breast_cancer.ipynb

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            "_view_name": "LayoutView",
            "grid_template_rows": null,
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            "_view_module": "@jupyter-widgets/base",
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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/Inyrkz/breast_cancer/blob/main/breast_cancer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "asosFGzliiRr"
      },
      "source": [
        "# install pandas-profiling\n",
        "!pip install -U pandas-profiling"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "2HeJHZ7cgroV",
        "outputId": "0baaf568-d817-4b79-fb86-edef46218b7d"
      },
      "source": [
        "# import relevant libraries\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.metrics import f1_score\n",
        "%matplotlib inline\n",
        "\n",
        "# load dataset\n",
        "dataset = pd.read_csv('breast_cancer_data.csv')\n",
        "dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>id</th>\n",
              "      <th>diagnosis</th>\n",
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              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
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              "      <th>Unnamed: 32</th>\n",
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              "      <td>184.60</td>\n",
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              "      <td>M</td>\n",
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              "      <td>17.77</td>\n",
              "      <td>132.90</td>\n",
              "      <td>1326.0</td>\n",
              "      <td>0.08474</td>\n",
              "      <td>0.07864</td>\n",
              "      <td>0.08690</td>\n",
              "      <td>0.07017</td>\n",
              "      <td>0.1812</td>\n",
              "      <td>0.05667</td>\n",
              "      <td>0.5435</td>\n",
              "      <td>0.7339</td>\n",
              "      <td>3.398</td>\n",
              "      <td>74.08</td>\n",
              "      <td>0.005225</td>\n",
              "      <td>0.01308</td>\n",
              "      <td>0.01860</td>\n",
              "      <td>0.01340</td>\n",
              "      <td>0.01389</td>\n",
              "      <td>0.003532</td>\n",
              "      <td>24.990</td>\n",
              "      <td>23.41</td>\n",
              "      <td>158.80</td>\n",
              "      <td>1956.0</td>\n",
              "      <td>0.12380</td>\n",
              "      <td>0.18660</td>\n",
              "      <td>0.2416</td>\n",
              "      <td>0.1860</td>\n",
              "      <td>0.2750</td>\n",
              "      <td>0.08902</td>\n",
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              "      <th>2</th>\n",
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              "      <td>M</td>\n",
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              "      <td>130.00</td>\n",
              "      <td>1203.0</td>\n",
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              "      <td>0.7869</td>\n",
              "      <td>4.585</td>\n",
              "      <td>94.03</td>\n",
              "      <td>0.006150</td>\n",
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              "      <td>0.02058</td>\n",
              "      <td>0.02250</td>\n",
              "      <td>0.004571</td>\n",
              "      <td>23.570</td>\n",
              "      <td>25.53</td>\n",
              "      <td>152.50</td>\n",
              "      <td>1709.0</td>\n",
              "      <td>0.14440</td>\n",
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              "      <td>0.2430</td>\n",
              "      <td>0.3613</td>\n",
              "      <td>0.08758</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>84348301</td>\n",
              "      <td>M</td>\n",
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              "      <td>20.38</td>\n",
              "      <td>77.58</td>\n",
              "      <td>386.1</td>\n",
              "      <td>0.14250</td>\n",
              "      <td>0.28390</td>\n",
              "      <td>0.24140</td>\n",
              "      <td>0.10520</td>\n",
              "      <td>0.2597</td>\n",
              "      <td>0.09744</td>\n",
              "      <td>0.4956</td>\n",
              "      <td>1.1560</td>\n",
              "      <td>3.445</td>\n",
              "      <td>27.23</td>\n",
              "      <td>0.009110</td>\n",
              "      <td>0.07458</td>\n",
              "      <td>0.05661</td>\n",
              "      <td>0.01867</td>\n",
              "      <td>0.05963</td>\n",
              "      <td>0.009208</td>\n",
              "      <td>14.910</td>\n",
              "      <td>26.50</td>\n",
              "      <td>98.87</td>\n",
              "      <td>567.7</td>\n",
              "      <td>0.20980</td>\n",
              "      <td>0.86630</td>\n",
              "      <td>0.6869</td>\n",
              "      <td>0.2575</td>\n",
              "      <td>0.6638</td>\n",
              "      <td>0.17300</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84358402</td>\n",
              "      <td>M</td>\n",
              "      <td>20.29</td>\n",
              "      <td>14.34</td>\n",
              "      <td>135.10</td>\n",
              "      <td>1297.0</td>\n",
              "      <td>0.10030</td>\n",
              "      <td>0.13280</td>\n",
              "      <td>0.19800</td>\n",
              "      <td>0.10430</td>\n",
              "      <td>0.1809</td>\n",
              "      <td>0.05883</td>\n",
              "      <td>0.7572</td>\n",
              "      <td>0.7813</td>\n",
              "      <td>5.438</td>\n",
              "      <td>94.44</td>\n",
              "      <td>0.011490</td>\n",
              "      <td>0.02461</td>\n",
              "      <td>0.05688</td>\n",
              "      <td>0.01885</td>\n",
              "      <td>0.01756</td>\n",
              "      <td>0.005115</td>\n",
              "      <td>22.540</td>\n",
              "      <td>16.67</td>\n",
              "      <td>152.20</td>\n",
              "      <td>1575.0</td>\n",
              "      <td>0.13740</td>\n",
              "      <td>0.20500</td>\n",
              "      <td>0.4000</td>\n",
              "      <td>0.1625</td>\n",
              "      <td>0.2364</td>\n",
              "      <td>0.07678</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>564</th>\n",
              "      <td>926424</td>\n",
              "      <td>M</td>\n",
              "      <td>21.56</td>\n",
              "      <td>22.39</td>\n",
              "      <td>142.00</td>\n",
              "      <td>1479.0</td>\n",
              "      <td>0.11100</td>\n",
              "      <td>0.11590</td>\n",
              "      <td>0.24390</td>\n",
              "      <td>0.13890</td>\n",
              "      <td>0.1726</td>\n",
              "      <td>0.05623</td>\n",
              "      <td>1.1760</td>\n",
              "      <td>1.2560</td>\n",
              "      <td>7.673</td>\n",
              "      <td>158.70</td>\n",
              "      <td>0.010300</td>\n",
              "      <td>0.02891</td>\n",
              "      <td>0.05198</td>\n",
              "      <td>0.02454</td>\n",
              "      <td>0.01114</td>\n",
              "      <td>0.004239</td>\n",
              "      <td>25.450</td>\n",
              "      <td>26.40</td>\n",
              "      <td>166.10</td>\n",
              "      <td>2027.0</td>\n",
              "      <td>0.14100</td>\n",
              "      <td>0.21130</td>\n",
              "      <td>0.4107</td>\n",
              "      <td>0.2216</td>\n",
              "      <td>0.2060</td>\n",
              "      <td>0.07115</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>565</th>\n",
              "      <td>926682</td>\n",
              "      <td>M</td>\n",
              "      <td>20.13</td>\n",
              "      <td>28.25</td>\n",
              "      <td>131.20</td>\n",
              "      <td>1261.0</td>\n",
              "      <td>0.09780</td>\n",
              "      <td>0.10340</td>\n",
              "      <td>0.14400</td>\n",
              "      <td>0.09791</td>\n",
              "      <td>0.1752</td>\n",
              "      <td>0.05533</td>\n",
              "      <td>0.7655</td>\n",
              "      <td>2.4630</td>\n",
              "      <td>5.203</td>\n",
              "      <td>99.04</td>\n",
              "      <td>0.005769</td>\n",
              "      <td>0.02423</td>\n",
              "      <td>0.03950</td>\n",
              "      <td>0.01678</td>\n",
              "      <td>0.01898</td>\n",
              "      <td>0.002498</td>\n",
              "      <td>23.690</td>\n",
              "      <td>38.25</td>\n",
              "      <td>155.00</td>\n",
              "      <td>1731.0</td>\n",
              "      <td>0.11660</td>\n",
              "      <td>0.19220</td>\n",
              "      <td>0.3215</td>\n",
              "      <td>0.1628</td>\n",
              "      <td>0.2572</td>\n",
              "      <td>0.06637</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>566</th>\n",
              "      <td>926954</td>\n",
              "      <td>M</td>\n",
              "      <td>16.60</td>\n",
              "      <td>28.08</td>\n",
              "      <td>108.30</td>\n",
              "      <td>858.1</td>\n",
              "      <td>0.08455</td>\n",
              "      <td>0.10230</td>\n",
              "      <td>0.09251</td>\n",
              "      <td>0.05302</td>\n",
              "      <td>0.1590</td>\n",
              "      <td>0.05648</td>\n",
              "      <td>0.4564</td>\n",
              "      <td>1.0750</td>\n",
              "      <td>3.425</td>\n",
              "      <td>48.55</td>\n",
              "      <td>0.005903</td>\n",
              "      <td>0.03731</td>\n",
              "      <td>0.04730</td>\n",
              "      <td>0.01557</td>\n",
              "      <td>0.01318</td>\n",
              "      <td>0.003892</td>\n",
              "      <td>18.980</td>\n",
              "      <td>34.12</td>\n",
              "      <td>126.70</td>\n",
              "      <td>1124.0</td>\n",
              "      <td>0.11390</td>\n",
              "      <td>0.30940</td>\n",
              "      <td>0.3403</td>\n",
              "      <td>0.1418</td>\n",
              "      <td>0.2218</td>\n",
              "      <td>0.07820</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>567</th>\n",
              "      <td>927241</td>\n",
              "      <td>M</td>\n",
              "      <td>20.60</td>\n",
              "      <td>29.33</td>\n",
              "      <td>140.10</td>\n",
              "      <td>1265.0</td>\n",
              "      <td>0.11780</td>\n",
              "      <td>0.27700</td>\n",
              "      <td>0.35140</td>\n",
              "      <td>0.15200</td>\n",
              "      <td>0.2397</td>\n",
              "      <td>0.07016</td>\n",
              "      <td>0.7260</td>\n",
              "      <td>1.5950</td>\n",
              "      <td>5.772</td>\n",
              "      <td>86.22</td>\n",
              "      <td>0.006522</td>\n",
              "      <td>0.06158</td>\n",
              "      <td>0.07117</td>\n",
              "      <td>0.01664</td>\n",
              "      <td>0.02324</td>\n",
              "      <td>0.006185</td>\n",
              "      <td>25.740</td>\n",
              "      <td>39.42</td>\n",
              "      <td>184.60</td>\n",
              "      <td>1821.0</td>\n",
              "      <td>0.16500</td>\n",
              "      <td>0.86810</td>\n",
              "      <td>0.9387</td>\n",
              "      <td>0.2650</td>\n",
              "      <td>0.4087</td>\n",
              "      <td>0.12400</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568</th>\n",
              "      <td>92751</td>\n",
              "      <td>B</td>\n",
              "      <td>7.76</td>\n",
              "      <td>24.54</td>\n",
              "      <td>47.92</td>\n",
              "      <td>181.0</td>\n",
              "      <td>0.05263</td>\n",
              "      <td>0.04362</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.1587</td>\n",
              "      <td>0.05884</td>\n",
              "      <td>0.3857</td>\n",
              "      <td>1.4280</td>\n",
              "      <td>2.548</td>\n",
              "      <td>19.15</td>\n",
              "      <td>0.007189</td>\n",
              "      <td>0.00466</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.00000</td>\n",
              "      <td>0.02676</td>\n",
              "      <td>0.002783</td>\n",
              "      <td>9.456</td>\n",
              "      <td>30.37</td>\n",
              "      <td>59.16</td>\n",
              "      <td>268.6</td>\n",
              "      <td>0.08996</td>\n",
              "      <td>0.06444</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.2871</td>\n",
              "      <td>0.07039</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>569 rows × 33 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           id diagnosis  ...  fractal_dimension_worst  Unnamed: 32\n",
              "0      842302         M  ...                  0.11890          NaN\n",
              "1      842517         M  ...                  0.08902          NaN\n",
              "2    84300903         M  ...                  0.08758          NaN\n",
              "3    84348301         M  ...                  0.17300          NaN\n",
              "4    84358402         M  ...                  0.07678          NaN\n",
              "..        ...       ...  ...                      ...          ...\n",
              "564    926424         M  ...                  0.07115          NaN\n",
              "565    926682         M  ...                  0.06637          NaN\n",
              "566    926954         M  ...                  0.07820          NaN\n",
              "567    927241         M  ...                  0.12400          NaN\n",
              "568     92751         B  ...                  0.07039          NaN\n",
              "\n",
              "[569 rows x 33 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KI1SjuJxinkj"
      },
      "source": [
        "### Exploratory Data Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0eJWvhOTigyq",
        "outputId": "189b3cb3-84e4-4818-9bcf-13b5b108c4d0"
      },
      "source": [
        "# check dataset info\n",
        "dataset.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 569 entries, 0 to 568\n",
            "Data columns (total 33 columns):\n",
            " #   Column                   Non-Null Count  Dtype  \n",
            "---  ------                   --------------  -----  \n",
            " 0   id                       569 non-null    int64  \n",
            " 1   diagnosis                569 non-null    object \n",
            " 2   radius_mean              569 non-null    float64\n",
            " 3   texture_mean             569 non-null    float64\n",
            " 4   perimeter_mean           569 non-null    float64\n",
            " 5   area_mean                569 non-null    float64\n",
            " 6   smoothness_mean          569 non-null    float64\n",
            " 7   compactness_mean         569 non-null    float64\n",
            " 8   concavity_mean           569 non-null    float64\n",
            " 9   concave points_mean      569 non-null    float64\n",
            " 10  symmetry_mean            569 non-null    float64\n",
            " 11  fractal_dimension_mean   569 non-null    float64\n",
            " 12  radius_se                569 non-null    float64\n",
            " 13  texture_se               569 non-null    float64\n",
            " 14  perimeter_se             569 non-null    float64\n",
            " 15  area_se                  569 non-null    float64\n",
            " 16  smoothness_se            569 non-null    float64\n",
            " 17  compactness_se           569 non-null    float64\n",
            " 18  concavity_se             569 non-null    float64\n",
            " 19  concave points_se        569 non-null    float64\n",
            " 20  symmetry_se              569 non-null    float64\n",
            " 21  fractal_dimension_se     569 non-null    float64\n",
            " 22  radius_worst             569 non-null    float64\n",
            " 23  texture_worst            569 non-null    float64\n",
            " 24  perimeter_worst          569 non-null    float64\n",
            " 25  area_worst               569 non-null    float64\n",
            " 26  smoothness_worst         569 non-null    float64\n",
            " 27  compactness_worst        569 non-null    float64\n",
            " 28  concavity_worst          569 non-null    float64\n",
            " 29  concave points_worst     569 non-null    float64\n",
            " 30  symmetry_worst           569 non-null    float64\n",
            " 31  fractal_dimension_worst  569 non-null    float64\n",
            " 32  Unnamed: 32              0 non-null      float64\n",
            "dtypes: float64(31), int64(1), object(1)\n",
            "memory usage: 146.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        },
        "id": "dOs5iWA0iq9V",
        "outputId": "7499502d-286d-4894-990c-671f1c2f38e2"
      },
      "source": [
        "dataset.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>id</th>\n",
              "      <th>radius_mean</th>\n",
              "      <th>texture_mean</th>\n",
              "      <th>perimeter_mean</th>\n",
              "      <th>area_mean</th>\n",
              "      <th>smoothness_mean</th>\n",
              "      <th>compactness_mean</th>\n",
              "      <th>concavity_mean</th>\n",
              "      <th>concave points_mean</th>\n",
              "      <th>symmetry_mean</th>\n",
              "      <th>fractal_dimension_mean</th>\n",
              "      <th>radius_se</th>\n",
              "      <th>texture_se</th>\n",
              "      <th>perimeter_se</th>\n",
              "      <th>area_se</th>\n",
              "      <th>smoothness_se</th>\n",
              "      <th>compactness_se</th>\n",
              "      <th>concavity_se</th>\n",
              "      <th>concave points_se</th>\n",
              "      <th>symmetry_se</th>\n",
              "      <th>fractal_dimension_se</th>\n",
              "      <th>radius_worst</th>\n",
              "      <th>texture_worst</th>\n",
              "      <th>perimeter_worst</th>\n",
              "      <th>area_worst</th>\n",
              "      <th>smoothness_worst</th>\n",
              "      <th>compactness_worst</th>\n",
              "      <th>concavity_worst</th>\n",
              "      <th>concave points_worst</th>\n",
              "      <th>symmetry_worst</th>\n",
              "      <th>fractal_dimension_worst</th>\n",
              "      <th>Unnamed: 32</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5.690000e+02</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>569.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>3.037183e+07</td>\n",
              "      <td>14.127292</td>\n",
              "      <td>19.289649</td>\n",
              "      <td>91.969033</td>\n",
              "      <td>654.889104</td>\n",
              "      <td>0.096360</td>\n",
              "      <td>0.104341</td>\n",
              "      <td>0.088799</td>\n",
              "      <td>0.048919</td>\n",
              "      <td>0.181162</td>\n",
              "      <td>0.062798</td>\n",
              "      <td>0.405172</td>\n",
              "      <td>1.216853</td>\n",
              "      <td>2.866059</td>\n",
              "      <td>40.337079</td>\n",
              "      <td>0.007041</td>\n",
              "      <td>0.025478</td>\n",
              "      <td>0.031894</td>\n",
              "      <td>0.011796</td>\n",
              "      <td>0.020542</td>\n",
              "      <td>0.003795</td>\n",
              "      <td>16.269190</td>\n",
              "      <td>25.677223</td>\n",
              "      <td>107.261213</td>\n",
              "      <td>880.583128</td>\n",
              "      <td>0.132369</td>\n",
              "      <td>0.254265</td>\n",
              "      <td>0.272188</td>\n",
              "      <td>0.114606</td>\n",
              "      <td>0.290076</td>\n",
              "      <td>0.083946</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.250206e+08</td>\n",
              "      <td>3.524049</td>\n",
              "      <td>4.301036</td>\n",
              "      <td>24.298981</td>\n",
              "      <td>351.914129</td>\n",
              "      <td>0.014064</td>\n",
              "      <td>0.052813</td>\n",
              "      <td>0.079720</td>\n",
              "      <td>0.038803</td>\n",
              "      <td>0.027414</td>\n",
              "      <td>0.007060</td>\n",
              "      <td>0.277313</td>\n",
              "      <td>0.551648</td>\n",
              "      <td>2.021855</td>\n",
              "      <td>45.491006</td>\n",
              "      <td>0.003003</td>\n",
              "      <td>0.017908</td>\n",
              "      <td>0.030186</td>\n",
              "      <td>0.006170</td>\n",
              "      <td>0.008266</td>\n",
              "      <td>0.002646</td>\n",
              "      <td>4.833242</td>\n",
              "      <td>6.146258</td>\n",
              "      <td>33.602542</td>\n",
              "      <td>569.356993</td>\n",
              "      <td>0.022832</td>\n",
              "      <td>0.157336</td>\n",
              "      <td>0.208624</td>\n",
              "      <td>0.065732</td>\n",
              "      <td>0.061867</td>\n",
              "      <td>0.018061</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.670000e+03</td>\n",
              "      <td>6.981000</td>\n",
              "      <td>9.710000</td>\n",
              "      <td>43.790000</td>\n",
              "      <td>143.500000</td>\n",
              "      <td>0.052630</td>\n",
              "      <td>0.019380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.106000</td>\n",
              "      <td>0.049960</td>\n",
              "      <td>0.111500</td>\n",
              "      <td>0.360200</td>\n",
              "      <td>0.757000</td>\n",
              "      <td>6.802000</td>\n",
              "      <td>0.001713</td>\n",
              "      <td>0.002252</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.007882</td>\n",
              "      <td>0.000895</td>\n",
              "      <td>7.930000</td>\n",
              "      <td>12.020000</td>\n",
              "      <td>50.410000</td>\n",
              "      <td>185.200000</td>\n",
              "      <td>0.071170</td>\n",
              "      <td>0.027290</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.156500</td>\n",
              "      <td>0.055040</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>8.692180e+05</td>\n",
              "      <td>11.700000</td>\n",
              "      <td>16.170000</td>\n",
              "      <td>75.170000</td>\n",
              "      <td>420.300000</td>\n",
              "      <td>0.086370</td>\n",
              "      <td>0.064920</td>\n",
              "      <td>0.029560</td>\n",
              "      <td>0.020310</td>\n",
              "      <td>0.161900</td>\n",
              "      <td>0.057700</td>\n",
              "      <td>0.232400</td>\n",
              "      <td>0.833900</td>\n",
              "      <td>1.606000</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>0.005169</td>\n",
              "      <td>0.013080</td>\n",
              "      <td>0.015090</td>\n",
              "      <td>0.007638</td>\n",
              "      <td>0.015160</td>\n",
              "      <td>0.002248</td>\n",
              "      <td>13.010000</td>\n",
              "      <td>21.080000</td>\n",
              "      <td>84.110000</td>\n",
              "      <td>515.300000</td>\n",
              "      <td>0.116600</td>\n",
              "      <td>0.147200</td>\n",
              "      <td>0.114500</td>\n",
              "      <td>0.064930</td>\n",
              "      <td>0.250400</td>\n",
              "      <td>0.071460</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>9.060240e+05</td>\n",
              "      <td>13.370000</td>\n",
              "      <td>18.840000</td>\n",
              "      <td>86.240000</td>\n",
              "      <td>551.100000</td>\n",
              "      <td>0.095870</td>\n",
              "      <td>0.092630</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.033500</td>\n",
              "      <td>0.179200</td>\n",
              "      <td>0.061540</td>\n",
              "      <td>0.324200</td>\n",
              "      <td>1.108000</td>\n",
              "      <td>2.287000</td>\n",
              "      <td>24.530000</td>\n",
              "      <td>0.006380</td>\n",
              "      <td>0.020450</td>\n",
              "      <td>0.025890</td>\n",
              "      <td>0.010930</td>\n",
              "      <td>0.018730</td>\n",
              "      <td>0.003187</td>\n",
              "      <td>14.970000</td>\n",
              "      <td>25.410000</td>\n",
              "      <td>97.660000</td>\n",
              "      <td>686.500000</td>\n",
              "      <td>0.131300</td>\n",
              "      <td>0.211900</td>\n",
              "      <td>0.226700</td>\n",
              "      <td>0.099930</td>\n",
              "      <td>0.282200</td>\n",
              "      <td>0.080040</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>8.813129e+06</td>\n",
              "      <td>15.780000</td>\n",
              "      <td>21.800000</td>\n",
              "      <td>104.100000</td>\n",
              "      <td>782.700000</td>\n",
              "      <td>0.105300</td>\n",
              "      <td>0.130400</td>\n",
              "      <td>0.130700</td>\n",
              "      <td>0.074000</td>\n",
              "      <td>0.195700</td>\n",
              "      <td>0.066120</td>\n",
              "      <td>0.478900</td>\n",
              "      <td>1.474000</td>\n",
              "      <td>3.357000</td>\n",
              "      <td>45.190000</td>\n",
              "      <td>0.008146</td>\n",
              "      <td>0.032450</td>\n",
              "      <td>0.042050</td>\n",
              "      <td>0.014710</td>\n",
              "      <td>0.023480</td>\n",
              "      <td>0.004558</td>\n",
              "      <td>18.790000</td>\n",
              "      <td>29.720000</td>\n",
              "      <td>125.400000</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>0.146000</td>\n",
              "      <td>0.339100</td>\n",
              "      <td>0.382900</td>\n",
              "      <td>0.161400</td>\n",
              "      <td>0.317900</td>\n",
              "      <td>0.092080</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>9.113205e+08</td>\n",
              "      <td>28.110000</td>\n",
              "      <td>39.280000</td>\n",
              "      <td>188.500000</td>\n",
              "      <td>2501.000000</td>\n",
              "      <td>0.163400</td>\n",
              "      <td>0.345400</td>\n",
              "      <td>0.426800</td>\n",
              "      <td>0.201200</td>\n",
              "      <td>0.304000</td>\n",
              "      <td>0.097440</td>\n",
              "      <td>2.873000</td>\n",
              "      <td>4.885000</td>\n",
              "      <td>21.980000</td>\n",
              "      <td>542.200000</td>\n",
              "      <td>0.031130</td>\n",
              "      <td>0.135400</td>\n",
              "      <td>0.396000</td>\n",
              "      <td>0.052790</td>\n",
              "      <td>0.078950</td>\n",
              "      <td>0.029840</td>\n",
              "      <td>36.040000</td>\n",
              "      <td>49.540000</td>\n",
              "      <td>251.200000</td>\n",
              "      <td>4254.000000</td>\n",
              "      <td>0.222600</td>\n",
              "      <td>1.058000</td>\n",
              "      <td>1.252000</td>\n",
              "      <td>0.291000</td>\n",
              "      <td>0.663800</td>\n",
              "      <td>0.207500</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 id  radius_mean  ...  fractal_dimension_worst  Unnamed: 32\n",
              "count  5.690000e+02   569.000000  ...               569.000000          0.0\n",
              "mean   3.037183e+07    14.127292  ...                 0.083946          NaN\n",
              "std    1.250206e+08     3.524049  ...                 0.018061          NaN\n",
              "min    8.670000e+03     6.981000  ...                 0.055040          NaN\n",
              "25%    8.692180e+05    11.700000  ...                 0.071460          NaN\n",
              "50%    9.060240e+05    13.370000  ...                 0.080040          NaN\n",
              "75%    8.813129e+06    15.780000  ...                 0.092080          NaN\n",
              "max    9.113205e+08    28.110000  ...                 0.207500          NaN\n",
              "\n",
              "[8 rows x 32 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
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            "1acbd6bc5c2a4bec9d224b0bcd155802",
            "e5871ab3354341a1a5bb6715c390381c",
            "742add9bd22c44f5979cd1c0082e50dd",
            "70fbd40e7e874a019da06d35c0c4de42",
            "111b570475a94ac89acea67a9c08c94d",
            "dc8f5bd4efb34abdb97b109f5f423d10",
            "b482f1fc199042d6881e3055ddcc68ab",
            "08d5ec76549f4b0fab5953b857775ee6",
            "ac5492e63b744fe1b2abc891f5ab3417",
            "e5c13f9bf7e2442fb2794ce49f1b5c32",
            "af038a96442b4c9bbe59f0c488dfd01d",
            "8e4eca83c70d475f9411a385ee62fb65",
            "4d3dfa5e670945d79af6735f37e6810e"
          ]
        },
        "id": "vOaneerhiz2r",
        "outputId": "17d4770d-06d4-4c7b-e067-2c1992128679"
      },
      "source": [
        "# Exploratory data analysis with pandas profiling\n",
        "import pandas_profiling\n",
        "\n",
        "profile = dataset.profile_report(title='Breast Cancer Profiling Report')\n",
        "profile\n",
        "\n",
        "# import pandas_profiling as pp\n",
        "# pp.ProfileReport(dataset)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4b04c11228c64c0c9ac32b23e3a30a8b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a2e985062e14eabb6b7018a48ec20a4",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "742add9bd22c44f5979cd1c0082e50dd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "ea4deb37f5ef4808a0d3bb496b629987",
            "9a40116440464224b391dfd57357e0ad",
            "9f7fae4151d746cda7ced93f9ac828d9",
            "9981596b6335421ca242150110f841ff",
            "65854e8af7444acfac78edffb602218f",
            "fea04e8a2dc54a739105444c9e3d50d7",
            "19b8122dd0844fb586c56e2da87611a9",
            "b45b2850d0d54961b0c83b01d1102651",
            "633892a35a374a19bc610c764f83a254",
            "d2af09ad879742f9abc9738ab4b74837",
            "308a5cb47d32490eb5edc35bba8bf8c0"
          ]
        },
        "id": "K0q-55ugv0an",
        "outputId": "eafbc5b5-3ee0-4267-c23a-fbc3a59f6656"
      },
      "source": [
        "profile.to_file(output_file='Breast_Cancer_Report.html')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ea4deb37f5ef4808a0d3bb496b629987",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sFHLN-9krA3m"
      },
      "source": [
        "profile"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "z6Nk8LpQmLBB",
        "outputId": "02511bf7-66cc-4317-a275-60b1ebef4b58"
      },
      "source": [
        "# Pearson's Corrrelation Plot with Seaborn\n",
        "fig, ax = plt.subplots(figsize=(20,20))  # Sample figsize in inches\n",
        "sns.heatmap(dataset[dataset.columns.difference(['id', 'diagnosis', 'Unnamed: 32'])].corr(method='pearson'),\n",
        "            annot=True, linewidths=.5, ax=ax)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f05a675d6d0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9p4vP_UrwgQk"
      },
      "source": [
        "#### Plot Labels Count in Pie Chart"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "hQpe0YI2mbNh",
        "outputId": "4e47896b-e5a7-445c-8b8b-b5d3ae27fbc8"
      },
      "source": [
        "# Check the number of unique values in the label column\n",
        "label_count = dataset['diagnosis'].value_counts()\n",
        "print(label_count.index)\n",
        "print(label_count)\n",
        "\n",
        "# Draw a pie chart with the values above\n",
        "plt.figure(figsize=(10, 10))\n",
        "plt.pie(label_count, labels=label_count.index,\n",
        "        explode = [0, 0.01],\n",
        "        autopct='%1.1f%%')\n",
        "# plt.title('Crops Count in Dataset')\n",
        "plt.axis('equal')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Index(['B', 'M'], dtype='object')\n",
            "B    357\n",
            "M    212\n",
            "Name: diagnosis, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAdFMQSamgz7",
        "outputId": "61907c00-13c9-4326-9581-6a8dd08d33d6"
      },
      "source": [
        "# Features Extraction\n",
        "X = dataset.iloc[:, 2:-1].values\n",
        "y = dataset. iloc [:, 1].values\n",
        "print(\"Matrix of features\", X, sep='\\n')\n",
        "print(\"--------------------------------------------------\")\n",
        "print(\"Target Variable\", y, sep='\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matrix of features\n",
            "[[1.799e+01 1.038e+01 1.228e+02 ... 2.654e-01 4.601e-01 1.189e-01]\n",
            " [2.057e+01 1.777e+01 1.329e+02 ... 1.860e-01 2.750e-01 8.902e-02]\n",
            " [1.969e+01 2.125e+01 1.300e+02 ... 2.430e-01 3.613e-01 8.758e-02]\n",
            " ...\n",
            " [1.660e+01 2.808e+01 1.083e+02 ... 1.418e-01 2.218e-01 7.820e-02]\n",
            " [2.060e+01 2.933e+01 1.401e+02 ... 2.650e-01 4.087e-01 1.240e-01]\n",
            " [7.760e+00 2.454e+01 4.792e+01 ... 0.000e+00 2.871e-01 7.039e-02]]\n",
            "--------------------------------------------------\n",
            "Target Variable\n",
            "['M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M'\n",
            " 'M' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M'\n",
            " 'M' 'B' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M'\n",
            " 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B'\n",
            " 'M' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'M' 'M' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B'\n",
            " 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'B'\n",
            " 'M' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B'\n",
            " 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M'\n",
            " 'M' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B'\n",
            " 'M' 'M' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'M' 'M'\n",
            " 'M' 'M' 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'M' 'M' 'M'\n",
            " 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'M'\n",
            " 'B' 'B' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B'\n",
            " 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B'\n",
            " 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B'\n",
            " 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'M'\n",
            " 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
            " 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'M' 'M' 'B']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pt1dVj07mjWi",
        "outputId": "2f271099-8988-4285-d810-3632763ad2d8"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "\n",
        "label_encoder = LabelEncoder()\n",
        "encoded_y = label_encoder.fit_transform(y)\n",
        "label_encoder_name_mapping = dict(zip(label_encoder.classes_,\n",
        "                                     label_encoder.transform(label_encoder.classes_)))\n",
        "print(\"Mapping of Label Encoded Classes\", label_encoder_name_mapping, sep=\"\\n\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mapping of Label Encoded Classes\n",
            "{'B': 0, 'M': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-OFYgY_ImmdJ",
        "outputId": "f00efde7-ead5-449b-ec9c-a36e650a59f6"
      },
      "source": [
        "# Split dataset into training and test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, encoded_y, test_size=0.2,\n",
        "                                                  random_state=42,\n",
        "                                                  stratify=encoded_y)\n",
        "\n",
        "print(\"Matrix of features (Training set)\", X_train, sep='\\n')\n",
        "print(\"--------------------------------------------------\")\n",
        "print(\"Target Variable (Training set)\", y_train, sep='\\n')\n",
        "print(\"--------------------------------------------------\")\n",
        "print(\"Matrix of features (Test set)\", X_test, sep='\\n')\n",
        "print(\"--------------------------------------------------\")\n",
        "print(\"Target Variable (Test set)\", y_test, sep='\\n')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Matrix of features (Training set)\n",
            "[[1.602e+01 2.324e+01 1.027e+02 ... 9.975e-02 2.948e-01 8.452e-02]\n",
            " [1.232e+01 1.239e+01 7.885e+01 ... 9.391e-02 2.827e-01 6.771e-02]\n",
            " [1.285e+01 2.137e+01 8.263e+01 ... 5.601e-02 2.488e-01 8.151e-02]\n",
            " ...\n",
            " [1.300e+01 2.513e+01 8.261e+01 ... 5.921e-02 2.306e-01 6.291e-02]\n",
            " [1.420e+01 2.053e+01 9.241e+01 ... 1.339e-01 2.534e-01 7.858e-02]\n",
            " [1.706e+01 2.100e+01 1.118e+02 ... 1.827e-01 2.623e-01 7.599e-02]]\n",
            "--------------------------------------------------\n",
            "Target Variable (Training set)\n",
            "[1 0 0 1 1 1 0 1 1 1 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 0 1 1 0 0 0\n",
            " 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 1 1 0 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0\n",
            " 0 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 0 1 1 1\n",
            " 0 0 0 0 0 1 0 0 1 1 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 1 1 1 0\n",
            " 1 0 0 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 1 1 0 0 1 1\n",
            " 0 0 1 0 1 0 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 1 0 0 1 0 1 0 0 1 0 1 0 1\n",
            " 1 1 0 0 1 0 0 1 1 0 0 1 1 1 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 1 1 0 0 0 1 1 0\n",
            " 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 1 1 1 0 1 1 1 0\n",
            " 0 0 0 1 1 1 0 0 0 0 1 1 1 0 1 0 1 0 1 1 1 0 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0\n",
            " 1 0 0 1 1 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 1 1\n",
            " 0 1 0 1 0 0 1 1 0 0 1 0 1 1 1 0 0 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 1\n",
            " 0 1 0 1 1 1 0 1 0 0 0 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1\n",
            " 1 0 0 0 0 0 0 0 0 0 1]\n",
            "--------------------------------------------------\n",
            "Matrix of features (Test set)\n",
            "[[1.141e+01 1.082e+01 7.334e+01 ... 8.958e-02 3.016e-01 8.523e-02]\n",
            " [2.094e+01 2.356e+01 1.389e+02 ... 2.105e-01 3.126e-01 7.849e-02]\n",
            " [1.617e+01 1.607e+01 1.063e+02 ... 1.251e-01 3.153e-01 8.960e-02]\n",
            " ...\n",
            " [1.453e+01 1.934e+01 9.425e+01 ... 9.594e-02 2.471e-01 7.463e-02]\n",
            " [1.512e+01 1.668e+01 9.878e+01 ... 1.252e-01 3.415e-01 9.740e-02]\n",
            " [1.607e+01 1.965e+01 1.041e+02 ... 1.520e-01 2.650e-01 6.387e-02]]\n",
            "--------------------------------------------------\n",
            "Target Variable (Test set)\n",
            "[0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 0 0\n",
            " 0 1 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 1 1 0\n",
            " 0 0 0 1 0 1 1 1 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0\n",
            " 0 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sFDB_R9GmtZh",
        "outputId": "802a029b-1f4a-4c71-e40f-125284aacf6e"
      },
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "scaler = StandardScaler()\n",
        "scaled_X_train = scaler.fit_transform(X_train)\n",
        "scaled_X_test = scaler.transform(X_test)\n",
        "print(\"Scaled Matrix of features (Training set)\",scaled_X_train, sep='\\n', end='\\n\\n')\n",
        "print(\"Scaled Matrix of features (Testing set)\", scaled_X_test, sep='\\n', end='\\n\\n')\n",
        "print(\"Number of records in the training set: \", len(scaled_X_train))\n",
        "print(\"Number of records in the test set: \", len(scaled_X_test))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Scaled Matrix of features (Training set)\n",
            "[[ 0.51855873  0.89182579  0.4246317  ... -0.23574392  0.05456632\n",
            "   0.02183673]\n",
            " [-0.51636409 -1.63971029 -0.54134872 ... -0.32320788 -0.13757624\n",
            "  -0.90440164]\n",
            " [-0.36811839  0.45551496 -0.38824993 ... -0.89082504 -0.675893\n",
            "  -0.14401559]\n",
            " ...\n",
            " [-0.32616206  1.33280304 -0.38905998 ... -0.84289958 -0.96490082\n",
            "  -1.16888375]\n",
            " [ 0.00948859  0.25952507  0.00786279 ...  0.27571059 -0.60284707\n",
            "  -0.30545988]\n",
            " [ 0.80945595  0.36918608  0.79320285 ...  1.00657384 -0.46151907\n",
            "  -0.44817001]]\n",
            "\n",
            "Scaled Matrix of features (Testing set)\n",
            "[[-0.77089916 -2.00602473 -0.76451652 ... -0.38805702  0.16254727\n",
            "   0.06095804]\n",
            " [ 1.89472636  0.96648861  1.89081582 ...  1.42292627  0.33722232\n",
            "  -0.31041892]\n",
            " [ 0.56051506 -0.78108791  0.57044007 ...  0.14391558  0.38009711\n",
            "   0.30174696]\n",
            " ...\n",
            " [ 0.10179251 -0.01812727  0.08238707 ... -0.29280517 -0.70288824\n",
            "  -0.52310661]\n",
            " [ 0.26682074 -0.63876192  0.26586259 ...  0.14541325  0.79614133\n",
            "   0.73153038]\n",
            " [ 0.53254417  0.05420233  0.48133495 ...  0.54678897 -0.41864428\n",
            "  -1.11598733]]\n",
            "\n",
            "Number of records in the training set:  455\n",
            "Number of records in the test set:  114\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HSWdDnA2165w"
      },
      "source": [
        "### 5-Fold Cross Validation for Training  Set and Validation Set\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Bzc_iWKE2EKk"
      },
      "source": [
        "# K-Fold Cross-Validation\n",
        "from sklearn.model_selection import cross_validate\n",
        "\n",
        "def cross_validation(model, _X, _y, _cv=5):\n",
        "  '''Function to perform 5 Folds Cross-Validation\n",
        "  \n",
        "  model: machine learning algorithm is add as argument,\n",
        "  _X: the matrix of features,\n",
        "  _y: the target variable,\n",
        "  _cv: number of folds for cross-validation. Default is 5.\n",
        "\n",
        "  Output:\n",
        "  Function returns a dictionary containing the metrics ('accuracy', 'precision', 'recall', 'f1')\n",
        "  for both training set [if the return_train_score=True] and validation set.\n",
        "  '''\n",
        "\n",
        "  scoring = ['accuracy', 'precision', 'recall', 'f1']\n",
        "  results = cross_validate(estimator=model,\n",
        "                         X=_X,\n",
        "                         y=_y,\n",
        "                         cv=_cv,\n",
        "                         scoring=scoring,\n",
        "                         return_train_score=True)\n",
        "  \n",
        "  return {\"Training Accuracy scores\": results['train_accuracy'],\n",
        "          \"Mean Training Accuracy\": results['train_accuracy'].mean()*100,\n",
        "          \"Training Precision scores\": results['train_precision'],\n",
        "          \"Mean Training Precision\": results['train_precision'].mean(),\n",
        "          \"Training Recall scores\": results['train_recall'],\n",
        "          \"Mean Training Recall\": results['train_recall'].mean(),\n",
        "          \"Training F1 scores\": results['train_f1'],\n",
        "          \"Mean Training F1 Score\": results['train_f1'].mean(),\n",
        "          \"Validation Accuracy scores\": results['test_accuracy'],\n",
        "          \"Mean Validation Accuracy\": results['test_accuracy'].mean()*100,\n",
        "          \"Validation Precision scores\": results['test_precision'],\n",
        "          \"Mean Validation Precision\": results['test_precision'].mean(),\n",
        "          \"Validation Recall scores\": results['test_recall'],\n",
        "          \"Mean Validation Recall\": results['test_recall'].mean(),\n",
        "          \"Validation F1 scores\": results['test_f1'],\n",
        "          \"Mean Validation F1 Score\": results['test_f1'].mean()\n",
        "          }\n",
        "\n",
        "\n",
        "# Grouped Bar Chart for both training and validation data\n",
        "def plot_result(x_label, y_label, plot_title, train_data, val_data):\n",
        "    ''' \n",
        "      Function to plot a grouped bar chart showing the training and validation\n",
        "      results of the ML model in each fold after applying K-fold cross validation.\n",
        "\n",
        "      x_label: str, name of the algorithm used as x_label e.g \n",
        "      y_label: str, y_label e.g Accuracy\n",
        "      plot_title: str, plot title e.g Accuracy Plot\n",
        "      train_data: list, list containing either training precision, accuracy, f1 score\n",
        "      val_data: list, list containing either validation precision, accuracy, f1 score\n",
        "    '''\n",
        "    # Accuracies Plot\n",
        "    plt.figure(figsize=(12,6))\n",
        "    # colour = ['green', 'blue', 'orange', 'violet', 'brown']\n",
        "\n",
        "    labels = [\"1st Fold\", \"2nd Fold\", \"3rd Fold\", \"4th Fold\", \"5th Fold\"]\n",
        "    \n",
        "    X_axis = np.arange(len(labels))\n",
        "\n",
        "    ax = plt.gca()\n",
        "    plt.ylim(0.40000, 1)\n",
        "    plt.bar(X_axis-0.2, train_data, 0.4, color='blue', label='Training')\n",
        "    plt.bar(X_axis+0.2, val_data, 0.4, color='red', label='Validation')\n",
        "    plt.title(plot_title, fontsize=30)\n",
        "    plt.xticks(X_axis, labels)\n",
        "    plt.xlabel(x_label, fontsize=14)\n",
        "    plt.ylabel(y_label, fontsize=14)\n",
        "    plt.legend()\n",
        "    plt.grid(True)\n",
        "    plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zC6bL40f1y2Y"
      },
      "source": [
        "### Confusion Matrix for Final Evaluation on the Test Set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "burO9_2fnAC1"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score, recall_score\n",
        "from sklearn.metrics import precision_score, f1_score, ConfusionMatrixDisplay\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "def evaluate_model(y_val, y_pred):\n",
        "  \"\"\"Function to evaluate model and return the metric of the model\n",
        "  \n",
        "  It returns a dictionary with the classification metrics.\n",
        "  \"\"\"\n",
        "  accuracy = accuracy_score(y_val, y_pred)\n",
        "  precision = precision_score(y_val, y_pred, average='macro')\n",
        "  recall = recall_score(y_val, y_pred, average='macro')\n",
        "  f1 = f1_score(y_val, y_pred, average='macro')\n",
        "  result = {\"accuracy_score\": accuracy,\n",
        "            \"precision_score\": precision,\n",
        "            \"recall_score\": recall,\n",
        "            \"f1_score\": f1}\n",
        "  return result\n",
        "\n",
        "def plot_confusion_matrix(y_val, y_pred, label):\n",
        "  '''function to plot confusion matrix\n",
        "\n",
        "  Args\n",
        "  y_val: array. The validation set of the target variable.\n",
        "  y_pred: array. Model's prediction.\n",
        "  label: list. A list containing all the classes in the target variable\n",
        "\n",
        "  Returns\n",
        "  It returns a plot of the confusion matrix\n",
        "  '''\n",
        "  cm = confusion_matrix(y_val, y_pred)\n",
        "  fig, ax = plt.subplots(figsize=(10,10))\n",
        "  ax.grid(False)\n",
        "  ConfusionMatrixDisplay(cm, display_labels=label).plot(ax=ax, values_format='',\n",
        "                                                        xticks_rotation ='vertical')\n",
        "\n",
        "# class labels\n",
        "label = ['B', 'M']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d55ZQXwMAexF"
      },
      "source": [
        "# Training with Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJi8TpsE38Nb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6e9dcacb-d684-482f-c944-feea49900082"
      },
      "source": [
        "# Using GridSearchCV to find the best parameters\n",
        "\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "logistic_regression_model_2 = LogisticRegression(max_iter=2000)\n",
        "\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'C': np.logspace(-5,8,15),\n",
        "               'penalty': ['l1', 'l2']}]\n",
        "# n_jobs=-1 uses all the system's processors             \n",
        "grid_search = GridSearchCV(estimator=logistic_regression_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(scaled_X_train, y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best f1 {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best accuracy 96.68 %\n",
            "Best Parameters: {'C': 3.727593720314938, 'penalty': 'l2'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "75 fits failed out of a total of 150.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "75 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py\", line 681, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py\", line 449, in _check_solver\n",
            "    % (solver, penalty)\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_search.py:972: UserWarning: One or more of the test scores are non-finite: [       nan 0.                nan 0.01142857        nan 0.76612429\n",
            "        nan 0.92348368        nan 0.9562022         nan 0.96369123\n",
            "        nan 0.96677614        nan 0.96122992        nan 0.95294495\n",
            "        nan 0.94474176        nan 0.93903766        nan 0.94177407\n",
            "        nan 0.95605831        nan 0.94191643        nan 0.94200412]\n",
            "  category=UserWarning,\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "VvOd16l15p17",
        "outputId": "86d4e71a-7d2d-4a8a-ffb2-4ea7b6c0cd60"
      },
      "source": [
        "# # Plot train and test accuracy for different values of the regularization (C) parameter\n",
        "# # setup arrays to store training and test accuracy\n",
        "# C_param = np.logspace(-5,8,15)\n",
        "# lr_train_accuracy = np.empty(len(C_param))\n",
        "# lr_test_accuracy = np.empty(len(C_param))\n",
        "\n",
        "# # loop over different values of K\n",
        "# for i,c in enumerate(C_param):\n",
        "#   lr = LogisticRegression(C=c, penalty='l2', max_iter=1000)\n",
        "#   lr.fit(scaled_X_train, y_train)\n",
        "#   # compute accuracy on the training set\n",
        "#   y_train_pred = lr.predict(scaled_X_train)\n",
        "#   lr_train_accuracy[i] = f1_score(y_train, y_train_pred)\n",
        "#   # compute accuracy on the test set\n",
        "#   y_pred = lr.predict(scaled_X_test)\n",
        "#   lr_test_accuracy[i] = f1_score(y_test, y_pred)\n",
        "\n",
        "# # Generate plot\n",
        "# plt.title('Logistic Regression: Varying the value of C')\n",
        "# plt.plot(C_param, lr_test_accuracy, label='Testing F1-Score')\n",
        "# plt.plot(C_param, lr_train_accuracy, label='Training F1-Score')\n",
        "# plt.legend()\n",
        "# plt.xlabel('Regularization Parameter, C')\n",
        "# plt.ylabel('F1-Score')\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "b4taA51_kCre",
        "outputId": "06c72c2f-42c0-49ba-8559-f0f9b49d4406"
      },
      "source": [
        "# Use the best parameters to train the logistic regression algorithm\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr_model = LogisticRegression(C=3.727593720314938, penalty='l2', max_iter=2000)\n",
        "lr_train_val_result = cross_validation(lr_model, scaled_X_train, y_train, 5)\n",
        "print(lr_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"Logistic Regression\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=lr_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=lr_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "lr_model.fit(scaled_X_train, y_train)\n",
        "lr_y_pred = lr_model.predict(scaled_X_test)\n",
        "# Get test result\n",
        "lr_test_result = evaluate_model(y_test, lr_y_pred)\n",
        "print(lr_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(y_test, lr_y_pred, label)\n",
        "print(classification_report(y_test, lr_y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.99175824, 0.98626374, 0.99725275, 0.98626374, 0.99175824]), 'Mean Training Accuracy': 99.06593406593407, 'Training Precision scores': array([1.       , 0.9924812, 1.       , 1.       , 1.       ]), 'Mean Training Precision': 0.9984962406015038, 'Training Recall scores': array([0.97794118, 0.97058824, 0.99264706, 0.96323529, 0.97794118]), 'Mean Training Recall': 0.9764705882352942, 'Training F1 scores': array([0.98884758, 0.98141264, 0.99630996, 0.98127341, 0.98884758]), 'Mean Training F1 Score': 0.9873382356061564, 'Validation Accuracy scores': array([0.97802198, 0.98901099, 0.95604396, 1.        , 0.95604396]), 'Mean Validation Accuracy': 97.58241758241759, 'Validation Precision scores': array([1.        , 0.97142857, 1.        , 1.        , 0.94117647]), 'Mean Validation Precision': 0.9825210084033614, 'Validation Recall scores': array([0.94117647, 1.        , 0.88235294, 1.        , 0.94117647]), 'Mean Validation Recall': 0.9529411764705882, 'Validation F1 scores': array([0.96969697, 0.98550725, 0.9375    , 1.        , 0.94117647]), 'Mean Validation F1 Score': 0.9667761373324033}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9736842105263158, 'precision_score': 0.9741062479117941, 'recall_score': 0.9692460317460317, 'f1_score': 0.9715828832571667}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.97      0.99      0.98        72\n",
            "           1       0.98      0.95      0.96        42\n",
            "\n",
            "    accuracy                           0.97       114\n",
            "   macro avg       0.97      0.97      0.97       114\n",
            "weighted avg       0.97      0.97      0.97       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjUAAAItCAYAAADBkItuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAf3ElEQVR4nO3de7ytdV0n8M+Xc0Tu14N4EhBMkkENUvLaOAiamjNKk2OZFa+iUadSq6nJnC6jNWXTxXLK8qQWZt6VwDJAUV5qkygoKoIKXlFB5KaACLLPd/7Y68SO4Ox9Dmet9Zz1vN+v13rt9Txrref5rf1iw5fP71bdHQCAnd0u824AAMCOoKgBABaCogYAWAiKGgBgIShqAICFsH7eDQAAZusJj92zr7l2aSb3uuBjt5zV3U+cxb0UNQAwMtdcu5QPnnXYTO61buOlG2Zyo+h+AgAWhKQGAEamk2zO5nk3Y4eT1AAAC0FSAwCj01nqYSQ1VfWAJG9ccep+SX4jyWsm5w9P8vkkT+/u67Z2LUkNADA33f2p7j62u49N8tAk30xyWpIXJDmnu49Mcs7keKsUNQAwMstjanomj210YpLPdPcXkjw1yamT86cmOWm1D+t+AgCmaUNVnb/ieFN3b7qL9/5IktdPnh/c3VdMnl+Z5ODVbqSoAYARmuHsp6u7+7jV3lRVuyZ5SpJfveNr3d1VtWrso/sJABiCJyX5cHd/dXL81aramCSTn1etdgFJDQCMTKez1Ns83mXanpHbu56S5IwkJyd5yeTn6atdQFIDAMxVVe2Z5PFJ3rbi9EuSPL6qLk3yuMnxVklqAGCEtmNm0tR0901JDrzDuWuyPBtqzSQ1AMBCUNQAAAtB9xMAjEwnWRpQ99OOIqkBABaCpAYARmhIA4V3FEkNALAQJDUAMDKdDHHxvbtNUgMALARJDQCM0My2s5whSQ0AsBAkNQAwMp22Tg0AwFBJagBgbDpZWrygRlIDACwGSQ0AjEzH7CcAgMGS1ADA6FSWUvNuxA4nqQEAFoKiBgBYCLqfAGBkOslmU7oBAIZJUgMAI2SgMADAQElqAGBkOpIaAIDBktQAwAhtbkkNAMAgSWoAYGSMqQEAGDBJDQCMTKeytIC5xuJ9IwBglCQ1ADBCZj8BAAyUpAYARsbsJwCAARtkUrPhgHV9+KH3mHczYHQ+/bE95t0EGKVv5abc2rcsXnQyY4Msag4/9B754FmHzrsZMDpP+I5j590EGKXz+pwZ37Gy1IvXWbN43wgAGKVBJjUAwPR0ks0LmGss3jcCAEZJUgMAI2RKNwDAQElqAGBkus1+AgAYLEkNAIzQZmNqAACGSVIDACOzvKHl4uUai/eNAIBRktQAwOiY/QQAMFiSGgAYGXs/AQAMmKIGAFgIup8AYISW2uJ7AACDJKkBgJHplMX3AACGSlIDACO02eJ7AADDJKkBgJGxoSUAwIBJagBgZDplnRoAgKGS1ADACNnQEgBgoCQ1ADAy3cmSdWoAAIZJUgMAo1PZHLOfAAAGSVEDAMxVVe1XVW+pqk9W1SVV9ciqOqCq3llVl05+7r/adRQ1ADAyneWBwrN4rNGfJDmzu49KckySS5K8IMk53X1kknMmx1ulqAEA5qaq9k3ymCSvSpLuvrW7r0/y1CSnTt52apKTVruWgcIAMEIz3NByQ1Wdv+J4U3dvWnF8RJKvJfmrqjomyQVJnp/k4O6+YvKeK5McvNqNFDUAwDRd3d3HbeX19UkekuS53X1eVf1J7tDV1N1dVb3ajRQ1ADAyncrm4Wxo+aUkX+ru8ybHb8lyUfPVqtrY3VdU1cYkV612IWNqAIC56e4rk1xeVQ+YnDoxycVJzkhy8uTcyUlOX+1akhoAGKEZjqlZi+cm+duq2jXJZ5P8ZJaDlzdV1SlJvpDk6atdRFEDAMxVd1+Y5M7G3Zy4LddR1ADAyHSSzTa0BAAYJkkNAIxOZcmGlgAAwySpAYCRMaYGAGDAJDUAMELG1AAADJSkBgBGpruMqQEAGCpFDQCwEHQ/AcAILel+AgAYJkkNAIxMJ9lsSjcAwDBJagBgdMqYGgCAoZLUAMDILG9oaUwNAMAgSWoAYISWFjDXWLxvBACMkqQGAEamU8bUAAAMlaQGAEZo8wLmGov3jQCAUZLUAMDIdCdLxtQAAAyTogYAWAi6nwBghEzpBgAYKEkNAIzM8uJ7i5drLN43AgBGSVIDACO0FGNqAAAGSVIDACPTMfsJAGCwJDUAMDpmPwEADJakBgBGaLPZTwAAwySpAYCR6U6WzH4CABgmSQ0AjJDZTwAAA6WoAQAWgu4nABiZTtkmAQBgqCQ1ADBCFt8DABgoSQ0AjEwnxtQAAAyVpAYARsjiewAAAyWpAYCxaevUAAAMlqQGAEamY50aAIDBktQAwAgZUwMAMFCSGgAYGSsKAwAMmKIGAFgIup8AYIR0PwEADJSkhu12+WX3zO885/B/Ob7yi7vmx3/5ymy49635mz+8dy6/dLe87B2fzncdc/P8Ggkj8It/9MU8/HE35Pqr1+fZJzxg3s1hJ9CxTcJ2qaqlqrqwqj5aVR+uqkdN+57MxqH3vyV//q5P5c/f9an86Vmfyj1335xHP+n6HH7Ut/Ibr/x8HvyIm+bdRBiFs994QP7nM4+YdzNg7maR1Nzc3ccmSVU9IcnvJvkPM7gvM3Th+/bOxvvekoMP+fa8mwKjc9F5e+XgQ26ddzPYydgm4e7bJ8l1M74nM3Du6fvl+JOun3czABixWSQ1u1fVhUl2S7IxyQl39qaqelaSZyXJYfcx1Gdn8u1bKx84e9/81AuvmHdTAFiLHtbsp6r6fJIbkiwlua27j6uqA5K8McnhST6f5OndvdVgZBZJzc3dfWx3H5XkiUleU1X/5jfZ3Zu6+7juPu6gA9fNoFnsKB969965/4O/mf0Pum3eTQFg5/XYSb1w3OT4BUnO6e4jk5wzOd6qmUYi3f3PVbUhyUFJrprlvZmec/9uf11PADuRnWSbhKcmOX7y/NQk5yb5la19YKZjaqrqqCTrklwzy/syPd/65i758Pv2zvf9wO1FzT/947555kOPziUX7JFf//H75YXPuN8cWwiL7wUv/0Je+vZLc8h3fiuvPf/iPOEZ/hXLoGyoqvNXPJ51J+/pJGdX1QUrXj+4u7eMa7gyycGr3WiWY2qSpJKc3N1LM7gvM7DbHpvzlk9c9K/OPfpJX8+jn/T1ObUIxuclP3PfeTeBndAMk5qrV3Qp3ZXv6+4vV9W9kryzqj658sXu7qrq1W409aKmuw2QAQDuUnd/efLzqqo6LcnDkny1qjZ29xVVtTFrGLZimwQAGJktKwrP4rGaqtqzqvbe8jzJ9ye5KMkZSU6evO3kJKevdi1zpwGAeTo4yWmTidHrk7yuu8+sqg8leVNVnZLkC0mevtqFFDUAMEI9kNlP3f3ZJMfcyflrkpy4LdfS/QQALARFDQCwEHQ/AcAI2dASAGCgJDUAMDI9sA0tdxRJDQCwECQ1ADBCQ5nSvSNJagCAhSCpAYDRWdsWBjsbSQ0AsBAkNQAwQsbUAAAMlKQGAEamY50aAIDBktQAwNj08qrCi0ZSAwAsBEkNAIyQXboBAAZKUQMALATdTwAwMh2L7wEADJakBgBGx4aWAACDJakBgBGy+B4AwEBJagBghMx+AgAYKEkNAIxMt6QGAGCwJDUAMELWqQEAGChJDQCMkHVqAAAGSlIDACNk9hMAwEApagCAhaD7CQBGplO6nwAAhkpSAwAjtIAzuiU1AMBikNQAwNjY0BIAYLgkNQAwRgs4qEZSAwAsBEkNAIyQMTUAAAMlqQGAEWpjagAAhklSAwAj0zGmBgBgsCQ1ADA2nURSAwAwTIoaAGAh6H4CgBEypRsAYKAkNQAwRpIaAIBhktQAwOiUxfcAAIZKUgMAY2RMDQDAMElqAGBs2oaWAACDJakBgDEypgYAYJgkNQAwSsbUAAAMkqQGAMbImBoAgGFS1AAAc1dV66rqI1X195PjI6rqvKq6rKreWFW7rnYNRQ0AjFHP6LF2z09yyYrj30vy0u6+f5Lrkpyy2gUUNQDAXFXVIUmenOSVk+NKckKSt0zecmqSk1a7joHCADA2nWR22yRsqKrzVxxv6u5Nd3jPHyf5H0n2nhwfmOT67r5tcvylJPdZ7UaKGgBgmq7u7uPu6sWq+o9JruruC6rq+LtzI0UNAIxQD2dK96OTPKWqfiDJbkn2SfInSfarqvWTtOaQJF9e7ULG1AAAc9Pdv9rdh3T34Ul+JMm7u/uZSd6T5GmTt52c5PTVrqWoAYAxGt7spzv6lSS/WFWXZXmMzatW+4DuJwBgELr73CTnTp5/NsnDtuXzihoAGKPZzX6aGd1PAMBCkNQAwAjVcGY/7TB3WdRU1f/NVob4dPfzptIiAIDtsLWk5vytvAYA7Kzu/sykQbrLoqa7T115XFV7dPc3p98kAIBtt+pA4ap6ZFVdnOSTk+NjqurlU28ZADAltTz7aRaPGVrL7Kc/TvKEJNckSXd/NMljptkoAIBttaYp3d19+R1OLU2hLQAA220tU7ovr6pHJemqukeS5ye5ZLrNAgCmagEHCq8lqXlOkp9Ncp8kX0ly7OQYAGAwVk1quvvqJM+cQVsAgFkZY1JTVferqrdX1deq6qqqOr2q7jeLxgEArNVaup9el+RNSTYm+Y4kb07y+mk2CgCYsp7RY4bWUtTs0d1/0923TR6vTbLbtBsGALAttrb30wGTp/9YVS9I8oYs11w/nOQdM2gbADANnZkvjDcLWxsofEGWv/aWb/3sFa91kl+dVqMAALbV1vZ+OmKWDQEAZqcWcPbTWhbfS1U9KMnRWTGWprtfM61GAQBsq1WLmqr6zSTHZ7moeUeSJyV5fxJFDQDsrBYwqVnL7KenJTkxyZXd/ZNJjkmy71RbBQCwjdZS1Nzc3ZuT3FZV+yS5Ksmh020WAMC2WcuYmvOrar8kf5nlGVE3JvnnqbYKAGAbrWXvp5+ZPP2LqjozyT7d/bHpNgsAmKZRzX6qqods7bXu/vB0mpR8+uN75ImHHTetywN34dOvPHbeTYBRuuXFOkB2hK0lNX+4ldc6yQk7uC0AwKyMaUXh7n7sLBsCAHB3rGX2EwDA4K1pRWEAYIF0Rrv4HgDA4K1a1NSyH6uq35gcH1ZVD5t+0wCAqekZPWZoLUnNy5M8MskzJsc3JPmzqbUIAGA7rGVMzcO7+yFV9ZEk6e7rqmrXKbcLAJiiRVx8by1Jzberal0mIVJVHZRk81RbBQCwjdZS1LwsyWlJ7lVV/zvJ+5P8zlRbBQBM1wKOqVnL3k9/W1UXJDkxSSU5qbsvmXrLAAC2wapFTVUdluSbSd6+8lx3f3GaDQMApmgBx9SsZaDwP2T5q1eS3ZIckeRTSR44xXYBAGyTtXQ/PXjl8WT37p+ZWosAgKmqHu/sp3+luz+c5OFTaAsAwHZby5iaX1xxuEuShyT5ytRaBABMX9e8W7DDrWVMzd4rnt+W5TE2b51OcwAAts9Wi5rJont7d/cvzag9AMAsjGlMTVWt7+6lJI+eYXsAALbL1pKaD2Z5/MyFVXVGkjcnuWnLi939tim3DQBgzdYypma3JNckOSG3r1fTSRQ1ALCTWsQp3Vsrau41mfl0UW4vZrZYwF8FALAz21pRsy7JXvnXxcwWihoA2Jkt4H/Jt1bUXNHdL55ZSwAA7oatFTWLtyoPAJCMcJuEE2fWCgCAu+kuk5ruvnaWDQEAZmhkSQ0AwE5jLevUAACLRlIDADBMkhoAGKGxzX4CANhpKGoAgIWgqAEAFoIxNQAwRsbUAAAMk6IGAFgIup8AYGxGuKElAMBOQ1IDAGMkqQEAGCZJDQCMkaQGAGCYJDUAMDIVs58AAHaoqtqtqj5YVR+tqk9U1Ysm54+oqvOq6rKqemNV7bratRQ1ADBGPaPH6m5JckJ3H5Pk2CRPrKpHJPm9JC/t7vsnuS7JKatdSFEDAMxNL7txcniPyaOTnJDkLZPzpyY5abVrGVMDAGMz2xWFN1TV+SuON3X3ppVvqKp1SS5Icv8kf5bkM0mu7+7bJm/5UpL7rHYjRQ0AME1Xd/dxW3tDdy8lObaq9ktyWpKjtudGihoAGKMBzn7q7uur6j1JHplkv6paP0lrDkny5dU+b0wNADA3VXXQJKFJVe2e5PFJLknyniRPm7zt5CSnr3YtSQ0AjNFwkpqNSU6djKvZJcmbuvvvq+riJG+oqt9O8pEkr1rtQooaAGBuuvtjSb7nTs5/NsnDtuVaup8AgIUgqQGAEbJNAgDAQElqAGCMJDUAAMMkqQGAsVn7ZpM7FUkNALAQJDUAMEJmPwEADJSkBgDGSFIDADBMkhoAGCFjagAABkpSAwBjJKkBABgmSQ0AjI0VhQEAhktRAwAsBN1PADAyNXksGkkNALAQJDUAMEYGCgMADJOkBgBGyDYJAAADJakBgDGS1AAADJOkBgDGSFIDADBMkhoAGJs2+wkAYLAkNQAwRpIaAIBhktQAwAgZUwMAMFCKGgBgIeh+AoAx0v0EADBMkhoAGCEDhQEABkpSAwBj0zGmBgBgqCQ1ADBGkhoAgGGS1ADAyFTMfgIAGCxJDQCMkaQGAGCYJDUAMELVixfVSGoAgIUgqQGAsbGiMADAcClqAICFoPsJAEbI4nsAAAMlqQGAMVrApEZRww6xYeOt+eWXfi77HXRb0sk7Xrchp7/64Hk3Cxbb5s5hv3Vxbtt/13zleUdm/dduycZNn826G2/LLffdI1f89BHJeoE84zG1f9qrqqvqtSuO11fV16rq76d1T+Zn81LlL3/70Dz7xAfm5596VP7TT3wthx1587ybBQttv3d9Nbdu3P1fjg9665dy/eMPzud/98FZ2nN99n3f1XNsHUNXPZvHLE2zhL8pyYOqastf3OOTfHmK92OOrr3qHrnsoj2SJDfftC6XX7ZbDrz3t+fcKlhc66+9NXt97Ov5+r/fsHyiO3t88obc8ND9kyTfeNSB2evC6+fYQpi9aeeS70jy5MnzZyR5/ZTvxwAcfMgt+c4HfjOf+sie824KLKyD3nh5vva0Q5JaPt7lxtuytPu6ZN3yidv23zXrr7t1ji1k8HpGjxmadlHzhiQ/UlW7JfnuJOfd1Rur6llVdX5Vnf/tvmXKzWJadttjKb/2is/mFS86NN+8cd28mwMLac+PXp+lvdfnlsP9jwOsNNWBwt39sao6PMspzTtWee+mJJuSZJ9dDljAMdmLb936zq+/4rN5z2kH5J/O3H/ezYGFtftlN2bPj16fIz7+9dS3N2eXb23Ovd5wedbdvJQsdbKusv66W3Pb/rvOu6kM1RzGu8zCLGY/nZHkD5Icn+TAGdyPuej8wu9/Pl+8bLe87ZVmPcE0Xf1Dh+TqHzokSbL7J7+R/c/+aq78r/fLxj//TPa+4Lrc8LADss//uyY3HrvfnFsKszWLoubVSa7v7o9X1fEzuB9z8MDvvSmP+6Fr87lLds+f/ePFSZK//j/3yYfes++cWwbjcfXTDsnGV3wmB5725dxy2B75xvdtmHeTGDJJzbbr7i8ledm078N8feJDe+WJhz103s2A0bn5qH1y81H7JEm+fdA988VfO3rOLYL5mVpR09173cm5c5OcO617AgCrqyzmmBpLTQIAC8E2CQAwRr14UY2kBgBYCIoaAGBuqurQqnpPVV1cVZ+oqudPzh9QVe+sqksnP1ddAE1RAwAjNKANLW9L8t+7++gkj0jys1V1dJIXJDmnu49Mcs7keKsUNQDA3HT3Fd394cnzG5JckuQ+SZ6a5NTJ205NctJq1zJQGADGZrabTW6oqvNXHG+abI30b0y2VvqeLO8VeXB3XzF56cokqy5Xr6gBAKbp6u4+brU3VdVeSd6a5Oe7+xtV9S+vdXdXrd6ZpagBgBGqzfNuwe2q6h5ZLmj+trvfNjn91ara2N1XVNXGJFetdh1jagCAuanlSOZVSS7p7j9a8dIZSU6ePD85yemrXUtSAwBjNJy19x6d5MeTfLyqLpyce2GSlyR5U1WdkuQLSZ6+2oUUNQDA3HT3+7O8HdWdOXFbrqWoAYARsqElAMBASWoAYGw6NrQEABgqSQ0AjJAxNQAAAyWpAYAxktQAAAyTogYAWAi6nwBgZCoGCgMADJakBgDGptviewAAQyWpAYARMqYGAGCgJDUAMEaSGgCAYZLUAMAIGVMDADBQkhoAGJtOsnnxohpJDQCwECQ1ADBGixfUSGoAgMUgqQGAETL7CQBgoBQ1AMBC0P0EAGPUi9f/JKkBABaCpAYARshAYQCAgZLUAMDYdCy+BwAwVJIaABiZSlJmPwEADJOkBgDGaPO8G7DjSWoAgIUgqQGAETKmBgBgoCQ1ADA21qkBABguSQ0AjE7bpRsAYKgkNQAwQnbpBgAYKEUNALAQdD8BwBgZKAwAMEySGgAYm07KhpYAAMMkqQGAMTKmBgBgmCQ1ADBGixfUSGoAgMUgqQGAESpjagAAhklSAwBjJKkBABgmSQ0AjE0nsaIwAMAwSWoAYGQqbfYTAMBQKWoAgIWg+wkAxkj3EwDAMElqAGCMJDUAAMMkqQGAsbH4HgDAcElqAGCELL4HALADVdWrq+qqqrpoxbkDquqdVXXp5Of+a7mWogYAxqh7No/V/XWSJ97h3AuSnNPdRyY5Z3K8KkUNADA33f3eJNfe4fRTk5w6eX5qkpPWci1jagBgdNacouwIG6rq/BXHm7p70yqfObi7r5g8vzLJwWu5kaIGAJimq7v7uO39cHd3Va2pAlPUAMDYdIa+ovBXq2pjd19RVRuTXLWWDxlTAwAMzRlJTp48PznJ6Wv5kKQGAMZoICsKV9Xrkxyf5bE3X0rym0lekuRNVXVKki8kefparqWoAQDmprufcRcvnbit19L9BAAsBEkNAIyQbRIAAAZKUgMAYySpAQAYJkkNAIxNJ9ksqQEAGCRJDQCMzkw3tJwZSQ0AsBAkNQAwRpIaAIBhktQAwBhJagAAhklSAwBjY50aAIDhGmRSc0Nfd/U7v/2GL8y7HWyXDUmunncj2E6nvGHeLeDu8fe387rvbG/XSW+e7S1nYJBFTXcfNO82sH2q6vzuPm7e7YAx8vfH2Ol+AgAWwiCTGgBgykzphlVtmncDYMT8/TFqkhp2qO72L1WYE39/rJkp3QAAwyWpAYAxMqYGAGCYJDUAMEaSGti6qtpQVTXvdgAwPpIatltVPSLJS5Jcm+S3kvxNlpdp36WqfqK7z5xn+2CRVdUZW3u9u58yq7awM+qFTGoUNdwdf5rkhUn2TfLuJE/q7g9U1VFJXp9EUQPT88gkl2f5b+28JBJSRk9Rw92xvrvPTpKqenF3fyBJuvuTeqBg6u6d5PFJnpHkR5P8Q5LXd/cn5toqdg6dZPPibWhpTA13x8q/iJvv8Nri5ZowIN291N1ndvfJSR6R5LIk51bVz825aTA3khrujmOq6htZjr13nzzP5Hi3+TULxqGq7pnkyVlOaw5P8rIkp82zTexEjKmB23X3unm3Acaqql6T5EFJ3pHkRd190ZybBHOnqAHYOf1YkpuSPD/J81aMY6sk3d37zKth7CQkNQAMQXcbEwl34I8CAFgIkhoAGJ1ONi9e95OkBuaoqpaq6sKquqiq3lxVe9yNa/11VT1t8vyVVXX0Vt57fFU9ajvu8fmq2rDW83d4z43beK//VVW/tK1tBMZLUQPzdXN3H9vdD0pya5LnrHyxqrYrTe3un+7ui7fyluOTbHNRAyyITro3z+QxS4oaGI73Jbn/JEV532Rvn4ural1V/X5VfaiqPlZVz06SWvanVfWpqnpXknttuVBVnVtVx02eP7GqPlxVH62qc6rq8CwXT78wSYn+fVUdVFVvndzjQ1X16MlnD6yqs6vqE1X1yqxhKf6q+ruqumDymWfd4bWXTs6fU1UHTc59Z1WdOfnM+ybbbABsM2NqYAAmicyTcvt+WQ9J8qDu/tykMPh6d3/vZLG1f6qqs5N8T5IHJDk6ycFJLk7y6jtc96Akf5nkMZNrHdDd11bVXyS5sbv/YPK+1yV5aXe/v6oOS3JWkn+X5DeTvL+7X1xVT05yyhq+zk9N7rF7kg9V1Vu7+5okeyY5v7t/oap+Y3Ltn0uyKclzuvvSqnp4kpcnOWE7fo3AtljAMTWKGpiv3avqwsnz9yV5VZa7hT7Y3Z+bnP/+JN+9ZbxMljcQPTLJY7K8189Skq9U1bvv5PqPSPLeLdfq7mvvoh2PS3L0irVO9qmqvSb3+M+Tz/5DVV23hu/0vKr6wcnzQydtvSbL22q8cXL+tUneNrnHo5K8ecW977mGewD8G4oamK+bu/vYlScm/3G/aeWpJM/t7rPu8L4f2IHt2CXJI7r7W3fSljWrquOzXCA9sru/WVXn5q63zOjJfa+/4+8AmIEFXHzPmBoYvrOS/LequkeSVNV3VdWeSd6b5IcnY242JnnsnXz2A0keU1VHTD57wOT8DUn2XvG+s5M8d8tBVW0pMt6b5R2gU1VPSrL/Km3dN8l1k4LmqCwnRVvskmRL2vSjWe7W+kaSz1XVf5nco6rqmFXuAXCnFDUwfK/M8niZD1fVRUlekeWU9bQkl05ee02Sf77jB7v7a0meleWuno/m9u6ftyf5wS0DhZM8L8lxk4HIF+f2WVgvynJR9Iksd0N9cZW2nplkfVVdkuQlWS6qtrgpycMm3+GEJC+enH9mklMm7ftEkqeu4XcC3B3dyebNs3nMUPUCxk8AwF3bd92GfuReT5nJvc76xl9d0N3HzeJextQAwBgtYKih+wkAWAiSGgAYoZ7xeJdZkNQAAAtBUgMAo9PG1AAADJWiBgBYCLqfAGBsOgu5oaWkBgBYCJIaABijNqUbAGCQJDUAMDKdpI2pAQAYJkkNAIxNtzE1AABDJakBgBEypgYAYKAkNQAwRsbUAAAMU/UCbj0OANy1qjozyYYZ3e7q7n7iLG6kqAEAFoLuJwBgIShqAICFoKgBABaCogYAWAiKGgBgIfx/kGbN7chtE88AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "njtqi3JVAexv"
      },
      "source": [
        "# Training with KNN Algorithm"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JgmZI5D6F4kR",
        "outputId": "0aa7b193-544f-45e7-bc08-52429a710c49"
      },
      "source": [
        "# Use GridSearchCV to find the best parameters\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_model_2 = KNeighborsClassifier()\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'n_neighbors':np.arange(1,15),\n",
        "               'metric':['minkowski'],\n",
        "               'p':np.arange(1,4)}\n",
        "              ]\n",
        "# n_jobs=-1 uses all the system's processors             \n",
        "grid_search = GridSearchCV(estimator=knn_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(scaled_X_train, y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 95.71 %\n",
            "Best Parameters: {'metric': 'minkowski', 'n_neighbors': 5, 'p': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIovJ2iDtSuB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "acd19da4-2543-4e12-e4ce-2c26cf735635"
      },
      "source": [
        "# # setup arrays to store training and test accuracy\n",
        "# neighbors = np.arange(1,15)\n",
        "# train_accuracy = np.empty(len(neighbors))\n",
        "# test_accuracy = np.empty(len(neighbors))\n",
        "\n",
        "# # loop over different values of K\n",
        "# for i,k in enumerate(neighbors):\n",
        "#   knn = KNeighborsClassifier(n_neighbors=k, metric='minkowski', p=1)\n",
        "#   knn.fit(scaled_X_train, y_train)\n",
        "#   # compute accuracy on the training set\n",
        "#   y_train_pred = knn.predict(scaled_X_train)\n",
        "#   train_accuracy[i] = f1_score(y_train, y_train_pred)\n",
        "#   # compute accuracy on the test set\n",
        "#   y_pred = knn.predict(scaled_X_test)\n",
        "#   test_accuracy[i] = f1_score(y_test, y_pred)\n",
        "\n",
        "# # Generate plot\n",
        "# plt.title('KNN: Varying number of K')\n",
        "# plt.plot(neighbors, test_accuracy, label='Testing F1 Score')\n",
        "# plt.plot(neighbors, train_accuracy, label='Training F1 Score')\n",
        "# plt.legend()\n",
        "# plt.xlabel('Number of Neighbors')\n",
        "# plt.ylabel('F1 Score')\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dlpKQjfmmkPn",
        "outputId": "0898e43b-d192-4525-e126-8b26cb1642b3"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "knn_model = KNeighborsClassifier(n_neighbors=5, metric='minkowski', p=1)\n",
        "knn_train_val_result = cross_validation(knn_model, scaled_X_train, y_train, 5)\n",
        "print(knn_train_val_result)\n",
        "\n",
        "model_name = \"KNN\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=knn_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=knn_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "knn_model.fit(scaled_X_train, y_train)\n",
        "knn_y_pred = knn_model.predict(scaled_X_test)\n",
        "# Get test result\n",
        "knn_test_result = evaluate_model(y_test, knn_y_pred)\n",
        "print(lr_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(y_test, knn_y_pred, label)\n",
        "print(classification_report(y_test, knn_y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.97252747, 0.96978022, 0.98076923, 0.96978022, 0.98351648]), 'Mean Training Accuracy': 97.52747252747251, 'Training Precision scores': array([0.9921875 , 0.98449612, 0.99236641, 0.99212598, 1.        ]), 'Mean Training Precision': 0.9922352040993434, 'Training Recall scores': array([0.93382353, 0.93382353, 0.95588235, 0.92647059, 0.95588235]), 'Mean Training Recall': 0.9411764705882353, 'Training F1 scores': array([0.96212121, 0.95849057, 0.97378277, 0.9581749 , 0.97744361]), 'Mean Training F1 Score': 0.9660026127320102, 'Validation Accuracy scores': array([0.96703297, 0.98901099, 0.95604396, 0.97802198, 0.95604396]), 'Mean Validation Accuracy': 96.92307692307693, 'Validation Precision scores': array([1.     , 1.     , 1.     , 1.     , 0.96875]), 'Mean Validation Precision': 0.99375, 'Validation Recall scores': array([0.91176471, 0.97058824, 0.88235294, 0.94117647, 0.91176471]), 'Mean Validation Recall': 0.9235294117647059, 'Validation F1 scores': array([0.95384615, 0.98507463, 0.9375    , 0.96969697, 0.93939394]), 'Mean Validation F1 Score': 0.9571023379605469}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9736842105263158, 'precision_score': 0.9741062479117941, 'recall_score': 0.9692460317460317, 'f1_score': 0.9715828832571667}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      0.99      0.97        72\n",
            "           1       0.97      0.90      0.94        42\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.96      0.95      0.95       114\n",
            "weighted avg       0.96      0.96      0.96       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_4zsDiD0AexX"
      },
      "source": [
        "# Training with SVM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iY36mPc72dIl",
        "outputId": "f098bc8b-80bd-47c6-e12f-f3f299ace677"
      },
      "source": [
        "from sklearn.svm import SVC\n",
        "svm_model_2 = SVC()\n",
        "\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'C':np.logspace(-5,8,15),\n",
        "               'kernel':['linear', 'rbf', 'sigmoid', 'poly']}]\n",
        "# n_jobs=-1 uses all the system's processors             \n",
        "grid_search = GridSearchCV(estimator=svm_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(scaled_X_train, y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 96.70 %\n",
            "Best Parameters: {'C': 3.727593720314938, 'kernel': 'rbf'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "TUeHun-GAexY",
        "outputId": "27186da0-8c45-4e0d-de1a-fce5f3b4d430"
      },
      "source": [
        "# # Plot train and test accuracy for different values of the regularization (C) parameter\n",
        "# # setup arrays to store training and test accuracy\n",
        "# C_param = np.logspace(-5,8,15)\n",
        "# train_accuracy = np.empty(len(C_param))\n",
        "# test_accuracy = np.empty(len(C_param))\n",
        "\n",
        "# # loop over different values of K\n",
        "# for i,c in enumerate(C_param):\n",
        "#   svm = SVC(C=c, kernel='rbf')\n",
        "#   svm.fit(scaled_X_train, y_train)\n",
        "#   # compute accuracy on the training set\n",
        "#   y_train_pred = svm.predict(scaled_X_train)\n",
        "#   train_accuracy[i] = f1_score(y_train, y_train_pred)\n",
        "#   # compute accuracy on the test set\n",
        "#   y_pred = svm.predict(scaled_X_test)\n",
        "#   test_accuracy[i] = f1_score(y_test, y_pred)\n",
        "\n",
        "# # Generate plot\n",
        "# plt.title('SVM: Varying the value of C')\n",
        "# plt.plot(C_param, lr_test_accuracy, label='Testing F1-Score')\n",
        "# plt.plot(C_param, lr_train_accuracy, label='Training F1-Score')\n",
        "# plt.legend()\n",
        "# plt.xlabel('Regularization Parameter, C')\n",
        "# plt.ylabel('F1-Score')\n",
        "# plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "8fOu8-giQAsc",
        "outputId": "c60789ef-9088-43d4-8514-cacbaae4572b"
      },
      "source": [
        "# Use the best parameters to train the logistic regression algorithm\n",
        "from sklearn.svm import SVC\n",
        "\n",
        "svm_model = SVC(C=3.727593720314938, kernel='rbf')\n",
        "svm_train_val_result = cross_validation(svm_model, scaled_X_train, y_train, 5)\n",
        "print(svm_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"SVM\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=svm_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=svm_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "svm_model.fit(scaled_X_train, y_train)\n",
        "svm_y_pred = svm_model.predict(scaled_X_test)\n",
        "# Get test result\n",
        "svm_test_result = evaluate_model(y_test, svm_y_pred)\n",
        "print(svm_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(y_test, svm_y_pred, label)\n",
        "print(classification_report(y_test, svm_y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.98901099, 0.98626374, 0.99450549, 0.98351648, 0.99175824]), 'Mean Training Accuracy': 98.9010989010989, 'Training Precision scores': array([1., 1., 1., 1., 1.]), 'Mean Training Precision': 1.0, 'Training Recall scores': array([0.97058824, 0.96323529, 0.98529412, 0.95588235, 0.97794118]), 'Mean Training Recall': 0.9705882352941175, 'Training F1 scores': array([0.98507463, 0.98127341, 0.99259259, 0.97744361, 0.98884758]), 'Mean Training F1 Score': 0.9850463640727287, 'Validation Accuracy scores': array([0.96703297, 1.        , 0.96703297, 0.97802198, 0.96703297]), 'Mean Validation Accuracy': 97.58241758241759, 'Validation Precision scores': array([1.        , 1.        , 1.        , 0.97058824, 0.94285714]), 'Mean Validation Precision': 0.9826890756302522, 'Validation Recall scores': array([0.91176471, 1.        , 0.91176471, 0.97058824, 0.97058824]), 'Mean Validation Recall': 0.9529411764705882, 'Validation F1 scores': array([0.95384615, 1.        , 0.95384615, 0.97058824, 0.95652174]), 'Mean Validation F1 Score': 0.966960456423372}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9736842105263158, 'precision_score': 0.98, 'recall_score': 0.9642857142857143, 'f1_score': 0.9712773998488284}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      1.00      0.98        72\n",
            "           1       1.00      0.93      0.96        42\n",
            "\n",
            "    accuracy                           0.97       114\n",
            "   macro avg       0.98      0.96      0.97       114\n",
            "weighted avg       0.97      0.97      0.97       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vVOY90PsADf6"
      },
      "source": [
        "# Training with Decision Tree Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c-6O__WY2tGR",
        "outputId": "28ff5b88-1cc1-4a82-9324-569a908d4672"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "decision_tree_model_2 = DecisionTreeClassifier()\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'min_samples_split': np.arange(2, 70),\n",
        "               'criterion':['gini', 'entropy']},\n",
        "              ]\n",
        "# n_jobs=-1 uses all the system's processors             \n",
        "grid_search = GridSearchCV(estimator=decision_tree_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(scaled_X_train, y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 92.41 %\n",
            "Best Parameters: {'criterion': 'entropy', 'min_samples_split': 5}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-RZP6kI973pf",
        "outputId": "4e8ad167-40ef-4148-a6ad-f4bfc0aa5c63"
      },
      "source": [
        "# Use the best parameters to train the Decision Tree algorithm\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "\n",
        "dt_model = DecisionTreeClassifier(criterion=\"entropy\",\n",
        "                                             min_samples_split=5,\n",
        "                                             random_state=0)\n",
        "\n",
        "dt_train_val_result = cross_validation(dt_model, scaled_X_train, y_train, 5)\n",
        "print(dt_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"Decision Tree\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=dt_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=dt_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "dt_model.fit(scaled_X_train, y_train)\n",
        "dt_y_pred = dt_model.predict(scaled_X_test)\n",
        "# Get test result\n",
        "dt_test_result = evaluate_model(y_test, dt_y_pred)\n",
        "print(dt_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(y_test, dt_y_pred, label)\n",
        "print(classification_report(y_test, dt_y_pred))\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.99450549, 0.99725275, 0.99450549, 1.        , 0.98901099]), 'Mean Training Accuracy': 99.50549450549451, 'Training Precision scores': array([1.        , 0.99270073, 0.98550725, 1.        , 1.        ]), 'Mean Training Precision': 0.9956415952607639, 'Training Recall scores': array([0.98529412, 1.        , 1.        , 1.        , 0.97058824]), 'Mean Training Recall': 0.9911764705882353, 'Training F1 scores': array([0.99259259, 0.996337  , 0.99270073, 1.        , 0.98507463]), 'Mean Training F1 Score': 0.9933409891444536, 'Validation Accuracy scores': array([0.96703297, 0.94505495, 0.92307692, 0.96703297, 0.87912088]), 'Mean Validation Accuracy': 93.62637362637362, 'Validation Precision scores': array([0.96969697, 0.89189189, 0.88571429, 0.96969697, 0.81081081]), 'Mean Validation Precision': 0.9055621855621855, 'Validation Recall scores': array([0.94117647, 0.97058824, 0.91176471, 0.94117647, 0.88235294]), 'Mean Validation Recall': 0.9294117647058823, 'Validation F1 scores': array([0.95522388, 0.92957746, 0.89855072, 0.95522388, 0.84507042]), 'Mean Validation F1 Score': 0.916729274631131}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9473684210526315, 'precision_score': 0.9539473684210527, 'recall_score': 0.933531746031746, 'f1_score': 0.9422297297297297}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.93      0.99      0.96        72\n",
            "           1       0.97      0.88      0.93        42\n",
            "\n",
            "    accuracy                           0.95       114\n",
            "   macro avg       0.95      0.93      0.94       114\n",
            "weighted avg       0.95      0.95      0.95       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hXeWC7ZRAqzo"
      },
      "source": [
        "# Training with Random Forest Classifier"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JQcPS_f_SkSX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e486cb6-764e-4ad4-ddad-2c3f9c1aee5d"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "random_forest_model_2 = RandomForestClassifier()\n",
        "\n",
        "# setting different parameter combinations\n",
        "parameters = [{'min_samples_split': np.arange(10,70, 5),\n",
        "               'criterion': ['gini', 'entropy'],\n",
        "               'n_estimators': np.arange(80,150, 10)}\n",
        "              ]\n",
        "# n_jobs=-1 uses all the system's processors             \n",
        "grid_search = GridSearchCV(estimator=random_forest_model_2,\n",
        "                           param_grid=parameters,\n",
        "                           scoring='f1',\n",
        "                           cv=5,\n",
        "                           n_jobs=-1)\n",
        "\n",
        "grid_search.fit(scaled_X_train, y_train)\n",
        "# get the best accuracy score\n",
        "best_f1 = grid_search.best_score_\n",
        "# get the parameters that gave the best score\n",
        "best_parameters = grid_search.best_params_\n",
        "print(\"Best F1 score {:.2f} %\".format(best_f1*100))\n",
        "print(\"Best Parameters:\", best_parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best F1 score 94.70 %\n",
            "Best Parameters: {'criterion': 'entropy', 'min_samples_split': 10, 'n_estimators': 130}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJL3wfPI_rhT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "96faec02-1a01-4197-e910-000ddbd27904"
      },
      "source": [
        "# Use the best parameters to train the Random Forest algorithm\n",
        "rf_model = RandomForestClassifier(n_estimators=130,\n",
        "                                  min_samples_split=10,\n",
        "                                  criterion=\"entropy\",\n",
        "                                  random_state=0)\n",
        "\n",
        "rf_train_val_result = cross_validation(rf_model, scaled_X_train, y_train, 5)\n",
        "print(rf_train_val_result)\n",
        "\n",
        "# Plot Training and Validation Result with F1-score as metric\n",
        "model_name = \"Random Forest\"\n",
        "plot_result(x_label=model_name,\n",
        "            y_label=\"F1 Score\",\n",
        "            plot_title=\"F1 scores in 5 Folds\",\n",
        "            train_data=rf_train_val_result[\"Training F1 scores\"],\n",
        "            val_data=rf_train_val_result[\"Validation F1 scores\"])\n",
        "\n",
        "\n",
        "# Evaluate model on Test Set and plot results\n",
        "rf_model.fit(scaled_X_train, y_train)\n",
        "rf_y_pred = rf_model.predict(scaled_X_test)\n",
        "# Get test result\n",
        "rf_test_result = evaluate_model(y_test, rf_y_pred)\n",
        "print(rf_test_result)\n",
        "# Plot confusion matrix\n",
        "plot_confusion_matrix(y_test, rf_y_pred, label)\n",
        "print(classification_report(y_test, rf_y_pred))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'Training Accuracy scores': array([0.98901099, 0.99175824, 0.99450549, 0.98351648, 0.98901099]), 'Mean Training Accuracy': 98.95604395604396, 'Training Precision scores': array([1.        , 1.        , 0.99264706, 0.98507463, 0.99253731]), 'Mean Training Precision': 0.9940517998244074, 'Training Recall scores': array([0.97058824, 0.97794118, 0.99264706, 0.97058824, 0.97794118]), 'Mean Training Recall': 0.9779411764705882, 'Training F1 scores': array([0.98507463, 0.98884758, 0.99264706, 0.97777778, 0.98518519]), 'Mean Training F1 Score': 0.9859064464590575, 'Validation Accuracy scores': array([0.96703297, 1.        , 0.92307692, 0.96703297, 0.91208791]), 'Mean Validation Accuracy': 95.3846153846154, 'Validation Precision scores': array([1.        , 1.        , 0.93548387, 0.96969697, 0.86111111]), 'Mean Validation Precision': 0.9532583903551647, 'Validation Recall scores': array([0.91176471, 1.        , 0.85294118, 0.94117647, 0.91176471]), 'Mean Validation Recall': 0.9235294117647059, 'Validation F1 scores': array([0.95384615, 1.        , 0.89230769, 0.95522388, 0.88571429]), 'Mean Validation F1 Score': 0.9374184024930294}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'accuracy_score': 0.9649122807017544, 'precision_score': 0.9736842105263157, 'recall_score': 0.9523809523809523, 'f1_score': 0.9614864864864865}\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.95      1.00      0.97        72\n",
            "           1       1.00      0.90      0.95        42\n",
            "\n",
            "    accuracy                           0.96       114\n",
            "   macro avg       0.97      0.95      0.96       114\n",
            "weighted avg       0.97      0.96      0.96       114\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pTUh7eiJUFwR"
      },
      "source": [
        "### Visualize Result"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jo-yv2AiT7cv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "outputId": "1c020883-7467-47e2-b477-aba3d96763c7"
      },
      "source": [
        "# Create empty list for accuracy, precision, recall and f1-score of each ml algorithm\n",
        "\n",
        "accuracy_score_tf = [lr_test_result['accuracy_score'], knn_test_result['accuracy_score'],\n",
        "                     dt_test_result['accuracy_score'], rf_test_result['accuracy_score'],\n",
        "                     svm_test_result['accuracy_score']]\n",
        "\n",
        "precision_score_tf = [lr_test_result['precision_score'], knn_test_result['precision_score'],\n",
        "                     dt_test_result['precision_score'], rf_test_result['precision_score'],\n",
        "                     svm_test_result['precision_score']]\n",
        "\n",
        "recall_score_tf = [lr_test_result['recall_score'], knn_test_result['recall_score'],\n",
        "                   dt_test_result['recall_score'], rf_test_result['recall_score'],\n",
        "                   svm_test_result['recall_score']]\n",
        "\n",
        "f1_score_tf = [lr_test_result['f1_score'], knn_test_result['f1_score'],\n",
        "               dt_test_result['f1_score'], rf_test_result['f1_score'],\n",
        "               svm_test_result['f1_score']]\n",
        "\n",
        "models = [\"Logistic Regression\", \"KNN\", \"Decision Tree\", \"Random Forest\", \"SVM\"]\n",
        "\n",
        "# 5 is the number of models used\n",
        "X = np.arange(5)\n",
        "fig = plt.figure(figsize=(12,6))\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.bar(X + 0.00, accuracy_score_tf, color = 'b', width = 0.25)\n",
        "ax.bar(X + 0.20, precision_score_tf, color = 'g', width = 0.25)\n",
        "ax.bar(X + 0.40, recall_score_tf, color = 'r', width = 0.25)\n",
        "ax.bar(X + 0.60, f1_score_tf, color = 'yellow', width = 0.25)\n",
        "plt.xticks(X+0.30, models)\n",
        "plt.ylim(0.8, 1)\n",
        "plt.xlabel(\"ML Algorithms\")\n",
        "plt.ylabel(\"Scores\")\n",
        "plt.legend([\"Accuracy\", \"Precision\", \"Recall\", \"F1\"])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f59eb4f37d0>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}