HeartDiseasePrediction.ipynb

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        "<a href=\"https://colab.research.google.com/github/avinashkolla532/Heart-Disease-Prediction/blob/main/HeartDiseasePrediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('/content/heart.csv')"
      ],
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        "id": "4nqAImWfEzMR"
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      "execution_count": null,
      "outputs": []
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        "dataset.head()"
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              "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
              "0   52    1   0       125   212    0        1      168      0      1.0      2   \n",
              "1   53    1   0       140   203    1        0      155      1      3.1      0   \n",
              "2   70    1   0       145   174    0        1      125      1      2.6      0   \n",
              "3   61    1   0       148   203    0        1      161      0      0.0      2   \n",
              "4   62    0   0       138   294    1        1      106      0      1.9      1   \n",
              "\n",
              "   ca  thal  target  \n",
              "0   2     3       0  \n",
              "1   0     3       0  \n",
              "2   0     3       0  \n",
              "3   1     3       0  \n",
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            "text/plain": [
              "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
              "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
              "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
              "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
              "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
              "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
              "\n",
              "      slope  ca  thal  target  \n",
              "1020      2   0     2       1  \n",
              "1021      1   1     3       0  \n",
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              "    async function quickchart(key) {\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 47,\n        \"max\": 60,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          60,\n          54,\n          47\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"trestbps\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 110,\n        \"max\": 140,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          125,\n          120\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"chol\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34,\n        \"min\": 188,\n        \"max\": 275,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          258,\n          188\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"restecg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23,\n        \"min\": 113,\n        \"max\": 164,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          141\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exang\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.1610340218959994,\n        \"min\": 0.0,\n        \"max\": 2.8,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.std()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jr0l3y7_FAAY",
        "outputId": "67c6305a-951c-4732-87c2-66af2bc4477b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "age          9.072290\n",
              "sex          0.460373\n",
              "cp           1.029641\n",
              "trestbps    17.516718\n",
              "chol        51.592510\n",
              "fbs          0.356527\n",
              "restecg      0.527878\n",
              "thalach     23.005724\n",
              "exang        0.472772\n",
              "oldpeak      1.175053\n",
              "slope        0.617755\n",
              "ca           1.030798\n",
              "thal         0.620660\n",
              "target       0.500070\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0oQBRfrVFD28",
        "outputId": "8281d035-f02e-4a53-d546-c63d474080c7"
      },
      "execution_count": null,
      "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": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset['target'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P-bQq_mDFPQM",
        "outputId": "33e0bb32-04c7-4d82-a525-a5109235e889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target\n",
              "1    526\n",
              "0    499\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.describe()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "QIXJtxQGFXPs",
        "outputId": "ef31a5d9-e3a0-406c-aa0d-238fefb03d7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               age          sex           cp     trestbps        chol  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.00000   \n",
              "mean     54.434146     0.695610     0.942439   131.611707   246.00000   \n",
              "std       9.072290     0.460373     1.029641    17.516718    51.59251   \n",
              "min      29.000000     0.000000     0.000000    94.000000   126.00000   \n",
              "25%      48.000000     0.000000     0.000000   120.000000   211.00000   \n",
              "50%      56.000000     1.000000     1.000000   130.000000   240.00000   \n",
              "75%      61.000000     1.000000     2.000000   140.000000   275.00000   \n",
              "max      77.000000     1.000000     3.000000   200.000000   564.00000   \n",
              "\n",
              "               fbs      restecg      thalach        exang      oldpeak  \\\n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  1025.000000   \n",
              "mean      0.149268     0.529756   149.114146     0.336585     1.071512   \n",
              "std       0.356527     0.527878    23.005724     0.472772     1.175053   \n",
              "min       0.000000     0.000000    71.000000     0.000000     0.000000   \n",
              "25%       0.000000     0.000000   132.000000     0.000000     0.000000   \n",
              "50%       0.000000     1.000000   152.000000     0.000000     0.800000   \n",
              "75%       0.000000     1.000000   166.000000     1.000000     1.800000   \n",
              "max       1.000000     2.000000   202.000000     1.000000     6.200000   \n",
              "\n",
              "             slope           ca         thal       target  \n",
              "count  1025.000000  1025.000000  1025.000000  1025.000000  \n",
              "mean      1.385366     0.754146     2.323902     0.513171  \n",
              "std       0.617755     1.030798     0.620660     0.500070  \n",
              "min       0.000000     0.000000     0.000000     0.000000  \n",
              "25%       1.000000     0.000000     2.000000     0.000000  \n",
              "50%       1.000000     0.000000     2.000000     1.000000  \n",
              "75%       2.000000     1.000000     3.000000     1.000000  \n",
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              "      <th></th>\n",
              "      <th>age</th>\n",
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              "      <th>ca</th>\n",
              "      <th>thal</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1025.000000</td>\n",
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              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.00000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "      <td>1025.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>54.434146</td>\n",
              "      <td>0.695610</td>\n",
              "      <td>0.942439</td>\n",
              "      <td>131.611707</td>\n",
              "      <td>246.00000</td>\n",
              "      <td>0.149268</td>\n",
              "      <td>0.529756</td>\n",
              "      <td>149.114146</td>\n",
              "      <td>0.336585</td>\n",
              "      <td>1.071512</td>\n",
              "      <td>1.385366</td>\n",
              "      <td>0.754146</td>\n",
              "      <td>2.323902</td>\n",
              "      <td>0.513171</td>\n",
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              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>9.072290</td>\n",
              "      <td>0.460373</td>\n",
              "      <td>1.029641</td>\n",
              "      <td>17.516718</td>\n",
              "      <td>51.59251</td>\n",
              "      <td>0.356527</td>\n",
              "      <td>0.527878</td>\n",
              "      <td>23.005724</td>\n",
              "      <td>0.472772</td>\n",
              "      <td>1.175053</td>\n",
              "      <td>0.617755</td>\n",
              "      <td>1.030798</td>\n",
              "      <td>0.620660</td>\n",
              "      <td>0.500070</td>\n",
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              "    <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>126.00000</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>48.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>211.00000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>132.000000</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",
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              "      <th>50%</th>\n",
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              "      <td>1.000000</td>\n",
              "      <td>130.000000</td>\n",
              "      <td>240.00000</td>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>61.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>140.000000</td>\n",
              "      <td>275.00000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>166.000000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
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              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>202.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
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240.0,\n          1025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"fbs\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.31633396418835,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.14926829268292682,\n          1.0,\n          0.3565266897271575\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"restecg\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.1373492815066,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          0.5297560975609756,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thalach\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 322.14920068771664,\n        \"min\": 23.005723745977207,\n        \"max\": 1025.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          149.11414634146342,\n          152.0,\n          1025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"exang\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.2505698153287,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.33658536585365856,\n          1.0,\n          0.47277237600371186\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"oldpeak\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 361.83970171608024,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1025.0,\n          1.0715121951219515,\n          1.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"slope\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 361.9886284380937,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          1.3853658536585365,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ca\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.05191221025626,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          0.7541463414634146,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"thal\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 361.7399759382844,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1025.0,\n          2.32390243902439,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"target\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 362.1897651954653,\n        \"min\": 0.0,\n        \"max\": 1025.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.5131707317073171,\n          1.0,\n          0.5000704980788014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the data"
      ],
      "metadata": {
        "id": "f5FKsQnuGD0M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = dataset.drop(columns='target', axis=1)\n",
        "y = dataset['target']"
      ],
      "metadata": {
        "id": "0PBX6CnEJM6g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RrM1Cy6Jad3",
        "outputId": "f7379dea-dee1-4b45-a467-6d47b62432f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  \\\n",
            "0      52    1   0       125   212    0        1      168      0      1.0   \n",
            "1      53    1   0       140   203    1        0      155      1      3.1   \n",
            "2      70    1   0       145   174    0        1      125      1      2.6   \n",
            "3      61    1   0       148   203    0        1      161      0      0.0   \n",
            "4      62    0   0       138   294    1        1      106      0      1.9   \n",
            "...   ...  ...  ..       ...   ...  ...      ...      ...    ...      ...   \n",
            "1020   59    1   1       140   221    0        1      164      1      0.0   \n",
            "1021   60    1   0       125   258    0        0      141      1      2.8   \n",
            "1022   47    1   0       110   275    0        0      118      1      1.0   \n",
            "1023   50    0   0       110   254    0        0      159      0      0.0   \n",
            "1024   54    1   0       120   188    0        1      113      0      1.4   \n",
            "\n",
            "      slope  ca  thal  \n",
            "0         2   2     3  \n",
            "1         0   0     3  \n",
            "2         0   0     3  \n",
            "3         2   1     3  \n",
            "4         1   3     2  \n",
            "...     ...  ..   ...  \n",
            "1020      2   0     2  \n",
            "1021      1   1     3  \n",
            "1022      1   1     2  \n",
            "1023      2   0     2  \n",
            "1024      1   1     3  \n",
            "\n",
            "[1025 rows x 13 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DXNHPgUFJlr9",
        "outputId": "1f79d68d-e189-4566-fab7-453bac3104ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0       0\n",
            "1       0\n",
            "2       0\n",
            "3       0\n",
            "4       0\n",
            "       ..\n",
            "1020    1\n",
            "1021    0\n",
            "1022    0\n",
            "1023    1\n",
            "1024    0\n",
            "Name: target, Length: 1025, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,stratify=y,random_state=2)"
      ],
      "metadata": {
        "id": "ZQsIDeLjJmrf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(x.shape,x_train.shape,x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T-Mj91KAJ7ce",
        "outputId": "dbe9529f-7186-4658-e5b0-3e4d0ea531e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1025, 13) (820, 13) (205, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "vFpkK7KXJ_7z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 220
        },
        "id": "wopI2cAUKQS6",
        "outputId": "ce3079fd-47eb-46ac-da6c-1f1687715df1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_logistic.py:458: 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",
            "  n_iter_i = _check_optimize_result(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_prediction = model.predict(x_train)\n",
        "accuracy = accuracy_score(x_train_prediction , y_train)"
      ],
      "metadata": {
        "id": "LbBJfD-hKYry"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zxYilBVsK0v1",
        "outputId": "9c207374-183e-482f-cf10-0f24f9d52f61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8524390243902439\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test_prediction = model.predict(x_test)\n",
        "accuracy_test = accuracy_score(x_test_prediction , y_test)"
      ],
      "metadata": {
        "id": "VVYS_WrcK3ah"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQl5maniLQaw",
        "outputId": "86764c7f-c84e-4d74-b683-d7964725bf86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8048780487804879\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = (34,0,1,118,210,0,1,192,0,0.7,2,0,2)\n",
        "\n",
        "input_data_toarray = np.asarray(input_data)\n",
        "\n",
        "input_data_reshape = input_data_toarray.reshape(1,-1)\n",
        "\n",
        "prediction = model.predict(input_data_reshape)\n",
        "\n",
        "print(prediction)\n",
        "\n",
        "if(prediction[0]==0):\n",
        "  print(\"there is no heart disease\")\n",
        "else:\n",
        "  print(\"having heart disease , recommanded to doctor\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEtcDk-WLWtX",
        "outputId": "95af8a03-df95-445e-e2e9-4cb3f73870c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "having heart disease , recommanded to doctor\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:439: UserWarning: X does not have valid feature names, but LogisticRegression was fitted with feature names\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GR2F41oELv4s"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}