/HTvvfdq8+bNt1QTHTgAAGAq+fEorfbt29/w/IYNG+y+dlxcnKRr3bR/8/f3V0ZGhhISEuTv76+4uDj5+vrm+Pv8/Pys17hZdOAAAABuQ1JSkiTl2Kc2+332+eTk5FyfNuXp6Wkdc7PowAEAAFPJj2fZ306HzYzowAEAANwGT89rmx2npKTYHM9+olT2eQ8PD6Wmpub4fFJSknXMzSLAAQAAU3GxWBz+yk/Zz2e//j62uLg4ubm5ydvb2zouPj5e1z8EKz4+/paf8U6AAwAApmK2VahGvL29VatWLa1Zs8bmeHR0tFq0aKHixYtLkkJDQ5WUlKTt27dbx8THxys2NlahoaG39J3cAwcAAHADly5dUkxMjCQpMTFRqamp1rDWrFkzeXl5acSIERozZox8fHwUFBSk6Oho7d69WwsXLrReJzAwUCEhIRo/frxefPFFlShRQjNmzFCdOnXUsWPHW6qJh9kDMA0eZg+YUCE8zH7/PbUdfs179h+0+7PHjh3LcxuS+fPnKygoSNK1R2lFRkbq+PHj8vX11XPPPae2bdvajE9JSdH06dP13XffKTMzUyEhIZowYYKqVKlySzUR4ACYBgEOMCECnCkxhQoAAEzFwh36hghwAADAVPLjSQx3GjIuAACAk6EDBwAATIUGnDECHAAAMBWmUI0xhQoAAOBk6MABAABToQFnjA4cAACAk6EDBwAATCW/Hz5/JyDAAQAAUyG/GWMKFQAAwMnQgQMAAKbCNiLG6MABAAA4GTpwAADAVGjAGSPAAQAAUyHAGWMKFQAAwMnQgQMAAKZicaEFZ4QABwAATIUpVGNMoQIAADgZOnAAAMBUeJSWMTpwAAAAToYOHAAAMBUacMYIcAAAwFR4lJYxplABAACcDB04AABgKjTgjNGBAwAAcDJ04AAAgKlwD5wxAhwAADAV8psxplABAACcDB04AABgKkyhGiPAAQAAU7EwP2iInwgAAMDJ0IEDAACmwhSqMTpwAAAAToYOHAAAMBcXOnBGCHAAAMBcmEI1dFMBrl27drc8H22xWLR+/Xq7igIAAEDebirANWvWjBsKAQBAgSBzGLupAPfqq6/mdx0AAAC4SdwDBwAAzIVFDIbs3kYkNTVVc+fO1eOPP66ePXtq9+7dkqQLFy7ok08+0ZEjRxxWJAAAKEIsFse/7lFryAMAACAASURBVDB2deD+/vtv9e/fX3///bdq1qypuLg4paWlSZLKlSunxYsXKzExURMmTHBosQAAALAzwL3++utKS0vT8uXL5eXlpeDgYJvzHTp00KZNmxxRHwAAKGIsTKEasmsKdevWrXrsscdUu3btXFeKeHt768SJE7ddHAAAKIKYQjVkV4C7fPmyvLy88jyfPZ0KAAAAx7MrwPn7++vnn3/O8/z69etVr149u4sCAABFl8XF4vDXncauADdw4EBFR0dr7ty5Sk1NlSRlZWXpyJEjev7557Vr1y4NGjTIkXUCAADgf9m1iKFHjx46fvy43nnnHc2cOVOS9MQTTygrK0suLi4aPXq0OnTo4NBCAQBAEXEH3rPmaHZv5Dt06FD16NFD69at05EjR3T16lX5+PioY8eO8vb2dmSNAACgKLkDpzwd7baexFCtWjWmSgEAAArYbQW4/fv3KyYmRomJiZKkGjVqqFWrVqpTp45DigMAAEUPD7M3ZleAS09PV0REhFasWGG9702Srl69qrfeekvdunXTtGnTVLx4cYcWCwAAADsD3BtvvKHly5frkUceUf/+/eXj4yOLxaIjR45owYIFioqKkqenp1566SVH1wsAAO503ANnyK5tRL755hv16NFDERER8vPzU7FixeTq6io/Pz9NmjRJ3bp10zfffOPoWgEAQFHAkxgM2RXgMjMz1ahRozzPBwYG6sqVK3YXBQAAgLzZFeBCQkL0ww8/5Hl+y5Ytatmypd1FAQCAosvi4vjX7diwYYN69+6twMBAhYSEaNSoUUpISMgxbunSperUqZMCAgLUvXt3bdy48fa++AZu6k+6cOGCzWvUqFE6duyYhg8fru3btysxMVGJiYnatm2bhg0bpuPHj2vUqFH5VjQAALiDmWgKdceOHRo+fLhq166t9957T+PHj9e+ffs0ePBgXb582Tpu9erVmjhxosLCwhQZGanGjRtr+PDh2rVrlyN+kRwsWVlZWUaD6tatm2NJb/bH8jru4uKi2NhYR9XpcOsqVSvsEgBcp+ORPwu7BADXK+1Z4F958aEQh1+z9Jd5zxzeSEREhLZu3ar169dbM8+PP/6ogQMHatGiRbr//vslSZ06dVKDBg301ltvWT/bt29flS1bVpGRkbf/B1znplahDhs2jD1ZAABAgTDTw+czMzPl7u5uk4PKli0r6f+aVgkJCTp8+LCef/55m8+Gh4fr9ddfV3p6usO3VrupADdixAiHfikAAEBBat++/Q3Pb9iwIdfjvXr10ooVK7Ro0SJ1795dFy5c0Ntvv6169eqpSZMmkqS4uDhJkq+vr81n/f39lZGRoYSEBPn7+zvgr/g/t3lbHwAAgIOZ6B64+++/X7Nnz9Zbb72l+++/Xx06dNDZs2cVGRkpV1dXSVJSUpIkycPDw+az2e+zzzvSbT1Ka+fOnYqNjVVKSoquXr1qc85isWjYsGG3VRwAACiC8mEKNa8Om5Fff/1VL7zwgh5++GG1adNGFy5c0Pvvv6+nnnpKn3/+uUqWLOngSm+OXQHuwoULGjJkiHbv3q2srCxZLBabRQ3ZxwhwAADAmU2bNk3NmzfX2LFjrccaN26sNm3aaMWKFerTp488Pa8t9EhJSVGlSpWs45KTkyXJet6R7JpCff311/XXX3/prbfe0vr165WVlaV58+Zp7dq16tu3r+69915t2bLF0bUCAIAiwGKxOPxlr0OHDqlu3bo2x6pWrary5cvr6NGjkiQ/Pz9J/3cvXLa4uDi5ubnJ29vb7u/Pi10BbvPmzerTp4/Cw8Pl7u5+7UIuLqpZs6YmTZqk6tWr65VXXnFooQAAAAWtWrVqObZFS0xM1Pnz51W9enVJkre3t2rVqqU1a9bYjIuOjlaLFi0cvgJVsnMKNTk5WbVr15Yka4BLS0uznm/ZsqVmzJjhgPIAAECRY6JtRPr27atXXnlF06ZNU7t27XThwgV98MEHqlChgsLCwqzjRowYoTFjxsjHx0dBQUGKjo7W7t27tXDhwnypy64AV7lyZZ05c0aSVLx4cVWoUEH79u1Thw4dJEknT55k3zgAAGAfE2WIAQMGqHjx4oqKitJXX30ld3d3NW7cWDNnzlT58uWt47p27apLly4pMjJSc+fOla+vr2bPnq3AwMB8qcuuANe0aVNt27ZNQ4cOlSSFhYVp3rx5cnV11dWrV/XZZ5+pVatWDi0UAACgoFksFvXr10/9+vUzHNu7d2/17t27AKqyM8ANGjRI27Zts+4sPGLECB08eFDvvPOOpGsBb8KECQ4tFAAAFA3M4hmzK8DVqVNHderUsb739PTUp59+quTkZLm4uKhMmTIOKxAAABQxJroHzqxuayPf612/AzEAAAAc76YC3PLly+26eM+ePe36HAAAKLqYQjV2UwHu37sP3yyLxUKAAwAAyAc3FeDsfX4YAADALeMeOEM3FeCydxoGAADId0yhGrLrUVoAAAAoPA5dhQoAAHC7LEyhGqIDBwAA4GTowAEAAHPhHjhDBDgAAGAuTKEaYgoVAADAydxUB2727Nm3fGGLxaJhw4bd8ucKSse/finsEgBc52l378IuAcB15mQlF/h38iQGY0U2wAEAAJNiCtXQTQW4ffv25XcdAAAAuEksYgAAAObCFKohFjEAAAA4Gbs7cPv27dPChQsVGxurlJQUXb161ea8xWLR+vXrb7tAAABQxNCBM2RXB27Hjh3q3bu3Nm3apMqVKyshIUHe3t6qXLmyjh8/rtKlS6tp06aOrhUAABQFFovjX3cYuwLcu+++K29vb61Zs0avvPKKJGnIkCGKiorS4sWLdfLkSXXu3NmhhQIAAOAauwJcbGysHnroIZUpU0aurq6SZJ1CbdSokfr06aN33nnHcVUCAICiw8XF8a87jF1/kaurq9zd3SVJHh4eKlasmM6ePWs97+3trUOHDjmmQgAAANiwK8D5+Pjo8OHDkq4tVvDz87NZsLBp0yZVrFjRIQUCAIAihnvgDNkV4Fq3bq3Vq1crMzNTkvTf//5X69atU8eOHdWxY0d9//336tOnj0MLBQAARQQBzpBd24g888wzGjBggPX+twceeEAuLi5at26dXF1d9fTTT6tXr14OLRQAAADX2BXg3NzcVL58eZtjPXr0UI8ePRxSFAAAKMLuwI6Zo/EoLQAAYC534KpRR7MrwA0YMMBwjMVi0WeffWbP5QEAAHADdgW4rKysHMeuXr2q48eP68SJE6pZs6YqV65828UBAIAiiClUQ3YFuAULFuR5buPGjZo4caLGjRtnd1EAAADIm8Mnmdu2bavu3btbH7EFAABwS9hGxFC+3CXo4+OjPXv25MelAQDAnY4AZ8jhAS4zM1Pffvttjm1GAAAA4Bh23QOX1/1tKSkp2rVrl86cOaOxY8feVmEAAKCIYhsRQ3YFuB07duQ4ZrFY5Onpqfvuu0+9e/dWSEjIbRcHAACAnOwKcN9//72j6wAAALjmDrxnzdHs6lEuX75cx44dy/P8sWPHtHz5cruLAgAARRiLGAzZFeDGjRun3377Lc/zu3fvZh84AACAfOKwJzH828WLF+Xq6mpXQQAAoIi7AztmjnbTAW7fvn3at2+f9f0vv/yiK1eu5BiXnJysxYsXy9fX1zEVAgCAIsXCKlRDNx3g1q9fr9mzZ0u6tuJ0yZIlWrJkSa5jPTw89NprrzmmQgAAANi46QD38MMPq02bNsrKylLv3r01cuRIhYaG2oyxWCwqVaqUfHx8VKyYXbOzAACgqGMK1dBNp6zKlSurcuXKkqT58+erdu3a8vLyyrfCAAAAkDu7JpnvuecenTp1Ks/zf/31l5KSkuwuCgAAFGFsI2LIrgA3ffp0RURE5Hl+0qRJ3AMHAADsQ4AzZFeA+/HHH9WuXbs8z7dt21bbt2+3uygAAADkza6VBufOnVP58uXzPF+uXDmdPXvW7qIAAEARxjYihuz6hSpVqqTY2Ng8z//xxx8scAAAAMgndgW4Dh066KuvvtKGDRtynFu/fr2WLVumDh063HZxAACgCOIeOEN2TaGOGDFC27dv1/Dhw1W3bl3dfffdkqQDBw5o37598vf318iRIx1aKAAAKCLuwMDlaHZ14MqWLaslS5Zo6NChyszM1Nq1a7V27VplZmbqmWee0RdffCEPDw9H1woAAADZ2YGTpNKlS2vkyJF5dtqSkpLk6elpd2EAAKCIogNnyKHPu0pPT9eGDRu0cuVKbdmyRXv27HHk5QEAQFHAKlRDtx3gsrKytH37dq1cuVLfffedUlNT5eXlpa5duzqiPgAAgEL39ddf67PPPtOhQ4dUunRpBQQEaPbs2SpZsqQk6fvvv9fMmTMVHx+vatWq6amnntKDDz6Yb/XYHeD27t2rlStXavXq1Tpz5owsFovCw8PVv39/NW7cWBbanwAAwB4myxAffPCBIiMj9fTTT6tx48Y6f/68tm/fritXrkiSfvnlFw0fPlwPPfSQxo8frx9//FEvvfSS3N3d1blz53yp6ZYCXEJCgr755hutXLlSR44cUZUqVdStWzc1bNhQo0ePVqdOnRQYGJgvhQIAABS0uLg4zZ49W++//75at25tPd6pUyfr//3BBx+oYcOGmjp1qiSpefPmSkhI0Lvvvlv4Aa5Pnz7avXu3ypcvr06dOmnatGm6//77JUlHjx7Nl+IAAEARZKIO3LJly1SjRg2b8PZv6enp2rFjh8aMGWNzPDw8XKtWrdKxY8dUo0YNh9d10wHu999/V40aNTR27Fi1adNGxYo5dP0DAADANSZaxPD777/rnnvu0fvvv68FCxYoJSVFDRo00Lhx49SoUSMdPXpUGRkZ8vPzs/mcv7+/pGsdvEINcBMnTtSqVas0fPhweXp6qlOnTgoPD1dQUJDDiwIAAHCk9u3b3/B8bk+XkqTTp09r79692r9/vyZNmqRSpUppzpw5Gjx4sNatW6ekpCRJyrH/bfb77POOdtMB7tFHH9Wjjz6qhIQErVy5UqtWrdIXX3yhihUrKigoSBaLhYULAADg9pkoT2RlZenixYt65513VLduXUlSo0aN1K5dOy1cuFAhISGFUtctz4N6e3vrmWee0TPPPGNdiRodHa2srCxNmTJFmzdvVrt27RQcHKwSJUrkR80AAAC3JK8OmxEPDw+VK1fOGt4kqVy5cqpXr54OHjyoLl26SJJSUlJsPpecnCxJ+fZQg9uaZM6eA46JidHHH3+skJAQRUdHa+jQoWrevLmjagQAAEWJiR5mX7t27TzP/fPPP/Lx8ZGbm5vi4uJszmW/v/7eOEdxyF2CLi4uCg4O1quvvqpt27bp7bffJsABAAD7mCjAtW3bVhcuXNCff/5pPXb+/Hn98ccfql+/vooXL66goCCtXbvW5nPR0dHy9/fPlwUMkoMfpSVJJUqUUHh4uMLDwx19aQAAgALVoUMHBQQEaOTIkRo9erRKlCihuXPnqnjx4nrkkUckSUOHDtWAAQM0efJkhYWFaceOHVq1apVmzJiRb3WxFwgAADAXE20j4uLiorlz52r69OmKiIhQRkaG7r//fi1atEiVKlWSJN1///2aNWuWZs6cqS+//FLVqlXTtGnTFBYWlm91WbKysrLy7epmdu54YVcA4DpPV6hrPAhAgZqTlVzg33ll9hjjQbfIdfibDr9mYTJPxAUAAMBNYQoVAACYi4n2gTMrOnAAAABOhg4cAAAwFwv9JSMEOAAAYC4uTKEaIeICAAA4GTpwAADAXJhCNUSAAwAA5sIqVENEXAAAACdDBw4AAJiLiR6lZVb8QgAAAE6GDhwAADAX7oEzRIADAADmwipUQ/xCAAAAToYOHAAAMBemUA3RgQMAAHAydOAAAIC5sI2IIQIcAAAwF6ZQDRFxAQAAnAwdOAAAYC5sI2KIAAcAAMzFhSlUI0RcAAAAJ0MHDgAAmAtTqIb4hQAAAJwMHTgAAGAubCNiiAAHAADMhSlUQ/xCAAAAToYOHAAAMBe2ETFEBw4AAMDJ0IEDAADmwiIGQwQ4AABgLixiMMQvBAAA4GTowAEAAHNhEYMhAhwAADAXplAN8QsBAAA4GTpwAADAXFiFaogOHAAAgJOhAwcAAMyFe+AMEeAAAIC5sArVEBEXAADAydCBAwAA5sIUqiF+IQAAACdDBw4AAJgL24gYIsABAABzcWGC0AgBDqaWdvGS5i1arN//+FN7YvcpKSVF0ye8qF5dOucYG71+oz5dvFRxR47K1cVVd/vV0hP9+6pNyxaFUDlwZyjh7q7/PD9KvkH3q1az++TuVV6fDXpa2z/73GZcyBMD1ax/H1Wte49KlfNU0vET2r/pB62e8qrOHjlqM3ZOVnKu3/X12Ela+9qMfPtbgDsJAQ6mdj4pSe99PF/VqlZRnbv99dOvu3Idt2DpMk17e5baBDfX/wx9Sv+kp+vr6DUaMma8Zk2foo5tQgu4cuDOUKZiBXWdNFZnjxzVsd/3qE7b3P9b8g5spLPxR7T7m2hdPH9BFXxrKeTJgQro2lnTGgUr6cTfNuNj132vH+dH2RxL+O33/Poz4GyYQjVEgIOpVa7gpR9WfaVKFby058+/9NDgp3Mdt3Dp1wq4t67mvPmKLP/7H/5D3cLUqntvfR29lgAH2CnpxN96oWptJZ88JZ/7AjX+l5hcx0UNey7Hsd+Xr9L4nZvVfEC/HJ21U/sP6qdFS/KlZtwBWIVqiF8Ipla8eHFVquBlOC41LU0VypezhjdJKuPuLvdSpVSyRIn8LBG4o2Wmpyv55Cm7Pnv28LWp01LlPHM971aypIrx3ydgFwIc7gjNmjTWlh0/acHSZTp24m8dOnxUU96YqZTUNA14+MHCLg8oMty9vFS2UkX53BeoAZ+8L0n6a0POrl3zQY/onbS/NfvyaU364yc17de7oEuFmVksjn/dYZhCxR1hwugROn8hSdPenqVpb8+SJJUv56lPZ72lwID6hVwdUHS8mrhPbiVLSpJSz5zV4hHP68/1G23GHNr6o3Z+8bXOxB+WZ7W71GbYk3r883kq5emhzXPmFUbZgNNxugB3/vx5HTx4UE2bNi3sUmAiJUuWlK+Pt6pWrqQ2LVso7eJFfbr4S40YF6FFH7yrmt7VC7tEoEiYFfag3EqWVNV771FQ/z4q4V46x5g3QjravN/28QKN37lZPV6J0PZPFynj8uWCKhdmxTYihpzuF/rpp580YMCAwi4DJjPqpck6cfKUXp04Vp3btdaDXcO04L0ZysjI1IwPPyrs8oAiY/+mLfpjzXfaMOM9ze09UF0mjVWbYU/d8DNXMjK0afZcuZcvL5/7GhdQpTA1plANOV2AA66XkHhcW378Se1aBdscL+fpoSaNGujX3XsLqTKgaDsTF6+E33ar2aMPG449n5AoSXL3Kp/fZQF3BNNMoXbr1u2mxqWlpeVzJXA2Z86dlyRduXI1x7nMzCu6cuVKQZcE4H+5lSopt5tYaVrRr5YkKeX02XyuCE6BbUQMmSbAxcXFqXbt2qpXr94NxyUmJurEiRMFVBWcQc0a1eXi4qLoDRvV94Fu1q1E/j51Wr/8vlv3NQwo5AqBO5uLq6tKli2rixcu2Byv1fQ+VQ+or58/X2o9VqZiBaWesQ1pJcqUUbtnn1HK6TM6uvO3AqkZcHamCXB33323atasqenTp99w3Nq1a/Xzzz8XUFUwg4VLv1ZyaqpOnTkjSdr4wzb9feq0JOmx3g/Iq3w5Pdg1TEu/Wa2BI/5HHVu3UtrFi/p82Qr9888/GjLgkcIsH3B6bYY9pVLlPFWu2l2SpIBuYSpX49rCoI2zPpTFYtErCbHauWSZjv/xp9LTLqp6QH21+O+jupSUrOiXX7e5VqOeXbR75RqdP5ogj7uqquXg/irv461PH3tKVzIyCuVvhMmY+J61tLQ0hYWF6eTJk/ryyy8VEPB/TYKlS5fqo48+0vHjx+Xr66vRo0erbdu2+VKHaQJcw4YNtWXLlpsam5WVlc/VwEw+/nyJEv8+aX2/btMWrdt07f9Xunf+j8qWKaPJz49W3dr++nJltN6aEylJCri3rl6LGKemgY0KpW7gTvGfMSNUoVZN6/smD/ZQkwd7SJJ+WrhEF46f0NaP5qtO21Zq8lAPuZUqpaTjJ/Rz1Jf6dtobNs9CPbT1R/kFBynkiQFyr+Clf9LSdPinXzV/8DD9tXFzgf9tMCkTT6G+//77ud6as3r1ak2cOFFPP/20mjdvrujoaA0fPlyLFi1S48aOX5xjyTJJGjp69KgOHDig9u3b33Dc5cuXdfbsWVWvfpvbQpw7fnufB+BwT1eoW9glALjOnKzkAv/OK5sWO/yarm363vY1Dh06pIceekgvvviiJk2aZNOB69Spkxo0aKC33nrLOr5v374qW7asIiMjb/u7r2eaiOvj42MY3qRr+33ddngDAADm5WJx/MsBpk2bpr59+8rX19fmeEJCgg4fPqywsDCb4+Hh4dq+fbvS09Md8v3/ZpopVAAAAEn5MoVq1CTasGHDDc+vWbNG+/fv16xZs/THH3/YnIuLi5OkHMHO399fGRkZSkhIkL+/vx1V5800HTgAAAAzunTpkl599VWNHj1aZcqUyXE+KSlJkuTh4WFzPPt99nlHogMHAADMJR9WoRp12G7kgw8+UIUKFfTggw86sKLbQwcOAAAgD4mJifr44481cuRIpaSkKDk5WRcvXpQkXbx4UWlpafL09JQkpaSk2Hw2OfnaApDs845EBw4AAJiLibYROXbsmDIyMvTUUzmf6TtgwAA1atTIuvI0Li5Ofn5+1vNxcXFyc3OTt7e3w+siwAEAAFOxmGgj33vvvVfz58+3Ofbnn39q+vTpmjJligICAuTt7a1atWppzZo16tChg3VcdHS0WrRooeLFizu8LgIcAABAHjw8PBQUFJTrufr166t+/fqSpBEjRmjMmDHy8fFRUFCQoqOjtXv3bi1cuDBf6iLAAQAAczHRFOrN6tq1qy5duqTIyEjNnTtXvr6+mj17tgIDA/Pl+0zzJIYCx5MYANPhSQyA+RTGkxiubl/h8Gu6tOjh8GsWJjpwAADAXJywA1fQCHAAAMBcHPToqzsZERcAAMDJ0IEDAADmwhSqIQIcAAAwFxPtA2dWRFwAAAAnQwcOAACYC1OohviFAAAAnAwdOAAAYC7cA2eIAAcAAMyFKVRD/EIAAABOhg4cAAAwF57EYIgOHAAAgJOhAwcAAMyFe+AMEeAAAIC5sArVEBEXAADAydCBAwAA5sIUqiECHAAAMBemUA0RcQEAAJwMHTgAAGAuTKEa4hcCAABwMnTgAACAubjQXzJCgAMAAKZiYRGDISIuAACAk6EDBwAAzIVFDIb4hQAAAJwMHTgAAGAu3ANniAAHAADMhSlUQ/xCAAAAToYOHAAAMBemUA0R4AAAgLmwka8hfiEAAAAnQwcOAACYC1OohujAAQAAOBk6cAAAwFzYRsQQAQ4AAJgLU6iGiLgAAABOhg4cAAAwGTpwRujAAQAAOBk6cAAAwFy4B84QAQ4AAJgLAc4QU6gAAABOhg4cAAAwGTpwRghwAADAXJhCNcQUKgAAgJOhAwcAAMyFBpwhOnAAAABOhg4cAAAwGVpwRghwAADAXFjEYIgpVAAAACdDBw4AAJgLHThDdOAAAACcDB04AABgMnTgjBDgAACAuTCFaogpVAAAACdDgAMAACZjyYeXfb799lsNHTpUoaGhaty4sXr06KEvv/xSWVlZNuOWLl2qTp06KSAgQN27d9fGjRvt/s6bQYADAADmYrE4/mWnTz/9VKVKldLYsWP1wQcfKDQ0VBMnTtR7771nHbN69WpNnDhRYWFhioyMVOPGjTV8+HDt2rXLEb9GrixZ10fIouLc8cKuAMB1nq5Qt7BLAHCdOVnJBf6dWacOO/yalsq17PrcuXPn5OXlZXNs4sSJio6O1s8//ywXFxd16tRJDRo00FtvvWUd07dvX5UtW1aRkZG3U3ae6MABAABzMVEH7vrwJkn33nuvUlNTdfHiRSUkJOjw4cMKCwuzGRMeHq7t27crPT3d7u++EVahAgCAO1779u1veH7Dhg03fa2dO3eqSpUqKlOmjHbu3ClJ8vX1tRnj7++vjIwMJSQkyN/f/9YLNkAHDgAAmIx5FjFc75dfflF0dLQGDx4sSUpKSpIkeXh42IzLfp993tHowAEAAFOx5MM+cLfSYcvL33//rdGjRysoKEgDBgxwQFX2owMHAABgIDk5WU8++aTKlSunWbNmycXlWoTy9PSUJKWkpOQY/+/zjkaAAwAA5mKiRQySdPnyZQ0ZMkQpKSn66KOPVLZsWes5Pz8/SVJcXJzNZ+Li4uTm5iZvb+/b+u68EOAAAADykJmZqWeffVZxcXH66KOPVKVKFZvz3t7eqlWrltasWWNzPDo6Wi1atFDx4sXzpS7ugQMAACZjnmehTpkyRRs3btTYsWOVmppqszlvvXr1VLx4cY0YMUJjxoyRj4+PgoKCFB0drd27d2vhwoX5Vhcb+QIwDTbyBcynMDbyzZf/jfaqZtfH2rVrp8TExFzPbdiwQTVq1JB07VFakZGROn78uHx9ffXcc8+pbdu2dpdrhAAHwDQIcID5FPUAZ1ZMoQIAAHPJh21E7jQEOAAAYDIEOCOsQgUAAHAydOAAAIC5MIVqiA4cAACAk6EDBwAAzIUGnCECHAAAMBkSnBGmUAEAAJwMHTgAAGAuLGIwRAcOAADAydCBAwAA5kIHzhABDgAAmAwBzghTqAAAAE6GDhwAADAXplANEeAAAIC5EOAMMYUKAMD/b+cOXRoP4ziOf28cFmETQayyaNGkyMCgFkFYXRPTgslmGohFVvUv0OKfsDpBq8UmGwgahmmboghu1w4OD+6u7HfP9nrF38qnjL159vCDxDiBAwD+M07g/sQJHABAYpzAAQD/F3fg6NSiTAAAAklJREFU/ujbcDgcZj0CAIC/5y9UAIDECDgAgMQIOACAxAg4AIDECDgAgMQIOACAxAg4AIDECDgAgMQIOACAxAg4AIDECDgAgMQIOACAxAg4AIDECDiS1Gq1Ym9vL5aXl6NUKkW9Xo+Pj4+sZ8FEe3h4iFqtFuVyORYXF2NnZyfrSTC2vmc9AP5Vt9uN3d3dWFhYiNPT0+h0OnFychLv7+9Rq9WyngcT6/7+PprNZiwtLcVgMIjhcJj1JBhbAo7kXF5exuvra5ydncXMzExERHx+fsbR0VFUq9WYn5/PeCFMpo2Njdja2oqIiMPDw7i7u8t4EYwvf6GSnKurq1hbW/sZbxER29vbMRgM4vr6OsNlMNlyOT8pMCq+bSSn3W5HsVj85Vk+n4+5ublot9sZrQKA0RFwJKfX60U+n//yvFAoRLfbzWARAIyWgAMASIyAIzn5fD76/f6X591uNwqFQgaLAGC0BBzJKRaLX+669fv9eH5+/nI3DgDGkYAjOevr63FzcxO9Xu/ns0ajEblcLkqlUobLAGA0vAeO5FQqlbi4uIj9/f2oVqvR6XSiXq9HpVLxDjjI0NvbWzSbzYiIeHp6ipeXl2g0GhERsbKyErOzs1nOg7HybehV2SSo1WrF8fFx3N7exvT0dJTL5Tg4OIipqamsp8HEenx8jM3Nzd9+dn5+HqurqyNeBONLwAEAJMYdOACAxAg4AIDECDgAgMQIOACAxAg4AIDECDgAgMQIOACAxAg4AIDECDgAgMQIOACAxAg4AIDE/ABLNWK149qOJQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute ROC curve and ROC area for each class\n",
        "y_pred_prob_xgb_tuned = xgb_cv.predict_proba(X_test)[::,1]\n",
        "fpr, tpr, _ = metrics.roc_curve(y_test,  y_pred_prob_xgb_tuned)\n",
        "xgb_cv_auc = metrics.roc_auc_score(y_test, y_pred_prob_xgb_tuned)\n",
        "plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(xgb_cv_auc), color = \"red\")\n",
        "plt.legend(loc=4)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "FJ_i-NGIiHul",
        "outputId": "c48e3c81-b062-4899-807c-6571239bfd4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#F1, Acc, Recall, Precision, AUC.\n",
        "AlgorithmsScores[\"XGBoost\"] = [format(xgb_f1, '.3f'), \"{:.3f}\".format(xgb_accuracy), \"{:.3f}\".format(xgb_recall), \"{:.3f}\".format(xgb_precision), \"{:.3f}\".format(xgb_cv_auc)]\n",
        "AlgorithmsScores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tkvRuCJljxnI",
        "outputId": "52401747-4720-4da8-ecc2-2c4346589e6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Gaussian NB': ['0.877', '0.877', '0.856', '0.916', '0.928'],\n",
              " 'LR': ['0.859', '0.859', '0.843', '0.896', '0.926'],\n",
              " 'Random Forest': ['0.877', '0.877', '0.856', '0.916', '0.932'],\n",
              " 'SVM': ['0.873', '0.862', '0.856', '0.891', '0.921'],\n",
              " 'XGBoost': ['0.895', '0.895', '0.876', '0.931', '0.944']}"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multi Layer Perceptron"
      ],
      "metadata": {
        "id": "2hpVELcqxS6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_nn_train = X_train\n",
        "y_nn_train = y_train\n",
        "x_nn_test = X_test\n",
        "y_nn_test = y_test\n",
        "\n",
        "neural_network_classifier = MLPClassifier(random_state=1, max_iter=300).fit(x_nn_train, y_nn_train)\n",
        "y_default_nn_predict = neural_network_classifier.predict(x_nn_test)\n",
        "accuracy_default_nn = metrics.accuracy_score(y_nn_test, y_default_nn_predict)\n",
        "print('Accuracy of default Neural Network: %',100*accuracy_default_nn)\n",
        "prec_default_nn = metrics.precision_score(y_nn_test, y_default_nn_predict)\n",
        "print('Precision of default Neural Network: %',100*prec_default_nn)\n",
        "recall_default_nn = metrics.recall_score(y_nn_test, y_default_nn_predict)\n",
        "print('Recall of default Neural Network: %',100*recall_default_nn)\n",
        "f1_default_nn = metrics.f1_score(y_nn_test, y_default_nn_predict)\n",
        "print('F1 of default Neural Network: %',100*f1_default_nn)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UDHGcRdUggzr",
        "outputId": "805fb521-ef38-4a6a-d466-4d5bd3a6dddc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of default Neural Network: % 84.42028985507247\n",
            "Precision of default Neural Network: % 85.71428571428571\n",
            "Recall of default Neural Network: % 86.27450980392157\n",
            "F1 of default Neural Network: % 85.99348534201954\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_default_nn_prob = neural_network_classifier.predict_proba(x_nn_test)[::,1] \n",
        "fpr_default_nn, tpr_default_nn, _ = metrics.roc_curve(y_nn_test,  y_pred_default_nn_prob)\n",
        "auc_nn = metrics.roc_auc_score(y_nn_test, y_pred_default_nn_prob)\n",
        "\n",
        "plt.plot(fpr_default_nn,tpr_default_nn,label=\"MLP default= \"+str(auc_nn),color='gold')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "Rx87jTiHj3q-",
        "outputId": "b60142e9-4d06-4203-d6e1-28681f6ac74c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_default_nn = confusion_matrix(y_nn_test,y_default_nn_predict)\n",
        "class_names=[0,1]\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "\n",
        "sns.heatmap(pd.DataFrame(cm_default_nn), annot=True, cmap=\"YlOrBr\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix MLP', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "TgXZVg71lBXM",
        "outputId": "cb0d6b04-2b4f-4209-c256-58674592da6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parameters ={'hidden_layer_sizes': [100,110,120], 'activation':['identity', 'logistic', 'tanh', 'relu'], 'solver':['sgd', 'adam']} \n",
        "neural_network_classifier = MLPClassifier(random_state=1, max_iter=800)\n",
        "nn_cv = GridSearchCV(neural_network_classifier,parameters,cv=5, verbose = True) \n",
        "nn_cv.fit(x_nn_train,y_nn_train)\n",
        "print(\"Best Hyperparameters:\",nn_cv.best_params_)\n",
        "print(\"The accuracy is:\",nn_cv.best_score_)\n",
        "print('')\n",
        "nn_cv = nn_cv.best_estimator_\n",
        "nn_cv = nn_cv.fit(x_nn_train, y_nn_train)\n",
        "y_nn_predict = nn_cv.predict(x_nn_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j99FwnLXlmTN",
        "outputId": "c405fac4-b3a2-402e-cb39-d6e551fa3e6d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n",
            "Best Hyperparameters: {'activation': 'logistic', 'hidden_layer_sizes': 110, 'solver': 'adam'}\n",
            "The accuracy is: 0.8287427325581396\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_nn = metrics.accuracy_score(y_nn_test, y_nn_predict)\n",
        "print('Accuracy of Neural Network: %',100*accuracy_nn)\n",
        "prec_nn = metrics.precision_score(y_nn_test, y_nn_predict)\n",
        "print('Precision of Neural Network: %',100*prec_nn)\n",
        "recall_nn = metrics.recall_score(y_nn_test, y_nn_predict)\n",
        "print('Recall of Neural Network: %',100*recall_nn)\n",
        "f1_nn = metrics.f1_score(y_nn_test, y_nn_predict)\n",
        "print('F1 of Neural Network: %',100*f1_nn)\n",
        "\n",
        "y_pred_nn_prob = nn_cv.predict_proba(x_nn_test)[::,1] \n",
        "fpr_nn, tpr_nn, _ = metrics.roc_curve(y_nn_test,  y_pred_nn_prob)\n",
        "auc_nn = metrics.roc_auc_score(y_nn_test, y_pred_nn_prob)\n",
        "\n",
        "plt.plot(fpr_nn,tpr_nn,label=\"MLP = \"+str(auc_nn),color='gold')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "Dvk1gX2gf8Zp",
        "outputId": "17194f7b-4dbf-4d07-b695-0c2db0218a15"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Neural Network: % 84.05797101449275\n",
            "Precision of Neural Network: % 90.97744360902256\n",
            "Recall of Neural Network: % 79.08496732026144\n",
            "F1 of Neural Network: % 84.61538461538461\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_nn = confusion_matrix(y_nn_test,y_nn_predict)\n",
        "class_names=[0,1]\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "\n",
        "sns.heatmap(pd.DataFrame(cm_nn), annot=True, cmap=\"YlOrBr\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix MLP', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "dG9iCCUAhKlU",
        "outputId": "d42b3a4a-ebbd-43ad-cb93-b43246c96ce9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Uq622atvhPKoN4j8C0pkaaJIbTWYDnwWu5pFFkp4CPBXYq6rOGK8NK6Jl2/E/vsyTn7cNq625LvfdfjPnfv4gHrjrDrbf75OsttYTWXDPndzyq59xcjOybvdvXsXMx63O9FVmsuCeOznpH3bktut8TLcsH7jk122HMBD23vsjXHDBpdxxx12ss85avOc9uzJv3ld58MGFrLlmZxDD5pvP4qCD9m450kGxwaRmjkteO6Or351bfH1R65mrL4kIIMk0YCuWHKzw06paPJHvm4i0spmI1I7JTUSXvm6Vrn53PudrC1tPRH0bNVdVDwHn9at9SdKfGsCeOaf4kaRhMojPiExEkjRETESSpFZNmyrTJXTBRCRJQ2QACyITkSQNk1gRSZLaZEUkSWqVgxUkSa0awDxkIpKkYWJFJElqlYlIktSqAcxDJiJJGiZWRJKkVmWqLLvaBRORJA0RKyJJUqsGMA+ZiCRpmFgRSZJaZSKSJLVqAPOQiUiShokVkSSpVQ7fliS1yopIktSqAcxDDGARJ0laniRdbRNs8/1JLkvyyyTHJlk1ySZJzk9yTZLjk8zsNWYTkSQNkclOREk2BN4LbFlVzwSmA28GDgY+WVVPBe4A5vQas4lIkoZI0t02QTOA1ZLMAB4L3AhsB5zUnD8aeG2vMZuIJGmIdFsRJZmb5MJR29zR7VXVDcDHgd/SSUB3ARcBd1bVouay+cCGvcbsYAVJGiLdDlaoqnnAvOW3l7WAnYFNgDuBE4HZvUf4p0xEkjREMm3Sh829DLi+qm4FSHIKsDWwZpIZTVW0EXBDrzewa06ShsnkPyT6LfCCJI9NZ3TD9sDlwA+BNzbX7AKc2mvIy62IktwD1Mhu82c1n6uqVu/1ppKk/pjs94iq6vwkJwEXA4uAS+h05X0TOC7JR5tjR/R6j+Umoqp6Qq+NSpJaMvldc1TVAcABSx2+DthqMtqf0DOiJC8GNq2qI5OsCzyhqq6fjAAkSZNnWh8SUb+Nm4iSHABsCTwNOBKYCRxD52GVJGkqGcA5fiZSEb0O2IJO/yBV9fskdttJ0hTUh1FzfTeRRPRgVVWSAkjyuD7HJEnq1eDloQklohOSHEZnzPjuwDuBL/Q3LElSL4ZyGYiq+niSlwN3A5sBH6qq7/U9MklS9wbw7dCJzqzwC2A1Ou8R/aJ/4UiSVsQgVkTj5s4kfw9cALyezlu05yV5Z78DkyR1rx/rEfXbRCqifwK2qKrbAJKsA/wY+GI/A5Mk9WBq5JauTCQR3QbcM2r/nuaYJGmKGarh20n2bj5eA5yf5FQ6z4h2Bn6+EmKTJHVrinS3dWOsimjkpdVrm21EzzOsSpL6awDz0JiTnh64MgORJE2CYeqaG5HkicC+wDOAVUeOV9V2fYxLktSDqTISrhsTefXpK8CVdJaJPRD4NfDTPsYkSerR5K+L138TSUTrVNURwMKq+r+qeidgNSRJU9EAZqKJDN9e2Px5Y5Idgd8Da/cvJElSr4Zq+PYoH02yBrAP8BlgdeD9fY1KktSbwctDE5r09BvNx7uAl/Y3HEnSihjEwQpjvdD6GTovsC5TVb23LxFJkno3ZF1zF660KCRJk2IAC6IxX2g9emUGIkmaBAOYiSa6HpEkaQAM1TMiSdLgyRCv0CpJGgTDVBG1PWruAz84pp/NS39ij802aTsEPQoddtWCSW1v2LrmHDUnSYNmmIZvO2pOkgbQkFVEwMPLQOwHzMJlICRpaps+eKMVJroMxBW4DIQkTX0DOPu2y0BI0jDJtO62KcBlICRpmAzTYIVRXAZCkgbFFOlu64bLQEjSMBnGRJTkSJbxYmvzrEiSNJUMadfcN0Z9XhV4HZ3nRJKkqWaKDEDoxkS65k4evZ/kWOCcvkUkSerdAFZEvaTOTYE/m+xAJEmToA/vESVZM8lJSa5MckWSFyZZO8n3klzd/LlWryGPm4iS3JPk7pENOJ3OTAuSpKmmPy+0HgKcUVVPBzanM8nB/sCZVbUpcGaz35OJdM09odfGJUkr2SR3zTWv72wD7ApQVQ8CDybZGdi2uexo4Ef0WKRMpCI6cyLHJElTQJcVUZK5SS4ctc1dqsVNgFuBI5NckuTwJI8D1quqG5trbgLW6zXksdYjWhV4LLBu0/c3kmZXBzbs9YaSpD7qctRcVc0D5o1xyQzgucB7qur8JIewVDdcVVWS5a5fN56xuub2AP4R2AC4iEcS0d3AZ3u9oSSpjyZ/1Nx8YH5Vnd/sn0QnEd2cZP2qujHJ+sAtvd5guamzqg6pqk2AD1TVX1TVJs22eVWZiCRpKprkwQpVdRPwuyRPaw5tD1wOnAbs0hzbBTi115An8kLrQ0nWrKo7AZpuurdU1f/2elNJUp/0Z4qf9wBfSTITuA7YjU4hc0KSOcBvgDf12vhEEtHuVfW5kZ2quiPJ7oCJSJKmmj680FpVlwJbLuPU9pPR/kQS0fQkqaoCSDIdmDkZN5ckTbJhnOIHOAM4Pslhzf4ezTFJ0lQzjLNv03lBaS7w7mb/e8AX+haRJKl3wzjXXFU9VFWfr6o3VtUb6YyW+Ez/Q5Mkda0/U/z01UQqIpJsAbyFzqiI64FT+hmUJKlHUyS5dGOsmRU2o5N83gL8ATgeSFW5SqskTVXThmuwwpXA2cBOVXUNQJL3r5SoJEm9GcCKaKzU+XrgRuCHSb6QZHsemeZHkjQVDeAzorGm+Pl6Vb0ZeDrwQzrzzv1ZkkOTvGJlBShJ6sK0dLdNARMZNXdvVX21ql4NbARcggvjSdLUNIAV0YRGzY2oqjvoTBc+1pThkqS2DOnMCpKkQTF9etsRdM1EJEnDxIpIktSqWBFJklplRSRJapNdc5KkVpmIJEmtMhFJklplIpIktcpEJElqlYlIktQqE5EkqVUmIklSq0xEkqRWmYgkSa0yEUmSWmUikiS1ykQkSWqVy0BIklplRSRJapWJSJLUKhORJKlVJiJJUqtMRJKkVpmIJEmtGsBENHgRS5KWL9O62ybSZDI9ySVJvtHsb5Lk/CTXJDk+ycwVCdmKaIAtWLCQt737CB58cBGLFz/EK7d7Bu/dfXv2+dCJ/PLKG1hlxnSeNWsjDtr/NawyY/BectPU8Y5/P4xnvfRV3HPbrRy003MBeMO+/8Gzt9uRRQ8+yK2/u46j99+d+++5i8etuTZ7fPpY/vxZW/KTr32Z4w76x5ajf5TpT0X0PuAKYPVm/2Dgk1V1XJLPA3OAQ3tt3IpogM2cOYOjP7sbpx2zF1//8p6c/ZNruPSXv+M1szfnjOPfx+lf2YsFCxZy4qkXtR2qBtxPTvkyn57z6iWOXX7umRy44xZ85DVbcsv1V7PDHvsCsHDBA5x6yIGcfPD+bYSqSa6IkmwE7Agc3uwH2A44qbnkaOC1KxKyiWiAJeFxj30MAIsWLWbRosUE+JsXbUYSkvDsWRtx8y13tRuoBt7VF57DfXfdscSxK879Pg8tXgzAdT87nzWftCEAD95/H9de9GMWLnhgpccpuk5ESeYmuXDUNnepFj8F7As81OyvA9xZVYua/fnAhisSsl1zA27x4od4/a6H8tv5t/PWN2zF5s988sPnFi5azKnfvpR/2ftVLUaoR4Ot37ArF37rxLbDEMC07n6tV9U8YN6yziXZCbilqi5Ksu2KB7dsK70iSrLbGOcezszzjvr+ygxrYE2fPo1Tv7wn/3faB/j55Tdw1bU3P3zuwP86nS232Jgtn7NxewFq6O3wrv1YvHgR5592bNuhCOj8Wu9mG9PWwGuS/Bo4jk6X3CHAmklGMt5GwA0rGvHKduDyTlTVvKrasqq2nLvry1ZmTANv9Sesxl8/bxPOPu9qAD57+A+4/c57+eD7ZrccmYbZC1/3dp790ldxxD67tB2KRiTdbWOoqg9W1UZVtTHwZuAHVfU24IfAG5vLdgFOXZGQ+9I1l+TnyzsFrNePez4a3X7HvcyYMY3Vn7AaDzywkB9fcC27v/0lnHjqhZxz/jUc9ZndmDbNx4Dqj2e85BW8Yvd9+J+3vYyFD9zfdjgasXLeI9oPOC7JR4FLgCNWpLFU1aREtUSjyc3AK4E7lj4F/LiqNhi3kTtOmPzAhsyVV9/E/h85mcWLi6pi9vbPZK85L2XW1gewwZPWeHggw8u3ncVec17acrRT3x5//fa2Q5iy5nziSzxtq214/FrrcvdtN3P6pz/C7D32ZcbMmdx75+0AXHfpBXz1gL0A+NgPfsVqj1+d6avM5P577uSQ3XbkxmuvbPNHmLIOu2rB2GVJl+r3h3b1uzMbvHtS79+LfiWiI4Ajq+qcZZz7alW9ddxGTERayUxEasOkJ6IbP99dIlr/Xa0nor50zVXVnDHOjZ+EJEm9GcApfhy+LUlDpfUCp2smIkkaJuOMhJuKTESSNFTsmpMktcmKSJLUKgcrSJLaZUUkSWqTXXOSpFbZNSdJapcVkSSpTXbNSZLaFLvmJEntsiKSJLXJrjlJUrvsmpMktcmKSJLUKhORJKldds1JktpkRSRJapWJSJLULrvmJEltsiKSJLXLRCRJatO06W1H0DUTkSQNFSsiSVKbnH1bktQuKyJJUqtMRJKkNtk1J0lqlxWRJKlVJiJJUqvsmpMktckpfiRJ7TIRSZJaNXiJaPA6EyVJY5jW5Ta2JE9O8sMklye5LMn7muNrJ/lekqubP9dakYglScMi6W4b3yJgn6qaBbwA2DPJLGB/4Myq2hQ4s9nviYlIkoZKutzGVlU3VtXFzed7gCuADYGdgaOby44GXttrxD4jkqSh0r/6IsnGwBbA+cB6VXVjc+omYL1e27UikqSh0l1FlGRukgtHbXOX2WryeOBk4B+r6u7R56qqgOo1YisiSRomXb5HVFXzgHljN5lV6CShr1TVKc3hm5OsX1U3JlkfuKWXcMGKSJKGzOQ+I0oS4Ajgiqr6xKhTpwG7NJ93AU7tNWIrIkkaKpNeX2wNvB34RZJLm2P/DPwncEKSOcBvgDf1egMTkSQNlcl9obWqzhmj0e0n4x4mIkkaJs41J0lq1+A9+jcRSdJQsSKSJLXKRCRJapPPiCRJ7fIZkSSpVVZEkqQ2ZXrbEXTNRCRJQ8WKSJLUKhORJKlVJiJJUpviqDlJUqsGryJKZ2E9DZMkc5vFrqSVwv/ntCIGr4bTRCxzqV+pj/x/Tj0zEUmSWmUikiS1ykQ0nOyr18rm/3PqmYMVJEmtsiKSJLXKRCRJapWJaIgkmZ3kV0muSbJ/2/Fo+CX5YpJbkvyy7Vg0uExEQyLJdOBzwA7ALOAtSWa1G5UeBY4CZrcdhAabiWh4bAVcU1XXVdWDwHHAzi3HpCFXVWcBt7cdhwabiWh4bAj8btT+/OaYJE1pJiJJUqtMRMPjBuDJo/Y3ao5J0pRmIhoePwU2TbJJkpnAm4HTWo5JksZlIhoSVbUI2Av4DnAFcEJVXdZuVBp2SY4FfgI8Lcn8JHPajkmDxyl+JEmtsiKSJLXKRCRJapWJSJLUKhORJKlVJiJJUqtMRJKkVpmIJEmt+v+qgeWAHfsW6AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AlgorithmsScores[\"MLP\"] = [format(f1_nn, '.3f'), \"{:.3f}\".format(accuracy_nn), \"{:.3f}\".format(recall_nn), \"{:.3f}\".format(prec_nn), \"{:.3f}\".format(auc_nn)]\n",
        "AlgorithmsScores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bgB7-NWiha7C",
        "outputId": "9874ee18-2782-4587-98d9-697851937823"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Gaussian NB': ['0.877', '0.877', '0.856', '0.916', '0.928'],\n",
              " 'LR': ['0.859', '0.859', '0.843', '0.896', '0.926'],\n",
              " 'MLP': ['0.846', '0.841', '0.791', '0.910', '0.923'],\n",
              " 'Random Forest': ['0.877', '0.877', '0.856', '0.916', '0.932'],\n",
              " 'SVM': ['0.873', '0.862', '0.856', '0.891', '0.921'],\n",
              " 'XGBoost': ['0.895', '0.895', '0.876', '0.931', '0.944']}"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LightGBM"
      ],
      "metadata": {
        "id": "fQeYOvN1xErH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x_L_train = X_train\n",
        "y_L_train = y_train\n",
        "x_L_test = X_test\n",
        "y_L_test = y_test\n",
        "\n",
        "lb = lgb.LGBMClassifier(learning_rate=0.09,max_depth=-5,random_state=42)\n",
        "lb.fit(x_L_train, y_L_train)\n",
        "\n",
        "\n",
        "y_default_L_predict = lb.predict(x_L_test)\n",
        "\n",
        "accuracy_default_lb = metrics.accuracy_score(y_L_test, y_default_L_predict)\n",
        "print('Accuracy of default LightGBM: %',100*accuracy_default_lb)\n",
        "prec_default_lb = metrics.precision_score(y_L_test, y_default_L_predict)\n",
        "print('Precision of default LightGBM: %',100*prec_default_lb)\n",
        "recall_default_lb = metrics.recall_score(y_L_test, y_default_L_predict)\n",
        "print('Recall of default LightGBM: %',100*recall_default_lb)\n",
        "f1_default_lb = metrics.f1_score(y_L_test, y_default_L_predict)\n",
        "print('F1 of default LightGBM: %',100*f1_default_lb)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bF6UtOPPxLKv",
        "outputId": "1c970585-51bf-4cfd-d894-1a2cb41695c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of default LightGBM: % 88.40579710144928\n",
            "Precision of default LightGBM: % 92.90780141843972\n",
            "Recall of default LightGBM: % 85.62091503267973\n",
            "F1 of default LightGBM: % 89.1156462585034\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred_default_L_prob = lb.predict_proba(x_L_test)[::,1] \n",
        "fpr_default_L, tpr_default_L, _ = metrics.roc_curve(y_L_test,  y_pred_default_L_prob)\n",
        "auc_L = metrics.roc_auc_score(y_L_test, y_pred_default_L_prob)\n",
        "\n",
        "plt.plot(fpr_default_nn,tpr_default_nn,label=\"LightGBM default = \"+str(auc_L),color='purple')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ooe5UajXzOm_",
        "outputId": "7f8a2c32-40ef-46c3-ae9f-427826010914"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_default_L = confusion_matrix(y_L_test,y_default_L_predict)\n",
        "class_names=[0,1]\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "\n",
        "sns.heatmap(pd.DataFrame(cm_default_L), annot=True, cmap=\"Purples\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix LightGBM', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "spclWtDrz3vo",
        "outputId": "3f954026-5ae0-4e76-e1b1-77ff620a57a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 80
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AkyLiyUad01q24dB+HHjSCPr078Evjv3ze8Fk1l7mzp1L377rANCnT1/mzp2bc0UrsPZv5lwCnAdcVrHuGGByRJwq6Zj0/GhgZ2DjtGwF/Cr92aqGtIgkHQ1cSXZnjAfTImBiKnpZrxsraYqkKdPm3teI0lZIzzz2Mv/7lYkcu881jBzzX1nLyKxBJBXyVgRl0d5dcxFxNzCv2eqRwKXp8aXAqIr1l0XmfqCnpP5tnaNRLaIxwMcjYlHlSklnAo8Dp7b0oogYD4wH2Guz8/zP9nY287lX+c+bi1hvo97vDWYwaw+9e/dmzpzZ9O27DnPmzKZXr155l7TCqnXUnKSxwNiKVePTZ3Fr+kXErPT4JaBfejwQeLFiv+lp3Sxa0ahrREuAAS2s75+2WQfpO6DHe4MT+vTvwYAN1mbOzNdzrsrKZocdRnD99TcAcP31NzBihxE5V7TiamqRVrtExPiIGFaxtBVCS4mIYDmvPjeqRfQ9YLKkp3k/HdcHNgIOadA5VxiH/uTzfGzYQHr0XJXzbtmPa3/1AG+89h/2O2Z71lx7NY46dzeef+oVTj1oEptsPoCR39qCxYuXEEuCi35yJwvmv932ScyW4cgjj+DBhx5k/vz57DBiOIccfAjfPuAADh83jut+dy0DBgzgzDPOyrvMFVfH9Iq+LKl/RMxKXW9NXSwzgPUq9ls3rWuVsjBrf5K6AFuy9GCFhyLi3Wpe764562iXTzko7xJsBdS1W/t+A/WwPSfW9Nl5zpV7tXl+SRsAN0XE0PT8NGBuxWCFXhFxlKRdyRobu5ANUjgnIrZs6/gNGzUXEUuA+xt1fDMz+6D2HigiaSIwHOgjaTpwAtl1/qsljQFeAPZIu/+RLISmAW8C+1dzDs+sYGZWJu08xU9E7LWMTTu2sG8AB9d6DgeRmVmJFHHkvIPIzKxEivgdLgeRmVmZePZtMzPLUwEbRA4iM7My8f2IzMwsXwVsEjmIzMxKxIMVzMwsVyrgXeYcRGZmJeIWkZmZ5ctBZGZmeXLXnJmZ5cpdc2Zmli9/j8jMzPLkFpGZmeWqgDnkIDIzKxV3zZmZWZ7cNWdmZrkqYA45iMzMSsVdc2Zmlid3zZmZWa58PyIzM8tX8XLIQWRmViZduhZvsjkHkZlZifgakZmZ5at4OeQgMjMrE7eIzMwsVwXMIQeRmVmZlCqIJC0Aoulp+jPS44iINRtcm5mZ1ahUXXMR0aMjCzEzs+VXwByiqgHnkj4laf/0uI+kQY0ty8zM6iGppqUzaPMakaQTgGHAJsDFwMrA5cB2jS3NzMxq1UmypSbVtIh2B74ELASIiJmAu+3MzDqhRrSIJB0u6XFJj0maKGlVSYMkPSBpmqSrJK1cb83VBNE7ERGkgQuSutd7MjMzayyptqXt42kgcBgwLCKGAl2BPYGfAmdFxEbAq8CYemuuJoiulvRroKekbwN/Bi6o94RmZtY4qvG/KnUDVpPUDVgdmAWMAK5N2y8FRtVbc5vXiCLidEmfA14HPgocHxG31XtCMzNrnPa+RhQRMySdDvwbeAu4FXgYmB8Ri9Nu04GB9Z6j2i+0/hNYjax77p/1nszMzBqr1iCSNBYYW7FqfESMr9i+NjASGATMB64BdlruQitUM2ruAOB44HayL7OeK+nkiLioPQsxM7PlV+uQ7BQ641vZ5bPAcxExJx3/d2SjpntK6pZaResCM+qruLoW0f8Cm0fE3FREb+AvgIPIzKyTacDw7X8DW0tanaxrbkdgCnAHMBq4EtgXuKHeE1QzWGEusKDi+YK0zszMOpt2HjYXEQ+QDUp4hOzSTBeyFtTRwDhJ04DewIR6S25trrlx6eE04AFJN5BdIxoJPFrvCc3MrHEa8YXWiDgBOKHZ6meBLdvj+K11zTV9afWZtDSpu/llZmaN1Vmm7alFa5OentSRhZiZ2fIrYA5VNWquL3AU8HFg1ab1ETGigXWZmVkditgiqmawwhXAVLIx5CcBzwMPNbAmMzOrU3tP8dMRqgmi3hExAVgUEXdFxLfIpnYwM7NORjUunUE13yNalP6cJWlXYCbQq3ElmZlZvYrYNVdNEJ0iaS3gCOBcYE3g8IZWZWZmdSlgDlU16elN6eFrwA6NLcfMzJZHqVpEks4l3YOoJRFxWEMqMjOzuhUwh1ptEU3psCrMzKxdlKpFFBGXdmQhZma2/AqYQ1Xfj8jMzArAQWRmZrkqVdecmZkVTwFzqPOOmht/1wGNPLzZB+y40ol5l2AroDvj5HY9XtlaRB41Z2ZWNMXLIY+aMzMrk7K1iID3bgNxNDAE3wbCzKxTK2IQVXsbiCfxbSDMzDq9Ll1U09IZ+DYQZmYlUsT7Efk2EGZmJVLErjnfBsLMrERKGUS+DYSZWXEUMIeqGjV3MS18sTVdKzIzs06klC0i4KaKx6sCu5NdJzIzs05GnWQkXC2q6Zq7rvK5pInAvQ2ryMzM6lbABlFdk55uDKzT3oWYmdnyK2XXnKQFLH2N6CWymRbMzKyTKWAOVdU116MjCjEzs+VXxBZRmzMrSJpczTozM8ufpJqWzqC1+xGtCqwO9JG0Nu9PLr4mMLADajMzsxp1kmypSWtdc98BvgcMAB7m/SB6HTivwXWZmVk9CphErd2P6GzgbEmHRsS5HViTmZnVqbN0t9Wimtm3l0jq2fRE0tqSDmpgTWZmVqdGzL4tqaekayVNlfSkpG0k9ZJ0m6Sn059r11tzNUH07YiY3/QkIl4Fvl3vCc3MrHHURTUtVTobuDkiBgOfJLtH3THA5IjYGJicntelmiDqqoq2nqSuwMr1ntDMzBqnvVtE6e4L2wMTACLindQ4GQlcmna7FBhVb83VBNHNwFWSdpS0IzAxrTMzs06m1uHbksZKmlKxjG12yEHAHOBiSX+TdKGk7kC/iJiV9nkJ6FdvzdVM8XM0MBb4bnp+G3BBvSc0M7PGqXWwQkSMB8a3sks3YAvg0Ih4QNLZNOuGi4iQ9IG7NFSrzRZRRCyJiPMjYnREjAaeILtBnpmZdTINGKwwHZgeEQ+k59eSBdPLkvpn51R/YHa9NVfTNYekzSX9TNLzwMnA1HpPaGZmjdPeMytExEvAi5I2Sat2JGuQTAL2Tev2BW6ot+bWZlb4KLBXWl4BrgIUEb5Lq5lZJ9Wg7xEdClwhaWXgWWB/sobM1ZLGAC8Ae9R78NauEU0F7gF2i4hpAJIOr/dEZmbWeI3IoYj4OzCshU07tsfxW+ua+zIwC7hD0gVpxFzxvrJrZrYCKeKkp8sMooi4PiL2BAYDd5DNO7eOpF9J+nxHFWhmZtUrVRA1iYiFEfHbiPgisC7wN3xjPDOzTqkRU/w0Wk23Ck/T+7Q15tzMzHJSw7Q9nUZNQWRmZp1bZ2nl1MJBZGZWIirgmDIHkZlZmRQvhxxEZmZl0llGwtXCQWRmViIFzCEHkZlZmbhFZGZmuSpgDjmIzMzKxC0iMzPLVQFzyEFkZlYmbhGZmVmuulR1u9POxUFkZlYinlnBzMxyVcCeOQeRmVmZ+BqRmZnlqoA55CAyMysTt4jMzCxXBcwhB5GZWZm4RWRmZvkqXg45iMzMysQtIjMzy1UBc8hBZGZWJm4RmZlZrooXQw4iM7NScYvIzMxyVcAcchCZmZWJW0TWoV56+SVOOPFY5s2bh4Dddx/NXnvuw9nnnMnd99zFSiutxLoD1+WE40+mR4818y7XCuyoCaPYZrePMn/2Qvb/xC8A+NbJI9hu5GBiSfDq7IWcut/vmTtrAetv0oejL96djbfoz4RjJ3PVGfflXP2KpYA5hCIi7xpatOC1tztnYZ3IK6/M4ZVXXmHw4I+xcOFCvvHNPTn9tJ8ze/bLDBu2Jd26deOcc88C4LBDD8+52s7viz3/L+8SOq1NP/1h3nrjHX5w2ZffC6LVe6zCmwv+A8CXD92KDYasw5nfvZGefbvT78Nr8alRH+ONV992ELXhzji5XaNj6tTZNX12Dh68Tu7RVcB7+VmTPn36MnjwxwDo3r07Gwz6CLPnzGbrrbelW7essfuJoZsye/bsPMu0Enj0nhdYMO+tpdY1hRDAqt1XpukftfPnLOSpKTN5d9GSDq3RMpJqWqo8ZldJf5N0U3o+SNIDkqZJukrSystTs4OoJGbOnMFTT01l6Mc/sdT6STdez7bbbpdTVVZ2Y07Zkav/fQSf22dTLjr+9rzLMbKuuVqWKv0P8GTF858CZ0XERsCrwJjlqbnDg0jS/q1sGytpiqQpF18yoSPLKrQ333yTo445giPG/S9rrLHGe+snXHQBXbt2Zeedds2xOiuzCT+czB7rn8FtVzzK7odslXc5Rvu3iCStC+wKXJieCxgBXJt2uRQYtTw159EiOmlZGyJifEQMi4hh+++3XAG7wli8eBFHHT2Onb6wCyN2+Ox762+86QbuvfduTvnRTwo5isaK5c9XPMpnvjIk7zKsDpUNgLSMbbbLz4GjgKa+1t7A/IhYnJ5PBwYuTw0NGTUn6dFlbQL6NeKcK6KI4OQfncigQR/h6/t88731f/nrfVz2m0sYf/4EVl11tRwrtDIbuFEvZkybB8B2Iwfz76mv5FyRQe3DtyNiPDB+GcfaDZgdEQ9LGr781bWsUcO3+wFfIOs7rCTgLw065wrnH//4G3/8001stNHG7L3PHgAcdNChnH7GT1n0zjscfMiBAAwd+gl+8P3j8izVCu64345ms+GDWKvP6lzz4hFcfMIdbLXLxqy/SR+WLAlefuE1zjxwEgC9+q3Br6d8h9XXXIVYEoz+3tbsO+S8pQY3WOO0cwfIdsCXJO0CrAqsCZwN9JTULbWK1gVmLM9JGjJ8W9IE4OKIuLeFbb+NiL3bOoaHb1tH8/Bty0N7D99+5pm5NX12brhh76rOn1pER0bEbpKuAa6LiCslnQ88GhG/rL3aTEOuEUXEmJZCKG1rM4TMzKw+DRo119zRwDhJ08iuGS3X6DLPrGBmViJq0PzbEXEncGd6/CywZXsd20FkZlYmBRwk6yAyMyuRIn5bw0FkZlYijeqaayQHkZlZmRQvhxxEZmZlUsAcchCZmZVJEaf0chCZmZVJ8XLIQWRmViYFzCEHkZlZmRSxa843xjMzs1y5RWRmViIFbBA5iMzMyqSIXXMOIjOzEilgDjmIzMzKxFP8mJlZvoqXQw4iM7MycdecmZnlqoA55CAyMyuVAjaJHERmZiVSvBhyEJmZlUoBG0QOIjOzUilgEjmIzMxKpHgx5CAyMyuVAjaIHERmZuVSvCRyEJmZlUgRW0S+H5GZmeXKLSIzsxIpYovIQWRmVirFSyIHkZlZiRSxReRrRGZmliu3iMzMyqSALSIHkZlZiRTxDq3umjMzs2WStJ6kOyQ9IelxSf+T1veSdJukp9Ofa9d7DgeRmVmJSLUtVVgMHBERQ4CtgYMlDQGOASZHxMbA5PS8Lg4iMzNbpoiYFRGPpMcLgCeBgcBI4NK026XAqHrP4SAyMyuTGptEksZKmlKxjF32obUBsDnwANAvImalTS8B/eot2YMVzMxKpNahChExHhjf5nGlNYDrgO9FxOuq6NeLiJAUNZ76PW4RmZmViWpcqjmktBJZCF0REb9Lq1+W1D9t7w/MrrdkB5GZWYm0dw4pa/pMAJ6MiDMrNk0C9k2P9wVuqLdmd82ZmZVJ+8/xsx3wDeCfkv6e1v0AOBW4WtIY4AVgj3pP4CAyM7Nlioh7WXbjacf2OIeDyMysRIo3r4KDyMysXAqYRA4iM7MSKeJccw4iM7MyKV4OOYjMzMqkgDnkIDIzK5UCJpGDyMysVIqXRA4iM7MSKV4MOYjMzEql/SdWaDwHkZlZmRQwiTzpqZmZ5cotIjOzEilgg8gtIjMzy5dbRGZmJaICNoncIjIzs1wpou7bjFsnJWlsug+9WYfw75wtD7eIymls3gXYCse/c1Y3B5GZmeXKQWRmZrlyEJWT++qto/l3zurmwQpmZpYrt4jMzCxXDiIzM8uVg6hEJO0k6SlJ0yQdk3c9Vn6SLpI0W9JjeddixeUgKglJXYFfADsDQ4C9JA3JtypbAVwC7JR3EVZsDqLy2BKYFhHPRsQ7wJXAyJxrspKLiLuBeXnXYcXmICqPgcCLFc+np2VJJtwAAACJSURBVHVmZp2ag8jMzHLlICqPGcB6Fc/XTevMzDo1B1F5PARsLGmQpJWBPYFJOddkZtYmB1FJRMRi4BDgFuBJ4OqIeDzfqqzsJE0E/gpsImm6pDF512TF4yl+zMwsV24RmZlZrhxEZmaWKweRmZnlykFkZma5chCZmVmuHERmZpYrB5GZmeXq/wFoofoZ0WlH5QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "parameters ={'num_leaves ': [40,50,60,70,700], 'min_data_in_leaf ':[100,200,500,700], 'max_depth':[7,8,9]} \n",
        "lb_cv = GridSearchCV(lb,parameters,cv=5, verbose = True) \n",
        "lb_cv.fit(x_L_train,y_L_train)\n",
        "print(\"Best Hyperparameters:\",lb_cv.best_params_)\n",
        "print(\"The accuracy is:\",lb_cv.best_score_)\n",
        "print('')\n",
        "lb_cv = lb_cv.best_estimator_\n",
        "lb_cv = lb_cv.fit(x_L_train, y_L_train)\n",
        "y_L_predict = lb_cv.predict(x_L_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rJwk8UNY0HII",
        "outputId": "2d305e3f-516c-4d1f-f81a-7b7a5ee1bbd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 60 candidates, totalling 300 fits\n",
            "Best Hyperparameters: {'max_depth': 9, 'min_data_in_leaf ': 100, 'num_leaves ': 40}\n",
            "The accuracy is: 0.8443192829457364\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_lb = metrics.accuracy_score(y_L_test, y_L_predict)\n",
        "print('Accuracy of LightGBM: %',100*accuracy_lb)\n",
        "prec_lb = metrics.precision_score(y_L_test, y_L_predict)\n",
        "print('Precision of LightGBM: %',100*prec_lb)\n",
        "recall_lb = metrics.recall_score(y_L_test, y_L_predict)\n",
        "print('Recall of LightGBM: %',100*recall_lb)\n",
        "f1_lb = metrics.f1_score(y_L_test, y_L_predict)\n",
        "print('F1 of LightGBM: %',100*f1_lb)\n",
        "\n",
        "y_pred_L_prob = lb_cv.predict_proba(x_L_test)[::,1] \n",
        "fpr_L, tpr_L, _ = metrics.roc_curve(y_L_test,  y_pred_L_prob)\n",
        "auc_L = metrics.roc_auc_score(y_L_test, y_pred_L_prob)\n",
        "\n",
        "plt.plot(fpr_nn,tpr_nn,label=\"LightGBM = \"+str(auc_L),color='purple')\n",
        "plt.legend(loc=4)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "id": "-YqlAXkHvX14",
        "outputId": "7fec3f86-021f-43dc-d5b0-dd374f036113"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of LightGBM: % 89.85507246376811\n",
            "Precision of LightGBM: % 93.10344827586206\n",
            "Recall of LightGBM: % 88.23529411764706\n",
            "F1 of LightGBM: % 90.60402684563759\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm_L = confusion_matrix(y_L_test,y_L_predict)\n",
        "class_names=[0,1]\n",
        "fig, ax = plt.subplots()\n",
        "tick_marks = np.arange(len(class_names))\n",
        "plt.xticks(tick_marks, class_names)\n",
        "plt.yticks(tick_marks, class_names)\n",
        "\n",
        "\n",
        "sns.heatmap(pd.DataFrame(cm_L), annot=True, cmap=\"Purples\" ,fmt='g')\n",
        "ax.xaxis.set_label_position(\"top\")\n",
        "plt.tight_layout()\n",
        "plt.title('Confusion matrix LightGBM', y=1.1)\n",
        "plt.ylabel('Actual label')\n",
        "plt.xlabel('Predicted label')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 342
        },
        "id": "A1cDVpG_xsBS",
        "outputId": "11908587-82e8-4705-8626-cbd50c31ef1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 257.44, 'Predicted label')"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "AlgorithmsScores[\"LightGBM\"] = [format(f1_lb, '.3f'), \"{:.3f}\".format(accuracy_lb), \"{:.3f}\".format(recall_lb), \"{:.3f}\".format(prec_lb), \"{:.3f}\".format(auc_L)]\n",
        "AlgorithmsScores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kRL8IHG9x4KB",
        "outputId": "33979376-d2f3-49f6-a283-4ca85e8040c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Gaussian NB': ['0.877', '0.877', '0.856', '0.916', '0.928'],\n",
              " 'LR': ['0.859', '0.859', '0.843', '0.896', '0.926'],\n",
              " 'LightGBM': ['0.906', '0.899', '0.882', '0.931', '0.942'],\n",
              " 'MLP': ['0.846', '0.841', '0.791', '0.910', '0.923'],\n",
              " 'Random Forest': ['0.877', '0.877', '0.856', '0.916', '0.932'],\n",
              " 'SVM': ['0.873', '0.862', '0.856', '0.891', '0.921'],\n",
              " 'XGBoost': ['0.895', '0.895', '0.876', '0.931', '0.944']}"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Comparison**"
      ],
      "metadata": {
        "id": "ewzEHnuBwI1y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def roc_curve_and_score(y_test, pred_proba):\n",
        "    fpr, tpr, _ = roc_curve(y_test.ravel(), pred_proba.ravel())\n",
        "    roc_auc = roc_auc_score(y_test.ravel(), pred_proba.ravel())\n",
        "    return fpr, tpr, roc_auc\n",
        "\n",
        "plt.style.use('default')\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "fpr, tpr, roc_auc = roc_curve_and_score(y_test, y_pred_prob_rf_tuned)\n",
        "plt.plot(fpr, tpr, color='black', lw=2,\n",
        "         label='Random Forest AUC={0:.3f}'.format(roc_auc))\n",
        "fpr, tpr, roc_auc = roc_curve_and_score(y_test, y_pred_prob_lr_cv)\n",
        "plt.plot(fpr, tpr, color='green', lw=2,\n",
        "         label='Logistic Regression AUC={0:.3f}'.format(roc_auc))\n",
        "fpr, tpr, roc_auc = roc_curve_and_score(y_test, y_pred_SVM_prob)\n",
        "plt.plot(fpr, tpr, color='orange', lw=2,\n",
        "          label='SVM AUC={0:.3f}'.format(roc_auc))\n",
        "fpr, tpr, roc_auc = roc_curve_and_score(y_test, y_pred_prob_xgb_tuned)\n",
        "plt.plot(fpr, tpr, color='red', lw=2,\n",
        "          label='XGBoost AUC={0:.3f}'.format(roc_auc))\n",
        "fpr, tpr, roc_auc = roc_curve_and_score(y_test, y_pred_nn_prob)\n",
        "plt.plot(fpr, tpr, color='gold', lw=2,\n",
        "          label='MLP AUC={0:.3f}'.format(roc_auc))\n",
        "fpr, tpr, roc_auc = roc_curve_and_score(y_test, y_pred_L_prob)\n",
        "plt.plot(fpr, tpr, color='purple', lw=2,\n",
        "          label='LightGBM AUC={0:.3f}'.format(roc_auc))\n",
        "ROC(fpr_gaus,tpr_gaus,\"Gaussian NB=\",'{0:.3f}'.format(auc_gaus))\n",
        "#plt.plot([0, 1], [0, 1], color='red', lw=1, linestyle='--')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "-b6BMsjkvEqO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "outputId": "2cced0bb-ce96-447d-c986-36f43c9b0599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No handles with labels found to put in legend.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_style('darkgrid')\n",
        "x=[]\n",
        "y=[]\n",
        "colors = [\"green\", \"blue\", \"orange\", \"black\", \"red\", \"gold\", \"purple\"]\n",
        "plt.style.use('default')\n",
        "sns.set(style=\"whitegrid\")\n",
        "#plt.xticks(rotation=90)\n",
        "\n",
        "plt.tick_params(labelsize=8)\n",
        "plt.ylim(0.6, 0.95)\n",
        "for i in range(len(list(AlgorithmsScores.keys()))):\n",
        "  x.append(str(list(AlgorithmsScores.keys())[i]))\n",
        "  y.append(float(AlgorithmsScores[str(list(AlgorithmsScores.keys())[i])][1]))\n",
        "# print(x)\n",
        "# print(y)\n",
        "plot = sns.barplot(x,y, palette=colors)\n",
        "plot.set_xlabel(\"ML Model\",fontsize=12)\n",
        "plot.set_ylabel(\"Accuracy\",fontsize=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "KLCf6iQs4UGu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 512
        },
        "outputId": "62f4d7fc-0eb6-47e9-bb06-9a1a5201a841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}