621 lines
19 KiB
Plaintext
621 lines
19 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np \n",
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"import pandas as pd \n",
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"import os\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>gender</th>\n",
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" <th>age</th>\n",
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" <th>hypertension</th>\n",
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" <th>heart_disease</th>\n",
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" <th>ever_married</th>\n",
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" <th>work_type</th>\n",
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" <th>Residence_type</th>\n",
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" <th>avg_glucose_level</th>\n",
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" <th>bmi</th>\n",
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" <th>smoking_status</th>\n",
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" <th>stroke</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>30669</td>\n",
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" <td>Male</td>\n",
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" <td>3.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>No</td>\n",
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" <td>children</td>\n",
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" <td>Rural</td>\n",
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" <td>95.12</td>\n",
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" <td>18.0</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>30468</td>\n",
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" <td>Male</td>\n",
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" <td>58.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>Yes</td>\n",
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" <td>Private</td>\n",
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" <td>Urban</td>\n",
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" <td>87.96</td>\n",
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" <td>39.2</td>\n",
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" <td>never smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>16523</td>\n",
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" <td>Female</td>\n",
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" <td>8.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>No</td>\n",
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" <td>Private</td>\n",
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" <td>Urban</td>\n",
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" <td>110.89</td>\n",
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" <td>17.6</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>56543</td>\n",
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" <td>Female</td>\n",
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" <td>70.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>Yes</td>\n",
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" <td>Private</td>\n",
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" <td>Rural</td>\n",
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" <td>69.04</td>\n",
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" <td>35.9</td>\n",
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" <td>formerly smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>46136</td>\n",
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" <td>Male</td>\n",
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" <td>14.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>No</td>\n",
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" <td>Never_worked</td>\n",
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" <td>Rural</td>\n",
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" <td>161.28</td>\n",
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" <td>19.1</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>32257</td>\n",
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" <td>Female</td>\n",
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" <td>47.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>Yes</td>\n",
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" <td>Private</td>\n",
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" <td>Urban</td>\n",
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" <td>210.95</td>\n",
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" <td>50.1</td>\n",
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" <td>NaN</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>52800</td>\n",
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" <td>Female</td>\n",
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" <td>52.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>Yes</td>\n",
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" <td>Private</td>\n",
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" <td>Urban</td>\n",
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" <td>77.59</td>\n",
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" <td>17.7</td>\n",
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" <td>formerly smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>41413</td>\n",
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" <td>Female</td>\n",
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" <td>75.0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>Yes</td>\n",
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" <td>Self-employed</td>\n",
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" <td>Rural</td>\n",
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" <td>243.53</td>\n",
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" <td>27.0</td>\n",
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" <td>never smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>15266</td>\n",
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" <td>Female</td>\n",
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" <td>32.0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>Yes</td>\n",
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" <td>Private</td>\n",
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" <td>Rural</td>\n",
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" <td>77.67</td>\n",
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" <td>32.3</td>\n",
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" <td>smokes</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>28674</td>\n",
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" <td>Female</td>\n",
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" <td>74.0</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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|
" <td>Yes</td>\n",
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" <td>Self-employed</td>\n",
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" <td>Urban</td>\n",
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" <td>205.84</td>\n",
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" <td>54.6</td>\n",
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" <td>never smoked</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" id gender age hypertension heart_disease ever_married \\\n",
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"0 30669 Male 3.0 0 0 No \n",
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"1 30468 Male 58.0 1 0 Yes \n",
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"2 16523 Female 8.0 0 0 No \n",
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"3 56543 Female 70.0 0 0 Yes \n",
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"4 46136 Male 14.0 0 0 No \n",
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"5 32257 Female 47.0 0 0 Yes \n",
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"6 52800 Female 52.0 0 0 Yes \n",
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"7 41413 Female 75.0 0 1 Yes \n",
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"8 15266 Female 32.0 0 0 Yes \n",
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"9 28674 Female 74.0 1 0 Yes \n",
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"\n",
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" work_type Residence_type avg_glucose_level bmi smoking_status \\\n",
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"0 children Rural 95.12 18.0 NaN \n",
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"1 Private Urban 87.96 39.2 never smoked \n",
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"2 Private Urban 110.89 17.6 NaN \n",
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"3 Private Rural 69.04 35.9 formerly smoked \n",
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"4 Never_worked Rural 161.28 19.1 NaN \n",
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"5 Private Urban 210.95 50.1 NaN \n",
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"6 Private Urban 77.59 17.7 formerly smoked \n",
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"7 Self-employed Rural 243.53 27.0 never smoked \n",
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"8 Private Rural 77.67 32.3 smokes \n",
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"9 Self-employed Urban 205.84 54.6 never smoked \n",
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"\n",
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" stroke \n",
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"0 0 \n",
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"1 0 \n",
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"2 0 \n",
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"3 0 \n",
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"4 0 \n",
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"5 0 \n",
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"6 0 \n",
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"7 0 \n",
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"8 0 \n",
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"9 0 "
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df = pd.read_csv('trainFile.csv')\n",
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"df.head(10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 43400 entries, 0 to 43399\n",
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"Data columns (total 12 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 id 43400 non-null int64 \n",
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" 1 gender 43400 non-null object \n",
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" 2 age 43400 non-null float64\n",
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" 3 hypertension 43400 non-null int64 \n",
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" 4 heart_disease 43400 non-null int64 \n",
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" 5 ever_married 43400 non-null object \n",
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" 6 work_type 43400 non-null object \n",
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" 7 Residence_type 43400 non-null object \n",
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" 8 avg_glucose_level 43400 non-null float64\n",
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" 9 bmi 41938 non-null float64\n",
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" 10 smoking_status 30108 non-null object \n",
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" 11 stroke 43400 non-null int64 \n",
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"dtypes: float64(3), int64(4), object(5)\n",
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"memory usage: 4.0+ MB\n"
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]
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}
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],
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"source": [
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>id</th>\n",
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" <th>age</th>\n",
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" <th>hypertension</th>\n",
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" <th>heart_disease</th>\n",
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" <th>avg_glucose_level</th>\n",
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" <th>bmi</th>\n",
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" <th>stroke</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
|
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" <td>43400.000000</td>\n",
|
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" <td>43400.000000</td>\n",
|
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" <td>43400.000000</td>\n",
|
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" <td>43400.000000</td>\n",
|
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" <td>43400.000000</td>\n",
|
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" <td>41938.000000</td>\n",
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" <td>43400.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>36326.142350</td>\n",
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" <td>42.217894</td>\n",
|
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" <td>0.093571</td>\n",
|
|
" <td>0.047512</td>\n",
|
|
" <td>104.482750</td>\n",
|
|
" <td>28.605038</td>\n",
|
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" <td>0.018041</td>\n",
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>std</th>\n",
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" <td>21072.134879</td>\n",
|
|
" <td>22.519649</td>\n",
|
|
" <td>0.291235</td>\n",
|
|
" <td>0.212733</td>\n",
|
|
" <td>43.111751</td>\n",
|
|
" <td>7.770020</td>\n",
|
|
" <td>0.133103</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>min</th>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>0.080000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>55.000000</td>\n",
|
|
" <td>10.100000</td>\n",
|
|
" <td>0.000000</td>\n",
|
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" </tr>\n",
|
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" <tr>\n",
|
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" <th>25%</th>\n",
|
|
" <td>18038.500000</td>\n",
|
|
" <td>24.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>77.540000</td>\n",
|
|
" <td>23.200000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>50%</th>\n",
|
|
" <td>36351.500000</td>\n",
|
|
" <td>44.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>91.580000</td>\n",
|
|
" <td>27.700000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>75%</th>\n",
|
|
" <td>54514.250000</td>\n",
|
|
" <td>60.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" <td>112.070000</td>\n",
|
|
" <td>32.900000</td>\n",
|
|
" <td>0.000000</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>max</th>\n",
|
|
" <td>72943.000000</td>\n",
|
|
" <td>82.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" <td>291.050000</td>\n",
|
|
" <td>97.600000</td>\n",
|
|
" <td>1.000000</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
],
|
|
"text/plain": [
|
|
" id age hypertension heart_disease \\\n",
|
|
"count 43400.000000 43400.000000 43400.000000 43400.000000 \n",
|
|
"mean 36326.142350 42.217894 0.093571 0.047512 \n",
|
|
"std 21072.134879 22.519649 0.291235 0.212733 \n",
|
|
"min 1.000000 0.080000 0.000000 0.000000 \n",
|
|
"25% 18038.500000 24.000000 0.000000 0.000000 \n",
|
|
"50% 36351.500000 44.000000 0.000000 0.000000 \n",
|
|
"75% 54514.250000 60.000000 0.000000 0.000000 \n",
|
|
"max 72943.000000 82.000000 1.000000 1.000000 \n",
|
|
"\n",
|
|
" avg_glucose_level bmi stroke \n",
|
|
"count 43400.000000 41938.000000 43400.000000 \n",
|
|
"mean 104.482750 28.605038 0.018041 \n",
|
|
"std 43.111751 7.770020 0.133103 \n",
|
|
"min 55.000000 10.100000 0.000000 \n",
|
|
"25% 77.540000 23.200000 0.000000 \n",
|
|
"50% 91.580000 27.700000 0.000000 \n",
|
|
"75% 112.070000 32.900000 0.000000 \n",
|
|
"max 291.050000 97.600000 1.000000 "
|
|
]
|
|
},
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
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}
|
|
],
|
|
"source": [
|
|
"df.describe()"
|
|
]
|
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},
|
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{
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"cell_type": "code",
|
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
|
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"output_type": "stream",
|
|
"text": [
|
|
"['Male' 'Female' 'Other']\n",
|
|
"['children' 'Private' 'Never_worked' 'Self-employed' 'Govt_job']\n",
|
|
"['Rural' 'Urban']\n",
|
|
"[nan 'never smoked' 'formerly smoked' 'smokes']\n",
|
|
"['No' 'Yes']\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print(df['gender'].unique())\n",
|
|
"print(df['work_type'].unique())\n",
|
|
"print(df['Residence_type'].unique())\n",
|
|
"print(df['smoking_status'].unique())\n",
|
|
"print(df['ever_married'].unique())"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"id 0\n",
|
|
"gender 0\n",
|
|
"age 0\n",
|
|
"hypertension 0\n",
|
|
"heart_disease 0\n",
|
|
"ever_married 0\n",
|
|
"work_type 0\n",
|
|
"Residence_type 0\n",
|
|
"avg_glucose_level 0\n",
|
|
"bmi 1462\n",
|
|
"smoking_status 13292\n",
|
|
"stroke 0\n",
|
|
"dtype: int64"
|
|
]
|
|
},
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"df.isnull().sum()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df = df.dropna(how = 'any', axis=0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 8,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"<class 'pandas.core.frame.DataFrame'>\n",
|
|
"Int64Index: 29072 entries, 1 to 43399\n",
|
|
"Data columns (total 12 columns):\n",
|
|
" # Column Non-Null Count Dtype \n",
|
|
"--- ------ -------------- ----- \n",
|
|
" 0 id 29072 non-null int64 \n",
|
|
" 1 gender 29072 non-null object \n",
|
|
" 2 age 29072 non-null float64\n",
|
|
" 3 hypertension 29072 non-null int64 \n",
|
|
" 4 heart_disease 29072 non-null int64 \n",
|
|
" 5 ever_married 29072 non-null object \n",
|
|
" 6 work_type 29072 non-null object \n",
|
|
" 7 Residence_type 29072 non-null object \n",
|
|
" 8 avg_glucose_level 29072 non-null float64\n",
|
|
" 9 bmi 29072 non-null float64\n",
|
|
" 10 smoking_status 29072 non-null object \n",
|
|
" 11 stroke 29072 non-null int64 \n",
|
|
"dtypes: float64(3), int64(4), object(5)\n",
|
|
"memory usage: 2.9+ MB\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"df.info()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 9,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"df.to_excel('datasetCleaned.xlsx')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 10,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/html": [
|
|
" <script type=\"text/javascript\">\n",
|
|
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
|
|
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
|
|
" if (typeof require !== 'undefined') {\n",
|
|
" require.undef(\"plotly\");\n",
|
|
" requirejs.config({\n",
|
|
" paths: {\n",
|
|
" 'plotly': ['https://cdn.plot.ly/plotly-2.14.0.min']\n",
|
|
" }\n",
|
|
" });\n",
|
|
" require(['plotly'], function(Plotly) {\n",
|
|
" window._Plotly = Plotly;\n",
|
|
" });\n",
|
|
" }\n",
|
|
" </script>\n",
|
|
" "
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import cufflinks as cf\n",
|
|
"\n",
|
|
"cf.go_offline()\n",
|
|
"cf.set_config_file(offline=False, world_readable=True)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3.8.13 ('StrokePredictionModel')",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.13"
|
|
},
|
|
"orig_nbformat": 4,
|
|
"vscode": {
|
|
"interpreter": {
|
|
"hash": "6d6bab66b583e7661b89cead2220317a23c391a40fb8c52f2c1bcd3c04f3fbda"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|