Heart_Attack_Prediction_System.ipynb

{
  "cells": [
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/uday-uppal/Heart-Disease-Prediction-Project/blob/main/Heart_Attack_Prediction_System.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
       {
      "cell_type": "code",
      "execution_count": null,
      "id": "389b52c6",
      "metadata": {
        "id": "389b52c6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.metrics import confusion_matrix, classification_report\n",
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "from scipy.stats import mode"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "181d81af",
      "metadata": {
        "id": "181d81af"
      },
      "source": [
        "# Extracting the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "83bea429",
      "metadata": {
        "id": "83bea429"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('heart.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "93a01580",
      "metadata": {
        "id": "93a01580"
      },
      "source": [
        "# Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b1ccf130",
      "metadata": {
        "id": "b1ccf130",
        "outputId": "e0292c8c-dd83-4660-f5a6-83de8e79d240",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(303, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# print(df)\n",
        "df.head(n = 10)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "FJ2sXrMCO-YJ",
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      },
      "id": "FJ2sXrMCO-YJ",
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   age  sex  cp  trtbps  chol  fbs  restecg  thalachh  exng  oldpeak  slp  \\\n",
              "0   63    1   3     145   233    1        0       150     0      2.3    0   \n",
              "1   37    1   2     130   250    0        1       187     0      3.5    0   \n",
              "2   41    0   1     130   204    0        0       172     0      1.4    2   \n",
              "3   56    1   1     120   236    0        1       178     0      0.8    2   \n",
              "4   57    0   0     120   354    0        1       163     1      0.6    2   \n",
              "5   57    1   0     140   192    0        1       148     0      0.4    1   \n",
              "6   56    0   1     140   294    0        0       153     0      1.3    1   \n",
              "7   44    1   1     120   263    0        1       173     0      0.0    2   \n",
              "8   52    1   2     172   199    1        1       162     0      0.5    2   \n",
              "9   57    1   2     150   168    0        1       174     0      1.6    2   \n",
              "\n",
              "   caa  thall  output  \n",
              "0    0      1       1  \n",
              "1    0      2       1  \n",
              "2    0      2       1  \n",
              "3    0      2       1  \n",
              "4    0      2       1  \n",
              "5    0      1       1  \n",
              "6    0      2       1  \n",
              "7    0      3       1  \n",
              "8    0      3       1  \n",
              "9    0      2       1  "
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      "metadata": {
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      "source": [
        "### We have 303 rows and 14 coumns"
      ]
    },
    {
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      "id": "eb2e1035",
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      "outputs": [
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          "data": {
            "text/plain": [
              "              age         sex          cp      trtbps        chol         fbs  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
              "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
              "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
              "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
              "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
              "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
              "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
              "\n",
              "          restecg    thalachh        exng     oldpeak         slp         caa  \\\n",
              "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
              "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
              "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
              "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
              "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
              "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
              "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
              "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
              "\n",
              "            thall      output  \n",
              "count  303.000000  303.000000  \n",
              "mean     2.313531    0.544554  \n",
              "std      0.612277    0.498835  \n",
              "min      0.000000    0.000000  \n",
              "25%      2.000000    0.000000  \n",
              "50%      2.000000    1.000000  \n",
              "75%      3.000000    1.000000  \n",
              "max      3.000000    1.000000  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>cp</th>\n",
              "      <th>trtbps</th>\n",
              "      <th>chol</th>\n",
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              "      <th>thalachh</th>\n",
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              "      <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",
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              "      <td>303.000000</td>\n",
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              "      <td>303.000000</td>\n",
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              "      <th>mean</th>\n",
              "      <td>54.366337</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.966997</td>\n",
              "      <td>131.623762</td>\n",
              "      <td>246.264026</td>\n",
              "      <td>0.148515</td>\n",
              "      <td>0.528053</td>\n",
              "      <td>149.646865</td>\n",
              "      <td>0.326733</td>\n",
              "      <td>1.039604</td>\n",
              "      <td>1.399340</td>\n",
              "      <td>0.729373</td>\n",
              "      <td>2.313531</td>\n",
              "      <td>0.544554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.082101</td>\n",
              "      <td>0.466011</td>\n",
              "      <td>1.032052</td>\n",
              "      <td>17.538143</td>\n",
              "      <td>51.830751</td>\n",
              "      <td>0.356198</td>\n",
              "      <td>0.525860</td>\n",
              "      <td>22.905161</td>\n",
              "      <td>0.469794</td>\n",
              "      <td>1.161075</td>\n",
              "      <td>0.616226</td>\n",
              "      <td>1.022606</td>\n",
              "      <td>0.612277</td>\n",
              "      <td>0.498835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>29.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>94.000000</td>\n",
              "      <td>126.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>71.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>47.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>133.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>55.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>153.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>274.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>77.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>564.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.200000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0645578e-f629-45bd-a478-a38023d2fb83')\"\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",
              "    }\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-0645578e-f629-45bd-a478-a38023d2fb83 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-0645578e-f629-45bd-a478-a38023d2fb83');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<H1> Medical Definitions </H1>\n",
        "\n",
        "**Age :** Age of the patient\n",
        "\n",
        "**Sex :** Sex of the patient\n",
        "\n",
        "**cp :** Chest Pain type chest pain type <BR>\n",
        "Value 1: typical angina <BR>\n",
        "Value 2: atypical angina <BR>\n",
        "Value 3: non-anginal pain <BR>\n",
        "Value 4: asymptomatic\n",
        "\n",
        "**trtbps :** resting blood pressure (in mm Hg)\n",
        "\n",
        "**chol :** cholestoral in mg/dl fetched via BMI sensor\n",
        "\n",
        "**fbs :** (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
        "\n",
        "**thalachh :** maximum heart rate achieved\n",
        "\n",
        "**rest_ecg :** resting electrocardiographic results <BR>\n",
        "Value 0: normal <BR>\n",
        "Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) <BR>\n",
        "Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria\n",
        "\n",
        "**exng :** exercise induced angina (1 = yes; 0 = no)\n",
        "\n",
        "**oldpeak :** ST depression induced by exercise relative to rest\n",
        "\n",
        "**slp :** the slope of the peak exercise ST segment <BR>\n",
        "0 = unsloping <BR>\n",
        "1 = flat <BR>\n",
        "2 = downsloping\n",
        "\n",
        "**caa :** number of major vessels (0-3)\n",
        "\n",
        "**thall :** thalassemia <BR>\n",
        "0 = null <BR>\n",
        "1 = fixed defect <BR>\n",
        "2 = normal <BR>\n",
        "3 = reversable defect\n",
        "\n",
        "**output :** <BR>\n",
        "0= less chance of heart attack <BR> \n",
        "1= more chance of heart attack"
      ],
      "metadata": {
        "id": "tzcN2Xgvr1CY"
      },
      "id": "tzcN2Xgvr1CY"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "76ab319b",
      "metadata": {
        "scrolled": true,
        "id": "76ab319b",
        "outputId": "a704a5f1-d2c6-4773-a1dd-43f5ce3929b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          41\n",
              "sex           2\n",
              "cp            4\n",
              "trtbps       49\n",
              "chol        152\n",
              "fbs           2\n",
              "restecg       3\n",
              "thalachh     91\n",
              "exng          2\n",
              "oldpeak      40\n",
              "slp           3\n",
              "caa           5\n",
              "thall         4\n",
              "output        2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b43201a1",
      "metadata": {
        "scrolled": true,
        "id": "b43201a1",
        "outputId": "ec8f1cc8-d425-46ae-ca60-247409aa7f8e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age         0\n",
              "sex         0\n",
              "cp          0\n",
              "trtbps      0\n",
              "chol        0\n",
              "fbs         0\n",
              "restecg     0\n",
              "thalachh    0\n",
              "exng        0\n",
              "oldpeak     0\n",
              "slp         0\n",
              "caa         0\n",
              "thall       0\n",
              "output      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a6cf79bf",
      "metadata": {
        "id": "a6cf79bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "f722261c-2765-4fe8-cef4-7e8dd46db336"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8769eefd50>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('thall')[\"output\"].count().plot(kind=\"pie\", title=\"Thall : maximum heart rate achieved\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "75e0b76c",
      "metadata": {
        "id": "75e0b76c",
        "outputId": "6e6b31bc-3302-4b26-e6b1-d1c12a0cd3ea",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age           int64\n",
              "sex           int64\n",
              "cp            int64\n",
              "trtbps        int64\n",
              "chol          int64\n",
              "fbs           int64\n",
              "restecg       int64\n",
              "thalachh      int64\n",
              "exng          int64\n",
              "oldpeak     float64\n",
              "slp           int64\n",
              "caa           int64\n",
              "thall         int64\n",
              "output        int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dabfa089",
      "metadata": {
        "id": "dabfa089"
      },
      "source": [
        "### Gender of Patient"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "445b6896",
      "metadata": {
        "id": "445b6896"
      },
      "source": [
        "#### MALE : 0 \n",
        "#### FEMALE : 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "e5448b8b",
      "metadata": {
        "id": "e5448b8b",
        "outputId": "f5c44463-c977-412e-f8e9-af75aced6082",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f87699e7790>"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('sex')[\"output\"].count().plot(kind=\"pie\",title=\"Male & Female have heart attack\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a6a35e3",
      "metadata": {
        "id": "3a6a35e3",
        "outputId": "b9b92f1b-ba3f-4258-d787-9868347560f7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f87699b4ad0>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "df.groupby('fbs')[\"output\"].count().plot(kind=\"pie\",title=\"fasting blood sugar\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "761dea20",
      "metadata": {
        "id": "761dea20",
        "outputId": "aaae49ad-e6f4-4e51-9ee4-ff18079ad716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f876998b690>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.scatterplot(x=\"age\",y=\"chol\",data=df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1a99ebe2",
      "metadata": {
        "id": "1a99ebe2",
        "outputId": "da8c362d-758b-45f7-ef06-6897dfb3382a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x648 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "corrmat = df.corr()\n",
        "f, ax = plt.subplots(figsize=(12, 9))\n",
        "sns.heatmap(corrmat, vmax=.8, square=True, annot=True);"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1f7d6928",
      "metadata": {
        "id": "1f7d6928"
      },
      "source": [
        "# Pre-Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0df11e0c",
      "metadata": {
        "id": "0df11e0c"
      },
      "outputs": [],
      "source": [
        "X = df.iloc[: , 0:-1 ]\n",
        "Y = df['output']\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.25 ,random_state = 42)\n",
        "\n",
        "sc = StandardScaler()\n",
        "X_test = sc.fit_transform(X_test)\n",
        "X_train = sc.transform(X_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "630cec4e",
      "metadata": {
        "id": "630cec4e"
      },
      "source": [
        "# Implementing Classsifer"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d9ab4ac",
      "metadata": {
        "id": "1d9ab4ac"
      },
      "source": [
        "## Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def F1Score(y, y_predicted):\n",
        "    tp,tn,fp,fn = 0,0,0,0\n",
        "    r=[]\n",
        "    for i in range(len(y)):\n",
        "        if (y[i] == 1 and y_predicted[i] == 1):\n",
        "            tp += 1\n",
        "        elif y[i] == 1 and y_predicted[i] == 0:\n",
        "            fn += 1\n",
        "        elif y[i] == 0 and y_predicted[i] == 1:\n",
        "            fp += 1\n",
        "        elif (y[i] == 0 and y_predicted[i] == 0):\n",
        "            tn += 1\n",
        "    precision = tp / (tp + fp)\n",
        "    recall = tp / (tp + fn)\n",
        "    f1 = 2 * precision * recall / (precision + recall)\n",
        "    return f1    \n",
        "\n",
        "class LogisticRegression:\n",
        "  def fit(self, X_train, Y_train, alpha = 0.1, n = 2000):\n",
        "    X_train = np.insert(X_train, 0, values = 1, axis = 1)\n",
        "    m, k = X_train.shape \n",
        "    self.beta = np.zeros(k)\n",
        "    for i in range(n): \n",
        "      cost_gradient = np.zeros(k) \n",
        "      z = X_train.dot(self.beta) \n",
        "      p = 1 / (1 + np.exp(-z))  \n",
        "      for j in range(k):\n",
        "        cost_gradient[j] = np.sum((p - Y_train).dot(X_train[:,j])) \n",
        "      for j in range(k):\n",
        "        self.beta[j] = self.beta[j] - (alpha / m) * cost_gradient[j] \n",
        "    return self.beta\n",
        "\n",
        "  def predict(self, X_test):\n",
        "    X_test = np.insert(X_test, 0, values = 1, axis = 1)\n",
        "    z = X_test.dot(self.beta)\n",
        "    Y_predict = 1 / (1 + np.exp(-z)) \n",
        "    Y_label = np.zeros(len(Y_predict))\n",
        "    for i in range(len(Y_predict)):\n",
        "      if(Y_predict[i] >= 0.5): \n",
        "        Y_label[i] = 1\n",
        "    return Y_label"
      ],
      "metadata": {
        "id": "ROju8n60R43o"
      },
      "id": "ROju8n60R43o",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8bfd13ee",
      "metadata": {
        "id": "8bfd13ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52e44cac-e6e5-4b60-b395-1dde3dc7f52d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.001\n"
          ]
        }
      ],
      "source": [
        "def find_accuracy(y, y_predicted):\n",
        "      tp,tn,fp,fn = 0,0,0,0\n",
        "      r=[]\n",
        "      for i in range(len(y)):\n",
        "          if (y[i] == 1 and y_predicted[i] == 1):\n",
        "              tp += 1\n",
        "          elif y[i] == 1 and y_predicted[i] == 0:\n",
        "              fn += 1\n",
        "          elif y[i] == 0 and y_predicted[i] == 1:\n",
        "              fp += 1\n",
        "          elif (y[i] == 0 and y_predicted[i] == 0):\n",
        "              tn += 1\n",
        "      accuracy = (tp + tn) / (tp + tn + fp + fn)\n",
        "      return round(accuracy, 2)\n",
        "\n",
        "X_train_LR, y_train_LR, X_test_LR, Y_test_LR = np.array(X_train), np.array(y_train), np.array(X_test), np.array(y_test)\n",
        "max_accuracy, best_alpha = 0, 0.01\n",
        "for alpha in np.arange(0.001, 1, 0.001):\n",
        "  clf = LogisticRegression()\n",
        "  clf2 = clf.fit(X_train_LR, y_train_LR, alpha)\n",
        "  predictions = clf.predict(X_test_LR)\n",
        "  accuracy = find_accuracy(Y_test_LR,predictions)\n",
        "  #print(alpha, \" - \", accuracy)\n",
        "  if accuracy > max_accuracy:\n",
        "    max_accuracy = accuracy\n",
        "    best_alpha = alpha\n",
        "print(best_alpha)\n",
        "\n",
        "X_train_LR, y_train_LR, X_test_LR, Y_test_LR = np.array(X_train), np.array(y_train), np.array(X_test), np.array(y_test)\n",
        "clf2 = clf.fit(X_train_LR, y_train_LR, best_alpha)\n",
        "predictions = clf.predict(X_test_LR)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4402e53c",
      "metadata": {
        "id": "4402e53c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acf8bd44-0bb8-4255-f302-89a35a5ef44f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "cm = confusion_matrix(Y_test_LR, predictions)\n",
        "disp = ConfusionMatrixDisplay(cm, display_labels = ['No Risk','High Risk']).plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "31d98360",
      "metadata": {
        "id": "31d98360",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20c8651c-dd31-4b7b-c259-48d0656d4f29"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.83      0.87        35\n",
            "           1       0.86      0.93      0.89        41\n",
            "\n",
            "    accuracy                           0.88        76\n",
            "   macro avg       0.88      0.88      0.88        76\n",
            "weighted avg       0.88      0.88      0.88        76\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(Y_test_LR,predictions))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "556864c4",
      "metadata": {
        "id": "556864c4"
      },
      "source": [
        "## KNN\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def find_accuracy(y, y_predicted):\n",
        "      tp,tn,fp,fn = 0,0,0,0\n",
        "      r=[]\n",
        "      for i in range(len(y)):\n",
        "          if (y[i] == 1 and y_predicted[i] == 1):\n",
        "              tp += 1\n",
        "          elif y[i] == 1 and y_predicted[i] == 0:\n",
        "              fn += 1\n",
        "          elif y[i] == 0 and y_predicted[i] == 1:\n",
        "              fp += 1\n",
        "          elif (y[i] == 0 and y_predicted[i] == 0):\n",
        "              tn += 1\n",
        "      accuracy = (tp + tn) / (tp + tn + fp + fn)\n",
        "      return round(accuracy, 2)\n",
        "\n",
        "class K_Nearest_Neighbors_Classifier() :   \n",
        "    def __init__( self, K ) :      \n",
        "        self.K = K      \n",
        "\n",
        "    def fit( self, X_train, Y_train ) :\n",
        "        self.X_train = X_train\n",
        "        self.Y_train = Y_train\n",
        "        self.m, self.n = X_train.shape\n",
        "      \n",
        "    def predict( self, X_test ) : \n",
        "        self.X_test = X_test  \n",
        "        self.m_test, self.n = X_test.shape\n",
        "        Y_predict = np.zeros( self.m_test )  \n",
        "        for i in range( self.m_test ) :\n",
        "            x = self.X_test[i] \n",
        "            neighbors = np.zeros( self.K )\n",
        "            neighbors = self.find_neighbors( x )\n",
        "            Y_predict[i] = mode( neighbors )[0][0]    \n",
        "        return Y_predict\n",
        "            \n",
        "    def find_neighbors( self, x ) : \n",
        "        euclidean_distances = np.zeros( self.m )\n",
        "        for i in range( self.m ) :     \n",
        "            d = self.euclidean( x, self.X_train[i] ) \n",
        "            euclidean_distances[i] = d\n",
        "        ind = euclidean_distances.argsort()\n",
        "        Y_train_sorted = np.zeros( self.K )\n",
        "        self.Y_train=np.array(self.Y_train)\n",
        "        for i in range(self.K):\n",
        "            Y_train_sorted[i] = self.Y_train[ind[i]]\n",
        "        return Y_train_sorted\n",
        "              \n",
        "    def euclidean( self, x, x_train ) : \n",
        "        return np.sqrt( np.sum( np.square( x - x_train ) ) )\n",
        "  "
      ],
      "metadata": {
        "id": "EO8zHggc-w94"
      },
      "id": "EO8zHggc-w94",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_accuracy, best_k = 0, 1\n",
        "for k in range (1, len(X_train)):\n",
        "  myKNN = K_Nearest_Neighbors_Classifier(k)\n",
        "  myKNN.fit(X_train, y_train)\n",
        "  y_predict = myKNN.predict(X_test)\n",
        "  accuracy = find_accuracy(np.array(y_test),y_predict)\n",
        "  #print(k, \" - \", accuracy)\n",
        "  if accuracy > max_accuracy:\n",
        "    max_accuracy = accuracy\n",
        "    best_k = k\n",
        "print(best_k)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ARZUaWlHW12N",
        "outputId": "7261d48b-b649-47ca-fe6e-1d74ab39ae8c"
      },
      "id": "ARZUaWlHW12N",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "myKNN = K_Nearest_Neighbors_Classifier(best_k)\n",
        "myKNN.fit(X_train, y_train)\n",
        "y_predict = myKNN.predict(X_test)"
      ],
      "metadata": {
        "id": "6EdbZGaHCEMc"
      },
      "id": "6EdbZGaHCEMc",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "dfba2c74",
      "metadata": {
        "id": "dfba2c74",
        "outputId": "fe4d6c8b-0c39-40a1-c758-59f75c866df9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "cm = confusion_matrix(y_predict, y_test)\n",
        "disp = ConfusionMatrixDisplay(cm, display_labels = ['No Risk','High Risk']).plot()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "765cf525",
      "metadata": {
        "id": "765cf525",
        "outputId": "287c0d1f-01e6-46a2-9431-b1eb62aa574f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.91      0.90        35\n",
            "           1       0.93      0.90      0.91        41\n",
            "\n",
            "    accuracy                           0.91        76\n",
            "   macro avg       0.91      0.91      0.91        76\n",
            "weighted avg       0.91      0.91      0.91        76\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_test,y_predict))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RS681VRv1IXQ"
      },
      "source": [
        "## Naive Bayes Regression"
      ],
      "id": "RS681VRv1IXQ"
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "class NaiveBayes_Gaussian:\n",
        "    def fit(self, X, y):\n",
        "        self.samples, self.features = X.shape\n",
        "        self.classes = len(np.unique(y))\n",
        "        self.mean = np.zeros((self.classes, self.features))\n",
        "        self.variance = np.zeros((self.classes, self.features))\n",
        "        self.priors = np.zeros(self.classes)\n",
        "        for c in range(self.classes):\n",
        "            X_c = X[y == c]\n",
        "            self.mean[c, :] = np.mean(X_c, axis=0)\n",
        "            self.variance[c, :] = np.var(X_c, axis=0)\n",
        "            self.priors[c] = X_c.shape[0] / self.samples\n",
        "\n",
        "    def predict(self, X):\n",
        "        y_hat = [self.calc_prob(x) for x in X]\n",
        "        return np.array(y_hat)\n",
        "\n",
        "    def calc_prob(self, x):\n",
        "        posteriors_list = list()\n",
        "        for c in range(self.classes):\n",
        "            mean = self.mean[c]\n",
        "            variance = self.variance[c]\n",
        "            prior = np.log(self.priors[c])\n",
        "            posterior = np.sum(np.log(self.gaussian(x, mean, variance)))\n",
        "            posterior = prior + posterior\n",
        "            posteriors_list.append(posterior)\n",
        "        return np.argmax(posteriors_list)\n",
        "\n",
        "    def gaussian(self, x, mean, var):\n",
        "        const = 1 / np.sqrt(var * 2 * np.pi)\n",
        "        prob = np.exp(-0.5 * ((x - mean) ** 2 / var))\n",
        "        return const * prob"
      ],
      "metadata": {
        "id": "WaHn8dnoTxaY"
      },
      "id": "WaHn8dnoTxaY",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb = NaiveBayes_Gaussian()\n",
        "nb.fit(X_train, y_train)\n",
        "y_predict3 = nb.predict(X_test)"
      ],
      "metadata": {
        "id": "dOZdB-iUWYiO"
      },
      "id": "dOZdB-iUWYiO",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_predict3)\n",
        "disp = ConfusionMatrixDisplay(cm, display_labels = ['No Risk','High Risk']).plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97OcYuQBWYx9",
        "outputId": "694a2a29-3e72-4177-daaf-ac2ffb3d7780"
      },
      "id": "97OcYuQBWYx9",
      "execution_count": null,
      "outputs": [
        {
          "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": [
        "print(classification_report(y_test,y_predict3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lOmsNNtcWd1N",
        "outputId": "d1873880-f30d-407d-a1d8-c9ce5b0df40c"
      },
      "id": "lOmsNNtcWd1N",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.86      0.89      0.87        35\n",
            "           1       0.90      0.88      0.89        41\n",
            "\n",
            "    accuracy                           0.88        76\n",
            "   macro avg       0.88      0.88      0.88        76\n",
            "weighted avg       0.88      0.88      0.88        76\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<H1> Ensemble of KNN, Logistic Regression & Naive Bayes </H1>"
      ],
      "metadata": {
        "id": "AlESxfr47DW8"
      },
      "id": "AlESxfr47DW8"
    },
    {
      "cell_type": "code",
      "source": [
        "result = []\n",
        "for i in range(len(y_test)):\n",
        "  if y_predict3[i] + predictions[i] + y_predict[i] >= 2:\n",
        "    result.append(1)\n",
        "  else:\n",
        "    result.append(0)      \n",
        "print(confusion_matrix(y_test, result))\n",
        "print(classification_report(y_test,result)) "
      ],
      "metadata": {
        "id": "XS5-UMSg73D9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47bb2920-8ea8-4e62-f302-f674a680e1c4"
      },
      "id": "XS5-UMSg73D9",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[31  4]\n",
            " [ 3 38]]\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.91      0.89      0.90        35\n",
            "           1       0.90      0.93      0.92        41\n",
            "\n",
            "    accuracy                           0.91        76\n",
            "   macro avg       0.91      0.91      0.91        76\n",
            "weighted avg       0.91      0.91      0.91        76\n",
            "\n"
          ]
        }
      ]
    }
  ],
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