Heart_Attack_Prediction_Logistic_Regression.ipynb

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  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    },
    "colab": {
      "name": "Heart_Attack_Prediction_Logistic_Regression.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/harshchan/AI-ML-python-codes/blob/main/Heart_Attack_Prediction_Logistic_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FOHd-6n-uOjD"
      },
      "source": [
        "#Get all essential libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split, cross_val_score\n",
        "import io"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "f_K_RJ4VuOjM",
        "outputId": "ba5e3b24-f47f-4c49-95ff-ba59c6f8a540"
      },
      "source": [
        "#Get the data\n",
        "from  google.colab  import  files \n",
        "uploaded = files.upload()\n",
        "\n",
        "#data = pd.read_csv(\"/home/sagar/Desktop/Desk_folders/Artificial Intelligence - Machine Learning/4.Case_Study_Regression_Methods/Classification/heart.xls\")\n",
        "#data.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7c5fc167-bc00-4fec-bff2-57dacaf52725\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7c5fc167-bc00-4fec-bff2-57dacaf52725\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving heart.xls to heart (1).xls\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "8kamTo9UugY_",
        "outputId": "48d2b214-b302-42fd-c9ce-7c35900f6fc5"
      },
      "source": [
        "data = pd.read_csv(io.BytesIO(uploaded['heart.xls']))\r\n",
        "data.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>63</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>145</td>\n",
              "      <td>233</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>37</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>130</td>\n",
              "      <td>250</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>120</td>\n",
              "      <td>236</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>57</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>354</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>163</td>\n",
              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0   63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1   37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2   41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3   56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4   57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WwWxmp2yuOjP"
      },
      "source": [
        "Attribute Information\n",
        "1) age\n",
        "2) sex\n",
        "3) chest pain type (4 values)\n",
        "4) resting blood pressure\n",
        "5) serum cholestoral in mg/dl\n",
        "6)fasting blood sugar > 120 mg/dl\n",
        "7) resting electrocardiographic results (values 0,1,2)\n",
        "8) maximum heart rate achieved\n",
        "9) exercise induced angina\n",
        "10) oldpeak = ST depression induced by exercise relative to rest\n",
        "11)the slope of the peak exercise ST segment\n",
        "12) number of major vessels (0-3) colored by flourosopy\n",
        "13) thal: 0 = normal; 1 = fixed defect; 2 = reversable defect\n",
        "14) target: 0= less chance of heart attack 1= more chance of heart attack"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "cHtcwA_MuOjP",
        "outputId": "0ce0b3e3-1ccc-4fce-a4d1-496a577a0249"
      },
      "source": [
        "data.describe()"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalach</th>\n",
              "      <th>exang</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slope</th>\n",
              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "      <td>303.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              age         sex          cp  ...          ca        thal      target\n",
              "count  303.000000  303.000000  303.000000  ...  303.000000  303.000000  303.000000\n",
              "mean    54.366337    0.683168    0.966997  ...    0.729373    2.313531    0.544554\n",
              "std      9.082101    0.466011    1.032052  ...    1.022606    0.612277    0.498835\n",
              "min     29.000000    0.000000    0.000000  ...    0.000000    0.000000    0.000000\n",
              "25%     47.500000    0.000000    0.000000  ...    0.000000    2.000000    0.000000\n",
              "50%     55.000000    1.000000    1.000000  ...    0.000000    2.000000    1.000000\n",
              "75%     61.000000    1.000000    2.000000  ...    1.000000    3.000000    1.000000\n",
              "max     77.000000    1.000000    3.000000  ...    4.000000    3.000000    1.000000\n",
              "\n",
              "[8 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "guEPqq6JuOjQ",
        "outputId": "0e2e40da-b592-48bd-dc9b-449c4493564a"
      },
      "source": [
        "data.info()\r\n",
        "#small data set with only 303 values which will later get divided into train test and split"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 303 entries, 0 to 302\n",
            "Data columns (total 14 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       303 non-null    int64  \n",
            " 1   sex       303 non-null    int64  \n",
            " 2   cp        303 non-null    int64  \n",
            " 3   trestbps  303 non-null    int64  \n",
            " 4   chol      303 non-null    int64  \n",
            " 5   fbs       303 non-null    int64  \n",
            " 6   restecg   303 non-null    int64  \n",
            " 7   thalach   303 non-null    int64  \n",
            " 8   exang     303 non-null    int64  \n",
            " 9   oldpeak   303 non-null    float64\n",
            " 10  slope     303 non-null    int64  \n",
            " 11  ca        303 non-null    int64  \n",
            " 12  thal      303 non-null    int64  \n",
            " 13  target    303 non-null    int64  \n",
            "dtypes: float64(1), int64(13)\n",
            "memory usage: 33.3 KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOxIMckOuOjR",
        "outputId": "ea11bc1f-f983-4a20-cbf3-3de89df71943"
      },
      "source": [
        "data.isna().sum()"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trestbps    0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalach     0\n",
              "exang       0\n",
              "oldpeak     0\n",
              "slope       0\n",
              "ca          0\n",
              "thal        0\n",
              "target      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "WeHPi6LquOjR",
        "outputId": "2674806a-2f66-42fc-fa64-eb1597db23c1"
      },
      "source": [
        "# check for any correlations between variables\n",
        "corr = data.corr()\n",
        "sns.heatmap(corr)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fb7185fba58>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DQ61PevquOjS"
      },
      "source": [
        "# create x and y\n",
        "#these are main features which we think will contribute tiwards trainnig of model\n",
        "#feature_cols = ['cp','thalach','slope','restecg','chol','trestbps','fbs','oldpeak'] \n",
        "#x = data[feature_cols] # predictor\n",
        "y = data.target # response"
      ],
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "5CF0ZNmBuOjS",
        "outputId": "e2f76d0d-8f85-4ffe-f3a3-a6213c993dbe"
      },
      "source": [
        "x.head()"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cp</th>\n",
              "      <th>thalach</th>\n",
              "      <th>slope</th>\n",
              "      <th>restecg</th>\n",
              "      <th>chol</th>\n",
              "      <th>trestbps</th>\n",
              "      <th>fbs</th>\n",
              "      <th>oldpeak</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3</td>\n",
              "      <td>150</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>233</td>\n",
              "      <td>145</td>\n",
              "      <td>1</td>\n",
              "      <td>2.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>187</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>250</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>3.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>172</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>204</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>178</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>236</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>354</td>\n",
              "      <td>120</td>\n",
              "      <td>0</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   cp  thalach  slope  restecg  chol  trestbps  fbs  oldpeak\n",
              "0   3      150      0        0   233       145    1      2.3\n",
              "1   2      187      0        1   250       130    0      3.5\n",
              "2   1      172      2        0   204       130    0      1.4\n",
              "3   1      178      2        1   236       120    0      0.8\n",
              "4   0      163      2        1   354       120    0      0.6"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b5SHMkPAuOjT",
        "outputId": "2133f941-e989-4a44-cd6e-7e7d2b19281a"
      },
      "source": [
        "y.head()\r\n",
        "#only 1 or 0 ie heart attck will happen or not"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    1\n",
              "1    1\n",
              "2    1\n",
              "3    1\n",
              "4    1\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s38-UwPQuOjT"
      },
      "source": [
        "# split data into train and test\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.1,random_state = 2) \n",
        "# the test set will be 10% of the whole data set\n",
        "#90% values in trainig dataset "
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7zM2vydNuOjU",
        "outputId": "e6ca6cca-d332-4eea-8c4e-bdb349ba5733"
      },
      "source": [
        "print(x_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(272, 8)\n",
            "(31, 8)\n",
            "(272,)\n",
            "(31,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vJnWGX64uOjV",
        "outputId": "dfa59acf-e83f-44d1-87ec-de034ad942cd"
      },
      "source": [
        "# instantiate, fit\n",
        "logreg = LogisticRegression()\n",
        "logreg.fit(x_train, y_train)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8n9ZQzBuOjV"
      },
      "source": [
        "#logreg.predict(np.array([[0,108,1,0,250,160,1,1.5]]))"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "VP7xrJAauOjV",
        "outputId": "11b2aa10-165d-46f2-c07c-20c43c4bb629"
      },
      "source": [
        "data[data['target'] == 0]\r\n",
        "#just to get idea whether we could do prediction or not\r\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
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              "      <td>0</td>\n",
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              "      <td>45</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>138 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "165   67    1   0       160   286    0  ...      1      1.5      1   3     2       0\n",
              "166   67    1   0       120   229    0  ...      1      2.6      1   2     3       0\n",
              "167   62    0   0       140   268    0  ...      0      3.6      0   2     2       0\n",
              "168   63    1   0       130   254    0  ...      0      1.4      1   1     3       0\n",
              "169   53    1   0       140   203    1  ...      1      3.1      0   0     3       0\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "298   57    0   0       140   241    0  ...      1      0.2      1   0     3       0\n",
              "299   45    1   3       110   264    0  ...      0      1.2      1   0     3       0\n",
              "300   68    1   0       144   193    1  ...      0      3.4      1   2     3       0\n",
              "301   57    1   0       130   131    0  ...      1      1.2      1   1     3       0\n",
              "302   57    0   1       130   236    0  ...      0      0.0      1   1     2       0\n",
              "\n",
              "[138 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "VRltDj74uOjW",
        "outputId": "ecb92a9d-04b5-4ad5-cdd4-e52fd0880ab8"
      },
      "source": [
        "data[data['target'] == 1]"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>182</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>138</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>38</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>138</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>173</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>165 rows × 14 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trestbps  chol  fbs  ...  exang  oldpeak  slope  ca  thal  target\n",
              "0     63    1   3       145   233    1  ...      0      2.3      0   0     1       1\n",
              "1     37    1   2       130   250    0  ...      0      3.5      0   0     2       1\n",
              "2     41    0   1       130   204    0  ...      0      1.4      2   0     2       1\n",
              "3     56    1   1       120   236    0  ...      0      0.8      2   0     2       1\n",
              "4     57    0   0       120   354    0  ...      1      0.6      2   0     2       1\n",
              "..   ...  ...  ..       ...   ...  ...  ...    ...      ...    ...  ..   ...     ...\n",
              "160   56    1   1       120   240    0  ...      0      0.0      0   0     2       1\n",
              "161   55    0   1       132   342    0  ...      0      1.2      2   0     2       1\n",
              "162   41    1   1       120   157    0  ...      0      0.0      2   0     2       1\n",
              "163   38    1   2       138   175    0  ...      0      0.0      2   4     2       1\n",
              "164   38    1   2       138   175    0  ...      0      0.0      2   4     2       1\n",
              "\n",
              "[165 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "deNyi2OyuOjW",
        "outputId": "b9bb7acd-a790-4458-ce16-7600624d0b0d"
      },
      "source": [
        "logreg.predict(np.array([[3,150,0,0,233,130,0,3.5]]))"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IUYrSUiBuOjX",
        "outputId": "07a84ab5-7c46-4a26-e3f3-0a91d2c4519e"
      },
      "source": [
        "# Use score method to get accuracy of model\n",
        "score = logreg.score(x_test, y_test)\n",
        "print(score)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8387096774193549\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6c5j3LrRuOjX",
        "outputId": "d4aba708-2656-4b1e-e89a-efdef41f7175"
      },
      "source": [
        "#Confusion matrix\n",
        "from sklearn import metrics\n",
        "cm = metrics.confusion_matrix(y_test, logreg.predict(x_test))\n",
        "print(cm)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[11  4]\n",
            " [ 1 15]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-YFLsYvuOjX",
        "outputId": "77a1de34-f5a4-481a-be4b-4727b60663f2"
      },
      "source": [
        "#Precision - best value is 1 and worst is 0\n",
        "precision = metrics.precision_score(y_test, logreg.predict(x_test))\n",
        "print(precision)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.7894736842105263\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6TaT0Kl5uOjY",
        "outputId": "eab3c807-c27e-4176-f4f2-5f78460b2fbc"
      },
      "source": [
        "#Recall or sesitivity - best value is 1 and worst is 0\n",
        "recall = metrics.recall_score(y_test, logreg.predict(x_test))\n",
        "print(recall)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9375\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Okadg77guOjY",
        "outputId": "8aae83f5-fc36-44e3-8077-b3d2ee8deb36"
      },
      "source": [
        "#F1 score - best value is 1 and worst is 0\n",
        "f1 = metrics.f1_score(y_test, logreg.predict(x_test))\n",
        "print(f1)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8571428571428572\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YgwtHGf9xJ1C",
        "outputId": "027692b0-c7a7-4c5a-a31a-bcba06ca6b48"
      },
      "source": [
        "scores=cross_val_score(logreg,x,y,cv=10)\r\n",
        "scores"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/linear_model/_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.77419355, 0.74193548, 0.77419355, 0.83333333, 0.76666667,\n",
              "       0.73333333, 0.8       , 0.76666667, 0.73333333, 0.73333333])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4NQEoy7NxXva",
        "outputId": "6d7b899d-dbca-47e6-8942-e41bd8c5bf7d"
      },
      "source": [
        "scores.mean()"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7656989247311828"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    }
  ]
}