CAD.ipynb

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      "name": "CAD.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Pragati-64/CAD/blob/master/CAD.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###IMPORT LIBRARY"
      ],
      "metadata": {
        "id": "pMLtYfkNZVYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install Boruta"
      ],
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      "execution_count": 56,
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: Boruta in /usr/local/lib/python3.7/dist-packages (0.3)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.7/dist-packages (from Boruta) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.17.1 in /usr/local/lib/python3.7/dist-packages (from Boruta) (1.0.2)\n",
            "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from Boruta) (1.7.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.17.1->Boruta) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.17.1->Boruta) (3.1.0)\n"
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      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "4I7phEnrOclf"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "#scaling\n",
        "from sklearn.preprocessing import StandardScaler , MinMaxScaler\n",
        "#models\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "#accuracy\n",
        "from sklearn.metrics import mean_squared_error , accuracy_score , confusion_matrix, ConfusionMatrixDisplay , classification_report\n",
        "\n",
        "# boruta feature selection\n",
        "\n",
        "from boruta import BorutaPy\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# Create the parameter grid based on the results of random search \n",
        "param_grid = {\n",
        "    'bootstrap': [True, False],\n",
        "    'max_depth': [5, 10, 15],\n",
        "    'n_estimators': [500, 1000]}\n",
        "\n",
        "\n",
        "#For DFE\n",
        "import sklearn\n",
        "from sklearn import feature_selection\n",
        "from sklearn.datasets import make_classification\n",
        "from sklearn.feature_selection import RFECV\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "# Data Visualization\n",
        "import seaborn as sns # checking data balancing(heat map)\n",
        "import plotly.express as px # Data Visualization\n",
        "import matplotlib.pyplot as plt # heat map figure size\n",
        "\n",
        "from sklearn.model_selection import StratifiedKFold\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### LOAD DATASET"
      ],
      "metadata": {
        "id": "oDYLeXleYljf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data_set=pd.ExcelFile('/content/Z-Alizadeh sani dataset.xlsx')"
      ],
      "metadata": {
        "id": "yL-y-iP8QQUC"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df=data_set.parse('CAD_DATA',na_values='?')   "
      ],
      "metadata": {
        "id": "Qlz606YFQW2u"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
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        "outputId": "55bfd91e-23cc-4d8a-a805-5e2878866c37"
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      "execution_count": 60,
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              "   Age  Weight  Length    Sex        BMI  DM  HTN  Current Smoker  EX-Smoker  \\\n",
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      "execution_count": 61,
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              "     Age  Weight  Length    Sex        BMI  DM  HTN  Current Smoker  \\\n",
              "298   58      84     168   Male  29.761905   0    0               0   \n",
              "299   55      64     152  Fmale  27.700831   0    0               0   \n",
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              "301   57      90     159  Fmale  35.599858   1    0               0   \n",
              "302   56      85     170  Fmale  29.411765   0    1               1   \n",
              "\n",
              "     EX-Smoker  FH  ...    K   Na    WBC Lymph Neut  PLT EF-TTE  Region RWMA  \\\n",
              "298          0   0  ...  4.8  146   8500    34   58  251     45            0   \n",
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              "\n",
              "      VHD    Cath  \n",
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              "299  mild  Normal  \n",
              "300     N  Normal  \n",
              "301     N  Normal  \n",
              "302     N     Cad  \n",
              "\n",
              "[5 rows x 56 columns]"
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              "      <td>8500</td>\n",
              "      <td>34</td>\n",
              "      <td>58</td>\n",
              "      <td>251</td>\n",
              "      <td>45</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Cad</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299</th>\n",
              "      <td>55</td>\n",
              "      <td>64</td>\n",
              "      <td>152</td>\n",
              "      <td>Fmale</td>\n",
              "      <td>27.700831</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>139</td>\n",
              "      <td>11400</td>\n",
              "      <td>16</td>\n",
              "      <td>80</td>\n",
              "      <td>377</td>\n",
              "      <td>40</td>\n",
              "      <td>0</td>\n",
              "      <td>mild</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>48</td>\n",
              "      <td>77</td>\n",
              "      <td>160</td>\n",
              "      <td>Fmale</td>\n",
              "      <td>30.078125</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>140</td>\n",
              "      <td>9000</td>\n",
              "      <td>35</td>\n",
              "      <td>55</td>\n",
              "      <td>279</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>301</th>\n",
              "      <td>57</td>\n",
              "      <td>90</td>\n",
              "      <td>159</td>\n",
              "      <td>Fmale</td>\n",
              "      <td>35.599858</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>3.8</td>\n",
              "      <td>141</td>\n",
              "      <td>3800</td>\n",
              "      <td>48</td>\n",
              "      <td>40</td>\n",
              "      <td>208</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Normal</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>302</th>\n",
              "      <td>56</td>\n",
              "      <td>85</td>\n",
              "      <td>170</td>\n",
              "      <td>Fmale</td>\n",
              "      <td>29.411765</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>4.4</td>\n",
              "      <td>147</td>\n",
              "      <td>6000</td>\n",
              "      <td>32</td>\n",
              "      <td>55</td>\n",
              "      <td>302</td>\n",
              "      <td>55</td>\n",
              "      <td>0</td>\n",
              "      <td>N</td>\n",
              "      <td>Cad</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 56 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###info about data"
      ],
      "metadata": {
        "id": "YwrrYg0bZHv-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p7rOWsXSZxuE",
        "outputId": "0457a3d2-e982-4b7c-f5c8-ce0a5c91ee00"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 56 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   Age                    303 non-null    int64  \n",
            " 1   Weight                 303 non-null    int64  \n",
            " 2   Length                 303 non-null    int64  \n",
            " 3   Sex                    303 non-null    object \n",
            " 4   BMI                    303 non-null    float64\n",
            " 5   DM                     303 non-null    int64  \n",
            " 6   HTN                    303 non-null    int64  \n",
            " 7   Current Smoker         303 non-null    int64  \n",
            " 8   EX-Smoker              303 non-null    int64  \n",
            " 9   FH                     303 non-null    int64  \n",
            " 10  Obesity                303 non-null    object \n",
            " 11  CRF                    303 non-null    object \n",
            " 12  CVA                    303 non-null    object \n",
            " 13  Airway disease         303 non-null    object \n",
            " 14  Thyroid Disease        303 non-null    object \n",
            " 15  CHF                    303 non-null    object \n",
            " 16  DLP                    303 non-null    object \n",
            " 17  BP                     303 non-null    int64  \n",
            " 18  PR                     303 non-null    int64  \n",
            " 19  Edema                  303 non-null    int64  \n",
            " 20  Weak Peripheral Pulse  303 non-null    object \n",
            " 21  Lung rales             303 non-null    object \n",
            " 22  Systolic Murmur        303 non-null    object \n",
            " 23  Diastolic Murmur       303 non-null    object \n",
            " 24  Typical Chest Pain     303 non-null    int64  \n",
            " 25  Dyspnea                303 non-null    object \n",
            " 26  Function Class         303 non-null    int64  \n",
            " 27  Atypical               303 non-null    object \n",
            " 28  Nonanginal             303 non-null    object \n",
            " 29  Exertional CP          303 non-null    object \n",
            " 30  LowTH Ang              303 non-null    object \n",
            " 31  Q Wave                 303 non-null    int64  \n",
            " 32  St Elevation           303 non-null    int64  \n",
            " 33  St Depression          303 non-null    int64  \n",
            " 34  Tinversion             303 non-null    int64  \n",
            " 35  LVH                    303 non-null    object \n",
            " 36  Poor R Progression     303 non-null    object \n",
            " 37  BBB                    303 non-null    object \n",
            " 38  FBS                    303 non-null    int64  \n",
            " 39  CR                     303 non-null    float64\n",
            " 40  TG                     303 non-null    int64  \n",
            " 41  LDL                    303 non-null    int64  \n",
            " 42  HDL                    303 non-null    float64\n",
            " 43  BUN                    303 non-null    int64  \n",
            " 44  ESR                    303 non-null    int64  \n",
            " 45  HB                     303 non-null    float64\n",
            " 46  K                      303 non-null    float64\n",
            " 47  Na                     303 non-null    int64  \n",
            " 48  WBC                    303 non-null    int64  \n",
            " 49  Lymph                  303 non-null    int64  \n",
            " 50  Neut                   303 non-null    int64  \n",
            " 51  PLT                    303 non-null    int64  \n",
            " 52  EF-TTE                 303 non-null    int64  \n",
            " 53  Region RWMA            303 non-null    int64  \n",
            " 54  VHD                    303 non-null    object \n",
            " 55  Cath                   303 non-null    object \n",
            "dtypes: float64(5), int64(29), object(22)\n",
            "memory usage: 132.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YN-q0o85Zy2N",
        "outputId": "f623288f-e067-454c-9073-f56dada7df7a"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                      0\n",
              "Weight                   0\n",
              "Length                   0\n",
              "Sex                      0\n",
              "BMI                      0\n",
              "DM                       0\n",
              "HTN                      0\n",
              "Current Smoker           0\n",
              "EX-Smoker                0\n",
              "FH                       0\n",
              "Obesity                  0\n",
              "CRF                      0\n",
              "CVA                      0\n",
              "Airway disease           0\n",
              "Thyroid Disease          0\n",
              "CHF                      0\n",
              "DLP                      0\n",
              "BP                       0\n",
              "PR                       0\n",
              "Edema                    0\n",
              "Weak Peripheral Pulse    0\n",
              "Lung rales               0\n",
              "Systolic Murmur          0\n",
              "Diastolic Murmur         0\n",
              "Typical Chest Pain       0\n",
              "Dyspnea                  0\n",
              "Function Class           0\n",
              "Atypical                 0\n",
              "Nonanginal               0\n",
              "Exertional CP            0\n",
              "LowTH Ang                0\n",
              "Q Wave                   0\n",
              "St Elevation             0\n",
              "St Depression            0\n",
              "Tinversion               0\n",
              "LVH                      0\n",
              "Poor R Progression       0\n",
              "BBB                      0\n",
              "FBS                      0\n",
              "CR                       0\n",
              "TG                       0\n",
              "LDL                      0\n",
              "HDL                      0\n",
              "BUN                      0\n",
              "ESR                      0\n",
              "HB                       0\n",
              "K                        0\n",
              "Na                       0\n",
              "WBC                      0\n",
              "Lymph                    0\n",
              "Neut                     0\n",
              "PLT                      0\n",
              "EF-TTE                   0\n",
              "Region RWMA              0\n",
              "VHD                      0\n",
              "Cath                     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()     #only for numerical values till now"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "NN0iwhoAaD9k",
        "outputId": "fc5deec6-180f-4be0-db15-fb19757ec5dc"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age      Weight      Length         BMI          DM         HTN  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    58.897690   73.831683  164.716172   27.248339    0.297030    0.590759   \n",
              "std     10.392278   11.987358    9.327661    4.098865    0.457706    0.492507   \n",
              "min     30.000000   48.000000  140.000000   18.115413    0.000000    0.000000   \n",
              "25%     51.000000   65.000000  158.000000   24.514380    0.000000    0.000000   \n",
              "50%     58.000000   74.000000  165.000000   26.775510    0.000000    1.000000   \n",
              "75%     66.000000   81.000000  171.000000   29.411765    1.000000    1.000000   \n",
              "max     86.000000  120.000000  188.000000   40.900658    1.000000    1.000000   \n",
              "\n",
              "       Current Smoker   EX-Smoker          FH          BP  ...         ESR  \\\n",
              "count      303.000000  303.000000  303.000000  303.000000  ...  303.000000   \n",
              "mean         0.207921    0.033003    0.158416  129.554455  ...   19.462046   \n",
              "std          0.406491    0.178941    0.365734   18.938105  ...   15.936475   \n",
              "min          0.000000    0.000000    0.000000   90.000000  ...    1.000000   \n",
              "25%          0.000000    0.000000    0.000000  120.000000  ...    9.000000   \n",
              "50%          0.000000    0.000000    0.000000  130.000000  ...   15.000000   \n",
              "75%          0.000000    0.000000    0.000000  140.000000  ...   26.000000   \n",
              "max          1.000000    1.000000    1.000000  190.000000  ...   90.000000   \n",
              "\n",
              "               HB           K          Na           WBC       Lymph  \\\n",
              "count  303.000000  303.000000  303.000000    303.000000  303.000000   \n",
              "mean    13.153465    4.230693  140.996700   7562.046205   32.399340   \n",
              "std      1.610452    0.458202    3.807885   2413.739323    9.972592   \n",
              "min      8.900000    3.000000  128.000000   3700.000000    7.000000   \n",
              "25%     12.200000    3.900000  139.000000   5800.000000   26.000000   \n",
              "50%     13.200000    4.200000  141.000000   7100.000000   32.000000   \n",
              "75%     14.200000    4.500000  143.000000   8800.000000   39.000000   \n",
              "max     17.600000    6.600000  156.000000  18000.000000   60.000000   \n",
              "\n",
              "             Neut         PLT      EF-TTE  Region RWMA  \n",
              "count  303.000000  303.000000  303.000000   303.000000  \n",
              "mean    60.148515  221.488449   47.231023     0.620462  \n",
              "std     10.182493   60.796199    8.927194     1.132531  \n",
              "min     32.000000   25.000000   15.000000     0.000000  \n",
              "25%     52.500000  183.500000   45.000000     0.000000  \n",
              "50%     60.000000  210.000000   50.000000     0.000000  \n",
              "75%     67.000000  250.000000   55.000000     1.000000  \n",
              "max     89.000000  742.000000   60.000000     4.000000  \n",
              "\n",
              "[8 rows x 34 columns]"
            ],
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              "\n",
              "  <div id=\"df-4014a03c-46d4-4c12-9d17-821c4d3083eb\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Length</th>\n",
              "      <th>BMI</th>\n",
              "      <th>DM</th>\n",
              "      <th>HTN</th>\n",
              "      <th>Current Smoker</th>\n",
              "      <th>EX-Smoker</th>\n",
              "      <th>FH</th>\n",
              "      <th>BP</th>\n",
              "      <th>...</th>\n",
              "      <th>ESR</th>\n",
              "      <th>HB</th>\n",
              "      <th>K</th>\n",
              "      <th>Na</th>\n",
              "      <th>WBC</th>\n",
              "      <th>Lymph</th>\n",
              "      <th>Neut</th>\n",
              "      <th>PLT</th>\n",
              "      <th>EF-TTE</th>\n",
              "      <th>Region RWMA</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
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              "      <td>303.000000</td>\n",
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              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
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              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>58.897690</td>\n",
              "      <td>73.831683</td>\n",
              "      <td>164.716172</td>\n",
              "      <td>27.248339</td>\n",
              "      <td>0.297030</td>\n",
              "      <td>0.590759</td>\n",
              "      <td>0.207921</td>\n",
              "      <td>0.033003</td>\n",
              "      <td>0.158416</td>\n",
              "      <td>129.554455</td>\n",
              "      <td>...</td>\n",
              "      <td>19.462046</td>\n",
              "      <td>13.153465</td>\n",
              "      <td>4.230693</td>\n",
              "      <td>140.996700</td>\n",
              "      <td>7562.046205</td>\n",
              "      <td>32.399340</td>\n",
              "      <td>60.148515</td>\n",
              "      <td>221.488449</td>\n",
              "      <td>47.231023</td>\n",
              "      <td>0.620462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.392278</td>\n",
              "      <td>11.987358</td>\n",
              "      <td>9.327661</td>\n",
              "      <td>4.098865</td>\n",
              "      <td>0.457706</td>\n",
              "      <td>0.492507</td>\n",
              "      <td>0.406491</td>\n",
              "      <td>0.178941</td>\n",
              "      <td>0.365734</td>\n",
              "      <td>18.938105</td>\n",
              "      <td>...</td>\n",
              "      <td>15.936475</td>\n",
              "      <td>1.610452</td>\n",
              "      <td>0.458202</td>\n",
              "      <td>3.807885</td>\n",
              "      <td>2413.739323</td>\n",
              "      <td>9.972592</td>\n",
              "      <td>10.182493</td>\n",
              "      <td>60.796199</td>\n",
              "      <td>8.927194</td>\n",
              "      <td>1.132531</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>30.000000</td>\n",
              "      <td>48.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>18.115413</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.900000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>128.000000</td>\n",
              "      <td>3700.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>51.000000</td>\n",
              "      <td>65.000000</td>\n",
              "      <td>158.000000</td>\n",
              "      <td>24.514380</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>12.200000</td>\n",
              "      <td>3.900000</td>\n",
              "      <td>139.000000</td>\n",
              "      <td>5800.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>52.500000</td>\n",
              "      <td>183.500000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>58.000000</td>\n",
              "      <td>74.000000</td>\n",
              "      <td>165.000000</td>\n",
              "      <td>26.775510</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>13.200000</td>\n",
              "      <td>4.200000</td>\n",
              "      <td>141.000000</td>\n",
              "      <td>7100.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>210.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>66.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>171.000000</td>\n",
              "      <td>29.411765</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>14.200000</td>\n",
              "      <td>4.500000</td>\n",
              "      <td>143.000000</td>\n",
              "      <td>8800.000000</td>\n",
              "      <td>39.000000</td>\n",
              "      <td>67.000000</td>\n",
              "      <td>250.000000</td>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>86.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>188.000000</td>\n",
              "      <td>40.900658</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>90.000000</td>\n",
              "      <td>17.600000</td>\n",
              "      <td>6.600000</td>\n",
              "      <td>156.000000</td>\n",
              "      <td>18000.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>742.000000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 34 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4014a03c-46d4-4c12-9d17-821c4d3083eb')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4014a03c-46d4-4c12-9d17-821c4d3083eb button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4014a03c-46d4-4c12-9d17-821c4d3083eb');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Exploratory Data Analysis**"
      ],
      "metadata": {
        "id": "CXwHV_JJJGBv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"List of Numerical features: \\n\" , df.select_dtypes(include=np.number).columns.tolist())\n",
        "print(\"\\n\\nList of Categorical features: \\n\" , df.select_dtypes(include=['object']).columns.tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a62EnIC4JLsb",
        "outputId": "08fe4d6f-b61f-4b2f-b498-048deb0cdab0"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List of Numerical features: \n",
            " ['Age', 'Weight', 'Length', 'BMI', 'DM', 'HTN', 'Current Smoker', 'EX-Smoker', 'FH', 'BP', 'PR', 'Edema', 'Typical Chest Pain', 'Function Class', 'Q Wave', 'St Elevation', 'St Depression', 'Tinversion', 'FBS', 'CR', 'TG', 'LDL', 'HDL', 'BUN', 'ESR', 'HB', 'K', 'Na', 'WBC', 'Lymph', 'Neut', 'PLT', 'EF-TTE', 'Region RWMA']\n",
            "\n",
            "\n",
            "List of Categorical features: \n",
            " ['Sex', 'Obesity', 'CRF', 'CVA', 'Airway disease', 'Thyroid Disease', 'CHF', 'DLP', 'Weak Peripheral Pulse', 'Lung rales', 'Systolic Murmur', 'Diastolic Murmur', 'Dyspnea', 'Atypical', 'Nonanginal', 'Exertional CP', 'LowTH Ang', 'LVH', 'Poor R Progression', 'BBB', 'VHD', 'Cath']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Checking Data Distribution Over All Classes**"
      ],
      "metadata": {
        "id": "q2jcQYBIJ4t-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since it's a classification problem we have to check the distribution of data points over each class."
      ],
      "metadata": {
        "id": "6hZTpKbKJzrV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "px.histogram(df, x=\"Cath\", color=\"Cath\", width=600, height=400, title=\"Visualizaton of Data Distribution (Hover for the details)\") "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "4r36MTNAJfFE",
        "outputId": "a183bce3-23c4-4034-da49-d2ca7d47f881"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Visualising Multi-Collinearity**"
      ],
      "metadata": {
        "id": "Uij840d7K-Sd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a covariance matrix\n",
        "corr = df.corr()\n",
        "\n",
        "# Creating a mask the size of our covariance matrix\n",
        "mask = np.zeros_like(corr, dtype=bool)\n",
        "\n",
        "mask[np.triu_indices_from(mask)] = True\n",
        "\n",
        "# Set up the matplotlib figure\n",
        "f, ax = plt.subplots(figsize=(30,29))\n",
        "\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(150,275,as_cmap=True)\n",
        "\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "sns.heatmap(corr,mask=mask,cmap=cmap,vmax=1,center=0,square=True, \n",
        "            linewidth=.5, annot=True)\n",
        "\n",
        "ax.set_title('Multi-Collinearity of Features')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "VZ0hxwjjKEuz",
        "outputId": "2e1e692b-3fcd-4f91-d825-3f35a5dabee4"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Multi-Collinearity of Features')"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2160x2088 with 2 Axes>"
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**"
      ],
      "metadata": {
        "id": "_wMQV3JALGJJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Weight vs BMI**"
      ],
      "metadata": {
        "id": "SfcM_yb-MPVK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig = px.scatter(df, x=\"BMI\", y=\"Weight\", color=\"Cath\", symbol = \"Cath\", hover_data=['Age'], width=700, height = 600, \n",
        "                 title = \"Weight vs BMI (Hover for the details) <br />Correlation = 0.73\")\n",
        "fig.update_traces(marker_size=10, marker_line_width = 1,marker_line_color = \"black\")\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "ZbQginZRLN7F",
        "outputId": "14c50be1-0d51-4a41-c311-1a3c71f6a922"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
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          "metadata": {}
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## One hot encoding"
      ],
      "metadata": {
        "id": "AxB4CZYyfR4x"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df=pd.get_dummies(df,columns =[\"Sex\",\"Obesity\",\"CRF\",\"CVA\",\"Airway disease\",\"Thyroid Disease\",\"CHF\",\"DLP\",\"Weak Peripheral Pulse\",\"Lung rales\",\"Systolic Murmur\",\"Diastolic Murmur\",\"Dyspnea\",\"Atypical\",\"Nonanginal\",\"Exertional CP\",\"LowTH Ang\",\"LVH\",\"Poor R Progression\",\"BBB\",\"VHD\"])"
      ],
      "metadata": {
        "id": "P8i_gPJ2fXvI"
      },
      "execution_count": 69,
      "outputs": []
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    {
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      "source": [
        "df.head()"
      ],
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        "colab": {
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          "height": 0
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        "id": "-z_MnOmchmNe",
        "outputId": "8c137bf5-d71b-4642-fcf8-e392b0b8b76d"
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      "execution_count": 70,
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              "   Age  Weight  Length        BMI  DM  HTN  Current Smoker  EX-Smoker  FH  \\\n",
              "0   53      90     175  29.387755   0    1               1          0   0   \n",
              "1   67      70     157  28.398718   0    1               0          0   0   \n",
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              "4   50      87     153  37.165193   0    1               0          0   0   \n",
              "\n",
              "    BP  ...  LVH_Y  Poor R Progression_N  Poor R Progression_Y  BBB_LBBB  \\\n",
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              "4  110  ...      0                     1                     0         0   \n",
              "\n",
              "   BBB_N  BBB_RBBB  VHD_Moderate  VHD_N  VHD_Severe  VHD_mild  \n",
              "0      1         0             0      1           0         0  \n",
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              "3      1         0             0      0           1         0  \n",
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              "[5 rows x 79 columns]"
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          "metadata": {},
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        "df.columns"
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        "id": "6Rbwco1UhoLT",
        "outputId": "10af02ba-ee33-4aae-dc12-0b4cb88b5db1"
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      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Age', 'Weight', 'Length', 'BMI', 'DM', 'HTN', 'Current Smoker',\n",
              "       'EX-Smoker', 'FH', 'BP', 'PR', 'Edema', 'Typical Chest Pain',\n",
              "       'Function Class', 'Q Wave', 'St Elevation', 'St Depression',\n",
              "       'Tinversion', 'FBS', 'CR', 'TG', 'LDL', 'HDL', 'BUN', 'ESR', 'HB', 'K',\n",
              "       'Na', 'WBC', 'Lymph', 'Neut', 'PLT', 'EF-TTE', 'Region RWMA', 'Cath',\n",
              "       'Sex_Fmale', 'Sex_Male', 'Obesity_N', 'Obesity_Y', 'CRF_N', 'CRF_Y',\n",
              "       'CVA_N', 'CVA_Y', 'Airway disease_N', 'Airway disease_Y',\n",
              "       'Thyroid Disease_N', 'Thyroid Disease_Y', 'CHF_N', 'CHF_Y', 'DLP_N',\n",
              "       'DLP_Y', 'Weak Peripheral Pulse_N', 'Weak Peripheral Pulse_Y',\n",
              "       'Lung rales_N', 'Lung rales_Y', 'Systolic Murmur_N',\n",
              "       'Systolic Murmur_Y', 'Diastolic Murmur_N', 'Diastolic Murmur_Y',\n",
              "       'Dyspnea_N', 'Dyspnea_Y', 'Atypical_N', 'Atypical_Y', 'Nonanginal_N',\n",
              "       'Nonanginal_Y', 'Exertional CP_N', 'LowTH Ang_N', 'LowTH Ang_Y',\n",
              "       'LVH_N', 'LVH_Y', 'Poor R Progression_N', 'Poor R Progression_Y',\n",
              "       'BBB_LBBB', 'BBB_N', 'BBB_RBBB', 'VHD_Moderate', 'VHD_N', 'VHD_Severe',\n",
              "       'VHD_mild'],\n",
              "      dtype='object')"
            ]
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          "metadata": {},
          "execution_count": 71
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        "num_columns = ['Age', 'Weight', 'Length', 'BMI', 'DM', 'HTN', 'Current Smoker','EX-Smoker', 'FH', 'BP', 'PR', 'Edema', 'Typical Chest Pain','Function Class', 'Q Wave', 'St Elevation', 'St Depression',\n",
        "       'Tinversion', 'FBS', 'CR', 'TG', 'LDL', 'HDL', 'BUN', 'ESR', 'HB', 'K',\n",
        "       'Na', 'WBC', 'Lymph', 'Neut', 'PLT', 'EF-TTE', 'Region RWMA']\n",
        "cat_columns= list(set(df.columns) - set(num_columns) - {\"Cath\"})"
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      "execution_count": 72,
      "outputs": []
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      "execution_count": 73,
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              "   Age  Weight  Length        BMI  DM  HTN  Current Smoker  EX-Smoker  FH  \\\n",
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              "   BBB_N  BBB_RBBB  VHD_Moderate  VHD_N  VHD_Severe  VHD_mild  \n",
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              "[5 rows x 79 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>54</td>\n",
              "      <td>54</td>\n",
              "      <td>164</td>\n",
              "      <td>20.077335</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>66</td>\n",
              "      <td>67</td>\n",
              "      <td>158</td>\n",
              "      <td>26.838648</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50</td>\n",
              "      <td>87</td>\n",
              "      <td>153</td>\n",
              "      <td>37.165193</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>110</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 79 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64562cb8-c0fa-4b64-90c3-dc76ac8771b8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-64562cb8-c0fa-4b64-90c3-dc76ac8771b8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-64562cb8-c0fa-4b64-90c3-dc76ac8771b8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "num_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TawhT810PuH4",
        "outputId": "514de588-d94f-49ee-81e4-cb72e284e5fc"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Age',\n",
              " 'Weight',\n",
              " 'Length',\n",
              " 'BMI',\n",
              " 'DM',\n",
              " 'HTN',\n",
              " 'Current Smoker',\n",
              " 'EX-Smoker',\n",
              " 'FH',\n",
              " 'BP',\n",
              " 'PR',\n",
              " 'Edema',\n",
              " 'Typical Chest Pain',\n",
              " 'Function Class',\n",
              " 'Q Wave',\n",
              " 'St Elevation',\n",
              " 'St Depression',\n",
              " 'Tinversion',\n",
              " 'FBS',\n",
              " 'CR',\n",
              " 'TG',\n",
              " 'LDL',\n",
              " 'HDL',\n",
              " 'BUN',\n",
              " 'ESR',\n",
              " 'HB',\n",
              " 'K',\n",
              " 'Na',\n",
              " 'WBC',\n",
              " 'Lymph',\n",
              " 'Neut',\n",
              " 'PLT',\n",
              " 'EF-TTE',\n",
              " 'Region RWMA']"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cat_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kT1Z0LcPPxy1",
        "outputId": "fbb78978-6e84-4150-960a-68a877e945c9"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sex_Fmale',\n",
              " 'Atypical_Y',\n",
              " 'LowTH Ang_N',\n",
              " 'Weak Peripheral Pulse_N',\n",
              " 'LowTH Ang_Y',\n",
              " 'BBB_LBBB',\n",
              " 'Weak Peripheral Pulse_Y',\n",
              " 'CVA_Y',\n",
              " 'Thyroid Disease_N',\n",
              " 'Nonanginal_Y',\n",
              " 'CHF_Y',\n",
              " 'LVH_Y',\n",
              " 'Lung rales_Y',\n",
              " 'VHD_Moderate',\n",
              " 'Systolic Murmur_Y',\n",
              " 'VHD_N',\n",
              " 'Sex_Male',\n",
              " 'Airway disease_Y',\n",
              " 'Dyspnea_N',\n",
              " 'Nonanginal_N',\n",
              " 'Lung rales_N',\n",
              " 'Diastolic Murmur_N',\n",
              " 'BBB_RBBB',\n",
              " 'CVA_N',\n",
              " 'VHD_mild',\n",
              " 'Atypical_N',\n",
              " 'Airway disease_N',\n",
              " 'DLP_N',\n",
              " 'Systolic Murmur_N',\n",
              " 'Poor R Progression_Y',\n",
              " 'Diastolic Murmur_Y',\n",
              " 'CRF_N',\n",
              " 'Obesity_Y',\n",
              " 'Obesity_N',\n",
              " 'Thyroid Disease_Y',\n",
              " 'Exertional CP_N',\n",
              " 'LVH_N',\n",
              " 'VHD_Severe',\n",
              " 'DLP_Y',\n",
              " 'CRF_Y',\n",
              " 'Dyspnea_Y',\n",
              " 'BBB_N',\n",
              " 'Poor R Progression_N',\n",
              " 'CHF_N']"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mycol = df[[\"Cath\"]]\n",
        "for i in mycol:\n",
        "    cleanup_nums = {i: {\"Normal\": 0, \"Cad\": 1}}\n",
        "    df = df.replace(cleanup_nums)"
      ],
      "metadata": {
        "id": "xx1o17nOP1WB"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Cath'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JJREQ8VCTAOg",
        "outputId": "9242ab34-6872-4be2-824a-e15d99fd5586"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    216\n",
              "0     87\n",
              "Name: Cath, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Numerical encoding"
      ],
      "metadata": {
        "id": "78lt-HFDwl_g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "\n",
        "def get_features_and_target_arrays(df,num_columns,cat_columns,scaler):\n",
        "  x_numerical_scaled=scaler.fit_transform(df[num_columns])    \n",
        "  x_categorical=df[cat_columns].to_numpy()\n",
        "  x=np.hstack((x_categorical,x_numerical_scaled))    #merging all array values to one\n",
        "  y=df[\"Cath\"]                                        #a=[1,2,3] b= [4,5,6] c=np.hstack((a,b))  => [1,2,3,4,5,6] \n",
        "  return x , y\n",
        "          \n",
        "          \n"
      ],
      "metadata": {
        "id": "q5v5Bos3RI_P"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## spliting data"
      ],
      "metadata": {
        "id": "fIZBGXu_Qf0c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train , df_test = train_test_split(df, test_size= 0.1, random_state = 42)"
      ],
      "metadata": {
        "id": "_DMrHaIvQqP7"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(df_train) , len(df_test) , len(df)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELaGaJVnRHp6",
        "outputId": "d07e1cfd-fafb-4e13-c5e0-6e1bf8eddc9b"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(272, 31, 303)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x_train , y_train = get_features_and_target_arrays(df_train,num_columns,cat_columns,scaler)"
      ],
      "metadata": {
        "id": "P0RrA71dYJmJ"
      },
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Analysis without feature selection\n"
      ],
      "metadata": {
        "id": "nipcIL1Ncitx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## LOGISTIC REGRESSION"
      ],
      "metadata": {
        "id": "toHnJ1xOmKvX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Logisitic regreesion classifier\n",
        "lr_clf=LogisticRegression()\n",
        "lr_clf.fit(x_train , y_train)\n",
        "x_test , y_test = get_features_and_target_arrays(df_test,num_columns,cat_columns,scaler)\n",
        "lr_pred=lr_clf.predict(x_test)\n",
        "print(\"MEAN SQUARE ERROR: \",mean_squared_error(y_test,lr_pred))\n",
        "print(\"ACCURACY SCORE: \",accuracy_score(y_test,lr_pred))\n",
        "# print(confusion_matrix(y_test,lr_pred))\n",
        "\n",
        "# Plotting the confusion matrix\n",
        "\n",
        "cm = confusion_matrix(y_test,lr_pred, labels = lr_clf.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = lr_clf.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QiWBff3fcnet",
        "outputId": "62087e1d-7b9e-4a88-b3a3-0fad09d868e1"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEAN SQUARE ERROR:  0.0967741935483871\n",
            "ACCURACY SCORE:  0.9032258064516129\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0d6669ef10>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Decision Tree Classifier\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ZILB3fgSix__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#decision tree classifier\n",
        "dtree_clf=DecisionTreeClassifier()\n",
        "dtree_clf.fit(x_train,y_train)\n",
        "dtree_pred=dtree_clf.predict(x_test)\n",
        "print(\"MEAN SQUARE ERROR: \",mean_squared_error(y_test,dtree_pred))\n",
        "print(\"ACCURACY SCORE: \",accuracy_score(y_test,dtree_pred))\n",
        "print(confusion_matrix(y_test,dtree_pred))\n",
        "cm = confusion_matrix(y_test,dtree_pred, labels = dtree_clf.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = dtree_clf.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "QpuGllA6iIb2",
        "outputId": "b0c3f2f9-6ae3-44d5-8388-59441931dad9"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEAN SQUARE ERROR:  0.22580645161290322\n",
            "ACCURACY SCORE:  0.7741935483870968\n",
            "[[ 3  5]\n",
            " [ 2 21]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0d67567490>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Random Forest\n"
      ],
      "metadata": {
        "id": "LDA1AE1DnX6s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Random Forest classifier\n",
        "Rforest_clf=RandomForestClassifier()\n",
        "Rforest_clf.fit(x_train,y_train)\n",
        "Rforest_pred=Rforest_clf.predict(x_test)\n",
        "print(\"MEAN SQUARE ERROR: \",mean_squared_error(y_test,Rforest_pred))\n",
        "print(\"ACCURACY ERROR: \",accuracy_score(y_test,Rforest_pred))\n",
        "\n",
        "cm = confusion_matrix(y_test,Rforest_pred, labels = Rforest_clf.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = Rforest_clf.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "yTjagDhxnay8",
        "outputId": "41777b54-d90e-43e8-cc32-063344b15bb3"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEAN SQUARE ERROR:  0.0967741935483871\n",
            "ACCURACY ERROR:  0.9032258064516129\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0d68270210>"
            ]
          },
          "metadata": {},
          "execution_count": 84
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SVM"
      ],
      "metadata": {
        "id": "fygeyp2JpGcM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#SVM classifier\n",
        "SVM_clf=SVC()\n",
        "SVM_clf.fit(x_train,y_train)\n",
        "SVM_pred=SVM_clf.predict(x_test)\n",
        "print(\"MEAN SQUARE ERROR: \",mean_squared_error(y_test,SVM_pred))\n",
        "print(\"ACCURACY SCORE: \",accuracy_score(y_test,SVM_pred))\n",
        "cm = confusion_matrix(y_test,SVM_pred, labels = SVM_clf.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = SVM_clf.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "h__UNBeUoxnI",
        "outputId": "4614fed1-a9f3-4a68-8e6f-f29a20f5e7f8"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEAN SQUARE ERROR:  0.0967741935483871\n",
            "ACCURACY SCORE:  0.9032258064516129\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0d66541cd0>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAATIAAAEGCAYAAADmLRl+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAUxUlEQVR4nO3de5AdZZ3G8e8zkwmBQEJCIAQMAhLBFMsllQ2CLhXAksBahbiuipeiXKgIEmFdWQt3a8WFWgpFxRuuGyELigRBRAJCAobFgAWSwAY2XBTkmpshgQjkQjJzfvvH6QlDSOZ0T86l3zPPp6or5/Sc8/ZvEnjqfd9+u1sRgZlZyjpaXYCZ2Y5ykJlZ8hxkZpY8B5mZJc9BZmbJG9LqAvrqGjo8hu08qtVlWAEdmyutLsEK2LBpLZu612tH2jjxuOGx5uWeXJ996NE35kXEtB05Xh6lCrJhO49i0jFfaHUZVsCwZa+3ugQr4IGnr9rhNta83MOD8/bL9dnOcU+N2eED5lCqIDOz8gugQrl64g4yMyskCDZHvqFlszjIzKww98jMLGlB0FOySxsdZGZWWAUHmZklLIAeB5mZpc49MjNLWgCbPUdmZikLwkNLM0tcQE+5csxBZmbFVFf2l4uDzMwKEj3s0HXndecgM7NCqpP9DjIzS1h1HZmDzMwSV3GPzMxS5h6ZmSUvED0lu0u+g8zMCvPQ0sySFohN0dnqMt7CQWZmhVQXxHpoaWaJ82S/mSUtQvSEe2RmlriKe2RmlrLqZH+5oqNc/UMzK73eyf48W38kjZf0P5Iel/SYpPOy/aMl3SXpqezPUbVqcpCZWWE9oVxbDd3AlyJiIvBe4BxJE4ELgPkRMQGYn73vl4PMzArpXdmfZ+u3nYgVEfFw9vo14AlgX+AU4JrsY9cAH65VU7kGumaWhEr+s5ZjJC3q835mRMzc+kOS9geOBH4PjI2IFdmPVgJjax3EQWZmhVQvGs8dZKsjYnJ/H5C0K3AT8I8R8ar05pA0IkJSzRtrO8jMrJBAbK7TJUqSuqiG2M8i4pfZ7j9LGhcRKySNA1bVasdzZGZWSAT0REeurT+qdr2uAp6IiG/3+dEc4PTs9enALbVqco/MzApSvRbEvg/4DPB/khZn+/4FuBS4QdIZwPPAx2o15CAzs0IC6nKJUkTcB9tNxBOKtOUgM7PCfGNFM0taIN9Y0czSVn0cXLmio1zVmFkC/IBeM0tcUGhlf1M4yMysMPfIzCxpEXKPzMzSVp3s91OUzCxpvme/mSWuOtnvOTIzS5xX9ptZ0ryy38zagp80bmZJi4DNFQeZmSWsOrR0kJlZ4ryyf5CZfdnPWb+xi0pF9PR0cNZFp7S6JNuOrq4eLvvW3XR19dDZGdx373iu/emhrS6rdAbd8gtJ04DvAp3AlRFxaSOPV1Zf/PrJvPr6sFaXYTVs3tzBBV+eysaNXXR2Vvjm5fNZtHBvnnxyTKtLK5nyDS0bVo2kTuAK4CRgInBa9hRhs5ISGzd2ATBkSIUhnRWiZEOosqhk9+2vtTVLI3tkU4CnI+IZAEnXU32C8OMNPGbpRMBl58+FgFvvOYTbfntIq0uyfnR0VPjeFXexzz6vc9ucg/jDk3u0uqTSqZ61HDzXWu4LvNjn/VLgqK0/JGk6MB1gp2G7N7Cc1jj3kg+xeu1wdt9tA988fy4vrBjJo38c1+qybDsqlQ5mnH0iw4dv4t8u/B3v3H8tzz/Xfv9d7ogyLoht+UA3ImZGxOSImNw1dHiry6m71Wurv9Pa13bm3offySEHrm5xRZbHunVDefSRvZg8eWWrSymlsg0tGxlky4Dxfd6/I9s3aAwbupmdh23a8nryoct4dumoFldl2zNy5EaGD6/+ew0d2s2Rk1by4osjWlxV+fSetcyzNUsjh5YLgQmSDqAaYJ8APtnA45XOqJEbuHjGfAA6Oyv85oF3sXDJO1pclW3PqNEbOf+ff09HR6CO4N7f7seDv9+n1WWVUtnOWjYsyCKiW9IMYB7V5RezIuKxRh2vjFa8NIIzLzy11WVYTs89uzszPn9iq8sovQjRPViCDCAibgdub+QxzKz5yjbZ75X9ZlbIoFvZb2btyUFmZkkr4zoyB5mZFdbMNWJ5OMjMrJAI6PaNFc0sdR5amlnSPEdmZm0hHGRmljpP9ptZ0iI8R2ZmyRM9PmtpZqkr2xxZuWLVzEqvnvcjkzRL0ipJS/rs+5qkZZIWZ9vJtdpxkJlZMVGdJ8uz5XA1MG0b+y+PiCOyreYddDy0NLPC6nXWMiIWSNp/R9txj8zMColssj/PBoyRtKjPNj3nYWZIejQbeta8P7yDzMwKKzC0XN37cKFsm5mj+f8E3gUcAawAvlXrCx5amllhjTxrGRF/7n0t6cfAbbW+4x6ZmRVS7W0p1zYQkvo++PVUYMn2PtvLPTIzK6xeK/slzQamUp1LWwpcCEyVdATVlR7PAZ+r1Y6DzMwKy7m0Ikc7cdo2dl9VtB0HmZkVEoiKL1Eys9TVqUNWNw4yMysmynetpYPMzIorWZfMQWZmhSXTI5P0ffrJ3Yg4tyEVmVmpBVCpJBJkwKKmVWFm6QgglR5ZRFzT972kXSJifeNLMrOyq9c6snqpuRhE0tGSHgeezN4fLumHDa/MzMorcm5NkmdV23eAE4E1ABHxCHBsI4syszLLd51lM08I5DprGREvSm8pqqcx5ZhZEko2tMwTZC9KOgYISV3AecATjS3LzEorIEp21jLP0PIs4BxgX2A51ZudndPIosys7JRza46aPbKIWA18qgm1mFkqSja0zHPW8kBJt0p6KXts0y2SDmxGcWZWUgmetbwOuAEYB+wD3AjMbmRRZlZivQti82xNkifIdomIn0ZEd7ZdCwxrdGFmVl51fK5lXfR3reXo7OUdki4ArqeaxR8Haj4w08zaWMnOWvY32f8Q1eDqrbjvfbMD+EqjijKzclPJJvv7u9bygGYWYmaJaPJEfh65VvZLOhSYSJ+5sYj4SaOKMrMya+5Efh41g0zShVQf1zSR6tzYScB9gIPMbLAqWY8sz1nLjwInACsj4rPA4cDIhlZlZuVWybk1SZ6h5YaIqEjqljQCWAWMb3BdZlZWKd1YsY9FknYHfkz1TObrwP0NrcrMSi2Zs5a9IuLz2csfSZoLjIiIRxtblpmVWipBJmlSfz+LiIcbU5KZWTH99ci+1c/PAji+zrWgV9czdJ6feZKSO5YvbnUJVsCUE1+uSzvJDC0j4rhmFmJmiQiSukTJzGzbUumRmZltTzJDSzOz7SpZkOW5Q6wkfVrSV7P3+0ma0vjSzKy0ErxD7A+Bo4HTsvevAVc0rCIzKzVF/q1Z8gwtj4qISZL+FyAiXpE0tMF1mVmZJXjWcrOkTrKOoqQ9aerloGZWNmWb7M8ztPwecDOwl6T/oHoLn0saWpWZlVvJ5sjyXGv5M0kPUb2Vj4APR4SfNG42WDV5/iuPPGct9wPWA7cCc4B12T4zG6zq1COTNCt7Xu6SPvtGS7pL0lPZn6NqtZNnaPlr4Lbsz/nAM8AdOb5nZm1KlXxbDlcD07badwEwPyImUM2cC2o1UjPIIuKvIuKw7M8JwBR8PzIzq4OIWABsfSX7KcA12etrgA/Xaqfwyv6IeFjSUUW/Z2ZtJP8c2RhJfW9pMzMiZtb4ztiIWJG9XgmMrXWQPA8f+ac+bzuAScDyWt8zszZVbLJ/dURMHvChIkKqfbQ8PbLd+rzupjpXdtNACzOzNtDYs5Z/ljQuIlZIGkf1OSH96jfIsoWwu0XE+fWq0MzaQGODbA5wOnBp9ucttb7Q362uh0REt6T31a8+M0udyH1GsnZb0myqz80dI2kpcCHVALtB0hnA88DHarXTX4/sQarzYYslzQFuBNb1/jAifjng6s0sXXVcEBsRp23nRycUaSfPHNkwYA3Ve/QH1UAOwEFmNliVbGV/f0G2V3bGcglvBlivkv0aZtZUJUuA/oKsE9iVtwZYr5L9GmbWTGW71rK/IFsRERc1rRIzS0dCQVauO6eZWTlE/c5a1kt/QVborIGZDSKp9Mgioj6PJDaztpPSHJmZ2bY5yMwsaU2+jXUeDjIzK0R4aGlmbcBBZmbpc5CZWfIcZGaWtBI+Ds5BZmbFOcjMLHUpXaJkZrZNHlqaWdq8INbM2oKDzMxS5pX9ZtYWVClXkjnIzKwYz5GZWTvw0NLM0ucgM7PUuUdmZulzkJlZ0hJ7ipKZ2dt4HZmZtYcoV5I5yMysMPfIBpHJU1/lrIuX09kR3DF7NDf8YGyrS7KtrFrWxWXn7cfal7pAwcmfXsOpZ67mmm/szf3zRiLB7mM2c/53XmCPvbtbXW45DKYFsZJmAR8CVkXEoY06Tll1dATnXLKMr3ziQFav6OL7tz/FA/NG8sJTw1pdmvXROSSY/tXlTDhsA+tf72DGtHcz6djX+OjZqzj9yysB+NWVY7j28r057+tLW1xteZRtsr+jgW1fDUxrYPuldvCR61n+3FBWvrAT3Zs7uOeW3Tn6xL+0uizbyh5ju5lw2AYAdtm1wviD3mD1ii6G7/bm/6kbN3QgtarCclIl39YsDeuRRcQCSfs3qv2y22Pvzby0fOiW96tXdHHIpPUtrMhqWfniUP60ZOct/07/fene/ObG0Qwf0cM3fvF0i6srkaB0k/2N7JHlImm6pEWSFm3mjVaXY4PUhnUdXHzm/px10bItvbHPXrCSnz30OMd/5BXmzNqzxRWWiyLf1iwtD7KImBkRkyNichc7tbqculmzsos999m05f2YcZtZvaKrhRXZ9nRvhovP3J/jP/IK7z/57cP/4099hftuH9mCykoscm5N0vIga1d/WLwL+x6wibHj32BIV4Wpp6zlgTv9P0PZRMC3v7Qf4ye8wd997qUt+5c98+a0wP3zRjL+II8WevUuiC1Tj8zLLxqk0iOu+Nd9ueS6Z+johDuvH83zf/QZy7J57MHhzP/FaA54zwbO/sDBAHz2K8uZO3sPlv5pJzo6YK99N3Guz1i+KWLw3FhR0mxgKjBG0lLgwoi4qlHHK6OFd49g4d0jWl2G9ePQo9Yxb/nit+2fcsJrLagmIXXKMUnPAa8BPUB3REweSDuNPGt5WqPaNrPWqvOw8biIWL0jDXhoaWbFBFCyoaUn+82suPqdtQzgTkkPSZo+0HLcIzOzwgoMLcdIWtTn/cyImNnn/fsjYpmkvYC7JD0ZEQuK1uMgM7PCCpy1XN3fBH5ELMv+XCXpZmAKUDjIPLQ0s2LyDitrZJ2k4ZJ2630NfBBYMpCS3CMzs0KqC2LrMtk/FrhZ1SvyhwDXRcTcgTTkIDOz4upwZ4uIeAY4fMdbcpCZ2QDUqUdWNw4yMytmMN0h1sza1SC61tLM2piHlmaWND+g18zagntkZpa8cuWYg8zMilOlXGNLB5mZFRPUZUFsPTnIzKwQEV4Qa2ZtwEFmZslzkJlZ0jxHZmbtwGctzSxx4aGlmSUucJCZWRso18jSQWZmxXkdmZmlz0FmZkmLgJ5yjS0dZGZWnHtkZpY8B5mZJS0A37PfzNIWEJ4jM7OUBZ7sN7M24DkyM0ueg8zM0uaLxs0sdQH4Nj5mljz3yMwsbb5EycxSFxBeR2ZmyfPKfjNLnufIzCxpET5raWZtwD0yM0tbED09rS7iLRxkZlaMb+NjZm2hZMsvOlpdgJmlJYCoRK6tFknTJP1B0tOSLhhoTQ4yMysmshsr5tn6IakTuAI4CZgInCZp4kBK8tDSzAqr02T/FODpiHgGQNL1wCnA40UbUpToNKqkl4DnW11HA4wBVre6CCukXf/N3hkRe+5IA5LmUv37yWMYsLHP+5kRMTNr56PAtIg4M3v/GeCoiJhRtKZS9ch29C+4rCQtiojJra7D8vO/2fZFxLRW17A1z5GZWassA8b3ef+ObF9hDjIza5WFwARJB0gaCnwCmDOQhko1tGxjM1tdgBXmf7MGi4huSTOAeUAnMCsiHhtIW6Wa7DczGwgPLc0seQ4yM0ueg6yB6nX5hTWPpFmSVkla0upaLD8HWYPU8/ILa6qrgdKtk7L+OcgaZ8vlFxGxCei9/MJKLCIWAC+3ug4rxkHWOPsCL/Z5vzTbZ2Z15iAzs+Q5yBqnbpdfmFn/HGSNU7fLL8ysfw6yBomIbqD38osngBsGevmFNY+k2cD9wMGSlko6o9U1WW2+RMnMkucemZklz0FmZslzkJlZ8hxkZpY8B5mZJc9BlhBJPZIWS1oi6UZJu+xAW1dnT7FB0pX9XdAuaaqkYwZwjOckve1pO9vbv9VnXi94rK9JOr9ojdYeHGRp2RARR0TEocAm4Ky+P5Q0oFuXR8SZEdHfswSnAoWDzKxZHGTpuhc4KOst3StpDvC4pE5Jl0laKOlRSZ8DUNUPsvuj/QbYq7chSfdImpy9nibpYUmPSJovaX+qgfnFrDf4N5L2lHRTdoyFkt6XfXcPSXdKekzSlYBq/RKSfiXpoew707f62eXZ/vmS9sz2vUvS3Ow790o6pB5/mZY2P3wkQVnP6yRgbrZrEnBoRDybhcFfIuKvJe0E/E7SncCRwMFU7402lurTnGdt1e6ewI+BY7O2RkfEy5J+BLweEd/MPncdcHlE3CdpP6pXL7wHuBC4LyIukvS3QJ5V8f+QHWNnYKGkmyJiDTAcWBQRX5T01aztGVQfCnJWRDwl6Sjgh8DxA/hrtDbiIEvLzpIWZ6/vBa6iOuR7MCKezfZ/EDisd/4LGAlMAI4FZkdED7Bc0t3baP+9wILetiJie/fl+gAwUdrS4RohadfsGB/JvvtrSa/k+J3OlXRq9np8VusaoAL8PNt/LfDL7BjHADf2OfZOOY5hbc5BlpYNEXFE3x3Z/9Dr+u4CvhAR87b63Ml1rKMDeG9EbNxGLblJmko1FI+OiPWS7gGGbefjkR137dZ/B2aeI2s/84CzJXUBSHq3pOHAAuDj2RzaOOC4bXz3AeBYSQdk3x2d7X8N2K3P5+4EvtD7RlJvsCwAPpntOwkYVaPWkcArWYgdQrVH2KsD6O1VfpLqkPVV4FlJf58dQ5IOr3EMGwQcZO3nSqrzXw9nD9D4L6o975uBp7Kf/YTqHR7eIiJeAqZTHcY9wptDu1uBU3sn+4FzgcnZyYTHefPs6b9TDcLHqA4xX6hR61xgiKQngEupBmmvdcCU7Hc4Hrgo2/8p4Iysvsfw7cMN3/3CzNqAe2RmljwHmZklz0FmZslzkJlZ8hxkZpY8B5mZJc9BZmbJ+38bDN6/0mjLhwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KNN"
      ],
      "metadata": {
        "id": "Xb5Pr725sVE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#knn classifier\n",
        "knn_clf = KNeighborsClassifier(n_neighbors=6)\n",
        "knn_clf.fit(x_train,y_train)\n",
        "knn_pred = knn_clf.predict(x_test)\n",
        "print(\"MEAN SQUARE ERROR: \",mean_squared_error(y_test,knn_pred))\n",
        "print(\"ACCURACY SCORE: \",accuracy_score(y_test,knn_pred))\n",
        "#print(confusion_matrix(y_test,knn_pred))\n",
        "cm = confusion_matrix(y_test,knn_pred, labels = knn_clf.classes_)\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix = cm, display_labels = knn_clf.classes_)\n",
        "disp.plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "oEzVPeBRsWok",
        "outputId": "cff38c14-3418-426a-ba06-697e877b9e90"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEAN SQUARE ERROR:  0.0967741935483871\n",
            "ACCURACY SCORE:  0.9032258064516129\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f0d66b56350>"
            ]
          },
          "metadata": {},
          "execution_count": 86
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "vX_OszEosumr"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Feature Selection "
      ],
      "metadata": {
        "id": "cry6RvPmzySu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##boruta feature selection"
      ],
      "metadata": {
        "id": "IwTBzxsM0GDC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfc = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)\n",
        "boruta_selector = BorutaPy(rfc, n_estimators='auto', verbose=2, random_state=1)\n",
        "boruta_selector.fit(np.array(x_train), np.array(y_train)) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JTX9uo3lz05J",
        "outputId": "d240f37c-ca28-441f-ef9a-1341ad45d036"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: \t1 / 100\n",
            "Confirmed: \t0\n",
            "Tentative: \t78\n",
            "Rejected: \t0\n",
            "Iteration: \t2 / 100\n",
            "Confirmed: \t0\n",
            "Tentative: \t78\n",
            "Rejected: \t0\n",
            "Iteration: \t3 / 100\n",
            "Confirmed: \t0\n",
            "Tentative: \t78\n",
            "Rejected: \t0\n",
            "Iteration: \t4 / 100\n",
            "Confirmed: \t0\n",
            "Tentative: \t78\n",
            "Rejected: \t0\n",
            "Iteration: \t5 / 100\n",
            "Confirmed: \t0\n",
            "Tentative: \t78\n",
            "Rejected: \t0\n",
            "Iteration: \t6 / 100\n",
            "Confirmed: \t0\n",
            "Tentative: \t78\n",
            "Rejected: \t0\n",
            "Iteration: \t7 / 100\n",
            "Confirmed: \t0\n",
            "Tentative: \t78\n",
            "Rejected: \t0\n",
            "Iteration: \t8 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t10\n",
            "Rejected: \t61\n",
            "Iteration: \t9 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t10\n",
            "Rejected: \t61\n",
            "Iteration: \t10 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t10\n",
            "Rejected: \t61\n",
            "Iteration: \t11 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t10\n",
            "Rejected: \t61\n",
            "Iteration: \t12 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t10\n",
            "Rejected: \t61\n",
            "Iteration: \t13 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t9\n",
            "Rejected: \t62\n",
            "Iteration: \t14 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t9\n",
            "Rejected: \t62\n",
            "Iteration: \t15 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t9\n",
            "Rejected: \t62\n",
            "Iteration: \t16 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t9\n",
            "Rejected: \t62\n",
            "Iteration: \t17 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t9\n",
            "Rejected: \t62\n",
            "Iteration: \t18 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t9\n",
            "Rejected: \t62\n",
            "Iteration: \t19 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t8\n",
            "Rejected: \t63\n",
            "Iteration: \t20 / 100\n",
            "Confirmed: \t7\n",
            "Tentative: \t8\n",
            "Rejected: \t63\n",
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            "Confirmed: \t7\n",
            "Tentative: \t8\n",
            "Rejected: \t63\n",
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            "Confirmed: \t9\n",
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            "Confirmed: \t9\n",
            "Tentative: \t4\n",
            "Rejected: \t65\n",
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            "Confirmed: \t9\n",
            "Tentative: \t3\n",
            "Rejected: \t66\n",
            "Iteration: \t86 / 100\n",
            "Confirmed: \t9\n",
            "Tentative: \t3\n",
            "Rejected: \t66\n",
            "Iteration: \t87 / 100\n",
            "Confirmed: \t9\n",
            "Tentative: \t3\n",
            "Rejected: \t66\n",
            "Iteration: \t88 / 100\n",
            "Confirmed: \t9\n",
            "Tentative: \t3\n",
            "Rejected: \t66\n",
            "Iteration: \t89 / 100\n",
            "Confirmed: \t9\n",
            "Tentative: \t3\n",
            "Rejected: \t66\n",
            "Iteration: \t90 / 100\n",
            "Confirmed: \t10\n",
            "Tentative: \t2\n",
            "Rejected: \t66\n",
            "Iteration: \t91 / 100\n",
            "Confirmed: \t10\n",
            "Tentative: \t2\n",
            "Rejected: \t66\n",
            "Iteration: \t92 / 100\n",
            "Confirmed: \t10\n",
            "Tentative: \t2\n",
            "Rejected: \t66\n",
            "Iteration: \t93 / 100\n",
            "Confirmed: \t10\n",
            "Tentative: \t2\n",
            "Rejected: \t66\n",
            "Iteration: \t94 / 100\n",
            "Confirmed: \t10\n",
            "Tentative: \t2\n",
            "Rejected: \t66\n",
            "Iteration: \t95 / 100\n",
            "Confirmed: \t11\n",
            "Tentative: \t1\n",
            "Rejected: \t66\n",
            "Iteration: \t96 / 100\n",
            "Confirmed: \t11\n",
            "Tentative: \t1\n",
            "Rejected: \t66\n",
            "Iteration: \t97 / 100\n",
            "Confirmed: \t11\n",
            "Tentative: \t1\n",
            "Rejected: \t66\n",
            "Iteration: \t98 / 100\n",
            "Confirmed: \t11\n",
            "Tentative: \t1\n",
            "Rejected: \t66\n",
            "Iteration: \t99 / 100\n",
            "Confirmed: \t11\n",
            "Tentative: \t1\n",
            "Rejected: \t66\n",
            "\n",
            "\n",
            "BorutaPy finished running.\n",
            "\n",
            "Iteration: \t100 / 100\n",
            "Confirmed: \t11\n",
            "Tentative: \t0\n",
            "Rejected: \t66\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BorutaPy(estimator=RandomForestClassifier(max_depth=5, n_estimators=97,\n",
              "                                          random_state=RandomState(MT19937) at 0x7F0D67F788D0),\n",
              "         n_estimators='auto',\n",
              "         random_state=RandomState(MT19937) at 0x7F0D67F788D0, verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(boruta_selector)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NiHllL7e2Ehi",
        "outputId": "48e0d2f5-6509-4b88-c9cf-ba48870b86a3"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "boruta.boruta_py.BorutaPy"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Selected Features: \", boruta_selector.support_)    # check selected features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3RA-8aAj3kWC",
        "outputId": "b466c58b-98af-47f2-c4d5-64d4aeb7a055"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Selected Features:  [False  True False False False False False False False False False False\n",
            " False False False False False False False False False False False False\n",
            " False  True False False False False False False False False False False\n",
            " False False False False False False False False  True False False  True\n",
            " False False False False False  True False False  True False False False\n",
            " False False  True False  True False False False  True False False False\n",
            " False False False False  True  True]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Ranking: \",boruta_selector.ranking_)               # check ranking of features\n",
        "\n",
        "print(\"No. of significant features: \", boruta_selector.n_features_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LAbZX8VN3tAX",
        "outputId": "9c5d24ef-076f-48a7-a490-a7d3f103242f"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ranking:  [28  1 66 60 65 35 63 58 53  4 63 48 46 42 33 40 30 62 25  2 44 23 50 54\n",
            " 27  1 57 36 32 55 23 61 45 37 47 68 41 11 34 55 26 39 52 67  1  8 19  1\n",
            " 16  6 28 58 42  1  9 38  1 30 51 49 22 15  1 20  1 14 13 18  1 20  5 17\n",
            " 12  3  6 10  1  1]\n",
            "No. of significant features:  11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So boruta has selected 10 relavent features. (The features with a ranking of 1 are selected)."
      ],
      "metadata": {
        "id": "zFZSnLwCQbT6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_important_train = boruta_selector.transform(np.array(x_train))\n",
        "X_important_test = boruta_selector.transform(np.array(x_test))"
      ],
      "metadata": {
        "id": "PcCjpPxslkc0"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_important_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QoOI6jDXQzhy",
        "outputId": "5d981ba2-6560-4686-dd73-1033ad99eb4a"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(272, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_boruta = RandomForestClassifier(random_state=1, n_estimators=1000, max_depth=5)\n",
        "rf_boruta.fit(X_important_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2EgkY0vOUhZc",
        "outputId": "72b5b8ae-4801-4f5e-ed85-f386156a3a1a"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=5, n_estimators=1000, random_state=1)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, rf_boruta.predict(X_important_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BH4SJqLNUoVr",
        "outputId": "0ce5c46e-3364-455e-c67e-b80248f1e952"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8709677419354839"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rf_hyper = RandomForestClassifier(random_state = 1)\n",
        "\n",
        "# Grid search cv\n",
        "grid_search = GridSearchCV(estimator = rf_hyper, param_grid = param_grid, \n",
        "                          cv = 2, n_jobs = -1, verbose = 2)"
      ],
      "metadata": {
        "id": "1GGs7eqsUsqD"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.fit(X_important_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "25i40BehVBAl",
        "outputId": "0f63d060-7b86-4bb4-fbbf-505c2d847dcb"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 2 folds for each of 12 candidates, totalling 24 fits\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=2, estimator=RandomForestClassifier(random_state=1), n_jobs=-1,\n",
              "             param_grid={'bootstrap': [True, False], 'max_depth': [5, 10, 15],\n",
              "                         'n_estimators': [500, 1000]},\n",
              "             verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "grid_search.best_params_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6H0ssZruVDsW",
        "outputId": "1cd7ba0c-b84e-49e5-d1e5-8414b5c3270f"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bootstrap': False, 'max_depth': 5, 'n_estimators': 500}"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_score(y_test, grid_search.predict(X_important_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0jw8N1zVME2",
        "outputId": "b1811cf2-146b-4044-adfc-6b6d9d0877f7"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8709677419354839"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "nHgbCaxJVMrg"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DT-RFE"
      ],
      "metadata": {
        "id": "ccjhl4Q4VViC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "RFE is a wrapper-type feature selection algorithm. This means that a different machine learning algorithm is given and used in the core of the method, is wrapped by RFE, and used to help select features. This is in contrast to filter-based feature selections that score each feature and select those features with the largest (or smallest) score.\n",
        "\n",
        "Technically, RFE is a wrapper-style feature selection algorithm that also uses filter-based feature selection internally.\n",
        "\n",
        "RFE works by searching for a subset of features by starting with all features in the training dataset and successfully removing features until the desired number remains.\n",
        "\n",
        "This is achieved by fitting the given machine learning algorithm used in the core of the model, ranking features by importance, discarding the least important features, and re-fitting the model. This process is repeated until a specified number of features remains. *italicized text*"
      ],
      "metadata": {
        "id": "9GbDhBSwWISV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "XprIcjocWm-5"
      },
      "execution_count": 98,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(sklearn.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pazKbEe1VZyK",
        "outputId": "f53b018a-5b7b-4dad-f883-75ef16fee481"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "RFE is a transform. To use it, first the class is configured with the chosen algorithm specified via the “estimator” argument and the number of features to select via the “n_features_to_select” argument.\n",
        "\n",
        "The algorithm must provide a way to calculate important scores, such as a decision tree. The algorithm used in RFE does not have to be the algorithm that is fit on the selected features; different algorithms can be used.\n",
        "\n",
        "Once configured, the class must be fit on a training dataset to select the features by calling the fit() function. After the class is fit, the choice of input variables can be seen via the “support_” attribute that provides a True or False for each input variable."
      ],
      "metadata": {
        "id": "jPFV1RwIXcii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rfecv = RFECV(DecisionTreeClassifier(),cv=StratifiedKFold(9))\n",
        "rfecv.fit(x_train,y_train)\n",
        "# check the support\n",
        "rfecv.get_support()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6PndKp5RWhjh",
        "outputId": "74efa514-483d-4a46-f126-615c9b53e34c"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, False, False, False, False, False, False,\n",
              "       False, False, False, False, False, False, False, False, False,\n",
              "       False, False, False, False, False, False, False, False, False,\n",
              "       False, False, False, False, False, False, False, False, False,\n",
              "       False, False, False, False, False, False, False, False,  True,\n",
              "       False,  True,  True, False, False, False, False, False, False,\n",
              "       False, False,  True, False, False, False, False, False, False,\n",
              "       False, False,  True, False, False, False, False, False, False,\n",
              "       False, False, False, False, False,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " x_train_rfe = rfecv.transform(x_train)\n",
        " x_tets_rfe = rfecv.transform(x_test)"
      ],
      "metadata": {
        "id": "TZeT8KKhX9Up"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#decision tree classifier\n",
        "dt_clf=DecisionTreeClassifier()\n",
        "dt_clf.fit(x_train_rfe,y_train)\n",
        "dtree_pred=dt_clf.predict(x_tets_rfe)\n",
        "print(\"MEAN SQUARE ERROR: \",mean_squared_error(y_test,dtree_pred))\n",
        "print(\"ACCURACY SCORE: \",accuracy_score(y_test,dtree_pred))\n",
        "print(confusion_matrix(y_test,dtree_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rd23KwC6gcX4",
        "outputId": "53acf0ca-9d42-4676-9229-2777849f5986"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "MEAN SQUARE ERROR:  0.25806451612903225\n",
            "ACCURACY SCORE:  0.7419354838709677\n",
            "[[ 3  5]\n",
            " [ 3 20]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "V3qH_7jNwEja"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## gradient boosting "
      ],
      "metadata": {
        "id": "6fBPt9P663NU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. "
      ],
      "metadata": {
        "id": "0BZdRpwZ-BUl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]\n",
        "\n",
        "for learning_rate in lr_list:\n",
        "    gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=15, max_depth=2, random_state=0)\n",
        "    gb_clf.fit(x_train, y_train)\n",
        "\n",
        "    print(\"Learning rate: \", learning_rate)\n",
        "    print(\"Accuracy score (training): {0:.3f}\".format(gb_clf.score(x_train, y_train)))\n",
        "    print(\"Accuracy score (validation): {0:.3f}\".format(gb_clf.score(x_test, y_test)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N2pKBaqZ66BX",
        "outputId": "ac94219f-213b-4926-9746-bb03a77f81fd"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Learning rate:  0.05\n",
            "Accuracy score (training): 0.816\n",
            "Accuracy score (validation): 0.839\n",
            "Learning rate:  0.075\n",
            "Accuracy score (training): 0.868\n",
            "Accuracy score (validation): 0.871\n",
            "Learning rate:  0.1\n",
            "Accuracy score (training): 0.890\n",
            "Accuracy score (validation): 0.871\n",
            "Learning rate:  0.25\n",
            "Accuracy score (training): 0.938\n",
            "Accuracy score (validation): 0.903\n",
            "Learning rate:  0.5\n",
            "Accuracy score (training): 0.982\n",
            "Accuracy score (validation): 0.968\n",
            "Learning rate:  0.75\n",
            "Accuracy score (training): 0.996\n",
            "Accuracy score (validation): 0.871\n",
            "Learning rate:  1\n",
            "Accuracy score (training): 0.996\n",
            "Accuracy score (validation): 0.871\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gb_clf2 = GradientBoostingClassifier(n_estimators=20, learning_rate=0.5, max_features=15, max_depth=2, random_state=0)\n",
        "gb_clf2.fit(x_train, y_train)\n",
        "predictions = gb_clf2.predict(x_test)\n",
        "\n",
        "print(\"Confusion Matrix:\")\n",
        "print(confusion_matrix(y_test, predictions))\n",
        "\n",
        "print(\"Classification Report\")\n",
        "print(classification_report(y_test, predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a8_QtKaz8K5j",
        "outputId": "4b028237-271a-4701-b85e-fd1392a97918"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Confusion Matrix:\n",
            "[[ 7  1]\n",
            " [ 0 23]]\n",
            "Classification Report\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      0.88      0.93         8\n",
            "           1       0.96      1.00      0.98        23\n",
            "\n",
            "    accuracy                           0.97        31\n",
            "   macro avg       0.98      0.94      0.96        31\n",
            "weighted avg       0.97      0.97      0.97        31\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "sjG8g9-m9Y5_"
      },
      "execution_count": 104,
      "outputs": []
    }
  ]
}