Heart_Attack_Chance_Prediction.ipynb

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    "colab": {
      "name": "Heart Attack  Chance Prediction.ipynb",
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      "mount_file_id": "1FSUx0hSpqs-kRNDM7wzy6G2SsOxB_lv_",
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Durgaprasad-77/Heart-Attack-Chance-Prediction/blob/main/Heart_Attack_Chance_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p5DvFEPvegl6"
      },
      "source": [
        "Importing necessary Lib"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xH83SyFgZXzF"
      },
      "source": [
        "import pandas as pd \n",
        "import numpy as np\n",
        "from  sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vZILOrNZo-x"
      },
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/heart.csv')"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fw2TCVB-aDUY",
        "outputId": "74b29d2e-1c66-4982-b8c3-436e569855c8"
      },
      "source": [
        "data.head()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>exng</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slp</th>\n",
              "      <th>caa</th>\n",
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              "      <td>0</td>\n",
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              "      <td>1.4</td>\n",
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              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>0.6</td>\n",
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              "      <td>1</td>\n",
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            "text/plain": [
              "   age  sex  cp  trtbps  chol  fbs  ...  exng  oldpeak  slp  caa  thall  output\n",
              "0   63    1   3     145   233    1  ...     0      2.3    0    0      1       1\n",
              "1   37    1   2     130   250    0  ...     0      3.5    0    0      2       1\n",
              "2   41    0   1     130   204    0  ...     0      1.4    2    0      2       1\n",
              "3   56    1   1     120   236    0  ...     0      0.8    2    0      2       1\n",
              "4   57    0   0     120   354    0  ...     1      0.6    2    0      2       1\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
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        }
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTR-Zoo0emTA"
      },
      "source": [
        "Checking If there are any Null values"
      ]
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          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "f7453d9d-ef12-435c-c9c7-621537b79578"
      },
      "source": [
        "data.isnull().sum()"
      ],
      "execution_count": 16,
      "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"
            ]
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            "tags": []
          },
          "execution_count": 16
        }
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    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "my0s96itaceR",
        "outputId": "8af43ca0-d256-4572-a0c5-369be251e266"
      },
      "source": [
        "data.describe"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.describe of      age  sex  cp  trtbps  chol  fbs  ...  exng  oldpeak  slp  caa  thall  output\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",
              "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",
              "[303 rows x 14 columns]>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "96ei9JxxezdJ"
      },
      "source": [
        "Defining Training and Testing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PDtvelBian0p"
      },
      "source": [
        "X = data.drop(columns = ['output'])\n",
        "y = data.output"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPyAQyKLbT9h"
      },
      "source": [
        "x_train,x_test,y_train,y_test =  train_test_split(X,y,random_state = 1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vBdAMfNHb7fJ",
        "outputId": "f07a05fb-4671-48a6-898b-7fd572083eb3"
      },
      "source": [
        "x_train"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>3</td>\n",
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              "      <th>42</th>\n",
              "      <td>45</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>104</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>90</th>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
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              "      <td>124</td>\n",
              "      <td>255</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>175</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <td>68</td>\n",
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              "      <td>180</td>\n",
              "      <td>274</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "      <td>1.6</td>\n",
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              "      <td>142</td>\n",
              "      <td>309</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>147</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>3</td>\n",
              "      <td>3</td>\n",
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              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>204</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>202</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>140</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>1</td>\n",
              "      <td>1.6</td>\n",
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              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>54</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>150</td>\n",
              "      <td>232</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>165</td>\n",
              "      <td>0</td>\n",
              "      <td>1.6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>227 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trtbps  chol  fbs  ...  thalachh  exng  oldpeak  slp  caa  thall\n",
              "170   56    1   2     130   256    1  ...       142     1      0.6    1    1      1\n",
              "192   54    1   0     120   188    0  ...       113     0      1.4    1    1      3\n",
              "168   63    1   0     130   254    0  ...       147     0      1.4    1    1      3\n",
              "42    45    1   0     104   208    0  ...       148     1      3.0    1    0      2\n",
              "90    48    1   2     124   255    1  ...       175     0      0.0    2    2      2\n",
              "..   ...  ...  ..     ...   ...  ...  ...       ...   ...      ...  ...  ...    ...\n",
              "203   68    1   2     180   274    1  ...       150     1      1.6    1    0      3\n",
              "255   45    1   0     142   309    0  ...       147     1      0.0    1    3      3\n",
              "72    29    1   1     130   204    0  ...       202     0      0.0    2    0      2\n",
              "235   51    1   0     140   299    0  ...       173     1      1.6    2    0      3\n",
              "37    54    1   2     150   232    0  ...       165     0      1.6    2    0      3\n",
              "\n",
              "[227 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d0W8DlzPe1vZ"
      },
      "source": [
        "Bring in Logistical Regression and Fit in our train sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2q6m__y8b-KT"
      },
      "source": [
        "log_reg = LogisticRegression()\n",
        "log_reg.fit(x_train, y_train)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kXWskoBkcmNZ"
      },
      "source": [
        "y_pred = log_reg.predict(x_test)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rMNZ1lvOcos4",
        "outputId": "f5738691-4862-4407-d223-ad8d65b57953"
      },
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[24, 11],\n",
              "       [ 5, 36]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ETqx3Fzvcq8p"
      },
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ua6knxIRe9_b"
      },
      "source": [
        "Accuracy Test score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SfLTp5DRcudI",
        "outputId": "6c416e46-579c-4d12-89e7-bf96fb73e20a"
      },
      "source": [
        "accuracy_score(y_test,y_pred)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7894736842105263"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VY2ioNMXcxHp",
        "outputId": "c184f336-6551-4554-c500-658c04f1a72f"
      },
      "source": [
        "x_test.head()"
      ],
      "execution_count": 30,
      "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>trtbps</th>\n",
              "      <th>chol</th>\n",
              "      <th>fbs</th>\n",
              "      <th>restecg</th>\n",
              "      <th>thalachh</th>\n",
              "      <th>exng</th>\n",
              "      <th>oldpeak</th>\n",
              "      <th>slp</th>\n",
              "      <th>caa</th>\n",
              "      <th>thall</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>204</th>\n",
              "      <td>62</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>164</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "      <td>6.2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>159</th>\n",
              "      <td>56</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>130</td>\n",
              "      <td>221</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>163</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>219</th>\n",
              "      <td>48</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>256</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>206</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>132</td>\n",
              "      <td>1</td>\n",
              "      <td>2.4</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>184</th>\n",
              "      <td>50</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>243</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>128</td>\n",
              "      <td>0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     age  sex  cp  trtbps  chol  fbs  ...  thalachh  exng  oldpeak  slp  caa  thall\n",
              "204   62    0   0     160   164    0  ...       145     0      6.2    0    3      3\n",
              "159   56    1   1     130   221    0  ...       163     0      0.0    2    0      3\n",
              "219   48    1   0     130   256    1  ...       150     1      0.0    2    2      3\n",
              "174   60    1   0     130   206    0  ...       132     1      2.4    1    2      3\n",
              "184   50    1   0     150   243    0  ...       128     0      2.6    1    0      3\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3j_34LkbfCPZ"
      },
      "source": [
        "Finally Predicting the Chances of Heart Attack by random values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XYv0Njb7c5-o",
        "outputId": "b7be44bb-c407-436f-a46f-a426fac72973"
      },
      "source": [
        "log_reg.predict([[25,1,0,120,230,1,0,140,0,2,2.5,0,3]])"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6oG8tKfOfO7u"
      },
      "source": [
        "Predicitng the Probability \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iqbESOrIdg_p",
        "outputId": "87098b56-0e40-43f5-8ce1-c084de40d323"
      },
      "source": [
        "log_reg.predict_proba([[25,1,0,120,230,1,0,140,0,2,2.5,0,3]])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.85634028, 0.14365972]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    }
  ]
}