heartDisease.ipynb

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  "metadata": {
    "colab": {
      "name": "heartDisease.ipynb",
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      "collapsed_sections": [],
      "mount_file_id": "19Nl9rmRkOarRocH7kFPmfjbmQsTcrN01",
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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/tanvi029/heartDisease/blob/main/heartDisease.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HdjDaVPS-9Tt"
      },
      "source": [
        "Name - Tanvi Prasad\n",
        "USN - 1RN18IS113\n",
        "HEART DISEASE PREDICTION\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXcGUsBFwo5l"
      },
      "source": [
        "## **Importing Essential Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k_MZkEN2BkD_",
        "outputId": "336bce59-8a33-4555-ae46-b9ee541a243d"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "%matplotlib inline\n",
        "\n",
        "import os\n",
        "print(os.listdir())\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['.config', 'drive', 'sample_data']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C5R33M-2w3L4"
      },
      "source": [
        "# **II. Importing and understanding our dataset**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G6XovVtrpNW-",
        "outputId": "dde41872-7ed4-4ef7-8c75-c8fff2126c9c"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Urv0oxc8qUPl"
      },
      "source": [
        "path=\"/content/drive/MyDrive/heart.csv\"\n",
        "dataset = pd.read_csv(path)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EevMsStfx6Fv"
      },
      "source": [
        "Verifying it as a 'dataframe' object in pandas"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mAaN39H2BumS",
        "outputId": "17ae20c4-8547-4696-95b4-2a14ec727abc"
      },
      "source": [
        "type(dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SheeNz2hyBYy"
      },
      "source": [
        "pandas.core.frame.DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "notKTO8dyK6A"
      },
      "source": [
        "Shape of dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52XCERJ_BxpD",
        "outputId": "b5386060-dbb9-497c-e9c1-272352f3b52e"
      },
      "source": [
        "dataset.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cQX0in350E90"
      },
      "source": [
        "# **Printing out a few columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VHPx7hYA_j5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "82dd5cbe-26d1-4676-9ab3-41dce4289513"
      },
      "source": [
        "dataset.head(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
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              "      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-12ab9345-579f-4346-866b-b553f2573569 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-12ab9345-579f-4346-866b-b553f2573569');\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  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "23    61    1   2       150   243    1  ...      1      1.0      1   0     2       1\n",
              "45    52    1   1       120   325    0  ...      0      0.2      2   0     2       1\n",
              "293   67    1   2       152   212    0  ...      0      0.8      1   0     3       0\n",
              "145   70    1   1       156   245    0  ...      0      0.0      2   0     2       1\n",
              "41    48    1   1       130   245    0  ...      0      0.2      1   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9io3SaMhyVU4"
      },
      "source": [
        "# **Description**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        },
        "id": "oA0o3alBCptC",
        "outputId": "a3733343-e076-418b-e6de-47e66fc24663"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c30f6146-237a-4e9e-9ee1-94a1ac676a6d\">\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>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c30f6146-237a-4e9e-9ee1-94a1ac676a6d')\"\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-c30f6146-237a-4e9e-9ee1-94a1ac676a6d 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-c30f6146-237a-4e9e-9ee1-94a1ac676a6d');\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          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nX4ctwIeDUwD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "370d2420-d73b-40a8-92af-0ce811c8220c"
      },
      "source": [
        "dataset.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sOfnYRNMyZ0m"
      },
      "source": [
        "Let's understand our columns better:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtSFLMFhD_r2",
        "outputId": "5384fbee-f268-4ef4-82e6-5fa1633efdc3"
      },
      "source": [
        "info = [\"age\",\"1: male, 0: female\",\"chest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\",\"resting blood pressure\",\" serum cholestoral in mg/dl\",\"fasting blood sugar > 120 mg/dl\",\"resting electrocardiographic results (values 0,1,2)\",\" maximum heart rate achieved\",\"exercise induced angina\",\"oldpeak = ST depression induced by exercise relative to rest\",\"the slope of the peak exercise ST segment\",\"number of major vessels (0-3) colored by flourosopy\",\"thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\"]\n",
        "\n",
        "\n",
        "\n",
        "for i in range(len(info)):\n",
        "    print(dataset.columns[i]+\":\\t\\t\\t\"+info[i])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "age:\t\t\tage\n",
            "sex:\t\t\t1: male, 0: female\n",
            "cp:\t\t\tchest pain type, 1: typical angina, 2: atypical angina, 3: non-anginal pain, 4: asymptomatic\n",
            "trestbps:\t\t\tresting blood pressure\n",
            "chol:\t\t\t serum cholestoral in mg/dl\n",
            "fbs:\t\t\tfasting blood sugar > 120 mg/dl\n",
            "restecg:\t\t\tresting electrocardiographic results (values 0,1,2)\n",
            "thalach:\t\t\t maximum heart rate achieved\n",
            "exang:\t\t\texercise induced angina\n",
            "oldpeak:\t\t\toldpeak = ST depression induced by exercise relative to rest\n",
            "slope:\t\t\tthe slope of the peak exercise ST segment\n",
            "ca:\t\t\tnumber of major vessels (0-3) colored by flourosopy\n",
            "thal:\t\t\tthal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H1y_zQhOyfaI"
      },
      "source": [
        "Analysing the 'target' variable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Om7A1kS9EBhI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7bb35e1b-bca7-4707-d80a-85070f4b2fc8"
      },
      "source": [
        "dataset[\"target\"].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    303.000000\n",
              "mean       0.544554\n",
              "std        0.498835\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        1.000000\n",
              "75%        1.000000\n",
              "max        1.000000\n",
              "Name: target, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy6DYVVUyg1y"
      },
      "source": [
        "Clearly, this is a classification problem, with the target variable having values '0' and '1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZM5lRDREfqD",
        "outputId": "f35eef5b-7ae3-4a97-a656-82560a8e1db9"
      },
      "source": [
        "dataset[\"target\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWRRDNFvykUe"
      },
      "source": [
        "# **Checking correlation between columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfwYF8WIEjLN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "edc40705-dae0-49f6-b117-e41ee563de52"
      },
      "source": [
        "print(dataset.corr()[\"target\"].abs().sort_values(ascending=False))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "target      1.000000\n",
            "exang       0.436757\n",
            "cp          0.433798\n",
            "oldpeak     0.430696\n",
            "thalach     0.421741\n",
            "ca          0.391724\n",
            "slope       0.345877\n",
            "thal        0.344029\n",
            "sex         0.280937\n",
            "age         0.225439\n",
            "trestbps    0.144931\n",
            "restecg     0.137230\n",
            "chol        0.085239\n",
            "fbs         0.028046\n",
            "Name: target, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4xsV8AsyyK2"
      },
      "source": [
        "# **Exploratory Data Analysis (EDA)**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "6tZLLMmTFqTa",
        "outputId": "cc486d8e-e3f8-4d7f-b6a8-a23d09e3a5c7"
      },
      "source": [
        "y = dataset[\"target\"]\n",
        "\n",
        "sns.countplot(y)\n",
        "\n",
        "\n",
        "target_temp = dataset.target.value_counts()\n",
        "\n",
        "print(target_temp)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    165\n",
            "0    138\n",
            "Name: target, dtype: int64\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",
      "metadata": {
        "id": "5jqAVoGaEuIF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4cbadfae-9c4c-4e1f-e97f-ba42c007fbbd"
      },
      "source": [
        "print(\"Percentage of patience without heart problems: \"+str(round(target_temp[0]*100/303,2)))\n",
        "print(\"Percentage of patience with heart problems: \"+str(round(target_temp[1]*100/303,2)))\n",
        "\n",
        "#Alternatively,\n",
        "# print(\"Percentage of patience with heart problems: \"+str(y.where(y==1).count()*100/303))\n",
        "# print(\"Percentage of patience with heart problems: \"+str(y.where(y==0).count()*100/303))\n",
        "\n",
        "# #Or,\n",
        "# countNoDisease = len(df[df.target == 0])\n",
        "# countHaveDisease = len(df[df.target == 1])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of patience without heart problems: 45.54\n",
            "Percentage of patience with heart problems: 54.46\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-45JeAHpFUKW",
        "outputId": "8df7fa9f-9dbb-44c6-fea6-6c3e2ea8e7dd"
      },
      "source": [
        "dataset[\"sex\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "ZaZAe2zFFaC6",
        "outputId": "26373a62-6833-4975-be3a-e4a3f8ff9885"
      },
      "source": [
        "sns.barplot(dataset[\"sex\"],y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf321550>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "rj_GEB-Gy4wZ"
      },
      "source": [
        "# **Analysing the 'Chest Pain Type' feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNdCSkdjFejh",
        "outputId": "fe6ae04b-0357-4b8b-b4fc-af1e79f2c831"
      },
      "source": [
        "dataset[\"cp\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 2, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "6U302IKKGJXp",
        "outputId": "3c9329fa-1235-4201-9cb4-3f29c22efa84"
      },
      "source": [
        "sns.barplot(dataset[\"cp\"],y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf7dfcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "DEA_QuuTy-qM"
      },
      "source": [
        "# **Analysing the FBS feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-C0G5VBpGTry",
        "outputId": "c2c2f2f5-0f81-4217-d8f4-711d50c5e00e"
      },
      "source": [
        "dataset[\"fbs\"].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    303.000000\n",
              "mean       0.148515\n",
              "std        0.356198\n",
              "min        0.000000\n",
              "25%        0.000000\n",
              "50%        0.000000\n",
              "75%        0.000000\n",
              "max        1.000000\n",
              "Name: fbs, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYSb9bYDGZPu",
        "outputId": "3572069b-f774-49db-ea76-c395e05ecc52"
      },
      "source": [
        "dataset[\"fbs\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WO8F4pObtRE_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "MFBkAkX8GeBw",
        "outputId": "a3965446-f8cb-4ce7-d685-9ce54cacd62e"
      },
      "source": [
        "sns.barplot(dataset[\"fbs\"],y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf312d90>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "Db8AdjF0zQ9t"
      },
      "source": [
        "# **Analysing the restecg feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ow1_COyZG7rD",
        "outputId": "e4b3aeba-1d39-4441-dc38-c5a1667e9c20"
      },
      "source": [
        "dataset[\"restecg\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "-QhvZL4pHQnO",
        "outputId": "6625fbce-9109-469a-d6bb-b0d3eae534d4"
      },
      "source": [
        "sns.barplot(dataset[\"restecg\"],y)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf29f090>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "7lawP2OZzVtk"
      },
      "source": [
        "# Analysing the 'exang' feature"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-H3fYnqH5Sr",
        "outputId": "5412056b-6f71-4cb1-fa2c-8d8f92402eb9"
      },
      "source": [
        "dataset[\"exang\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sqFV9avwIBuA",
        "outputId": "dee9a574-a9ca-43e6-a1c7-568696b9f200"
      },
      "source": [
        "sns.barplot(dataset[\"exang\"],y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf1f0e10>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LJUntGASS1DmDQJI6ZxBIUucMAknqnEEgSZ0zCCSpcwaBJHXOIJCkzhkEktQ5g0CSOmcQSFLnDAJJ6pxBIEmdMwgkqXMGgSR1ziCQpM4ZBJLUuaZBkGRjkiNJFpPsGLP9Lyb5ncH2zya5uWU9kqQzNQuCJCuAPcCdwAZga5INI8PuBr5WVX8V+A3gfa3qkSSN1/KI4DZgsaqOVtWzwD5g88iYzcBHBo8fAN6YJA1rkiSNaBkEq4BjQ+3jg76xY6rqNPA08Fca1iRJGnHltAuYRJJtwLZB8+tJjkyznsvM9cDj0y7iUpB/+7PTLkEv5u/mC/7FRTlRctPZNrQMghPAmqH26kHfuDHHk1wJXAc8MbqjqtoL7G1UZ9eSLFTV7LTrkEb5u7l8Wp4aOgCsT7IuyUpgCzA3MmYOeOHPsLcAv19V1bAmSdKIZkcEVXU6yXZgP7AC+FBVHUyyC1ioqjngPwC/nWQReJKlsJAkLaP4B3jfkmwbnHqTLin+bi4fg0CSOucSE5LUOYOgU+db/kOaliQfSvJYki9Ou5ZeGAQdmnD5D2laPgxsnHYRPTEI+jTJ8h/SVFTVp1n6FKGWiUHQp0mW/5DUCYNAkjpnEPRpkuU/JHXCIOjTJMt/SOqEQdChwZLfLyz/cRi4v6oOTrcqaUmSjwEPAq9IcjzJ3dOu6XLnlcWS1DmPCCSpcwaBJHXOIJCkzhkEktQ5g0CSOmcQSFLnDAJJ6pxBII2R5G1J/jjJw0k+kOT2JI8kuSrJ1UkOJnlVkmuSfDLJ55J8IcnmwfNvTnI4yQcHYz+R5HsG21472NfDSd7vuvuaNoNAGpHkh4C/D7yuqn4E+DbwCpaW4fiXwG7gP1XVF4FvAn+7ql4DvAH4tSQZ7Go9sKeqfhh4CnjzoP+3gJ8b2rc0VVdOuwDpEvRG4FbgwOA9/XuAx4BdLK3T9E3gFwZjA/yrJK8HnmdpOe8fGGz7clU9PHj8EHBzku8Drq2qBwf99wE/3fbbkc7NIJDOFOAjVXXvizqTlwHXAH8BuAr4BvBWYAa4taqeS/KVwTaAbw09/dssBYp0yfHUkHSmTwJvSXIDQJLvT3IT8AHgnwMfBd43GHsd8NggBN4A3HSuHVfVU8AzSW4fdG1p8Q1IF8IjAmlEVR1K8l7gE0muAJ4DPg48V1X3De75/D+T/ARLofC7Sb4ALAD/a4KXuBv4YJLngT8Enm7yjUgTcvVRaZkluaaqvj54vAN4WVW9a8plqWMeEUjL76eS3MvS/79HgbumW4565xGBJHXOyWJJ6pxBIEmdMwgkqXMGgSR1ziCQpM4ZBJLUuf8P3z2eo5r6zawAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GcpTW6tzZTO"
      },
      "source": [
        "# **Analysing the Slope feature**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfcDnRFMIIrJ",
        "outputId": "af5f0dd4-f484-459c-8ac6-7a8b9cc18ac7"
      },
      "source": [
        "dataset[\"slope\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "tNCZTlLKHQi1",
        "outputId": "f272e1c2-c5ab-4d0d-fe3d-43da75f3e95e"
      },
      "source": [
        "sns.barplot(dataset[\"slope\"],y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf183510>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAASsUlEQVR4nO3df6zdd33f8ecrzjxEktJtvp2Z7WCLuWVuSSFczLZstAXSOWy1UUs3e7AVKcNjqykrLZ7RkKncSlPMBH9MroRpo9KW1KTpNG6FW2uioV0RP3xNk4BtOb0yBNvlipuEpAlUJF7e++OeVKfH59rXxt9z7vXn+ZCucr6f78ff87q6sV/3++N8v6kqJEntum7cASRJ42URSFLjLAJJapxFIEmNswgkqXHXjzvA5Vq1alWtX79+3DEkaVk5duzYo1U1MWzdsiuC9evXMz09Pe4YkrSsJHlkoXUeGpKkxlkEktQ4i0CSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1btl9oEySrrbdu3czOzvL6tWr2b9//7jjjJxFIKl5s7OznDt3btwxxsZDQ5LUOItAkhrXaREk2ZLkVJKZJHuGrL85yf1J/izJQ0ne2GUeSdKFOiuCJCuAA8AdwCZgR5JNA9PeB9xbVa8EtgO/2lUeSdJwXe4RbAZmqup0VT0DHAK2Dcwp4Ht6r18E/EWHeSRJQ3RZBGuAM33LZ3tj/X4JeGuSs8Bh4J3DNpRkZ5LpJNNzc3NdZJWkZo37ZPEO4Deqai3wRuC3klyQqaoOVtVkVU1OTAx9wI4k6Qp1WQTngHV9y2t7Y/3uBO4FqKrPAi8AVnWYSZI0oMsiOApsTLIhyUrmTwZPDcz5GvB6gCT/iPki8NiPJI1QZ0VQVeeBXcAR4CTzVwcdT7IvydbetF8A3p7kQeB3gLdVVXWVSZJ0oU5vMVFVh5k/Cdw/trfv9Qngti4zSJIubtwniyVJY2YRSFLjLAJJapxFIEmNswgkqXE+mEbSVXfb/1xeFwOufGIl13EdZ544s6yyf+adn7kq23GPQJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjOi2CJFuSnEoyk2TPkPUfSvJA7+vhJE90mUeSdKHO7jWUZAVwALgdOAscTTLVeyoZAFX1833z3wm8sqs8kqThutwj2AzMVNXpqnoGOARsu8j8Hcw/t1iSNEJdFsEa4Ezf8tne2AWSvATYAPzRAut3JplOMj03N3fVg0pSy5bKyeLtwH1V9f+Grayqg1U1WVWTExMTI44mSde2LovgHLCub3ltb2yY7XhYSJLGossiOApsTLIhyUrm/7GfGpyU5GXA3wE+22EWSdICOiuCqjoP7AKOACeBe6vqeJJ9Sbb2Td0OHKqq6iqLJGlhnT6qsqoOA4cHxvYOLP9Slxkk6VLqhcVzPEe9sM3fR31msaTmPXvbs+OOMFZL5aohSdKYWASS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjvOncErJ7925mZ2dZvXo1+/fvH3ccSY2wCJaQ2dlZzp1b6CFuktQNDw1JUuM6LYIkW5KcSjKTZM8Cc/51khNJjie5p8s8kqQLdXZoKMkK4ABwO3AWOJpkqqpO9M3ZCLwXuK2qvpnk+7rKI0karss9gs3ATFWdrqpngEPAtoE5bwcOVNU3AarqGx3mkSQN0WURrAHO9C2f7Y31+37g+5N8JsnnkmwZtqEkO5NMJ5mem5vrKK4ktWncJ4uvBzYCPwrsAD6S5HsHJ1XVwaqarKrJiYmJEUeUpGtbl0VwDljXt7y2N9bvLDBVVc9W1VeAh5kvBknSiHRZBEeBjUk2JFkJbAemBub8b+b3BkiyivlDRac7zCRJGtBZEVTVeWAXcAQ4CdxbVceT7EuytTftCPBYkhPA/cB7quqxrjJJki7U6SeLq+owcHhgbG/f6wLe3fuSJI3BuE8WS5LGzCKQpMZZBJLUOItAkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcZ0WQZItSU4lmUmyZ8j6tyWZS/JA7+s/dJlHknShzp5QlmQFcAC4nfmH1B9NMlVVJwamfryqdnWVQ5J0cV3uEWwGZqrqdFU9AxwCtnX4fpKkK9BlEawBzvQtn+2NDfqpJA8luS/JumEbSrIzyXSS6bm5uS6ySlKzxn2y+PeB9VV1C/B/gI8Om1RVB6tqsqomJyYmRhpQkq51XRbBOaD/N/y1vbG/VlWPVdV3eou/BryqwzySpCG6LIKjwMYkG5KsBLYDU/0Tkry4b3ErcLLDPJKkIS551VCSDVX1lUuNDaqq80l2AUeAFcDdVXU8yT5guqqmgJ9LshU4DzwOvO0Kvw9J0hVazOWjvwfcOjB2H4s4jFNVh4HDA2N7+16/F3jvIjJIkjqyYBEkeRnwg8CLkvxk36rvAV7QdTBJ0mhcbI/gB4B/BXwv8BN9408Bb+8ylCRpdBYsgqr6BPCJJP+kqj47wkySpBFazFVDjyX5VJIvAyS5Jcn7Os4lSRqRxRTBR5g/ofssQFU9xPyloJKka8BiiuCFVfWFgbHzXYSRJI3eYorg0SQvBQogyZuBr3eaSpI0Mov5HMHPAgeBlyU5B3wFeGunqSRJI3PJIqiq08AbktwAXFdVT3UfS5I0Kou5xcS7B5YBngSOVdUDHeWSJI3IYs4RTALvYP5ZAmuA/whsAT6SZHeH2SRJI7CYcwRrgVur6mmAJO8HPgm8FjgG7O8unrQ87N69m9nZWVavXs3+/f6V0PKymCL4PuA7fcvPAn+/qv4qyXcW+DNLwqve85vjjnBZbnr0KVYAX3v0qWWV/dgH/v24I4zd7Ows586du/REaQlaTBF8DPh8kk/0ln8CuKd38njwQfSSpGXmokWQ+TPDvwH8AXBbb/gdVTXde/2W7qJJkkbhokVQVZXkcFW9HJi+2FxJ0vK0mKuGvpjk1Vey8SRbkpxKMpNkz0Xm/VSSSjJ5Je8jSbpyizlH8BrgLUkeAb4FhPmdhVsu9oeSrAAOALcDZ4GjSaaq6sTAvJuAdwGfv4L8kqTv0mKK4F9c4bY3AzO9TyaT5BCwjQtPMP8ycBfwnit8H0nSd+GSh4aq6pGqegT4K+ZvPPf816WsAc70LZ/tjf21JLcC66rqkxfbUJKdSaaTTM/NzS3irSVJi3XJIkiyNcmfM3+zuT8Gvsr8VUTflSTXAR8EfuFSc6vqYFVNVtXkxMTEd/vWkqQ+izlZ/MvAPwYerqoNwOuBzy3iz50D1vUtr+2NPe8m4IeATyf5au89pjxhLEmjtZgieLaqHgOuS3JdVd3P/P2HLuUosDHJhiQrmX+q2dTzK6vqyapaVVXrq2o98+Wyte8zCpKkEVjMyeInktwI/AnwsSTfAJ6+1B+qqvNJdgFHgBXA3VV1PMk+YLqqpi6+BUnSKCymCB4Evg38PPOfJH4RcONiNl5Vh4HDA2N7F5j7o4vZpiTp6lpMEfxYVT0HPAd8FCDJQ52mkiSNzIJFkOQ/Af8ZeOnAP/w3AZ/pOpgkaTQutkdwD/OXif53oP/2EE9V1eOdppIkjcyCRVBVTzL/SModo4sjSRq1xVw+Kkm6hlkEktQ4i0CSGreYy0elsfjavpePO8KinX/87wLXc/7xR5ZV7pv3fmncEbQEuEcgSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjLAJJalynRZBkS5JTSWaS7Bmy/h1JvpTkgSR/mmRTl3kkSRfqrAiSrAAOAHcAm4AdQ/6hv6eqXl5VrwD2Ax/sKo8kabgu9wg2AzNVdbqqngEOAdv6J1TVX/Yt3gBUh3kkSUN0ea+hNcCZvuWzwGsGJyX5WeDdwErgdcM2lGQnsBPg5ptvvupBJallYz9ZXFUHquqlwH8F3rfAnINVNVlVkxMTE6MNKEnXuC6L4Bywrm95bW9sIYeAN3WYR5I0RJdFcBTYmGRDkpXAdmCqf0KSjX2L/xL48w7zSJKG6OwcQVWdT7ILOAKsAO6uquNJ9gHTVTUF7EryBuBZ4JvAz3SVR5I0XKcPpqmqw8DhgbG9fa/f1eX7S5IubewniyVJ4+WjKqWrYNULngPO9/4rLS8WgXQV/OItT4w7gnTFPDQkSY2zCCSpcRaBJDXOIpCkxlkEktQ4i0CSGmcRSFLjLAJJapwfKFtCnlt5w9/4rySNgkWwhHxr44+PO4KkBnloSJIaZxFIUuMsAklqXKdFkGRLklNJZpLsGbL+3UlOJHkoyaeSvKTLPJKkC3VWBElWAAeAO4BNwI4kmwam/RkwWVW3APcB+7vKI0karss9gs3ATFWdrqpngEPAtv4JVXV/VX27t/g5YG2HeSRJQ3RZBGuAM33LZ3tjC7kT+IMO80iShlgSnyNI8lZgEviRBdbvBHYC3HzzzSNMJknXvi73CM4B6/qW1/bG/oYkbwD+G7C1qr4zbENVdbCqJqtqcmJiopOwktSqLovgKLAxyYYkK4HtwFT/hCSvBD7MfAl8o8MskqQFdFYEVXUe2AUcAU4C91bV8ST7kmztTfsAcCPwu0keSDK1wOYkSR3p9BxBVR0GDg+M7e17/YYu31+SdGl+sliSGmcRSFLjLAJJapxFIEmNswgkqXEWgSQ1ziKQpMZZBJLUOItAkhpnEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIa12kRJNmS5FSSmSR7hqx/bZIvJjmf5M1dZpEkDddZESRZARwA7gA2ATuSbBqY9jXgbcA9XeWQJF1cl88s3gzMVNVpgCSHgG3AiecnVNVXe+ue6zCHJOkiujw0tAY407d8tjd22ZLsTDKdZHpubu6qhJMkzVsWJ4ur6mBVTVbV5MTExLjjSNI1pcsiOAes61te2xuTJC0hXRbBUWBjkg1JVgLbgakO30+SdAU6K4KqOg/sAo4AJ4F7q+p4kn1JtgIkeXWSs8BPAx9OcryrPJKk4bq8aoiqOgwcHhjb2/f6KPOHjCRJY7IsThZLkrpjEUhS4ywCSWqcRSBJjbMIJKlxFoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAklqnEUgSY2zCCSpcRaBJDXOIpCkxlkEktS4TosgyZYkp5LMJNkzZP3fTvLx3vrPJ1nfZR5J0oU6K4IkK4ADwB3AJmBHkk0D0+4EvllV/xD4EHBXV3kkScN1uUewGZipqtNV9QxwCNg2MGcb8NHe6/uA1ydJh5kkSQO6fGbxGuBM3/JZ4DULzamq80meBP4e8Gj/pCQ7gZ29xaeTnOok8dKwioHvf6nL//iZcUdYKpbdz473+3tXn2X388vPXdbP7yULrej04fVXS1UdBA6OO8coJJmuqslx59Dl82e3vLX88+vy0NA5YF3f8tre2NA5Sa4HXgQ81mEmSdKALovgKLAxyYYkK4HtwNTAnCng+eMKbwb+qKqqw0ySpAGdHRrqHfPfBRwBVgB3V9XxJPuA6aqaAn4d+K0kM8DjzJdF65o4BHaN8me3vDX784u/gEtS2/xksSQ1ziKQpMZZBEvEpW7HoaUryd1JvpHky+POosuXZF2S+5OcSHI8ybvGnWnUPEewBPRux/EwcDvzH7w7CuyoqhNjDaZFSfJa4GngN6vqh8adR5cnyYuBF1fVF5PcBBwD3tTS3z/3CJaGxdyOQ0tUVf0J81e9aRmqqq9X1Rd7r58CTjJ/14NmWARLw7DbcTT1P6K0FPTugPxK4PPjTTJaFoEkAUluBH4P+C9V9ZfjzjNKFsHSsJjbcUjqSJK/xXwJfKyq/te484yaRbA0LOZ2HJI60Lv1/a8DJ6vqg+POMw4WwRJQVeeB52/HcRK4t6qOjzeVFivJ7wCfBX4gydkkd447ky7LbcC/A16X5IHe1xvHHWqUvHxUkhrnHoEkNc4ikKTGWQSS1DiLQJIaZxFIUuMsAukyJPl0kiYfcK5rl0UgSY2zCKQFJLkhySeTPJjky0n+zcD6HUm+1Ft3V9/400k+1Lu3/aeSTPTGX5rkD5McS/J/k7xs1N+TNIxFIC1sC/AXVfXDvecM/OHzK5L8A+Au4HXAK4BXJ3lTb/UNwHRV/SDwx8D7e+MHgXdW1auAXwR+dTTfhnRxFoG0sC8Btye5K8k/r6on+9a9Gvh0Vc31bhHyMeC1vXXPAR/vvf5t4J/17mz5T4HfTfIA8GHgxSP5LqRLuH7cAaSlqqoeTnIr8EbgV5J86ko3xfwvXU9U1SuuWkDpKnGPQFpA7/DPt6vqt4EPALf2rf4C8CNJVvUeNbqD+cNAMP/36s291/8W+NPe/e2/kuSne9tOkh8exfchXYpFIC3s5cAXeody3g/8yvMrqurrwB7gfuBB4FhVfaK3+lvA5t7D7F8H7OuNvwW4M8mDwHF8HKmWCO8+Kl1lSZ6uqhvHnUNaLPcIJKlx7hFIUuPcI5CkxlkEktQ4i0CSGmcRSFLjLAJJatz/ByeB7SD/mBK/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YfugfYOJzeKu"
      },
      "source": [
        "# **Analysing the 'ca' feature**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oY13GYzXIYKY",
        "outputId": "8e51179b-dd6b-4d21-da06-d4f07a1c287c"
      },
      "source": [
        "dataset[\"ca\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 2, 1, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "VtxkJxPZId1U",
        "outputId": "bc95f9d7-f3ec-4131-ed99-bf80846db0c2"
      },
      "source": [
        "sns.countplot(dataset[\"ca\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf0f00d0>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "pcIcfSKqIj8e",
        "outputId": "6df219ff-2698-4c64-d3f3-423e164b4a8a"
      },
      "source": [
        "sns.barplot(dataset[\"ca\"],y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf0617d0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WldwPuITIuvv",
        "outputId": "4f392d57-cc8c-4467-9a1a-4fff9197a568"
      },
      "source": [
        "dataset[\"thal\"].unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MPc1n3FJI6Wd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "outputId": "bbcf05bd-d5b0-42ab-89aa-1c2587b34709"
      },
      "source": [
        "sns.barplot(dataset[\"thal\"],y)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddf059290>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "fqnQo6GsJFfz",
        "outputId": "67512662-343e-486e-b661-cd1142126681"
      },
      "source": [
        "sns.distplot(dataset[\"thal\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4ddefbbf10>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MiCivbGJqVjJpiaGp3I9PjCUl3k1lQ3tI7meiz1kThKq6RioQY8zwqSpbKxq5ePrw1l8KRkQoTE+kwhLEmGUbzxozCpxs7qCmpZMFIWpe6lGQkURtayftXTZhbiyyBGHMKFBe0QTA/MLxIb1vQXoiAFWNVosYiyxBGDMKlFc24o4RSkK8l3TBeH9HdWWDzYcYiyxBGDMKbKtsZFZeKgmxoe02TIxzkZUST4VNmBuTLEEYE+V8PmVbRRMLCkLbvNSjp6Pavzr/2NDe5WVrReOYX6xwsPtBGGMi1KHaNlo6PY4liIL0RLZUNNLU3u3I/SNJl8fHM1ur2F7VhNenJMa6+PhFk/nfK2YSc47Ll0QjR2sQIrJSRPaKyAERuSfI8R+KyNbAY5+INPY65u11bI2TcRoTzbZV+v/ZLAhxB3WPgvSefojR3VHd3uXlwbcOU17RyLIpGfz0lkVcVZLL/a8d5LOPbsHjHXtbsDpWgxARF/AzYDlQCWwUkTWquqvnHFX9Yq/zPwss6nWLdlVd6FR8xowW5RWNJMW5HFuWOy8tAVeMjOqOalXlqS2VVDW0c8vSIubmp3Hdgol8YH4ecyaO47vP72FWbiqfvbI43KGOKCdrEEuBA6p6SFW7gEeBVWc5/xbgEQfjMWZUKq9sYm5+2jmv4NoftyuGvLSEUT1hbltVEzuPN7O8JJe5+f+YSyIi3HXpNK5fMJEfv7yf7ZVNYYxy5DmZIPKBil6vKwNl7yEik4ApwCu9ihNEpExENojIDf29iYisDpxXVlNjexmZsaXL42PX8WYWOtS81KMgPYmqhvZRubJrR7eXP5cfpzA9kYuLg89E//aquWSmxPGvz2wfU531kTKK6WbgCVXtPWRgkqqWArcCPxKRacEuVNUHVLVUVUuzs7NHIlZjIsaek810eX3MD/EM6r4K0xPp8vo4UD369qnecKiO011erlswkZh+9vFOS4rlS8tnUF7ZxCt7qkc4wvBxMkFUAYW9XhcEyoK5mT7NS6paFfh5CHiNd/dPGGPwNy8Bjo1g6tHTUb21osHR9xlpnd1e/ra/lpm5qWd+x/58cHEBRRlJ/GDdvjFTi3AyQWwEikVkiojE4U8C7xmNJCKzgHTg773K0kUkPvA8C/+udrv6XmvMWFde0UhGctyZJTGckpkSR0JszJmENFq8fbie9m4vV8zKGfDcWFcMn71iOjuPN/P6vrHRnO1YglBVD3A3sBbYDTymqjtF5F4Rub7XqTcDj+q7U/JsoExEyoFXge/2Hv1kjPHbVtnIgoI0pJ+mkVCJESF/fOKZIbWjgU+Vtw/XMSUrmcKMs9ceely/cCKZyXE8vOGYw9FFBkcnyqnqc8Bzfcq+0ef1t4Jctx6Y52RsxkS71k4P+6tbuXZe3oi8X0F6Em8dqKWj2xvyJT3C4UB1Kw2nu1kxZ8Kgr4l3u7jpvEJ+/vpBqhrbyR/vbM0t3CKlk9oYM0TbK5tQdb7/oUf++EQ8PmX3iWB7iEWfdw7XkxznYs4QFzi8ZWkRCjzy9uivRViCMCZK9TT3OD2CqUdPP8e2UdAP0dLRzZ6TzSyZlI7bNbSPwcKMJC6bkc1TmyvxjcJhv71ZgjAmSpVXNlKQnkhmSvyIvF9aYixZKfGUj4J+iO1VTfgUFhWlD+v6Gxblc7ypg3eO1Ic4sshiCcKYKFVe0eTY+kvBiAgLCtJGRQ2ivKKRvLQEcsclDOv65SW5JMW5eHZrfyP3RwdLEMZEodrWTqoa20O+xehA5heM52BNK62dnhF931Cqb+uioqH9nPpukuLcXD1nAn/ddmJULwluCcKYKHRmBdcR6qDuMb8wDVWiek2i8hD13axaOJHmDg9v7KsNRVgRyRKEMVFoa0UTMcK7FpYbCT0JKZrnQ+w63kxRRhLjk+LO6T4XTssiNcHNul0nQxRZ5LEEYUwUKq9opDgnleT4kd3zq2fWdrT2QzS3d1PV2M6sCannfK84dwxXzMrhpd3Vo3IRQ7AEYUzUUVX/DOrCka099FhQMD5qRzLtPdUCwKwhzn3oz4qSCdS3dbHp6Ohao6qHJQhjokxFfTsNp7uZP8L9Dz3mF6RR2dBOXWtnWN7/XOw50cz4pFhyU0MzNPjSmdnEuWJGbTOTJQhjokzPt3en94DoT09i2lYVXc1M3V4fB2pamTVhXMjWrkqJd3Ph9Exe3HVqVK7wagnCmChTXtFInDuGmSFoRx+OeQVpiMC2iuhKEIdqWun2akj6H3pbUTKBo3Wn2Xdq9O2VYQnCmChTXtnInInjiB3iEhGhkhLvZlp2StSNZNp9soU4VwxTs5JDet+rSnIQgRd3jr5mJksQxkQRj9fHjqrmEZ//0Nf8gjTKK5uipllFVdl7soXpOSlDXntpIDmpCSwqHM+Lu06F9L6RwBKEMVFkf3Ur7d3esI1g6rGgYDy1rZ2caOoIaxyDdaKpg6b2bmbnOdMst2LOBLZXNXG8sd2R+4eLJQhjoki4ZlD31TMLOVqamfacbEGAGbnOJIjlJbkAvLx7dNUiLEEYE0W2VjSRmuBmcmZo29GHanbeONwxEjVbkO452UxBeiKpCbGO3H9qVjKTM5N4ZU+1I/cPF0cThIisFJG9InJARO4JcvxjIlIjIlsDj0/0OnaHiOwPPO5wMk5josWWYw0sLBxPTIyzW4wOJCHWxay81KioQbR0dFPZ0M7MCaGZHBeMiHD5rBzWH6yjvWv0LN7nWIIQERfwM+AaoAS4RURKgpz6J1VdGHj8KnBtBvBNYBmwFPimiAxv4XZjRomWjm72nmph8TD3MAi1+QXj2VbZFPGb5uw96Z897VT/Q48rZuXQ6fGx/uDoWbzPyRrEUuCAqh5S1S7gUWDVIK+9GlinqvWq2gCsA1Y6FKcxUWFrRSOqsGRSZCSIhYXjaenwcLAmssf/7znZQlpiLBOGuffDYC2dkkFynGtUNTM5mSDygYperysDZX19SES2icgTIlI4xGsRkdUiUiYiZTU1NaGI25iItPloIyKwsCi8HdQ9SgOJqiyC1yHq9vo4UN3KrAmpIZs93Z94t4uLi7N4ZU911Az/HUi4O6n/DExW1fn4awm/HeoNVPUBVS1V1dLs7OyQB2hMpNh0rIEZOamMc6ijdaimZCWTmRxH2ZHITRCHa9vo8vqY5WD/Q29XzMrhRFMHewLNWtHOyQRRBRT2el0QKDtDVetUtWfFr18BSwZ7rTFjic+nbDnWwOIIaV4Cf8fs4knpbDoaufsy7znZTKxLmJo9MqO+Lp+ZAzBqmpmcTBAbgWIRmSIiccDNwJreJ4hIXq+X1wO7A8/XAitEJD3QOb0iUGbMmHSgppWWDk/E9D/0KJ2UzpG609S0RN7KrqrKnhMtTM9JHbFlSXLGJTAvP80SxEBU1QPcjf+DfTfwmKruFJF7ReT6wGmfE5GdIlIOfA74WODaeuDb+JPMRuDeQJkxY1LPfgOLI6T/oUfpZH/CisT9EE41d9LY3h3yxfkGcvmsHLYca6ChrWtE39cJjqZVVX1OVWeo6jRV/U6g7Buquibw/GuqOkdVF6jq5aq6p9e1D6rq9MDjN07GaUyk23y0gfSkWKaEeKG5czU3P404d0xENjPtOdkMMOKr3l45Kwefwuv7on/QTLg7qY0xg7DpWAOLi9IdH4kzVPFuF/Pz0yKyBrH7RDP54xNHvFN/Xn4aWSnxvDwKmpksQRgT4RraujhU0xZRHdS9LZmczo6qZjq6I2cGcWunh8qGdmY5PDkumJgY4bKZ2by+txqP1zfi7x9KliCMiXBbKvzfziOtg7rHkqJ0urw+tkfQDnN7T7agMGLDW/u6clYOzR2eiKxZDYUlCGMi3KajDbhi5MwKqpGmJ3FF0nyIPSebGZfgZmKas7On+3NxcRaxLuGVvdHdzGQJwpgIV3akgZK8cSTFucMdSlCZKfFMzUqOmI5qj9fH/urQ7j09VKkJsZw3OYNXo7wfwhKEMRGso9vLlopGlk3JCHcoZ7VkUjqbjjZExBITh2vb6PL4Rnx4a19XzMph36lWKupPhzWOc2EJwpgItuVYI10eH+dPzQx3KGd13uQMGk53s786/Av37TzRTJwrhmk5KWGN44pZ/lnVr0ZxM5MlCGMi2IZDdcQInBfhNYgLpvkT2PoD4V3q2qfK7hPNFOemjNjs6f5MzU6J+k2ELEEYE8E2HKpjzsQ00hIjY4G+/hRmJFGUkcRbB+vCGkdlQzstHR7mTAzP6KW+ejYROt3lCXcow2IJwpgI1dHtZcuxxjPfziPdhdMy2XCoDm8YNxDadbyJGIGZuZGRIK6clUuXx8dbB8KbOIfLEoQxEWrz0Qa6vD7OnxrZzUs9LpyeRUuHhx1hmg+hquw83sy07BQS41xhiaGvpVMySE1ws3bnyXCHMiyWIIyJUK/vryHWJSybEh01iAsCHelvhWnLzf3VrdS1dVESIc1LAHHuGJbPzuXFnSfp8kTfrGpLEMZEqDf21bJkUjrJ8ZE5/6Gv7NR4Zk1I5W/7wpMgXgx8S58dptnT/bl2Xh7NHZ6wJc5zYQnCmAhU3dLB7hPNXDIjunZJvGxmDhuP1NPS0T3i77125ykK0xMZF2Ed+u+bkUVqvJvntp0IdyhDZgnCmAjU8y38kuLoShCXz8zG41PeGuHhrlWN7WyvamLOxMhbjiTe7eKqklxe3HWK7ihbvM8ShDER6I39NWQmx1GSF1nNJQNZPCmd1AQ3r+4Z2b0Qnt/u/3YeSf0PvV07L4+m9u4RT5znyhKEMRHG4/Xx+r4aLp2RTUxMZO3/MJBYVwyXFGfz2r7qEV1249mtx5lf4N+HIRK9rziLlHg3z22PrmYmRxOEiKwUkb0ickBE7gly/EsisktEtonIyyIyqdcxr4hsDTzW9L3WmNFq45EGGk93s7wkN9yhDMtlM7M51dzJjqrmEXm/gzWtbK9q4voFE0fk/YYjIdbFVbNzoq6ZybEEISIu4GfANUAJcIuIlPQ5bQtQqqrzgSeA7/U61q6qCwOP6zFmjFi36xRx7pio66DucdXsXFwxwvM7Rubb8pqtxxEhohME+JuZGk93sz7Ms82HwskaxFLggKoeUtUu4FFgVe8TVPVVVe1Z6nADUOBgPMZEPFVl3e6TXDQtM2qGt/aVnhzHBVMzeX7HScebmVSVZ7ZWccHUTHLGhWfvh8G6ZEY2qfFunt1aFe5QBs3JBJEPVPR6XRko68+dwPO9XieISJmIbBCRG/q7SERWB84rq6mJ/k3Czdi291QLFfXtLC+ZEO5Qzsk18yZwuLaNvadaHH2fDYfqOVp3mhuXRP53y4RYFx9YkMfz20/S2hkdazNFRCe1iNwOlALf71U8SVVLgVuBH4nItGDXquoDqlqqqqXZ2dFZJTemx1/KTxAjcFVJTrhDOScrSiYgAs9td3aJicfKKkhNcHPN3DxH3ydUblxSQHu3N2o6q51MEFVAYa/XBYGydxGRq4D/A1yvqp095apaFfh5CHgNWORgrMaEnarybHkVF03PIic1sptLBpKdGs/5UzJ5dmuVY81MTe3dPLf9BKsWToyYtZcGsrgonalZyTyxqTLcoQyKk42cG4FiEZmCPzHcjL82cIaILAJ+AaxU1epe5enAaVXtFJEs4CLe3YFtxog/vn0spPe7dVlRSO8XSlsqGqmob+fzV84IdyghceOSAr78eDkbjzSw1IH9LJ7eXEmnx8dNpZH737QvEeHG0gK+98JeDlS3Mj3MmxoNxLEahKp6gLuBtcBu4DFV3Ski94pIz6ik7wMpwON9hrPOBspEpBx4Ffiuqu5yKlZjIsGarceJc8dw9ZzoHN7a1zXzJpAc5+KJTRUDnzxEPp/y0PojLCoaz7yCyJs9fTb/vKSQWJfw8Iaj4Q5lQI4Ok1DV54Dn+pR9o9fzq/q5bj0wz8nYjIkknR4va8qPc9XsHFITImstoeFKinPz/vl5/HXbCb553ZyQjsp6dW81R+pO8+UVM0N2z5GSnRrPtfPyeHJTJV+5emZEj1aLiE5qY8a6F3acpL6ti1uWRk9zyWDcdF4hbV1entoS2qGdv37zMHlpCaycG52jvT56wSRaOj08HeK/S6hZgjAmAvxhwzEmZyZx0bSscIcSUouL0hLGBHUAAA2tSURBVFlQkMaDbx7GF6Kd5jYdrWf9wTo+duHksO87PVyLi9KZl5/Gr988HNYd+AYSnX9dY0aRfadaeOdIPbcuK4q6tZcGIiLc+b6pHK5t45U91QNfMAg/emk/mclxfOSCSQOfHKFEhE9dNo3DtW0jNuN8OCxBGBNmD7xxiITYGG5cUjjwyVHomrkTmJiWwP2vHTjnIa8bj9Tzt/21fPLSqSTFRW7b/WBcPWcCU7OSuf/VgyO6sOFQWIIwJowqG07zzJYqbj6viIzkuHCH44hYVwx3X1HM5mONvLR7+LUIr0+598+7yB0Xz+3nR2/toYcrxl+L2HWimbU7T4U7nKAsQRgTRr984xAAqy+ZGuZInPXh0gKmZiXzvRf24BnmaqaPlVWwvaqJr187O+prDz3+aVE+03NS+N7a4f9dnGQJwkQ9j8/H8cCOYn8/WMub+2v4+6E6tlU2cqKpPSL/4QEcqzvNI+9U8KHFBUwcnxjucBzldsXw1ZUz2V/dykPrjwz5+pNNHfzXC3tYOiUj4ldtHQq3K4avXj2TQzVtPLox9PNFztXoSMNmzGnr9FBe2cieEy0cqWvDc5aRIO4YYVJmEnMmpnHtvAmMT4qMppz/fH43rhjhi8tHx8zpgVw9ZwJXzc7he2v3ctnMnEHPIvb6lM8/uoUuj4/vfnAeIqOrI395SS5Lp2Tw/bV7WTl3QkRtemQJwkQNVeVgTRvvHKln9/FmvKrkpMazbEoGhRlJZKfGk5oQiztG8PiUlo5ualo6qag/zb7qVtaUH+eFHSe5fFY2d1wwmQumZYbtw2b9wVqe33GSLy2fwYS06F53abBEhP/7wXms+OEbfPaRLTx+1wWkDGKS2H+9sIe3D9fz/RvnMzU7spemGA4R4Ts3zOXan/yN7/x1Nz+8aWG4QzrDEoSJCgdrWnlp9ymO1p0mKc7F+VMzWDIp46wfrinxbvLSEplfMJ5rVTnR1MHpLi/Pbq1i7c5TLCgcz6cuncaKktwRHV7a0tHNV5/YRlFGEv/yvtHd99BXTmoCP7ppIXf+toxPPbyJX91RSrw7+EJ7qsr9rx3kgTcOcfv5RVGxpPdwFeemctel0/jpKwdYOXcCV8+JjAmAliBMRDta18a6Xac4VNvGuAQ31y+YSOmkdNxDnCAlIkwcn8ity4r46sqZPLm5kl+8foi7Ht7EzNxUvri8mBUlE0YkUdz7510cb2znsU9eEDWrkIbSZTNz+O4H5/GVJ7Zx0y82cP9ti9/TB3O6y8M3nt3JE5sqWbVwIvdeP3fUNS319dkrinl9Xw1febycORPHUZCeFO6QLEGYyLS1opH/fnEvf9tfS0q8mw/Mz+O8yRkhmTmbEOvitmWTuKm0kL9uP8GPX9rPXQ9vpiRvHF9aPoMrZ+c49mH0m7cO8/imSu6+fDqlk0O/wmm0+OfSQlLi3XzliW1c+d+v86El+Sybkkm8O4ZtlU088s4x6k938bkri/n8lcWjbgJhMHHuGH56yyLe/5M3uevhTTy6enBNcE6yBGEiyo6qJn64bh8v76kmIzmOa+ZOYNmUTOLcoR9w53bFsGphPu+fl8ea8uP8+OX9fOJ3ZSwoSOOLy2dw6YzskCaKv247wb1/2cWKktwx0zF9NtfMy6Nk4jh+9uoBHttYycMb/rG0++Uzs7n7iuksmTS2kuikzGR+eusiPvHbMj79h8386qOljvy/P1gSqTP4hqO0tFTLysrCHYYZhu2VTdz36n7W7jxFWmIsqy+Zyh0XTmbN1uMhfZ+z7QfR7fXx9OYqfvLKfiob2lkyKZ0vXFXMxdOzzjlRPLzhKP/27A4WF6Xz+zuXjtg4/mjZT+N0l4ejdafp9PiYlp087BVtQ/n7hnPvkMc2VvDVJ7dxyYxsfn77Ykf/fxGRTYHdO9/DahAmbFSV9Qfr+J/XDvLmgVpSE9x84api/tfFUxgXhiWvY10xfPi8Qm5YlM8Tmyq575X9fOTX7zAjN4WPnD+JGxblD/mDq6Gti2+u2cma8uNcPjOb+29bMib7HQaSFOdmdt64cIcRMT58XiGK8rWntp/ppynMGPk+CUsQZsQ1tHXx5OZKHnnnGAdr2shKieeea2Zx27KiiNgLIc4dw63LivjQknye3Xqc3//9KP/27E7+8/k9XFKczVUluVwxK+esS2McrWvjiU2VPPTWEdq7vXx5+Qw+ddm0IXeum7HrpvOKyEyO54uPbeX9P/kbX792Nh8uLRzR/hhLEGZE1LZ2sm7XKdbuPMn6A3V0eX0sKhrP926cz/ULJpIQG3nfquPdLj5cWsiHSwvZWtHI42UVvLT7FC/sPAnAlKxkinNSyEtLICHORbdHOdXSwa7jzRyubQPg6jm5fGn5TGZOSA3nr2Ki1FUluTz3uffx5cfLueep7fx+w1E+e8V0lpdMwDUCicLRBCEiK4EfAy7gV6r63T7H44HfAUuAOuAmVT0SOPY14E7AC3xOVdc6GWsk8Hh9tHV5aev0+B+B56e7vO9a7fGNfbVnnrtdQqwrhtgzP/3P490uYl0yYNu5E+2snR4vh2vb2H2imbIjDZQdaWDvqRYACjMS+egFk/jQkoKoalJYWDiehYXj+Y8b5rKjqpk39tewrbKRw7VtbDhUR6fHhztGyEqNZ0ZuKrcuLeLa+Xnkj/IlNIzzCjOS+NPq83lma9WZEXe54/y70l00LYulUzMca5J1LEGIiAv4GbAcqAQ2isiaPntL3wk0qOp0EbkZ+C/gJhEpAW4G5gATgZdEZIaqep2Kt4eq4lP/9H6fKl6f4lXF5+v9nDNlHd1e2ru9tHf5f/7jte/M654P+dY+H/z+5x7aOv2vOz2hXTNIgFh3DPHuGOJcgZ9uV+Cn/7G/uoXkODdJ8S5S4t0kxblJjnORFO/GFSS5KEp7l5e2Lg+tgbib27s52dzBqeYOjjd2cLSujZ6VL1Li3SyelM4H5udx5excZuelRvV4dhFhXkFa1O2DbKKbiPBPiwq4bv5EXtp9iic2VfKHt4/xm7eOECP+LzCP33VhyGsVTtYglgIHVPUQgIg8CqwCeieIVcC3As+fAO4T/6fHKuBRVe0EDovIgcD9/u5EoAvvfZG2Tk8gKYT+/q4YITku8AEc7yY53v8hnJGcFPhQ7vXhHP+P81LiXSTHuUmMcxHT60P1hR3+Jg4FvF4f3T6l2+Ojy+vD41W6vD66Aq87u73+nx5/WafHR2unh842H10eL/tOtdDW6Tmn39sdI+SkxpOblsDsvFSum5/H9NxUZuSmUJyTOiJVYWPGArcrhpVz81g5N4+Obi9bjjXy90N11Ld1OvLvzMkEkQ/0Xp6wEljW3zmq6hGRJiAzUL6hz7X5wd5ERFYDqwMvW0Vk77mHHvGygNoBzxpBB8MdwLv1+/e5bYQDiWDD+n9oLP39bovAf2dn853hX9rv5hpR30mtqg8AD4Q7jpEkImX9jVs29vcZDPsbDcz+Rs7uB1EF9N5DsSBQFvQcEXEDafg7qwdzrTHGGAc5mSA2AsUiMkVE4vB3Oq/pc84a4I7A8xuBV9Q/XGcNcLOIxIvIFKAYeMfBWI0xxvThWBNToE/hbmAt/mGuD6rqThG5FyhT1TXAr4HfBzqh6/EnEQLnPYa/Q9sDfGYkRjBFkTHVpDYM9vcZmP2NBjbm/0ajai0mY4wxoWPz/o0xxgRlCcIYY0xQliCiiIisFJG9InJARO4JdzyRRkQeFJFqEdkR7lgilYgUisirIrJLRHaKyOfDHVOkEZEEEXlHRMoDf6N/D3dM4WJ9EFEisHTJPnotXQLc0mfpkjFNRC4BWoHfqerccMcTiUQkD8hT1c0ikgpsAm6w/4/+IbCaQ7KqtopILPAm8HlV3TDApaOO1SCix5mlS1S1C+hZusQEqOob+EfDmX6o6glV3Rx43gLspp9VCsYq9WsNvIwNPMbkN2lLENEj2NIl9g/bDJuITAYWAW+HN5LIIyIuEdkKVAPrVHVM/o0sQRgzBolICvAk8AVVbQ53PJFGVb2quhD/Kg5LRWRMNllagogetvyICYlAu/qTwB9U9alwxxPJVLUReBVYGe5YwsESRPQYzNIlxpxVoAP218BuVf1BuOOJRCKSLSLjA88T8Q8M2RPeqMLDEkSUUFUP0LN0yW7gMVXdGd6oIouIPIJ/z5CZIlIpIneGO6YIdBHwEeAKEdkaeFwb7qAiTB7wqohsw//FbJ2q/iXMMYWFDXM1xhgTlNUgjDHGBGUJwhhjTFCWIIwxxgRlCcIYY0xQliCMMcYEZQnCmHMkIuNF5NOB55eJyJCGRIrIQyJyozPRGTN8liCMOXfjgU+HOwhjQs2xPamNGUO+C0wLLO7WDbSJyBPAXPzLad+uqioi3wCuAxKB9cAn1SYimQhmNQhjzt09wMHA4m5fwb9C6heAEmAq/tnLAPep6nmBvSoSgQ+EI1hjBssShDGh946qVqqqD9gKTA6UXy4ib4vIduAKYE64AjRmMKyJyZjQ6+z13Au4RSQBuB8oVdUKEfkWkBCO4IwZLKtBGHPuWoDUAc7pSQa1gb0YbNSSiXhWgzDmHKlqnYi8JSI7gHbgVJBzGkXkl8AO4CT+VUKNiWi2mqsxxpigrInJGGNMUJYgjDHGBGUJwhhjTFCWIIwxxgRlCcIYY0xQliCMMcYEZQnCGGNMUP8fh3N5qaw3Q0gAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5AKs__cLzqe3"
      },
      "source": [
        "# **IV. Train Test split**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCURsiWIJLWJ"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "predictors = dataset.drop(\"target\",axis=1)\n",
        "target = dataset[\"target\"]\n",
        "\n",
        "X_train,X_test,Y_train,Y_test = train_test_split(predictors,target,test_size=0.20,random_state=0)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OdjLxbGzJQCd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6e07524-7e89-4917-c7b1-64eff80f5734"
      },
      "source": [
        "X_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2cEy_I0oJVCI",
        "outputId": "ab9fed48-3e8d-4f57-a52b-2913f78a1693"
      },
      "source": [
        "X_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dWS7AputJYUW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "32cca021-1688-416a-9887-e93c9afa130f"
      },
      "source": [
        "Y_train.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(242,)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y9C20hxVKcol",
        "outputId": "999fb41b-9b9c-4e9e-c456-f28e78179895"
      },
      "source": [
        "Y_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hsi3YIdVzxip"
      },
      "source": [
        "# **V. Model Fitting**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1o1DIJ446onx"
      },
      "source": [
        "# **Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MKfxfkUSKgsU"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JK1ZEsG9KkxF"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "\n",
        "lr.fit(X_train,Y_train)\n",
        "\n",
        "Y_pred_lr = lr.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pZ_ubCy3Kr3O",
        "outputId": "f8b0a5e4-2a50-482d-bd35-fa6433a7ed60"
      },
      "source": [
        "Y_pred_lr.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y7eD2uLTKwAT",
        "outputId": "e43d1164-9e7a-4aa5-aa91-382a8a9c5675"
      },
      "source": [
        "score_lr = round(accuracy_score(Y_pred_lr,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Logistic Regression is: \"+str(score_lr)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 85.25 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GVW59dKtz4wI"
      },
      "source": [
        "# **Naive Bayes**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OWDAeQRiKzp5"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "nb = GaussianNB()\n",
        "\n",
        "nb.fit(X_train,Y_train)\n",
        "\n",
        "Y_pred_nb = nb.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tn4CpQUxLAuz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2b0a8e3-3260-4b06-e731-c5c0c1bf1bef"
      },
      "source": [
        "Y_pred_nb.shape\n",
        "       "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JKlNNIKbtb8X",
        "outputId": "0fb6c926-8236-42ad-e367-78cca32530b2"
      },
      "source": [
        "score_nb = round(accuracy_score(Y_pred_nb,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Naive Bayes is: \"+str(score_nb)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Naive Bayes is: 85.25 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bz6R4qgatgeh"
      },
      "source": [
        "#**SVM**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DMMcYfSztiGs"
      },
      "source": [
        "from sklearn import svm\n",
        "\n",
        "sv = svm.SVC(kernel='linear')\n",
        "\n",
        "sv.fit(X_train, Y_train)\n",
        "\n",
        "Y_pred_svm = sv.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTp7StyGtm4r",
        "outputId": "129bc9fb-4098-4b86-a7ab-38918b155608"
      },
      "source": [
        "Y_pred_svm.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBH0RUP2tp3R",
        "outputId": "5b069373-8627-4d2c-de83-379907fcf387"
      },
      "source": [
        "score_svm = round(accuracy_score(Y_pred_svm,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Linear SVM is: \"+str(score_svm)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Linear SVM is: 81.97 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a7zwgTjJtsmr"
      },
      "source": [
        "# **K Nearest Neighbours**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RC-gq7Nitz7Y"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn = KNeighborsClassifier(n_neighbors=7)\n",
        "knn.fit(X_train,Y_train)\n",
        "Y_pred_knn=knn.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-8jQD5YHt4jo",
        "outputId": "31d4c1b3-4da2-455d-d203-811b22c662e9"
      },
      "source": [
        "Y_pred_knn.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Uvca-C5t7td",
        "outputId": "b0bc8f7e-027e-4a4d-a8cc-42f7d876cf67"
      },
      "source": [
        "score_knn = round(accuracy_score(Y_pred_knn,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using KNN is: \"+str(score_knn)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using KNN is: 67.21 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zMOPi7-lt9EF"
      },
      "source": [
        "# **Decision Tree**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uSjsLpCIt_q_"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "max_accuracy = 0\n",
        "\n",
        "\n",
        "for x in range(200):\n",
        "    dt = DecisionTreeClassifier(random_state=x)\n",
        "    dt.fit(X_train,Y_train)\n",
        "    Y_pred_dt = dt.predict(X_test)\n",
        "    current_accuracy = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
        "    if(current_accuracy>max_accuracy):\n",
        "        max_accuracy = current_accuracy\n",
        "        best_x = x\n",
        "        \n",
        "#print(max_accuracy)\n",
        "#print(best_x)\n",
        "\n",
        "\n",
        "dt = DecisionTreeClassifier(random_state=best_x)\n",
        "dt.fit(X_train,Y_train)\n",
        "Y_pred_dt = dt.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4S5KtsyCt_vk",
        "outputId": "d34d3d62-b740-4093-9ae0-18fbee962e99"
      },
      "source": [
        "print(Y_pred_dt.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(61,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G9Zq9ddsuQFK",
        "outputId": "2fa2c4f1-17e4-4dc6-d1ca-df2b7958ffae"
      },
      "source": [
        "score_dt = round(accuracy_score(Y_pred_dt,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Decision Tree is: \"+str(score_dt)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Decision Tree is: 81.97 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZXE2FDouTmK"
      },
      "source": [
        "# **Random forest**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ru28PGuFuWKa"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "max_accuracy = 0\n",
        "\n",
        "\n",
        "for x in range(2000):\n",
        "    rf = RandomForestClassifier(random_state=x)\n",
        "    rf.fit(X_train,Y_train)\n",
        "    Y_pred_rf = rf.predict(X_test)\n",
        "    current_accuracy = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
        "    if(current_accuracy>max_accuracy):\n",
        "        max_accuracy = current_accuracy\n",
        "        best_x = x\n",
        "        \n",
        "#print(max_accuracy)\n",
        "#print(best_x)\n",
        "\n",
        "rf = RandomForestClassifier(random_state=best_x)\n",
        "rf.fit(X_train,Y_train)\n",
        "Y_pred_rf = rf.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "utNpU3sbuY6s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c938df45-ae21-4d90-f0a2-b7b6dac4c3aa"
      },
      "source": [
        "Y_pred_rf.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s4AQgqjAubg-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c0b789c-11c9-4978-dbf2-ca095f186cfb"
      },
      "source": [
        "score_rf = round(accuracy_score(Y_pred_rf,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Decision Tree is: \"+str(score_rf)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Decision Tree is: 90.16 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w5ZC6uYvvrfO"
      },
      "source": [
        "# **XG Boost**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DodFgJSavrBt"
      },
      "source": [
        "import xgboost as xgb\n",
        "\n",
        "xgb_model = xgb.XGBClassifier(objective=\"binary:logistic\", random_state=42)\n",
        "xgb_model.fit(X_train, Y_train)\n",
        "\n",
        "Y_pred_xgb = xgb_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NtPHSr1Yvq9T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "545eeef5-61e8-4676-fcca-12ec1b0a3f05"
      },
      "source": [
        "Y_pred_xgb.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61,)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39pTdp_Pv0yG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adc4f3b2-ff17-47c1-af0e-382d6dffa4d0"
      },
      "source": [
        "score_xgb = round(accuracy_score(Y_pred_xgb,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using XGBoost is: \"+str(score_xgb)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using XGBoost is: 85.25 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IxcCcoLav3DT"
      },
      "source": [
        "# **Neural Network**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Umx5SpOzv49B"
      },
      "source": [
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zUUhnh6wv9Kk"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(11,activation='relu',input_dim=13))\n",
        "model.add(Dense(1,activation='sigmoid'))\n",
        "\n",
        "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WPtYN0aUv_r9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9600d4ec-b631-4499-8158-3d8e88dba5af"
      },
      "source": [
        "model.fit(X_train,Y_train,epochs=300)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "8/8 [==============================] - 1s 4ms/step - loss: 32.1281 - accuracy: 0.4587\n",
            "Epoch 2/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 26.2329 - accuracy: 0.4587\n",
            "Epoch 3/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 19.9613 - accuracy: 0.4587\n",
            "Epoch 4/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 14.1361 - accuracy: 0.4669\n",
            "Epoch 5/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 8.3636 - accuracy: 0.5124\n",
            "Epoch 6/300\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 4.2208 - accuracy: 0.5620\n",
            "Epoch 7/300\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 3.1567 - accuracy: 0.6074\n",
            "Epoch 8/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 3.2309 - accuracy: 0.6198\n",
            "Epoch 9/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 3.1303 - accuracy: 0.6240\n",
            "Epoch 10/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.9704 - accuracy: 0.5950\n",
            "Epoch 11/300\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 2.8641 - accuracy: 0.6074\n",
            "Epoch 12/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.8448 - accuracy: 0.6033\n",
            "Epoch 13/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.8283 - accuracy: 0.5992\n",
            "Epoch 14/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.7395 - accuracy: 0.6116\n",
            "Epoch 15/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.6851 - accuracy: 0.6198\n",
            "Epoch 16/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.6515 - accuracy: 0.6157\n",
            "Epoch 17/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.5930 - accuracy: 0.6116\n",
            "Epoch 18/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 2.5404 - accuracy: 0.6198\n",
            "Epoch 19/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.4982 - accuracy: 0.6074\n",
            "Epoch 20/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 2.4719 - accuracy: 0.6116\n",
            "Epoch 21/300\n",
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            "8/8 [==============================] - 0s 3ms/step - loss: 0.3696 - accuracy: 0.8430\n",
            "Epoch 198/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3731 - accuracy: 0.8430\n",
            "Epoch 199/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3733 - accuracy: 0.8430\n",
            "Epoch 200/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3695 - accuracy: 0.8306\n",
            "Epoch 201/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3699 - accuracy: 0.8471\n",
            "Epoch 202/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3709 - accuracy: 0.8388\n",
            "Epoch 203/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3742 - accuracy: 0.8347\n",
            "Epoch 204/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3701 - accuracy: 0.8471\n",
            "Epoch 205/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3639 - accuracy: 0.8430\n",
            "Epoch 206/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3699 - accuracy: 0.8347\n",
            "Epoch 207/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3673 - accuracy: 0.8471\n",
            "Epoch 208/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3708 - accuracy: 0.8347\n",
            "Epoch 209/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3701 - accuracy: 0.8471\n",
            "Epoch 210/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3642 - accuracy: 0.8388\n",
            "Epoch 211/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3888 - accuracy: 0.8264\n",
            "Epoch 212/300\n",
            "8/8 [==============================] - 0s 6ms/step - loss: 0.3653 - accuracy: 0.8347\n",
            "Epoch 213/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3630 - accuracy: 0.8430\n",
            "Epoch 214/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3702 - accuracy: 0.8388\n",
            "Epoch 215/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3806 - accuracy: 0.8347\n",
            "Epoch 216/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3747 - accuracy: 0.8430\n",
            "Epoch 217/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3658 - accuracy: 0.8471\n",
            "Epoch 218/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3693 - accuracy: 0.8430\n",
            "Epoch 219/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3708 - accuracy: 0.8388\n",
            "Epoch 220/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3724 - accuracy: 0.8182\n",
            "Epoch 221/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3742 - accuracy: 0.8430\n",
            "Epoch 222/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3643 - accuracy: 0.8306\n",
            "Epoch 223/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3644 - accuracy: 0.8512\n",
            "Epoch 224/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3624 - accuracy: 0.8430\n",
            "Epoch 225/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3674 - accuracy: 0.8512\n",
            "Epoch 226/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3677 - accuracy: 0.8388\n",
            "Epoch 227/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3700 - accuracy: 0.8471\n",
            "Epoch 228/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3781 - accuracy: 0.8182\n",
            "Epoch 229/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3695 - accuracy: 0.8471\n",
            "Epoch 230/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3614 - accuracy: 0.8430\n",
            "Epoch 231/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3678 - accuracy: 0.8471\n",
            "Epoch 232/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3735 - accuracy: 0.8388\n",
            "Epoch 233/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3686 - accuracy: 0.8430\n",
            "Epoch 234/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3647 - accuracy: 0.8430\n",
            "Epoch 235/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3659 - accuracy: 0.8471\n",
            "Epoch 236/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3703 - accuracy: 0.8471\n",
            "Epoch 237/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3791 - accuracy: 0.8471\n",
            "Epoch 238/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3820 - accuracy: 0.8264\n",
            "Epoch 239/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3773 - accuracy: 0.8471\n",
            "Epoch 240/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3678 - accuracy: 0.8388\n",
            "Epoch 241/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3689 - accuracy: 0.8388\n",
            "Epoch 242/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3782 - accuracy: 0.8306\n",
            "Epoch 243/300\n",
            "8/8 [==============================] - 0s 2ms/step - loss: 0.3677 - accuracy: 0.8512\n",
            "Epoch 244/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3773 - accuracy: 0.8223\n",
            "Epoch 245/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3898 - accuracy: 0.8099\n",
            "Epoch 246/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3681 - accuracy: 0.8347\n",
            "Epoch 247/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3692 - accuracy: 0.8388\n",
            "Epoch 248/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3751 - accuracy: 0.8306\n",
            "Epoch 249/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3791 - accuracy: 0.8264\n",
            "Epoch 250/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3777 - accuracy: 0.8306\n",
            "Epoch 251/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3796 - accuracy: 0.8223\n",
            "Epoch 252/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3832 - accuracy: 0.8223\n",
            "Epoch 253/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3629 - accuracy: 0.8471\n",
            "Epoch 254/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3624 - accuracy: 0.8347\n",
            "Epoch 255/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3646 - accuracy: 0.8554\n",
            "Epoch 256/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3752 - accuracy: 0.8264\n",
            "Epoch 257/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3931 - accuracy: 0.8264\n",
            "Epoch 258/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.4044 - accuracy: 0.8182\n",
            "Epoch 259/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3707 - accuracy: 0.8512\n",
            "Epoch 260/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3755 - accuracy: 0.8264\n",
            "Epoch 261/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3730 - accuracy: 0.8388\n",
            "Epoch 262/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3673 - accuracy: 0.8512\n",
            "Epoch 263/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3624 - accuracy: 0.8430\n",
            "Epoch 264/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3650 - accuracy: 0.8306\n",
            "Epoch 265/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3620 - accuracy: 0.8388\n",
            "Epoch 266/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3596 - accuracy: 0.8430\n",
            "Epoch 267/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3634 - accuracy: 0.8471\n",
            "Epoch 268/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3603 - accuracy: 0.8388\n",
            "Epoch 269/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3585 - accuracy: 0.8512\n",
            "Epoch 270/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3617 - accuracy: 0.8471\n",
            "Epoch 271/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3618 - accuracy: 0.8554\n",
            "Epoch 272/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3594 - accuracy: 0.8471\n",
            "Epoch 273/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3688 - accuracy: 0.8388\n",
            "Epoch 274/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3875 - accuracy: 0.8388\n",
            "Epoch 275/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3945 - accuracy: 0.8223\n",
            "Epoch 276/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3808 - accuracy: 0.8430\n",
            "Epoch 277/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3617 - accuracy: 0.8430\n",
            "Epoch 278/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3638 - accuracy: 0.8388\n",
            "Epoch 279/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3619 - accuracy: 0.8471\n",
            "Epoch 280/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3610 - accuracy: 0.8430\n",
            "Epoch 281/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3599 - accuracy: 0.8430\n",
            "Epoch 282/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3925 - accuracy: 0.8430\n",
            "Epoch 283/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3955 - accuracy: 0.8099\n",
            "Epoch 284/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3681 - accuracy: 0.8471\n",
            "Epoch 285/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3836 - accuracy: 0.8388\n",
            "Epoch 286/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3631 - accuracy: 0.8512\n",
            "Epoch 287/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3722 - accuracy: 0.8430\n",
            "Epoch 288/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3634 - accuracy: 0.8471\n",
            "Epoch 289/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3868 - accuracy: 0.8388\n",
            "Epoch 290/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3546 - accuracy: 0.8512\n",
            "Epoch 291/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3662 - accuracy: 0.8347\n",
            "Epoch 292/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3857 - accuracy: 0.8264\n",
            "Epoch 293/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3729 - accuracy: 0.8306\n",
            "Epoch 294/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3618 - accuracy: 0.8388\n",
            "Epoch 295/300\n",
            "8/8 [==============================] - 0s 4ms/step - loss: 0.3606 - accuracy: 0.8471\n",
            "Epoch 296/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3630 - accuracy: 0.8388\n",
            "Epoch 297/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3693 - accuracy: 0.8471\n",
            "Epoch 298/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3831 - accuracy: 0.8430\n",
            "Epoch 299/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3737 - accuracy: 0.8264\n",
            "Epoch 300/300\n",
            "8/8 [==============================] - 0s 3ms/step - loss: 0.3678 - accuracy: 0.8306\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f4d6138be10>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pf79At1JwESU"
      },
      "source": [
        "Y_pred_nn = model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TPomrykYwGn8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4006e92f-18c9-40e4-a9c3-2334940e4fed"
      },
      "source": [
        "Y_pred_nn.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_j5dQ9nswJAX"
      },
      "source": [
        "rounded = [round(x[0]) for x in Y_pred_nn]\n",
        "\n",
        "Y_pred_nn = rounded"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6sA5u_TQwLxT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47ca5da4-3d3f-4562-b49a-05ab4e497888"
      },
      "source": [
        "score_nn = round(accuracy_score(Y_pred_nn,Y_test)*100,2)\n",
        "\n",
        "print(\"The accuracy score achieved using Neural Network is: \"+str(score_nn)+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Neural Network is: 81.97 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nz3NnAbNwZ0f"
      },
      "source": [
        "# **Output Final Score**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "34Gz3toIwc3T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad1649f0-54c0-4759-acf9-3ac7a4916f67"
      },
      "source": [
        "scores = [score_lr,score_nb,score_svm,score_knn,score_dt,score_rf,score_xgb,score_nn]\n",
        "algorithms = [\"Logistic Regression\",\"Naive Bayes\",\"Support Vector Machine\",\"K-Nearest Neighbors\",\"Decision Tree\",\"Random Forest\",\"XGBoost\",\"Neural Network\"]    \n",
        "\n",
        "for i in range(len(algorithms)):\n",
        "    print(\"The accuracy score achieved using \"+algorithms[i]+\" is: \"+str(scores[i])+\" %\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The accuracy score achieved using Logistic Regression is: 85.25 %\n",
            "The accuracy score achieved using Naive Bayes is: 85.25 %\n",
            "The accuracy score achieved using Support Vector Machine is: 81.97 %\n",
            "The accuracy score achieved using K-Nearest Neighbors is: 67.21 %\n",
            "The accuracy score achieved using Decision Tree is: 81.97 %\n",
            "The accuracy score achieved using Random Forest is: 90.16 %\n",
            "The accuracy score achieved using XGBoost is: 85.25 %\n",
            "The accuracy score achieved using Neural Network is: 81.97 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3AVf9Jjbwhqe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "c2bb5e69-221f-40a0-d224-45ef0cfd62bb"
      },
      "source": [
        "sns.set(rc={'figure.figsize':(15,8)})\n",
        "plt.xlabel(\"Algorithms\")\n",
        "plt.ylabel(\"Accuracy score\")\n",
        "\n",
        "sns.barplot(algorithms,scores)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4d602ea610>"
            ]
          },
          "metadata": {},
          "execution_count": 70
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Tpk2+rAsAAAAAkAcehb0ePXrop59+kiTNmTNHAwcO1KBBg7iMEwAAAAAKKY/C3q+//qrg4GBJ0scff6wFCxboP//5jxYvXuzT4gAAAAAAuePRPXtut1sul0uHDh2SMUZ/+9vfJElnz571aXEAAAAAgNzxKOw1atRIY8eO1YkTJ9SmTRtJ0qFDh1SuXDmfFgcAAAAAyB2PLuOcNGmSSpcurTp16qhfv36SpP3796tnz54+LQ4AAAAAkDsendkrV66cBg4cmGlay5YtfVEPAAAAAMAL+EN5AAAAAGAhwh4AAAAAWIiwBwAAAAAW8ijs7dmzx9d1AAAAAAC8yKOw16tXL0VERGju3Lk6fvy4r2sCAAAAAOSRR2FvzZo16t+/v7Zt26a2bduqT58+io6O1sWLF31dHwAAAAAgFzwKewEBAWrdurWmT5+u77//XuHh4Xr33Xd11113afDgwfrpp598XScAAAAAIAdy9AUt58+f18qVK7V8+XLFxcWpQ4cOuummm/Tiiy9qzJgxvqoRAAAAAJBDHv1R9dWrVys6Olrff/+9GjZsqK5du6p169YKCgqSJD388MNq1aqVRo0a5dNiAQAAAACe8Sjsvfrqq4qMjNSwYcNUuXLlq54vW7ashg8f7vXiAAAAAAC541HYi4mJue5runbtmudiAAAAAADe4dE9e88++6w2bdqUadqmTZvUv39/nxQFAAAAAMgbj8Lexo0bdccdd2SaFhwcrPXr1/ukKAAAAABA3ngU9gIDA6/6m3oXLlxQQIBHV4ECAAAAAPKZR2GvWbNmGjlypBITEyVJiYmJGjt2rJo3b+7T4gAAAAAAueNR2Bs6dKgSExMVGhqqJk2aKDQ0VImJiXwDJwAAAAAUUh5dh1mmTBnNmTNHx48f17Fjx1StWjVVqlTJ17UBAAAAAHIpRzfdVa5cWZUqVZIxRm63W5Lk5+fRyUEAAAAAQD7yKOzFxcVp7Nix2rRpk86dO5fpuZ9//tknhQEAAAAAcs+j03KjRo1SkSJFNH/+fBUvXlxLly5VWFiYxowZ4+v6AAAAAAC54NGZvS1btujbb79V8eLF5XK5dNttt2nChAnq3r27unXr5usaAQAAAAA55NGZPT8/P+dv6pUuXVrx8fEqXry44uLifFocAAAAACB3PDqz16BBA3333Xdq06aNmjVrpueee05FixZVvXr1fF0fAAAAACAXPAp7U6ZMcb59c/jw4Xrvvfd0/vx5Pfrooz4tDgAAAACQO9cNe+np6ZowYYLGjRsnSSpatKiefvppnxcGAAAAAMi9696z5+/vrx9++EEulys/6gEAAAAAeIFHX9Dy6KOPasaMGUpNTfV1PQAAAAAAL/Donr33339fJ0+e1Lx581S+fPlMZ/lWr17tq9oAAAAAALnkUdibOnWqr+sAAAAAAHiRR2EvNDTU13UAAAAAALzIo7AXFRWV5XMDBgzwWjEAAAAAAO/wKOwdO3Ys0+MTJ05o48aNat26tU+KAgAAAADkjUdhb9KkSVdN+/7777V8+XKvFwQAAAAAyDuP/vTCtTRr1kwrV670Zi0AAAAAAC/x6Mze4cOHMz2+ePGiYmNjVa1aNZ8UBQAAAADIG4/CXps2beRyuWSMkSQVK1ZMdevW1SuvvOLT4gAAAAAAueNR2NuzZ4+v6wAAAAAAeJFH9+z9/PPPOnr0aKZpR48eJQQCAAAAQCHlUdh78cUXlZaWlmlaamqqXnzxRZ8UBQAAAADIG4/C3pEjR1SjRo1M02rWrKn//e9/PikKAAAAAJA3HoW9qlWrateuXZmm7dq1S5UrV/ZJUQAAAACAvPEo7PXq1UtPP/20Fi5cqO+++04LFy7Us88+q969e+d4hjNnzlSdOnX0yy+/SJK2bt2qiIgItW3bVn369NGpU6dyPCYAAAAAIDOPvo2zW7duKlWqlD755BMdO3ZMVatW1ZAhQ9SuXbsczWzXrl3aunWrqlevLklyu9168cUXNWnSJIWEhGjWrFmaNm2aJk2alPPfBAAAAADg8CjsSVJ4eLjCw8NzPaOUlBSNHTtWr776qnr27ClJ2rlzp4KCghQSEiJJ6t69u+655x7CHgAAAADkkUeXcY4fP16bN2/ONG3z5s2aMGGCxzOKiopSRESEbrzxRmfa0aNHdcMNNziPy5cvL7fbrTNnzng8LgAAAADgah6d2YuNjdXgwYMzTatXr56eeeYZjRgx4ro/v2XLFu3cuVMvvPBC7qq8jgoVSvpkXGSvUqVSBV0CskF/Cj96VPjRo8KN/hR+9Kjwo0eFW17741HYc7lcMsZkmpaeni632+3RTDZu3Kh9+/bpnnvukSQdO3ZMjz32mB555BEdOXLEeV18fLz8/PxUtmxZT+uXJJ06lSi322T7GlZk7ztxIsGr49Ej76I/hR89KvzoUeFGfwo/elT40aPCzZP++Pm5sjz55dFlnCEhIXrjjTeccOd2uzVjxgznXrvr+fe//601a9Zo1apVWrVqlapWraq5c+fq8ccfV1JSkjZt2iRJWrx4cY6/9AUAAAAAcDWPzuyNGDFCTz75pJo1a6YbbrhBR48eVaVKlfTWW2/laeZ+fn6aMmWKRo0apeTkZFWvXl1Tp07N05gAAAAAAA/DXtWqVbV06VJt27ZNx44dU7Vq1VS/fv1cz3TVqlXO/xs2bKiYmJhcjwUAAAAAuJpHl3FKGWfh7rjjDoWHh6tYsWKaOnWqWrRo4cvaAAAAAAC55PHf2YuPj1dMTIyWLVumPXv2qFGjRh59EycAAAAAIP9lG/ZSU1O1atUqLV26VGvWrFHNmjXVoUMHHTlyRFFRUapQoUJ+1QkAAAAAyIFsw17Tpk3lcrl0//33q1+/frr99tslSYsWLcqX4gAAAAAAuZPtPXt16tRRQkKCtm3bph07dujs2bP5VRcAAAAAIA+yDXsLFy7U119/raZNm+q9995T06ZN9dRTT+nChQtKS0vLrxoBAAAAADl03W/jrF69up555hmtWLFC8+fPV6VKleTn56eIiAhNmTIlP2oEAAAAAOSQx9/GKUkhISEKCQnRSy+9pK+//lrLli3zVV0AAAAAgDzIUdi7JCgoSB07dlTHjh29XQ8AAAAAwAs8/qPqAAAAAIA/D8IeAAAAAFiIsAcAAAAAFiLsAQAAAICFCHsAAAAAYCHCHgAAAABYiLAHAAAAABYi7AEAAACAhQh7AAAAAGAhwh4AAAAAWIiwBwAAAAAWIuwBAAAAgIUIewAAAABgIcIeAAAAAFiIsAcAAAAAFiLsAQAAAICFCHsAAAAAYCHCHgAAAABYiLAHAAAAABYi7AEAAACAhQh7AAAAAGAhwh4AAAAAWIiwBwAAAAAWIuwBAAAAgIUIewAAAABgIcIeAAAAAFiIsAcAAAAAFiLsAQAAAICFCHsAAAAAYCHCHgAAAABYiLAHAAAAABYi7AEAAACAhQh7AAAAAGAhwh4AAAAAWIiwBwAAAAAWIuwBAAAAgIUIewAAAABgIcIeAAAAAFiIsAcAAAAAFiLsAQAAAICFCHsAAAAAYCHCHgAAAABYiLAHAAAAABYi7AEAAACAhQh7AAAAAGAhwh4AAAAAWIiwBwAAAAAWIuwBAAAAgIUIewAAAABgIcIeAAAAAFiIsAcAAAAAFiLsAQAAAICFCHsAAAAAYCHCHgAAAABYiLAHAAAAABYi7AEAAACAhQh7AAAAAGAhwh4AAAAAWIiwBwAAAAAWIuwBAAAAgIUIewAAAABgIcIeAAAAAFiIsAcAAAAAFiLsAQAAAICFCHsAAAAAYCHCHgAAAABYiLAHAAAAABYi7AEAAACAhQh7AAAAAGAhwh4AAAAAWIiwBwAAAAAWIuwBAAAAgIUIewAAAABgIcIeAAAAAFgoID9mcvr0aQ0ePFiHDh1SYGCgbrrpJo0dO1bly5fX1q1bNXLkSCUnJ6t69eqaOnWqKlSokB9lAQAAAIC18uXMnsvl0uOPP66vvvpKMTExqlGjhqZNmya3260XX3xRI0eO1FdffaWQkBBNmzYtP0oCAAAAAKvlS9grW7asGjdu7DwODg7WkSNHtHPnTgUFBSkkJESS1L17d3355Zf5URIAAAAAWC1fLuO8nNvt1qJFixQWFqajR4/qhhtucJ4rX7683G63zpw5o7Jly3o8ZoUKJX1RKq6jUqVSBV0CskF/Cj96VPjRo8KN/hR+9Kjwo0eFW177k+9hb9y4cSpevLh69Oihr7/+2itjnjqVKLfbZPsaVmTvO3Eiwavj0SPvoj+FHz0q/OhR4UZ/Cj96VPjRo8LNk/74+bmyPPmVr2Fv8uTJOnjwoN5++235+fmpWrVqOnLkiPN8fHy8/Pz8cnRWDwAAAABwtXz70wuvvfaadu7cqTfffFOBgYGSpHr16ikpKUmbNm2SJC1evFjt2rXLr5IAAAAAwFr5cmbv119/1ezZs3XzzTere/fukqQbb7xRb775pqZMmaJRo0Zl+tMLAAAAAIC8yZew9/e//1179+695nMNGzZUTExMfpQBAAAAAH8Z+XYZJwAAAAAg/xD2AAAAAMBChD0AAAAAsBBhDwAAAAAsRNgDAAAAAAsR9gAAAADAQoQ9AAAAALAQYQ8AAAAALETYAwAAAAALEfYAAAAAwEKEPQAAAACwEGEPAAAAACxE2AMAAAAACxH2AAAAAMBChD0AAAAAsBBhDwAAAAAsRNgDAAAAAAsR9gAAAADAQoQ9AAAAALAQYQ8AAAAALETYAwAAAAALEfYAAAAAwEKEPQAAAACwEGEPAAAAACxE2AMAAAAACxH2AAAAAMBChD0AAAAAsBBhDwAAAAAsRNgDAAAAAAsR9gAAAADAQoQ9AAAAALAQYQ8AAAAALETYAwAAAAALEfYAAAAAwEKEPQAAAACwEGEPAAAAACxE2AMAAAAACxH2AAAAAMBChD0AAAAAsBBhDwAAAAAsRNgDAAAAAAsR9gAAAADAQoQ9AAAAALAQYQ8AAAAALETYAwAAAAALEfYAAAAAwEKEPQAAAACwEGEPAAAAACxE2AMAAAAACxH2AAAAAMBChD0AAAAAsBBhDwAAAAAsRNgDAAAAAAsR9gAAAADAQoQ9AAAAALAQYQ8AAAAALETYAwAAAAALEfYAAAAAwEKEPQAAAACwEGEPAAAAACxE2AMAAAAACxH2AAAAAMBChD0AAAAAsBBhDwAAAAAsRNgDAAAAAAsR9gAAAADAQoQ9AAAAALAQYQ8AAAAALETYAwAAAAALEfYAAAAAwEKEPQAAAACwEGEPAAAAACxE2AMAAAAACxH2AAAAAMBChD0AAAAAsBBhDwAAAAAsRNgDAAAAAAsR9gAAAADAQoQ9AAAAALAQYQ8AAAAALETYAwAAAAALEfYAAAAAwEKEPQAAAACwUKEIewcOHNADDzygtm3b6oEHHtDvv/9e0CUBAAAAwJ9aoQh7o0aN0kMPPaSvvvpKDz30kEaOHFnQJQEAAADAn1pAQRdw6tQp7d69W/PmzZMkdezYUePGjVN8fLzKly/v0Rh+fi6PXlexXIlc14mrebrccyKwdAWvj/lX5Yv+VCzp2TYJz/iiR8Uqsg15ky96VKZsca+P+Vfli/6ULss25E2+6FGRUkW9PuZfmU+2o9KlvT7mX5Un/cnuNS5jjPFmQTm1c+dODRkyRMuXL3emtW/fXlOnTtXtt99egJUBAAAAwJ9XobiMEwAAAADgXQUe9qpVq6a4uDilp6dLktLT03X8+HFVq1atgCsDAAAAgD+vAg97FSpUUN26dRUbGytJio2NVd26dT2+Xw8AAAAAcLUCv2dPkvbt26ehQ4fq3LlzKl26tCZPnqxbb721oMsCAAAAgD+tQhH2AAAAAADeVeCXcQIAAAAAvI+wBwAAAAAWIuwBAAAAgIUIewAAAABgIcIeAAAAAFjoTx/2wsLC9Msvv3hlrG+++UaTJ0/O9jXr16/XmjVrnMdxcXF65JFHcjSf9evXq0GDBoqMjFTHjh3Vo0cP7du3L1c154eoqCh9/vnnBV2GwsLC1LFjR7nd7kzTPOl/ZGSkkpKSvFpLu3btFBkZqXbt2umll15Samqq18bPrS+++EL/+te/nLoGDRpU0CU5lixZogMHDlzzuR1NHaUAACAASURBVFGjRmnatGlXTX/kkUe0dOnSHM/rjz/+0EcffZTjn8tKWFiYmjVrpvT0dGfakiVLVKdOHb3//vu5Hnfo0KHX/PmwsDCNHDlSn3/+uS5evKjHHntMw4YNyzR/KWP5NGnSROfPn8807dtvv811Td4yY8YMpaSkZPl8nTp19Pjjj1817fLfJSuebs/Z7R+8+d7hDZf2KREREWrTpo369u2rzZs352nMRYsWaf78+dm+xpv797179yoyMlKRkZFq2bKlQkJCnMcffPCBV+aRXy7vR3h4uD7++GOfzMOX6+Ajjzyie+65x+nBc88957N5XXLu3Dm98847Pp+Pt5w5c0YtWrTQ9u3bnWlvv/22+vXrJ0nasWOHHnvsMYWFhen+++9Xp06dNHfuXOe1+XUs4O33NF/Ky7Gat2T13nqpltzWN3/+fJ06dcprdV7pjz/+UOPGjb0+7p8+7HnTPffcoyFDhmT7mg0bNuiHH35wHlepUkULFy7M8bxq1aql6OhoxcbGqkGDBpo0aVKOx8jOlQeFeTFgwAC1b9/ea+PlxYULFxQdHZ3jn4uOjlbRokW9Wsv06dMVHR2t5cuX67ffftPXX3/t1fFz6vjx4xozZozeeustRUdH64svvtBjjz1WoDVdkp6erqVLl+r333+/5vOdO3dWdHR0pvX28OHD2r17t9q1a5fj+f3vf//L9RtjWlraNadXrlw50wc9S5cu1e23356reXiiR48eatasmXr37q1bb71VEydOlL+//1WvK1asmObNm+ezOrJaHtczc+bM6x707N+/Xxs3bszx2L7YnnMjt8smK9OnT9dnn32mr7/+Wp06ddK///1vbdu2LdfjPfjgg+rVq1e2r/Hm/r1OnTqKjo5WdHS0+vfvr7vuust5/PDDDzuv8/Zy85VL/YiKitKYMWMUFxdX0CXl2EsvveT04I033sjRz+amT+fOndO7776b458rKGXLltXIkSM1bNgwpaSkaO/evfrggw80evRo7d27V0888YR69uypVatWacmSJXr33Xd15syZTGPkx7FAXt7TCkJuj9U8ldd9SG7rW7Bggc/Cni/3iwE+G7mALVu2zPn0pWbNmho7dqwqVKiglJQUjRs3Ths2bFD58uVVt25dnTx5UtOnT9eSJUu0evVqTZ8+Xfv379ewYcN08eJFud1uderUSc2aNdPixYvldru1du1adejQQe3bt1fnzp21fv16SdKWLVs0ZcoU59PpwYMHq1mzZtnWGhoaqtWrVzuPly5dqg8//FDp6ekqWbKkRo8erVtvvfW6tX/22WcqUaKEDh48qKlTpyolJUXTpk1zaunfv79atmypU6dOadCgQc4K26RJEw0fPlybN2/WuHHj5Ha7lZaWpr59+6pjx44aOnSo6tWrpx49euj8+fMaP368duzYISnjE/YnnnhCUsaniPXq1dPWrVt1/PhxhYeH64UXXvBeUyU9++yzmjlzpjp06KDAwMBMz7333ntavny50tPTFRQUpNGjR6tu3bqSMg5CNm/erJUrV2rFihV68803JWVsXC1bttSiRYtUo0YNzZkzRytWrFB6erqqVKmicePGqVKlStnWlJycrOTkZJUuXVqS9OOPP+qNN95QcnKy0tPT9dRTT6lDhw7avn27hg8frtjYWOdnIyIiNHr0aDVs2DDLvmfVlyudPHlSAQEBKlu2rCTJ5XLpH//4h6SMT4suX08vf3zp/506dXI+yBg1apRCQkKyfU7Keju7cn3s0qWLdu7cqfHjx+uNN97QkCFDdNdddzm1169fX2XLltWaNWt09913S8o4cxYeHq5ixYpluWwkafbs2YqNjZXL5VLx4sX14YcfauzYsfrjjz8UGRmpm266SdOnT9f27ds1YcIEXbhwQcWLF9eIESNUv35953e8//77tW7dOnXr1k0PPvjgVcu3U6dOWrJkie6++24dPnxYFy5cUO3atZ3ns+q7lHEFwPjx452w27FjRz355JOSpF9++UU9e/bUsWPHFBwc7FxdMHnyZP3666/q0qWLXC6XBg0apISEBB0+fFg1a9ZUVFSUJKlPnz6aOnWqVq5cKbfbrZMnTzpnvRITEzVp0iTt3btXycnJaty4sYYNGyZ/f//rbi/PPvusVq9erebNm+vxxx/PcpyZM2cqNjZWQUFBcrlcWrBggV5//XVJUvfu3eXn56eFCxc628fl+vXrp1dffVWLFy++6rn9+/dr4sSJOn36tFJTU/Xoo4+qc+fOTn2bN29WiRIltGnTJo0ZM0aS1LhxY33zzTeaPXu205svvvhCL7/8sk6cOKE+ffqoR48ezjw+++wzrV27VgkJCXr00Ued53KyrlSqVElRUVHy8/NTenq6Xn75Za98Mnvvvfdq+/btmjt3rqZPn66UlBS9/vrr2rhxo1JSUlSnTh2NHj1aJUqUUEJCgiZOnKidO3fK5XIpJCREI0eO1IwZM3ThwgUNGTKkQPfv11pu99xzj8aPH68jR44oOTlZHTp00FNPPXXd3heE2rVrq3Tp0oqLi1OVKlUUExOjBQsWOB9mDBkyRE2aNJGUcYYgMjJSa9euvWqdu3xd/ec//6nL/8zx9da5bt266b///a+SkpI0bdo0LV68WNu2bVPRokU1a9as675PXS4n63dWfXK73Ro7dqzWrVunwMBAFS9eXIsXL9bYsWOVkJCgyMhIFStW7JrbdmHTunVrffnll5o2bZo2btyoYcOGqUKFCpo0aZK6du3qvCdJUoUKFbK8YubKY4HstqeDBw9q5MiRio+PV0BAgJ5//nm1aNFCFy9e1JAhQ/Tbb78pICBAt9xyi6Kioq75nlaYZXesdvz48Sy3/cv37Vc+vvJ9KTw8XGPGjNHFixeVnJysbt26XffDrbzU99Zbb+n48ePq37+/goKC9Oqrr6p3795atmyZKlSooCeeeEIul0tz5szRqVOn1KlTJ33//ffX3a/edttt2rZtm8qUKaNRo0Y5daSkpGjw4MGqWrWqhgwZIpfLlateSJLMn1yrVq3M3r17M03bu3evadq0qYmLizPGGPP666+bAQMGGGOMWbBggenTp49JTU01SUlJpmvXrqZfv37GGGM+/fRT5//jxo0zb7/9tjPmmTNnjDHGTJ8+3bzyyivO9MOHD5vQ0FBjjDGnT582d911l/npp5+MMcakpaU5P3e5devWmU6dOhljjElPTzcvv/yymTlzpjHGmI0bN5onnnjCJCcnG2OMWb16tXnggQc8qj04ONgcPHjQGGPM2bNnTWRkpLMM4uLiTPPmzc3Zs2fNvHnzzMsvv3zV7/bUU0+ZmJgYY4wxbrfbnD171hhjzJAhQ8zChQuNMcZMmTLFDB482LjdbpOQkGDat29vVq9ebYwxpkePHmbAgAEmPT3dnDt3zoSGhpoDBw5k2bucutTrfv36mfnz52eaZowxp06dcl77ww8/mK5duzqPa9eubRITE82FCxdMaGio89pvvvnGPPLII8YYY5YtW2Zeeuklk56ebowx5oMPPjADBw7Mspa2bduaiIgIExwcbJ599lnnuTNnzpi0tDRjjDEnTpwwzZs3d5Zx165dzfr1640xGb2OjIx0/p9V37Pqy5XS09NN3759TWhoqOnXr5+ZN2+eiY+PN8ZkXk+vfHz48GFTu3Zts3TpUmNMxvrZvHlzk5ycnO1z2W1nV66PxmSsH6tWrbpm7cYYM2/ePNO/f3/nd2nZsqXZvHlztstmyZIlplu3biYhIcEYY5zf9/JtzBhjkpOTzd13323Wrl1rjMlYP+6+++5Mv+Py5cuzrK1Vq1Zmz549pl27dubMmTMmKirKLFiwINO2kV3fe/ToYd555x1nvEvr35AhQ0z37t1NUlKSSU5ONu3btzdr1qwxrVq1MvXq1TOPPfaYMSZjv9OmTRtz9uxZ43a7Te/evc1HH31kevToYQYNGmS6d+9uJkyYYIwxpkWLFk4fhg8f7vQuPT3dPP/88+ajjz7KVMOl5XHl9jJ79mzncVbjnD592jRq1MhcvHjRGGNMQkKCSU1NdcZITEzMcpleer5z587m66+/zjQtNTXVdOrUyfz222/OuPfee6/z+NLrkpOTTfPmzc3GjRuNMcasWLHC1K5d29kntGrVytlfHz582AQHBzs1tWrVygwdOtTpV9OmTc3PP/+c43XlvvvuM5s3bzbGZOzzL62LOXWt97IVK1aY8PBwY4wxb775pnnzzTed56ZMmWJee+01Y4wxQ4cONWPHjnX2XZd6e/n7VX7v3y9/P73WcuvVq5fZsGGDMSZj+3zwwQfNmjVrrtv7/HJ5PzZt2mTat2/v7IPi4+ON2+02xhizb98+07x580w/d611Ljk52TRr1sysW7fOGGPM8uXLnXXVk3Xu22+/NcYY884775hGjRqZ3bt3G2OMGTVqlLMeXKlHjx4mLCzMREREmIiICPPJJ5/keP3Oqk+7du0y7dq1c9a5S/u6K99r/izOnDlj7rjjDvPMM88408LDw519U1ayOxbIbnvq0qWL+c9//mOMMebXX391jktWrFhh+vTpk6kuY65+TyvMrnesltU6ZczV7xuXP77yfSkhIcHZJhMTE014eLizn7h8v+bN+q7cTw8aNMjExsaalJQU065dOxMeHm5SUlJMTEyMeeGFF4wx19+vPvnkk8775qXt5/Tp06ZHjx7m//7v/3K6+K/JyjN769ev1913363KlStLyvh0OTIy0nkuMjJSAQEBCggIUIcOHfTTTz9dNcY///lPTZ06VRcvXlTjxo115513Xne+W7duVa1atdSwYUNJkr+/v8qUKXPN1+7bt0+RkZGKi4tTyZIlnfsBVq1apT179qhr166SJGOMzp0751HtDRs2VM2aNSVlnGH8448/nE8PpIwzPQcPHlSDBg00f/58TZ48WaGhoc6Zx8aNG+utt97SoUOH1LRpUzVo0OCqun/88UcNHz5cLpdLJUuWVIcOHfTjjz86n3y1a9dOfn5+KlWqlGrVqqVDhw7p5ptvvu6yy4nnnntOPXv2VJcuXTJN37lzp2bPnq2zZ8/K5XJd85LBYsWKqXXr1oqNjVXPnj21dOlS3X///ZIylv3OnTvVqVMnSXLOImVl+vTpql27tpKTk9WvXz/Nnz9fvXr1Unx8vIYPH66DBw/K399fZ8+e1YEDBxQcHKxHHnlEH374oUJDQ/XBBx84lzZl13dP+iJJfn5+mjVrln755Rdt3LhRK1eu1Ny5cxUTE3PdZVqkSBFFREQ48ytatKj279+vkiVLZvncxo0bs9zOpMzroyciIiIUFRWlM2fOaPfu3SpWrJjuuOMOTZkyJctl8+233+rBBx90+lSuXLlrjn3gwAEVKVLE+fT9rrvuUpEiRXTgwAGVKFFCQUFBCg8Pz7Y+l8ul8PBwLV++XMuXL9fixYu1a9cu5/ms+v73v/9dW7ZsyXSpZfny5Z3/t27dWkFBQZKkf/zjHzp06JCkjEvEd+3a5Vw61qxZM+cT4/r16zuv2759u4wxev/997VmzRqdOXNGx48fl5SxXm3fvt2Zd1JSkqpUqSLp+tvLpe0gu3FKlSqlmjVrOlcwtGzZMttt5loGDhyoSZMmKSwszJn2+++/a9++fRo4cKAzLTU1Vfv371etWrWcafv371fRokWdM81t2rS56gzipUsUb7zxRpUuXVrHjh1zxri0D6lYsaJatmypDRs2yOVy5WhdufPOOzVp0iTde++9atGiRaazvXllLjvzs2rVKiUmJuqrr76SlPGp72233SYpYztYsmSJ/Pwy7sy4fP26pKD375cvtwsXLmjDhg2Kj493nj9//rz27dunKlWqeNT7/NC/f38ZY3To0CFFRUU5ZwAOHz6sQYMGKS4uTgEBATp58qROnDjhnF271jqXmpqqYsWKOWd927dvr5EjR0q6/v6pePHiatmypSTp9ttvV9WqVZ2z8LfffrvWrl2b5e/w0ksvqVWrVs7jvXv3erx+Z9enTp06KS0tTSNGjFDjxo0zzePP6Mcff1TJkiW1f/9+paSkXHW2R5LGjx+vjRs36tSpU/r4449VrVo1SVkfC2S1PTVq1Eg///yzc7b6b3/7m+rWrautW7fqtttu0759+zRmzBiFhoY6ff8zutaxWnbrVNOmTa875uXvS0lJSc7lti6XS8ePH9eePXs83k94o74mTZpo7dq1qlKlioKDg2WM0bZt27R27VonN1xvv3rfffcpIOD/x7GUlBQ99NBD6tev33WPSzxlZdjzhrZt2yo4OFg//PCD3nnnHX366afX/AKJ3KpVq5aWLFmilJQUDRw4UKNHj1ZUVJSMMercubMGDBiQ4zEvnfaWMg4S6tSpk+VN8UuXLtXatWsVHR2tOXPmaNGiRerVq5fCwsK0du1ajRs3Tk2bNtXzzz+foxouHbRKGWHXm/cOXnLrrbfq7rvvznTwnJKSogEDBuj999/X7bffrri4OLVo0eKaP9+pUydNnDhR9913nzZs2KApU6ZIylhmffv2vSpEXk9QUJBatmyp1atXq1evXho9erTCwsI0c+ZMuVwutW3bVsnJyZIyDpZee+017d69W+vXr9fEiROdeWfV95z2pXbt2qpdu7YefvhhtW/fXhs2bFD9+vUzHTheqseXLl8fPVG+fHk1a9ZMsbGx2rJlixPC87JNeKpYsWIeXSLRqVMnde3aVf/85z+vCpbZ9T07WW0ztWvXltvtVs+ePdWyZUudOHHCCdPly5d3DvSNMRo7dqy2bNmiw4cPq0KFCurTp4/z3KxZs1SjRo1M8/RkeylevLjz/6zGkaT//Oc/2rx5s9atW6f7779f7777rlObJ+666y5VqFBBn332Wab5lStXziv3fHh7n3TlujJ8+HDt3btX69at04ABA9S7d29169YtT/O4ZMeOHfr73/8uKWOZjBo1yjlIz6mC3r9fvtzcbrdcLpc++eQTFSlSJNPrfv31V6/1Pq8uHcR/8cUXGjZsmBo2bKiKFStq4MCBGjp0qFq3bi23260GDRpk2tY9XU6eXpZ1efDw8/PL9Njb77Oe9kmSli9frvXr12vt2rWaNm1arr5MqzCIj4/XxIkTNWfOHL333nuaPn26XnjhBdWtW1c7duxQ69atJWUEZynjg5NrLfMrjwVyo0aNGoqNjdW6dev0/fff6/XXX/foA9vC6FrHatdbp/z9/Z1jlWu9f17+vvTaa6+pUqVKeuWVVxQQEKA+ffrk6NgmN/Vd6c4779Sbb76pqlWr6s4775QxRuvWrdO6dev07LPPelTH5b+TlPHhe4MGDbRq1Srde++917xXP6es/IKWxo0b67vvvtOJEyckZRyMXLo/KDQ0VDExMUpLS1NycrK++OKLa45x8OBBVapUSffff7+eeeYZ51rbkiVLKiEh4Zo/ExwcrH379mnLli2SMs4MnT17NttaAwMDNXr0aP33v//V7t27FRYWpujoaB07dswZY+fOnTmqXZLuuOMOHTx4UOvWrXOmXToDcPjwYefThWHDhmnXrl1yu906cOCAatasqe7du6tnz57O73y5Jk2a6NNPP5UxRomJifr8888z3XuVX/r166cPP/zQuR8xJSVFaWlpzidtH374YZY/GxISosTERL322mtq3bq1ihUrJinjXosPP/zQ6VlKSor27Nlz3Vrcbrc2btzofMKdkJCg6tWry+Vy6YcfftDBgwed1xYpUkSdO3dW3759dd9992Wad1Z996QvUsZ9YZfWPUk6duyY4uPjdeONN6pixYpKTU11arn8vkEp45PzS28omzZtUlJSknNPXFbPZbedXcule4uy07lzZy1atEirV6/Wv/71r+sum1atWmnRokVKTEyUJJ0+fVpSxnZ6aZok3XLLLUpNTXW2hx9//FFpaWm65ZZbsq3nSjVq1NDzzz+vp59++qrnsup7iRIldMcdd2T6VsTLPzXMTrNmzdSpUyctW7ZM5cuXd75o4dLVA5KcM/Xdu3fXmjVrdPDgQR09elRSxrKbM2eOc2ASHx+vw4cP52h7yW6cxMRExcfHKzQ0VP3791ft2rX166+/Or/35T3IzqBBgzRjxgzn8S233KKiRYtq2bJlzrR9+/ZdNd6tt96qixcvOlc4rFy50jnr64lLB6fx8fH67rvv1Lhx4xyvK/v371edOnX06KOPKiIiIsvtM6dWrlypRYsWOcE9LCxM8+fPz3Q/5qVvcW7VqpXmzp3rHCRda/0qTPv3kiVLqlGjRpozZ44z7ejRozpx4oTHvc9P4eHhatq0qWbPni0pY1u/8cYbJUmffvpptt86e8mtt96qpKQkbdq0SZL05ZdfOuuqt/ZPnsjJvLLrU3x8vC5evKjmzZvrhRdeUKlSpZxji6SkpD/Nl/BI0pgxY9StWzfddtttGjFihGJjY7Vjxw498cQT+uijj/T99987r01JScn0LY6Xu/JYIKvtqWTJkqpbt66z/9m3b5/27Nmj4OBgHTt2TP7+/mrdurWGDRum+Ph4nTlz5qr3tD+LK4/VslunpIx7/y/tm64XchMSElS1alUFBATol19+cbYtX9Z35XFM9erV5e/vr6VLl6pJkyZq0qSJlixZooCAAN1www2Scr5fdblcmjhxokqWLKnnn3/eK9/uasWZvd69e2dKvjExMXrhhRecN8kaNWpo7NixkjIuNduzZ486dOigcuXKOQe0V/riiy8UExOjIkWKyOVyafjw4ZIyLrlatmyZIiMjnS9ouaRs2bKaMWOGXnnlFV24cEF+fn5XfRHFtVSsWFF9+vTRzJkzNWvWLD333HPq27ev0tPTlZqaqnbt2qlevXoe1y5JZcqU0axZszR16lRNnDhRqampqlGjht5++21t2LBB8+fPl5+fn9xut8aMGeN8icL69ev/X3v3H1NV/cdx/Hm59x4upoGAPyKpmRpb6oxNNBRybhHk7hWiGpqDjVUkoM5yc3AbhDmw4l6dXmdlQ9kaW1uKV6vLlvpHrgZuNrdq5T/YL7VprjJY0kW5/cGXM26IYl8IuL4eG9s993zO/XzuOZxzz/t8Pud9sNvtGIZhXsXqr6ysjK1bt+JyuYDeoXeD9aCNpOnTp5Obm8u+ffuA3h10w4YNPP3008TFxZGdnX3T5fPy8ti5c2dYz2deXh6///67eTN9KBRi9erVg/ZS9N2k293dzZw5cygvLwd6T1y3bNmCz+dj/vz5pKSkhC33zDPPsHv37rAkIGlpaYNu96FsF+hNNuPz+Th//jwOh4Oenh42btxoJml55ZVXKC4uJj4+fsDQkLi4OM6cOWNmUdu+fbt59XiweQ8++OCg+9mNFBQU8Prrr9PQ0DDofpGZmUlVVRWLFi0iMTHxlusmLy+PixcvUlBQgM1mY8KECTQ1NZGSksLMmTNxOp088MAD7Nq1i127doUlJeg/LOt2FBQU3PD9m213j8fDli1bcDqdREVF4XQ6KSkpGVJ9a9eupbW1lUAgQFlZmTkMs092djZff/01RUVFhEIhLl26ZAbGbreb+vp6cnNzzeGJbreb5OTk29pfBvscu93O+vXr6erqIhQK8dBDD/H4448DvYljioqKcDgcgyZo6TN//nzmzp3LuXPnALDZbLz99tvU1dXR0NBAT08PCQkJA7IJGoaB1+ulpqYG6L0glpCQwKRJk4a0bidPnkx+fj4dHR28+OKL5ja7nf8Vr9drDt29++67qa2tHVLdN7JhwwYMw+Dq1avMmjWLvXv3msMtS0pK2L17t5mwx2KxsG7dOmbNmkVlZSV1dXU4nU6sViuLFi0acJwYa8d3j8fDtm3bzLruuusuamtrmTJlypC2/X9t06ZN5Ofn88ILL1BZWUlZWRmxsbFkZmaaSbFuxjAMtm/fHpagpe9k0DCMYTs+DaUdt1PXYNupq6uLqqoqrl27xvXr13n00Ud5+OGHiYqKwuVy4XK5iI2NHfMJWgKBAN9//705cis2Npbq6mrcbjcHDx7knXfeYefOndTU1BAfH4/dbmft2rXm7Qsw+LnAzfYnj8dDdXU1jY2N2Gw23nzzTeLj4/n000/xer1Ab/BYUlLCtGnTSEhIGPCbNh7881wNbr7vV1ZWUl1dzaRJk26Zibu0tJTNmzdz4MABZs6cSVpa2oi3r6ioCLfbjcPhwOv1Mnv2bNLT0/niiy/M/4n+txbAvzuuWiwWXn31Vd544w3Ky8vx+XxhIwZulyXUf2zXHaKzs5OJEycSDAYpLS0lJyfHvB9orBvPbZdefSma+185Gk3/zNQ51HkiY0HfMRGgra2NyspKjh8/bt6/JiIicieLiJ6921VcXEwwGOSvv/5iyZIlYTd8jnXjue0Czz33HD/++CNvvfXWaDdFJCJ88sknNDY2EgqFMAwDj8ejQE9EROR/7siePRERERERkUiny58iIiIiIiIRSMGeiIiIiIhIBFKwJyIiIiIiEoEU7ImISESrqKhgx44dI/LZR44cMR8/ciMnT54clcfTiIiIgII9ERGJIIWFhaSlpQ3pQdfDYeXKlWHPaEpJSeGHH374T+oWERG5FQV7IiISEc6dO8epU6ewWCwcP358xOu7du3aiNchIiLy/1CwJyIiEcHv97NgwQKefPJJ/H7/oOXeffddMjIyyMjI4IMPPgjrjevo6GDz5s088sgjLF++nD179tDT0wNAc3Mzq1atoq6ujsWLF+Pz+Whubmb16tUArFmzBoDc3FxSU1MJBAJmnfv27SM9PZ2MjAwOHjxovl9RUUFNTQ3PP/88qamprFq1il9++YXa2lrS0tLIycnhm2++Mcvv3buXzMxMUlNTyc7OprW1dfhWoIiIRBwFeyIiEhEOHz6My+XC5XLx2Wefcfny5QFlTpw4QWNjI/v37+fo0aOcPHkybP7WrVvp6Ojg2LFjvPfeexw+fDgsOPvyyy9JTk7m888/p7S0BjV7fgAAA2lJREFUNGzZpqYmsx2nT59mxYoVAFy+fJmOjg5OnDhBbW0tr732GleuXDGXa2lpYePGjbS1tWEYBgUFBcydO5e2tjays7PZtm0bAGfPnqWpqYkDBw5w+vRpGhoauPfee4dn5YmISERSsCciIuPeqVOnuHDhAk888QTz5s0jOTmZjz76aEC5lpYW8vPzmTNnDjExMaxfv96cd/36dQKBAJs2bWLixInMmDGD4uJijhw5YpaZOnUqhYWF2Gw2HA7HkNpms9koLy/HbrezbNkyJkyYwHfffWfOz8rKYt68eURHR5OVlUV0dDR5eXlYrVZWrFjBt99+C4DVaiUYDNLe3k53dzczZszgvvvu+7erTERE7gAK9kREZNzz+/0sXbqU+Ph4AJxOJ4cOHRpQ7tKlS0yfPt2cvueee8zXv/32G93d3SQlJZnvJSUlcfHiRXO6/7JDFRcXh81mM6djYmL4888/zemEhATztcPhIDExMWy6r+z999+P2+3G5/OxZMkSXnrppbC2iYiI/JPt1kVERETGrq6uLlpaWujp6WHp0qUABINB/vjjD86cORNWdurUqWEB0s8//2y+njx5Mna7nQsXLjB79mxz/rRp08wyFotlJL/KLfUNU+3s7KS6uhqPx0N9ff2otklERMYu9eyJiMi4duzYMaxWKx9//DF+vx+/308gEGDhwoUDErXk5OTQ3NxMe3s7V69eZc+ePeY8q9VKTk4OO3bsoLOzk/Pnz7N//35Wrlw55LYkJiby008/Ddt36+/s2bO0trYSDAYxDIPo6GiiovQzLiIig9OvhIiIjGuHDh0iPz+fpKQkpkyZYv6tWbOGDz/8MOwRCcuWLaOwsJCioiKysrJYsGABAIZhAFBVVUVMTAyPPfYYzz77LE6nk6eeemrIbVm3bh0VFRUsXLgwLBvncAgGg3i9XhYvXkxGRga//vorL7/88rDWISIikcUSCoVCo90IERGR0dDe3o7T6eSrr74Ku69OREQkEqhnT0RE7ihHjx4lGAxy5coV6uvrWb58uQI9ERGJSAr2RETkjvL++++Tnp5OVlYWVquVmpqa0W6SiIjIiNAwThERERERkQiknj0REREREZEIpGBPREREREQkAinYExERERERiUAK9kRERERERCKQgj0REREREZEI9Df7uMu5yRuEbwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}