heart_failure.ipynb

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    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        pass\n",
    "#     print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
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     "text": [
      "2024-03-20 22:27:05.210805: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-03-20 22:27:05.210917: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-03-20 22:27:05.349905: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import sklearn as sk\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras import layers,models\n",
    "import time\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from sklearn.metrics import accuracy_score,f1_score,precision_score,recall_score\n",
    "from tensorflow.keras.losses import binary_crossentropy"
   ]
  },
  {
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   "source": [
    "df = pd.read_csv('/kaggle/input/heart-failure-prediction/heart.csv')"
   ]
  },
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       "      <th></th>\n",
       "      <th>Age</th>\n",
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       "      <th>ChestPainType</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
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       "      <th>ExerciseAngina</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>ST_Slope</th>\n",
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       "      <td>ASY</td>\n",
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       "      <td>214</td>\n",
       "      <td>0</td>\n",
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      "text/plain": [
       "   Age Sex ChestPainType  RestingBP  Cholesterol  FastingBS RestingECG  MaxHR  \\\n",
       "0   40   M           ATA        140          289          0     Normal    172   \n",
       "1   49   F           NAP        160          180          0     Normal    156   \n",
       "2   37   M           ATA        130          283          0         ST     98   \n",
       "3   48   F           ASY        138          214          0     Normal    108   \n",
       "4   54   M           NAP        150          195          0     Normal    122   \n",
       "\n",
       "  ExerciseAngina  Oldpeak ST_Slope  HeartDisease  \n",
       "0              N      0.0       Up             0  \n",
       "1              N      1.0     Flat             1  \n",
       "2              N      0.0       Up             0  \n",
       "3              Y      1.5     Flat             1  \n",
       "4              N      0.0       Up             0  "
      ]
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   "source": [
    "df.head()"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 12 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Age             918 non-null    int64  \n",
      " 1   Sex             918 non-null    object \n",
      " 2   ChestPainType   918 non-null    object \n",
      " 3   RestingBP       918 non-null    int64  \n",
      " 4   Cholesterol     918 non-null    int64  \n",
      " 5   FastingBS       918 non-null    int64  \n",
      " 6   RestingECG      918 non-null    object \n",
      " 7   MaxHR           918 non-null    int64  \n",
      " 8   ExerciseAngina  918 non-null    object \n",
      " 9   Oldpeak         918 non-null    float64\n",
      " 10  ST_Slope        918 non-null    object \n",
      " 11  HeartDisease    918 non-null    int64  \n",
      "dtypes: float64(1), int64(6), object(5)\n",
      "memory usage: 86.2+ KB\n"
     ]
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   "source": [
    "df.info()"
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     "data": {
      "text/plain": [
       "Age               0\n",
       "Sex               0\n",
       "ChestPainType     0\n",
       "RestingBP         0\n",
       "Cholesterol       0\n",
       "FastingBS         0\n",
       "RestingECG        0\n",
       "MaxHR             0\n",
       "ExerciseAngina    0\n",
       "Oldpeak           0\n",
       "ST_Slope          0\n",
       "HeartDisease      0\n",
       "dtype: int64"
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   "source": [
    "df = pd.get_dummies(df, columns=['Sex','RestingECG','ST_Slope','ChestPainType','ExerciseAngina']) * 1"
   ]
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>913</th>\n",
       "      <td>45</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>132</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>914</th>\n",
       "      <td>68</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>915</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>115</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>916</th>\n",
       "      <td>57</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>38</td>\n",
       "      <td>138</td>\n",
       "      <td>175</td>\n",
       "      <td>0</td>\n",
       "      <td>173</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>918 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
       "0     40        140          289          0    172      0.0             0   \n",
       "1     49        160          180          0    156      1.0             1   \n",
       "2     37        130          283          0     98      0.0             0   \n",
       "3     48        138          214          0    108      1.5             1   \n",
       "4     54        150          195          0    122      0.0             0   \n",
       "..   ...        ...          ...        ...    ...      ...           ...   \n",
       "913   45        110          264          0    132      1.2             1   \n",
       "914   68        144          193          1    141      3.4             1   \n",
       "915   57        130          131          0    115      1.2             1   \n",
       "916   57        130          236          0    174      0.0             1   \n",
       "917   38        138          175          0    173      0.0             0   \n",
       "\n",
       "     Sex_F  Sex_M  RestingECG_LVH  ...  RestingECG_ST  ST_Slope_Down  \\\n",
       "0        0      1               0  ...              0              0   \n",
       "1        1      0               0  ...              0              0   \n",
       "2        0      1               0  ...              1              0   \n",
       "3        1      0               0  ...              0              0   \n",
       "4        0      1               0  ...              0              0   \n",
       "..     ...    ...             ...  ...            ...            ...   \n",
       "913      0      1               0  ...              0              0   \n",
       "914      0      1               0  ...              0              0   \n",
       "915      0      1               0  ...              0              0   \n",
       "916      1      0               1  ...              0              0   \n",
       "917      0      1               0  ...              0              0   \n",
       "\n",
       "     ST_Slope_Flat  ST_Slope_Up  ChestPainType_ASY  ChestPainType_ATA  \\\n",
       "0                0            1                  0                  1   \n",
       "1                1            0                  0                  0   \n",
       "2                0            1                  0                  1   \n",
       "3                1            0                  1                  0   \n",
       "4                0            1                  0                  0   \n",
       "..             ...          ...                ...                ...   \n",
       "913              1            0                  0                  0   \n",
       "914              1            0                  1                  0   \n",
       "915              1            0                  1                  0   \n",
       "916              1            0                  0                  1   \n",
       "917              0            1                  0                  0   \n",
       "\n",
       "     ChestPainType_NAP  ChestPainType_TA  ExerciseAngina_N  ExerciseAngina_Y  \n",
       "0                    0                 0                 1                 0  \n",
       "1                    1                 0                 1                 0  \n",
       "2                    0                 0                 1                 0  \n",
       "3                    0                 0                 0                 1  \n",
       "4                    1                 0                 1                 0  \n",
       "..                 ...               ...               ...               ...  \n",
       "913                  0                 1                 1                 0  \n",
       "914                  0                 0                 1                 0  \n",
       "915                  0                 0                 0                 1  \n",
       "916                  0                 0                 1                 0  \n",
       "917                  1                 0                 1                 0  \n",
       "\n",
       "[918 rows x 21 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "46321797",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:15.828981Z",
     "iopub.status.busy": "2024-03-20T22:27:15.828586Z",
     "iopub.status.idle": "2024-03-20T22:27:15.840369Z",
     "shell.execute_reply": "2024-03-20T22:27:15.839172Z"
    },
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     "start_time": "2024-03-20T22:27:15.820153",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 918 entries, 0 to 917\n",
      "Data columns (total 21 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Age                918 non-null    int64  \n",
      " 1   RestingBP          918 non-null    int64  \n",
      " 2   Cholesterol        918 non-null    int64  \n",
      " 3   FastingBS          918 non-null    int64  \n",
      " 4   MaxHR              918 non-null    int64  \n",
      " 5   Oldpeak            918 non-null    float64\n",
      " 6   HeartDisease       918 non-null    int64  \n",
      " 7   Sex_F              918 non-null    int64  \n",
      " 8   Sex_M              918 non-null    int64  \n",
      " 9   RestingECG_LVH     918 non-null    int64  \n",
      " 10  RestingECG_Normal  918 non-null    int64  \n",
      " 11  RestingECG_ST      918 non-null    int64  \n",
      " 12  ST_Slope_Down      918 non-null    int64  \n",
      " 13  ST_Slope_Flat      918 non-null    int64  \n",
      " 14  ST_Slope_Up        918 non-null    int64  \n",
      " 15  ChestPainType_ASY  918 non-null    int64  \n",
      " 16  ChestPainType_ATA  918 non-null    int64  \n",
      " 17  ChestPainType_NAP  918 non-null    int64  \n",
      " 18  ChestPainType_TA   918 non-null    int64  \n",
      " 19  ExerciseAngina_N   918 non-null    int64  \n",
      " 20  ExerciseAngina_Y   918 non-null    int64  \n",
      "dtypes: float64(1), int64(20)\n",
      "memory usage: 150.7 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4b7d4bb0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:15.859954Z",
     "iopub.status.busy": "2024-03-20T22:27:15.859222Z",
     "iopub.status.idle": "2024-03-20T22:27:15.894142Z",
     "shell.execute_reply": "2024-03-20T22:27:15.893178Z"
    },
    "papermill": {
     "duration": 0.045773,
     "end_time": "2024-03-20T22:27:15.896510",
     "exception": false,
     "start_time": "2024-03-20T22:27:15.850737",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>Sex_F</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>RestingECG_LVH</th>\n",
       "      <th>...</th>\n",
       "      <th>RestingECG_ST</th>\n",
       "      <th>ST_Slope_Down</th>\n",
       "      <th>ST_Slope_Flat</th>\n",
       "      <th>ST_Slope_Up</th>\n",
       "      <th>ChestPainType_ASY</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "      <th>ExerciseAngina_N</th>\n",
       "      <th>ExerciseAngina_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.254399</td>\n",
       "      <td>-0.095282</td>\n",
       "      <td>0.198039</td>\n",
       "      <td>-0.382045</td>\n",
       "      <td>0.258612</td>\n",
       "      <td>0.282039</td>\n",
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       "      <td>0.055750</td>\n",
       "      <td>0.145727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.136798</td>\n",
       "      <td>0.138397</td>\n",
       "      <td>0.185568</td>\n",
       "      <td>-0.258067</td>\n",
       "      <td>0.166607</td>\n",
       "      <td>-0.218165</td>\n",
       "      <td>-0.011335</td>\n",
       "      <td>0.032042</td>\n",
       "      <td>-0.215793</td>\n",
       "      <td>0.215793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RestingBP</th>\n",
       "      <td>0.254399</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100893</td>\n",
       "      <td>0.070193</td>\n",
       "      <td>-0.112135</td>\n",
       "      <td>0.164803</td>\n",
       "      <td>0.107589</td>\n",
       "      <td>-0.005133</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>0.053166</td>\n",
       "      <td>...</td>\n",
       "      <td>0.090447</td>\n",
       "      <td>-0.007912</td>\n",
       "      <td>0.099207</td>\n",
       "      <td>-0.096146</td>\n",
       "      <td>0.048824</td>\n",
       "      <td>-0.046153</td>\n",
       "      <td>-0.041348</td>\n",
       "      <td>0.049855</td>\n",
       "      <td>-0.155101</td>\n",
       "      <td>0.155101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cholesterol</th>\n",
       "      <td>-0.095282</td>\n",
       "      <td>0.100893</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.260974</td>\n",
       "      <td>0.235792</td>\n",
       "      <td>0.050148</td>\n",
       "      <td>-0.232741</td>\n",
       "      <td>0.200092</td>\n",
       "      <td>-0.200092</td>\n",
       "      <td>0.177077</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.133106</td>\n",
       "      <td>-0.083371</td>\n",
       "      <td>-0.050953</td>\n",
       "      <td>0.094027</td>\n",
       "      <td>-0.120531</td>\n",
       "      <td>0.150954</td>\n",
       "      <td>-0.006634</td>\n",
       "      <td>0.017365</td>\n",
       "      <td>0.034166</td>\n",
       "      <td>-0.034166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FastingBS</th>\n",
       "      <td>0.198039</td>\n",
       "      <td>0.070193</td>\n",
       "      <td>-0.260974</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.131438</td>\n",
       "      <td>0.052698</td>\n",
       "      <td>0.267291</td>\n",
       "      <td>-0.120076</td>\n",
       "      <td>0.120076</td>\n",
       "      <td>-0.011656</td>\n",
       "      <td>...</td>\n",
       "      <td>0.127110</td>\n",
       "      <td>0.105102</td>\n",
       "      <td>0.107006</td>\n",
       "      <td>-0.161730</td>\n",
       "      <td>0.131176</td>\n",
       "      <td>-0.140514</td>\n",
       "      <td>-0.039249</td>\n",
       "      <td>0.026885</td>\n",
       "      <td>-0.060451</td>\n",
       "      <td>0.060451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MaxHR</th>\n",
       "      <td>-0.382045</td>\n",
       "      <td>-0.112135</td>\n",
       "      <td>0.235792</td>\n",
       "      <td>-0.131438</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.160691</td>\n",
       "      <td>-0.400421</td>\n",
       "      <td>0.189186</td>\n",
       "      <td>-0.189186</td>\n",
       "      <td>0.125793</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.157879</td>\n",
       "      <td>-0.073316</td>\n",
       "      <td>-0.342581</td>\n",
       "      <td>0.383397</td>\n",
       "      <td>-0.354963</td>\n",
       "      <td>0.253735</td>\n",
       "      <td>0.134580</td>\n",
       "      <td>0.100025</td>\n",
       "      <td>0.370425</td>\n",
       "      <td>-0.370425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oldpeak</th>\n",
       "      <td>0.258612</td>\n",
       "      <td>0.164803</td>\n",
       "      <td>0.050148</td>\n",
       "      <td>0.052698</td>\n",
       "      <td>-0.160691</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.403951</td>\n",
       "      <td>-0.105734</td>\n",
       "      <td>0.105734</td>\n",
       "      <td>0.086794</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055958</td>\n",
       "      <td>0.322130</td>\n",
       "      <td>0.283295</td>\n",
       "      <td>-0.450577</td>\n",
       "      <td>0.280026</td>\n",
       "      <td>-0.262124</td>\n",
       "      <td>-0.106212</td>\n",
       "      <td>0.032231</td>\n",
       "      <td>-0.408752</td>\n",
       "      <td>0.408752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeartDisease</th>\n",
       "      <td>0.282039</td>\n",
       "      <td>0.107589</td>\n",
       "      <td>-0.232741</td>\n",
       "      <td>0.267291</td>\n",
       "      <td>-0.400421</td>\n",
       "      <td>0.403951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.305445</td>\n",
       "      <td>0.305445</td>\n",
       "      <td>0.010670</td>\n",
       "      <td>...</td>\n",
       "      <td>0.102527</td>\n",
       "      <td>0.122527</td>\n",
       "      <td>0.554134</td>\n",
       "      <td>-0.622164</td>\n",
       "      <td>0.516716</td>\n",
       "      <td>-0.401924</td>\n",
       "      <td>-0.212964</td>\n",
       "      <td>-0.054790</td>\n",
       "      <td>-0.494282</td>\n",
       "      <td>0.494282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_F</th>\n",
       "      <td>-0.055750</td>\n",
       "      <td>-0.005133</td>\n",
       "      <td>0.200092</td>\n",
       "      <td>-0.120076</td>\n",
       "      <td>0.189186</td>\n",
       "      <td>-0.105734</td>\n",
       "      <td>-0.305445</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.049518</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.063715</td>\n",
       "      <td>-0.066036</td>\n",
       "      <td>-0.116077</td>\n",
       "      <td>0.150942</td>\n",
       "      <td>-0.183876</td>\n",
       "      <td>0.161522</td>\n",
       "      <td>0.066486</td>\n",
       "      <td>0.004031</td>\n",
       "      <td>0.190664</td>\n",
       "      <td>-0.190664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex_M</th>\n",
       "      <td>0.055750</td>\n",
       "      <td>0.005133</td>\n",
       "      <td>-0.200092</td>\n",
       "      <td>0.120076</td>\n",
       "      <td>-0.189186</td>\n",
       "      <td>0.105734</td>\n",
       "      <td>0.305445</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.049518</td>\n",
       "      <td>...</td>\n",
       "      <td>0.063715</td>\n",
       "      <td>0.066036</td>\n",
       "      <td>0.116077</td>\n",
       "      <td>-0.150942</td>\n",
       "      <td>0.183876</td>\n",
       "      <td>-0.161522</td>\n",
       "      <td>-0.066486</td>\n",
       "      <td>-0.004031</td>\n",
       "      <td>-0.190664</td>\n",
       "      <td>0.190664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RestingECG_LVH</th>\n",
       "      <td>0.145727</td>\n",
       "      <td>0.053166</td>\n",
       "      <td>0.177077</td>\n",
       "      <td>-0.011656</td>\n",
       "      <td>0.125793</td>\n",
       "      <td>0.086794</td>\n",
       "      <td>0.010670</td>\n",
       "      <td>0.049518</td>\n",
       "      <td>-0.049518</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.248892</td>\n",
       "      <td>0.043755</td>\n",
       "      <td>0.015091</td>\n",
       "      <td>-0.037582</td>\n",
       "      <td>0.002289</td>\n",
       "      <td>-0.085791</td>\n",
       "      <td>0.035299</td>\n",
       "      <td>0.081407</td>\n",
       "      <td>0.016382</td>\n",
       "      <td>-0.016382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RestingECG_Normal</th>\n",
       "      <td>-0.230566</td>\n",
       "      <td>-0.116851</td>\n",
       "      <td>-0.038470</td>\n",
       "      <td>-0.093028</td>\n",
       "      <td>0.023801</td>\n",
       "      <td>-0.116719</td>\n",
       "      <td>-0.091580</td>\n",
       "      <td>0.010634</td>\n",
       "      <td>-0.010634</td>\n",
       "      <td>-0.623227</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.602314</td>\n",
       "      <td>-0.060564</td>\n",
       "      <td>-0.047172</td>\n",
       "      <td>0.078563</td>\n",
       "      <td>-0.063606</td>\n",
       "      <td>0.107941</td>\n",
       "      <td>0.005010</td>\n",
       "      <td>-0.057719</td>\n",
       "      <td>0.072924</td>\n",
       "      <td>-0.072924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RestingECG_ST</th>\n",
       "      <td>0.136798</td>\n",
       "      <td>0.090447</td>\n",
       "      <td>-0.133106</td>\n",
       "      <td>0.127110</td>\n",
       "      <td>-0.157879</td>\n",
       "      <td>0.055958</td>\n",
       "      <td>0.102527</td>\n",
       "      <td>-0.063715</td>\n",
       "      <td>0.063715</td>\n",
       "      <td>-0.248892</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.030345</td>\n",
       "      <td>0.043017</td>\n",
       "      <td>-0.058936</td>\n",
       "      <td>0.076438</td>\n",
       "      <td>-0.046111</td>\n",
       "      <td>-0.042236</td>\n",
       "      <td>-0.011611</td>\n",
       "      <td>-0.107036</td>\n",
       "      <td>0.107036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_Slope_Down</th>\n",
       "      <td>0.138397</td>\n",
       "      <td>-0.007912</td>\n",
       "      <td>-0.083371</td>\n",
       "      <td>0.105102</td>\n",
       "      <td>-0.073316</td>\n",
       "      <td>0.322130</td>\n",
       "      <td>0.122527</td>\n",
       "      <td>-0.066036</td>\n",
       "      <td>0.066036</td>\n",
       "      <td>0.043755</td>\n",
       "      <td>...</td>\n",
       "      <td>0.030345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.272040</td>\n",
       "      <td>-0.235904</td>\n",
       "      <td>0.103407</td>\n",
       "      <td>-0.097754</td>\n",
       "      <td>-0.040816</td>\n",
       "      <td>0.016651</td>\n",
       "      <td>-0.136439</td>\n",
       "      <td>0.136439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_Slope_Flat</th>\n",
       "      <td>0.185568</td>\n",
       "      <td>0.099207</td>\n",
       "      <td>-0.050953</td>\n",
       "      <td>0.107006</td>\n",
       "      <td>-0.342581</td>\n",
       "      <td>0.283295</td>\n",
       "      <td>0.554134</td>\n",
       "      <td>-0.116077</td>\n",
       "      <td>0.116077</td>\n",
       "      <td>0.015091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043017</td>\n",
       "      <td>-0.272040</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.870951</td>\n",
       "      <td>0.303645</td>\n",
       "      <td>-0.304667</td>\n",
       "      <td>-0.072031</td>\n",
       "      <td>-0.010486</td>\n",
       "      <td>-0.382237</td>\n",
       "      <td>0.382237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ST_Slope_Up</th>\n",
       "      <td>-0.258067</td>\n",
       "      <td>-0.096146</td>\n",
       "      <td>0.094027</td>\n",
       "      <td>-0.161730</td>\n",
       "      <td>0.383397</td>\n",
       "      <td>-0.450577</td>\n",
       "      <td>-0.622164</td>\n",
       "      <td>0.150942</td>\n",
       "      <td>-0.150942</td>\n",
       "      <td>-0.037582</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.058936</td>\n",
       "      <td>-0.235904</td>\n",
       "      <td>-0.870951</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.359443</td>\n",
       "      <td>0.357588</td>\n",
       "      <td>0.093583</td>\n",
       "      <td>0.002087</td>\n",
       "      <td>0.455676</td>\n",
       "      <td>-0.455676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChestPainType_ASY</th>\n",
       "      <td>0.166607</td>\n",
       "      <td>0.048824</td>\n",
       "      <td>-0.120531</td>\n",
       "      <td>0.131176</td>\n",
       "      <td>-0.354963</td>\n",
       "      <td>0.280026</td>\n",
       "      <td>0.516716</td>\n",
       "      <td>-0.183876</td>\n",
       "      <td>0.183876</td>\n",
       "      <td>0.002289</td>\n",
       "      <td>...</td>\n",
       "      <td>0.076438</td>\n",
       "      <td>0.103407</td>\n",
       "      <td>0.303645</td>\n",
       "      <td>-0.359443</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.522432</td>\n",
       "      <td>-0.577670</td>\n",
       "      <td>-0.249003</td>\n",
       "      <td>-0.430034</td>\n",
       "      <td>0.430034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <td>-0.218165</td>\n",
       "      <td>-0.046153</td>\n",
       "      <td>0.150954</td>\n",
       "      <td>-0.140514</td>\n",
       "      <td>0.253735</td>\n",
       "      <td>-0.262124</td>\n",
       "      <td>-0.401924</td>\n",
       "      <td>0.161522</td>\n",
       "      <td>-0.161522</td>\n",
       "      <td>-0.085791</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.046111</td>\n",
       "      <td>-0.097754</td>\n",
       "      <td>-0.304667</td>\n",
       "      <td>0.357588</td>\n",
       "      <td>-0.522432</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.256767</td>\n",
       "      <td>-0.110679</td>\n",
       "      <td>0.300365</td>\n",
       "      <td>-0.300365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <td>-0.011335</td>\n",
       "      <td>-0.041348</td>\n",
       "      <td>-0.006634</td>\n",
       "      <td>-0.039249</td>\n",
       "      <td>0.134580</td>\n",
       "      <td>-0.106212</td>\n",
       "      <td>-0.212964</td>\n",
       "      <td>0.066486</td>\n",
       "      <td>-0.066486</td>\n",
       "      <td>0.035299</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.042236</td>\n",
       "      <td>-0.040816</td>\n",
       "      <td>-0.072031</td>\n",
       "      <td>0.093583</td>\n",
       "      <td>-0.577670</td>\n",
       "      <td>-0.256767</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.122381</td>\n",
       "      <td>0.166030</td>\n",
       "      <td>-0.166030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ChestPainType_TA</th>\n",
       "      <td>0.032042</td>\n",
       "      <td>0.049855</td>\n",
       "      <td>0.017365</td>\n",
       "      <td>0.026885</td>\n",
       "      <td>0.100025</td>\n",
       "      <td>0.032231</td>\n",
       "      <td>-0.054790</td>\n",
       "      <td>0.004031</td>\n",
       "      <td>-0.004031</td>\n",
       "      <td>0.081407</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.011611</td>\n",
       "      <td>0.016651</td>\n",
       "      <td>-0.010486</td>\n",
       "      <td>0.002087</td>\n",
       "      <td>-0.249003</td>\n",
       "      <td>-0.110679</td>\n",
       "      <td>-0.122381</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.128105</td>\n",
       "      <td>-0.128105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExerciseAngina_N</th>\n",
       "      <td>-0.215793</td>\n",
       "      <td>-0.155101</td>\n",
       "      <td>0.034166</td>\n",
       "      <td>-0.060451</td>\n",
       "      <td>0.370425</td>\n",
       "      <td>-0.408752</td>\n",
       "      <td>-0.494282</td>\n",
       "      <td>0.190664</td>\n",
       "      <td>-0.190664</td>\n",
       "      <td>0.016382</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.107036</td>\n",
       "      <td>-0.136439</td>\n",
       "      <td>-0.382237</td>\n",
       "      <td>0.455676</td>\n",
       "      <td>-0.430034</td>\n",
       "      <td>0.300365</td>\n",
       "      <td>0.166030</td>\n",
       "      <td>0.128105</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExerciseAngina_Y</th>\n",
       "      <td>0.215793</td>\n",
       "      <td>0.155101</td>\n",
       "      <td>-0.034166</td>\n",
       "      <td>0.060451</td>\n",
       "      <td>-0.370425</td>\n",
       "      <td>0.408752</td>\n",
       "      <td>0.494282</td>\n",
       "      <td>-0.190664</td>\n",
       "      <td>0.190664</td>\n",
       "      <td>-0.016382</td>\n",
       "      <td>...</td>\n",
       "      <td>0.107036</td>\n",
       "      <td>0.136439</td>\n",
       "      <td>0.382237</td>\n",
       "      <td>-0.455676</td>\n",
       "      <td>0.430034</td>\n",
       "      <td>-0.300365</td>\n",
       "      <td>-0.166030</td>\n",
       "      <td>-0.128105</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Age  RestingBP  Cholesterol  FastingBS     MaxHR  \\\n",
       "Age                1.000000   0.254399    -0.095282   0.198039 -0.382045   \n",
       "RestingBP          0.254399   1.000000     0.100893   0.070193 -0.112135   \n",
       "Cholesterol       -0.095282   0.100893     1.000000  -0.260974  0.235792   \n",
       "FastingBS          0.198039   0.070193    -0.260974   1.000000 -0.131438   \n",
       "MaxHR             -0.382045  -0.112135     0.235792  -0.131438  1.000000   \n",
       "Oldpeak            0.258612   0.164803     0.050148   0.052698 -0.160691   \n",
       "HeartDisease       0.282039   0.107589    -0.232741   0.267291 -0.400421   \n",
       "Sex_F             -0.055750  -0.005133     0.200092  -0.120076  0.189186   \n",
       "Sex_M              0.055750   0.005133    -0.200092   0.120076 -0.189186   \n",
       "RestingECG_LVH     0.145727   0.053166     0.177077  -0.011656  0.125793   \n",
       "RestingECG_Normal -0.230566  -0.116851    -0.038470  -0.093028  0.023801   \n",
       "RestingECG_ST      0.136798   0.090447    -0.133106   0.127110 -0.157879   \n",
       "ST_Slope_Down      0.138397  -0.007912    -0.083371   0.105102 -0.073316   \n",
       "ST_Slope_Flat      0.185568   0.099207    -0.050953   0.107006 -0.342581   \n",
       "ST_Slope_Up       -0.258067  -0.096146     0.094027  -0.161730  0.383397   \n",
       "ChestPainType_ASY  0.166607   0.048824    -0.120531   0.131176 -0.354963   \n",
       "ChestPainType_ATA -0.218165  -0.046153     0.150954  -0.140514  0.253735   \n",
       "ChestPainType_NAP -0.011335  -0.041348    -0.006634  -0.039249  0.134580   \n",
       "ChestPainType_TA   0.032042   0.049855     0.017365   0.026885  0.100025   \n",
       "ExerciseAngina_N  -0.215793  -0.155101     0.034166  -0.060451  0.370425   \n",
       "ExerciseAngina_Y   0.215793   0.155101    -0.034166   0.060451 -0.370425   \n",
       "\n",
       "                    Oldpeak  HeartDisease     Sex_F     Sex_M  RestingECG_LVH  \\\n",
       "Age                0.258612      0.282039 -0.055750  0.055750        0.145727   \n",
       "RestingBP          0.164803      0.107589 -0.005133  0.005133        0.053166   \n",
       "Cholesterol        0.050148     -0.232741  0.200092 -0.200092        0.177077   \n",
       "FastingBS          0.052698      0.267291 -0.120076  0.120076       -0.011656   \n",
       "MaxHR             -0.160691     -0.400421  0.189186 -0.189186        0.125793   \n",
       "Oldpeak            1.000000      0.403951 -0.105734  0.105734        0.086794   \n",
       "HeartDisease       0.403951      1.000000 -0.305445  0.305445        0.010670   \n",
       "Sex_F             -0.105734     -0.305445  1.000000 -1.000000        0.049518   \n",
       "Sex_M              0.105734      0.305445 -1.000000  1.000000       -0.049518   \n",
       "RestingECG_LVH     0.086794      0.010670  0.049518 -0.049518        1.000000   \n",
       "RestingECG_Normal -0.116719     -0.091580  0.010634 -0.010634       -0.623227   \n",
       "RestingECG_ST      0.055958      0.102527 -0.063715  0.063715       -0.248892   \n",
       "ST_Slope_Down      0.322130      0.122527 -0.066036  0.066036        0.043755   \n",
       "ST_Slope_Flat      0.283295      0.554134 -0.116077  0.116077        0.015091   \n",
       "ST_Slope_Up       -0.450577     -0.622164  0.150942 -0.150942       -0.037582   \n",
       "ChestPainType_ASY  0.280026      0.516716 -0.183876  0.183876        0.002289   \n",
       "ChestPainType_ATA -0.262124     -0.401924  0.161522 -0.161522       -0.085791   \n",
       "ChestPainType_NAP -0.106212     -0.212964  0.066486 -0.066486        0.035299   \n",
       "ChestPainType_TA   0.032231     -0.054790  0.004031 -0.004031        0.081407   \n",
       "ExerciseAngina_N  -0.408752     -0.494282  0.190664 -0.190664        0.016382   \n",
       "ExerciseAngina_Y   0.408752      0.494282 -0.190664  0.190664       -0.016382   \n",
       "\n",
       "                   ...  RestingECG_ST  ST_Slope_Down  ST_Slope_Flat  \\\n",
       "Age                ...       0.136798       0.138397       0.185568   \n",
       "RestingBP          ...       0.090447      -0.007912       0.099207   \n",
       "Cholesterol        ...      -0.133106      -0.083371      -0.050953   \n",
       "FastingBS          ...       0.127110       0.105102       0.107006   \n",
       "MaxHR              ...      -0.157879      -0.073316      -0.342581   \n",
       "Oldpeak            ...       0.055958       0.322130       0.283295   \n",
       "HeartDisease       ...       0.102527       0.122527       0.554134   \n",
       "Sex_F              ...      -0.063715      -0.066036      -0.116077   \n",
       "Sex_M              ...       0.063715       0.066036       0.116077   \n",
       "RestingECG_LVH     ...      -0.248892       0.043755       0.015091   \n",
       "RestingECG_Normal  ...      -0.602314      -0.060564      -0.047172   \n",
       "RestingECG_ST      ...       1.000000       0.030345       0.043017   \n",
       "ST_Slope_Down      ...       0.030345       1.000000      -0.272040   \n",
       "ST_Slope_Flat      ...       0.043017      -0.272040       1.000000   \n",
       "ST_Slope_Up        ...      -0.058936      -0.235904      -0.870951   \n",
       "ChestPainType_ASY  ...       0.076438       0.103407       0.303645   \n",
       "ChestPainType_ATA  ...      -0.046111      -0.097754      -0.304667   \n",
       "ChestPainType_NAP  ...      -0.042236      -0.040816      -0.072031   \n",
       "ChestPainType_TA   ...      -0.011611       0.016651      -0.010486   \n",
       "ExerciseAngina_N   ...      -0.107036      -0.136439      -0.382237   \n",
       "ExerciseAngina_Y   ...       0.107036       0.136439       0.382237   \n",
       "\n",
       "                   ST_Slope_Up  ChestPainType_ASY  ChestPainType_ATA  \\\n",
       "Age                  -0.258067           0.166607          -0.218165   \n",
       "RestingBP            -0.096146           0.048824          -0.046153   \n",
       "Cholesterol           0.094027          -0.120531           0.150954   \n",
       "FastingBS            -0.161730           0.131176          -0.140514   \n",
       "MaxHR                 0.383397          -0.354963           0.253735   \n",
       "Oldpeak              -0.450577           0.280026          -0.262124   \n",
       "HeartDisease         -0.622164           0.516716          -0.401924   \n",
       "Sex_F                 0.150942          -0.183876           0.161522   \n",
       "Sex_M                -0.150942           0.183876          -0.161522   \n",
       "RestingECG_LVH       -0.037582           0.002289          -0.085791   \n",
       "RestingECG_Normal     0.078563          -0.063606           0.107941   \n",
       "RestingECG_ST        -0.058936           0.076438          -0.046111   \n",
       "ST_Slope_Down        -0.235904           0.103407          -0.097754   \n",
       "ST_Slope_Flat        -0.870951           0.303645          -0.304667   \n",
       "ST_Slope_Up           1.000000          -0.359443           0.357588   \n",
       "ChestPainType_ASY    -0.359443           1.000000          -0.522432   \n",
       "ChestPainType_ATA     0.357588          -0.522432           1.000000   \n",
       "ChestPainType_NAP     0.093583          -0.577670          -0.256767   \n",
       "ChestPainType_TA      0.002087          -0.249003          -0.110679   \n",
       "ExerciseAngina_N      0.455676          -0.430034           0.300365   \n",
       "ExerciseAngina_Y     -0.455676           0.430034          -0.300365   \n",
       "\n",
       "                   ChestPainType_NAP  ChestPainType_TA  ExerciseAngina_N  \\\n",
       "Age                        -0.011335          0.032042         -0.215793   \n",
       "RestingBP                  -0.041348          0.049855         -0.155101   \n",
       "Cholesterol                -0.006634          0.017365          0.034166   \n",
       "FastingBS                  -0.039249          0.026885         -0.060451   \n",
       "MaxHR                       0.134580          0.100025          0.370425   \n",
       "Oldpeak                    -0.106212          0.032231         -0.408752   \n",
       "HeartDisease               -0.212964         -0.054790         -0.494282   \n",
       "Sex_F                       0.066486          0.004031          0.190664   \n",
       "Sex_M                      -0.066486         -0.004031         -0.190664   \n",
       "RestingECG_LVH              0.035299          0.081407          0.016382   \n",
       "RestingECG_Normal           0.005010         -0.057719          0.072924   \n",
       "RestingECG_ST              -0.042236         -0.011611         -0.107036   \n",
       "ST_Slope_Down              -0.040816          0.016651         -0.136439   \n",
       "ST_Slope_Flat              -0.072031         -0.010486         -0.382237   \n",
       "ST_Slope_Up                 0.093583          0.002087          0.455676   \n",
       "ChestPainType_ASY          -0.577670         -0.249003         -0.430034   \n",
       "ChestPainType_ATA          -0.256767         -0.110679          0.300365   \n",
       "ChestPainType_NAP           1.000000         -0.122381          0.166030   \n",
       "ChestPainType_TA           -0.122381          1.000000          0.128105   \n",
       "ExerciseAngina_N            0.166030          0.128105          1.000000   \n",
       "ExerciseAngina_Y           -0.166030         -0.128105         -1.000000   \n",
       "\n",
       "                   ExerciseAngina_Y  \n",
       "Age                        0.215793  \n",
       "RestingBP                  0.155101  \n",
       "Cholesterol               -0.034166  \n",
       "FastingBS                  0.060451  \n",
       "MaxHR                     -0.370425  \n",
       "Oldpeak                    0.408752  \n",
       "HeartDisease               0.494282  \n",
       "Sex_F                     -0.190664  \n",
       "Sex_M                      0.190664  \n",
       "RestingECG_LVH            -0.016382  \n",
       "RestingECG_Normal         -0.072924  \n",
       "RestingECG_ST              0.107036  \n",
       "ST_Slope_Down              0.136439  \n",
       "ST_Slope_Flat              0.382237  \n",
       "ST_Slope_Up               -0.455676  \n",
       "ChestPainType_ASY          0.430034  \n",
       "ChestPainType_ATA         -0.300365  \n",
       "ChestPainType_NAP         -0.166030  \n",
       "ChestPainType_TA          -0.128105  \n",
       "ExerciseAngina_N          -1.000000  \n",
       "ExerciseAngina_Y           1.000000  \n",
       "\n",
       "[21 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f5109a97",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:15.915196Z",
     "iopub.status.busy": "2024-03-20T22:27:15.914495Z",
     "iopub.status.idle": "2024-03-20T22:27:16.710097Z",
     "shell.execute_reply": "2024-03-20T22:27:16.709159Z"
    },
    "papermill": {
     "duration": 0.809589,
     "end_time": "2024-03-20T22:27:16.714077",
     "exception": false,
     "start_time": "2024-03-20T22:27:15.904488",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=200)\n",
    "sns.heatmap(df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "87e23999",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.738022Z",
     "iopub.status.busy": "2024-03-20T22:27:16.737657Z",
     "iopub.status.idle": "2024-03-20T22:27:16.755584Z",
     "shell.execute_reply": "2024-03-20T22:27:16.754805Z"
    },
    "papermill": {
     "duration": 0.032231,
     "end_time": "2024-03-20T22:27:16.757504",
     "exception": false,
     "start_time": "2024-03-20T22:27:16.725273",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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>RestingBP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>FastingBS</th>\n",
       "      <th>MaxHR</th>\n",
       "      <th>Oldpeak</th>\n",
       "      <th>HeartDisease</th>\n",
       "      <th>Sex_F</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>RestingECG_LVH</th>\n",
       "      <th>...</th>\n",
       "      <th>RestingECG_ST</th>\n",
       "      <th>ST_Slope_Down</th>\n",
       "      <th>ST_Slope_Flat</th>\n",
       "      <th>ST_Slope_Up</th>\n",
       "      <th>ChestPainType_ASY</th>\n",
       "      <th>ChestPainType_ATA</th>\n",
       "      <th>ChestPainType_NAP</th>\n",
       "      <th>ChestPainType_TA</th>\n",
       "      <th>ExerciseAngina_N</th>\n",
       "      <th>ExerciseAngina_Y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40</td>\n",
       "      <td>140</td>\n",
       "      <td>289</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49</td>\n",
       "      <td>160</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>156</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>130</td>\n",
       "      <td>283</td>\n",
       "      <td>0</td>\n",
       "      <td>98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>48</td>\n",
       "      <td>138</td>\n",
       "      <td>214</td>\n",
       "      <td>0</td>\n",
       "      <td>108</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>54</td>\n",
       "      <td>150</td>\n",
       "      <td>195</td>\n",
       "      <td>0</td>\n",
       "      <td>122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
       "0   40        140          289          0    172      0.0             0   \n",
       "1   49        160          180          0    156      1.0             1   \n",
       "2   37        130          283          0     98      0.0             0   \n",
       "3   48        138          214          0    108      1.5             1   \n",
       "4   54        150          195          0    122      0.0             0   \n",
       "\n",
       "   Sex_F  Sex_M  RestingECG_LVH  ...  RestingECG_ST  ST_Slope_Down  \\\n",
       "0      0      1               0  ...              0              0   \n",
       "1      1      0               0  ...              0              0   \n",
       "2      0      1               0  ...              1              0   \n",
       "3      1      0               0  ...              0              0   \n",
       "4      0      1               0  ...              0              0   \n",
       "\n",
       "   ST_Slope_Flat  ST_Slope_Up  ChestPainType_ASY  ChestPainType_ATA  \\\n",
       "0              0            1                  0                  1   \n",
       "1              1            0                  0                  0   \n",
       "2              0            1                  0                  1   \n",
       "3              1            0                  1                  0   \n",
       "4              0            1                  0                  0   \n",
       "\n",
       "   ChestPainType_NAP  ChestPainType_TA  ExerciseAngina_N  ExerciseAngina_Y  \n",
       "0                  0                 0                 1                 0  \n",
       "1                  1                 0                 1                 0  \n",
       "2                  0                 0                 1                 0  \n",
       "3                  0                 0                 0                 1  \n",
       "4                  1                 0                 1                 0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "596abc41",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.783599Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = df.drop('HeartDisease',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fbe7f23a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.814423Z",
     "iopub.status.busy": "2024-03-20T22:27:16.813775Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = df['HeartDisease']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2d2cc6bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.844473Z",
     "iopub.status.busy": "2024-03-20T22:27:16.843745Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(918, 20)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7ac7e6f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.877539Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(918,)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a7237a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.910131Z",
     "iopub.status.busy": "2024-03-20T22:27:16.909737Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "xx,xtest,yy,ytest = train_test_split(x,y,random_state=101,test_size=0.2)\n",
    "xtrain,xval,ytrain,yval = train_test_split(xx,yy,random_state=101,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5a8b371b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.946844Z",
     "iopub.status.busy": "2024-03-20T22:27:16.946455Z",
     "iopub.status.idle": "2024-03-20T22:27:16.951356Z",
     "shell.execute_reply": "2024-03-20T22:27:16.950252Z"
    },
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     "exception": false,
     "start_time": "2024-03-20T22:27:16.933759",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def convert_to_binary(predictions, threshold):\n",
    "    binary_predictions = (predictions > threshold).astype(int)\n",
    "    return binary_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ce5cad9f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:16.978573Z",
     "iopub.status.busy": "2024-03-20T22:27:16.977786Z",
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     "shell.execute_reply": "2024-03-20T22:27:30.528769Z"
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     "duration": 13.567525,
     "end_time": "2024-03-20T22:27:30.532272",
     "exception": false,
     "start_time": "2024-03-20T22:27:16.964747",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m6/6\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 11ms/step\n"
     ]
    }
   ],
   "source": [
    "mymodel = models.Sequential([\n",
    "    layers.Input(shape=(20,)),\n",
    "    layers.Flatten(), \n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(64, activation='relu'),\n",
    "    layers.Dense(1, activation='sigmoid')\n",
    "])\n",
    "mymodel.compile(tf.keras.optimizers.Adam(learning_rate=1e-3),loss=binary_crossentropy)\n",
    "early_stopping = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=7, restore_best_weights=True)\n",
    "history = mymodel.fit(xtrain,ytrain,epochs=50,batch_size=4,verbose=0,callbacks=[early_stopping],validation_data=(xval,yval))\n",
    "ypreds = predictions = mymodel.predict(xtest)\n",
    "predictions = ypreds\n",
    "threshold = 0.95\n",
    "binary_predictions = convert_to_binary(predictions, threshold)\n",
    "acc = accuracy_score(ytest,binary_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7a22eccd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:30.558262Z",
     "iopub.status.busy": "2024-03-20T22:27:30.557856Z",
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     "shell.execute_reply": "2024-03-20T22:27:30.563077Z"
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7608695652173914"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f33f006c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:30.591966Z",
     "iopub.status.busy": "2024-03-20T22:27:30.591571Z",
     "iopub.status.idle": "2024-03-20T22:27:30.791723Z",
     "shell.execute_reply": "2024-03-20T22:27:30.790617Z"
    },
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     "exception": false,
     "start_time": "2024-03-20T22:27:30.578417",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7898e4187040>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y=history.history['loss']\n",
    "plt.plot(range(len(Y)),Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "942b60f4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:30.821891Z",
     "iopub.status.busy": "2024-03-20T22:27:30.821490Z",
     "iopub.status.idle": "2024-03-20T22:27:31.072620Z",
     "shell.execute_reply": "2024-03-20T22:27:31.071393Z"
    },
    "papermill": {
     "duration": 0.267875,
     "end_time": "2024-03-20T22:27:31.075248",
     "exception": false,
     "start_time": "2024-03-20T22:27:30.807373",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "random = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "17a419d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:31.102680Z",
     "iopub.status.busy": "2024-03-20T22:27:31.102282Z",
     "iopub.status.idle": "2024-03-20T22:27:31.327107Z",
     "shell.execute_reply": "2024-03-20T22:27:31.325932Z"
    },
    "papermill": {
     "duration": 0.241695,
     "end_time": "2024-03-20T22:27:31.329728",
     "exception": false,
     "start_time": "2024-03-20T22:27:31.088033",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8478260869565217\n"
     ]
    }
   ],
   "source": [
    "random.fit(xtrain,ytrain)\n",
    "ypreds = random.predict(xtest)\n",
    "threshold = 0.95\n",
    "binary_predictions = convert_to_binary(ypreds, threshold)\n",
    "accRF = accuracy_score(ytest,binary_predictions)\n",
    "print(accRF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6711b038",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-03-20T22:27:31.360670Z",
     "iopub.status.busy": "2024-03-20T22:27:31.360296Z",
     "iopub.status.idle": "2024-03-20T22:27:31.479850Z",
     "shell.execute_reply": "2024-03-20T22:27:31.478727Z"
    },
    "papermill": {
     "duration": 0.138313,
     "end_time": "2024-03-20T22:27:31.483293",
     "exception": false,
     "start_time": "2024-03-20T22:27:31.344980",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8152173913043478\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "logmod = LogisticRegression(max_iter=1000)\n",
    "logmod.fit(xtrain,ytrain)\n",
    "ypreds = logmod.predict(xtest)\n",
    "threshold = 0.95\n",
    "binary_predictions = convert_to_binary(ypreds, threshold)\n",
    "accLR = accuracy_score(ytest,binary_predictions)\n",
    "print(accLR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de2ab1d5",
   "metadata": {
    "papermill": {
     "duration": 0.022789,
     "end_time": "2024-03-20T22:27:31.529245",
     "exception": false,
     "start_time": "2024-03-20T22:27:31.506456",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 1582403,
     "sourceId": 2603715,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30664,
   "isGpuEnabled": false,
   "isInternetEnabled": true,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 34.426206,
   "end_time": "2024-03-20T22:27:33.082714",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-03-20T22:26:58.656508",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}