a b/Diabetes.ipynb 

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 "cells": [





  
  

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  {





  
  

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   "cell_type": "code",





  
  

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   "execution_count": 19,





  
  

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   "id": "50eddcda",





  
  

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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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      "  HeartDisease    BMI Smoking AlcoholDrinking Stroke  PhysicalHealth  \\\n",





  
  

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      "0           No  16.60     Yes              No     No             3.0   \n",





  
  

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      "1           No  20.34      No              No    Yes             0.0   \n",





  
  

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      "2           No  26.58     Yes              No     No            20.0   \n",





  
  

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      "3           No  24.21      No              No     No             0.0   \n",





  
  

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      "4           No  23.71      No              No     No            28.0   \n",





  
  

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      "\n",





  
  

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      "   MentalHealth DiffWalking     Sex  AgeCategory   Race Diabetic  \\\n",





  
  

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      "0          30.0          No  Female        55-59  White      Yes   \n",





  
  

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      "1           0.0          No  Female  80 or older  White       No   \n",





  
  

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      "2          30.0          No    Male        65-69  White      Yes   \n",





  
  

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      "3           0.0          No  Female        75-79  White       No   \n",





  
  

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      "4           0.0         Yes  Female        40-44  White       No   \n",





  
  

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      "\n",





  
  

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      "  PhysicalActivity  GenHealth  SleepTime Asthma KidneyDisease SkinCancer  \n",





  
  

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      "0              Yes  Very good        5.0    Yes            No        Yes  \n",





  
  

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      "1              Yes  Very good        7.0     No            No         No  \n",





  
  

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      "2              Yes       Fair        8.0    Yes            No         No  \n",





  
  

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      "3               No       Good        6.0     No            No        Yes  \n",





  
  

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      "4              Yes  Very good        8.0     No            No         No  \n"





  
  

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     ]





  
  

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    }





  
  

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   ],





  
  

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   "source": [





  
  

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    "import pandas as pd\n",





  
  

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    "import numpy as np\n",





  
  

  39
  
    "import matplotlib.pyplot as plt\n",





  
  

  40
  
    "cardio_data_2 = pd.read_csv('heart_data.csv')\n",





  
  

  41
  
    "print(cardio_data_2.head())"





  
  

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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": 20,





  
  

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   "id": "34be506a",





  
  

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   "metadata": {},





  
  

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   "outputs": [],





  
  

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   "source": [





  
  

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    "cardio_data_2['HeartDisease'] =  cardio_data_2['HeartDisease'].map({'Yes': 1, 'No': 0})\n",





  
  

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    "diabetes = cardio_data_2[cardio_data_2['Diabetic']=='Yes']\n",





  
  

  53
  
    "non_diabetes = cardio_data_2[cardio_data_2['Diabetic']=='No']\n",





  
  

  54
  
    "diabetes.describe()\n",





  
  

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    "diabetes_stdev = diabetes['HeartDisease'].std()\n"





  
  

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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": 21,





  
  

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   "id": "f7d4a6e3",





  
  

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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>HeartDisease</th>\n",





  
  

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       "      <th>BMI</th>\n",





  
  

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       "      <th>PhysicalHealth</th>\n",





  
  

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       "      <th>MentalHealth</th>\n",





  
  

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       "      <th>SleepTime</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>269653.000000</td>\n",





  
  

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       "      <td>269653.000000</td>\n",





  
  

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       "      <td>269653.000000</td>\n",





  
  

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       "      <td>269653.000000</td>\n",





  
  

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       "      <td>269653.000000</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>0.064969</td>\n",





  
  

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       "      <td>27.754661</td>\n",





  
  

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       "      <td>2.845535</td>\n",





  
  

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       "      <td>3.787382</td>\n",





  
  

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       "      <td>7.096769</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>0.246471</td>\n",





  
  

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       "      <td>6.030478</td>\n",





  
  

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       "      <td>7.261770</td>\n",





  
  

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       "      <td>7.774713</td>\n",





  
  

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       "      <td>1.379846</td>\n",





  
  

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       "    </tr>\n",





  
  

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       "    <tr>\n",





  
  

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       "      <th>min</th>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>12.020000</td>\n",





  
  

  121
  
       "      <td>0.000000</td>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>1.000000</td>\n",





  
  

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       "    </tr>\n",





  
  

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       "    <tr>\n",





  
  

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       "      <th>25%</th>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>23.670000</td>\n",





  
  

  129
  
       "      <td>0.000000</td>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>6.000000</td>\n",





  
  

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       "    </tr>\n",





  
  

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       "    <tr>\n",





  
  

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       "      <th>50%</th>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>26.630000</td>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>7.000000</td>\n",





  
  

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       "    </tr>\n",





  
  

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       "    <tr>\n",





  
  

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       "      <th>75%</th>\n",





  
  

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       "      <td>0.000000</td>\n",





  
  

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       "      <td>30.680000</td>\n",





  
  

  145
  
       "      <td>1.000000</td>\n",





  
  

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       "      <td>3.000000</td>\n",





  
  

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       "      <td>8.000000</td>\n",





  
  

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       "    </tr>\n",





  
  

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       "    <tr>\n",





  
  

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       "      <th>max</th>\n",





  
  

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       "      <td>1.000000</td>\n",





  
  

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       "      <td>94.850000</td>\n",





  
  

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       "      <td>30.000000</td>\n",





  
  

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       "      <td>30.000000</td>\n",





  
  

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       "      <td>24.000000</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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       "        HeartDisease            BMI  PhysicalHealth   MentalHealth  \\\n",





  
  

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       "count  269653.000000  269653.000000   269653.000000  269653.000000   \n",





  
  

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       "mean        0.064969      27.754661        2.845535       3.787382   \n",





  
  

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       "std         0.246471       6.030478        7.261770       7.774713   \n",





  
  

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       "min         0.000000      12.020000        0.000000       0.000000   \n",





  
  

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       "25%         0.000000      23.670000        0.000000       0.000000   \n",





  
  

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       "50%         0.000000      26.630000        0.000000       0.000000   \n",





  
  

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       "75%         0.000000      30.680000        1.000000       3.000000   \n",





  
  

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       "max         1.000000      94.850000       30.000000      30.000000   \n",





  
  

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       "\n",





  
  

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       "           SleepTime  \n",





  
  

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       "count  269653.000000  \n",





  
  

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       "mean        7.096769  \n",





  
  

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       "std         1.379846  \n",





  
  

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       "min         1.000000  \n",





  
  

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       "25%         6.000000  \n",





  
  

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       "50%         7.000000  \n",





  
  

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       "75%         8.000000  \n",





  
  

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       "max        24.000000  "





  
  

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      ]





  
  

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     },





  
  

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     "execution_count": 21,





  
  

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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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    "non_diabetes.describe()"





  
  

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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": 22,





  
  

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   "id": "a361496e",





  
  

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   "metadata": {},





  
  

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   "outputs": [





  
  

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    {





  
  

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     "data": {





  
  

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      "text/plain": [





  
  

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       "<AxesSubplot: >"





  
  

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      ]





  
  

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     },





  
  

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     "execution_count": 22,





  
  

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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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     "data": {





  
  

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      "image/png": 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",





  
  

  211
  
      "text/plain": [





  
  

  212
  
       "<Figure size 640x480 with 1 Axes>"





  
  

  213
  
      ]





  
  

  214
  
     },





  
  

  215
  
     "metadata": {},





  
  

  216
  
     "output_type": "display_data"





  
  

  217
  
    }





  
  

  218
  
   ],





  
  

  219
  
   "source": [





  
  

  220
  
    "diabetes['HeartDisease'].hist()"





  
  

  221
  
   ]





  
  

  222
  
  },





  
  

  223
  
  {





  
  

  224
  
   "cell_type": "code",





  
  

  225
  
   "execution_count": 23,





  
  

  226
  
   "id": "d1cc0119",





  
  

  227
  
   "metadata": {},





  
  

  228
  
   "outputs": [





  
  

  229
  
    {





  
  

  230
  
     "data": {





  
  

  231
  
      "text/plain": [





  
  

  232
  
       "<AxesSubplot: >"





  
  

  233
  
      ]





  
  

  234
  
     },





  
  

  235
  
     "execution_count": 23,





  
  

  236
  
     "metadata": {},





  
  

  237
  
     "output_type": "execute_result"





  
  

  238
  
    },





  
  

  239
  
    {





  
  

  240
  
     "data": {





  
  

  241
  
      "image/png": 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",





  
  

  242
  
      "text/plain": [





  
  

  243
  
       "<Figure size 640x480 with 1 Axes>"





  
  

  244
  
      ]





  
  

  245
  
     },





  
  

  246
  
     "metadata": {},





  
  

  247
  
     "output_type": "display_data"





  
  

  248
  
    }





  
  

  249
  
   ],





  
  

  250
  
   "source": [





  
  

  251
  
    "non_diabetes['HeartDisease'].hist()"





  
  

  252
  
   ]





  
  

  253
  
  },





  
  

  254
  
  {





  
  

  255
  
   "cell_type": "code",





  
  

  256
  
   "execution_count": 24,





  
  

  257
  
   "id": "d137624b",





  
  

  258
  
   "metadata": {},





  
  

  259
  
   "outputs": [





  
  

  260
  
    {





  
  

  261
  
     "name": "stdout",





  
  

  262
  
     "output_type": "stream",





  
  

  263
  
     "text": [





  
  

  264
  
      "The percent of people that have diabetes and heart diseases 21.952355276702125\n",





  
  

  265
  
      "The percent of people that don't have diabetes but have heart diseases 6.4968681972757585\n"





  
  

  266
  
     ]





  
  

  267
  
    }





  
  

  268
  
   ],





  
  

  269
  
   "source": [





  
  

  270
  
    "diabetes_heart = diabetes[diabetes['HeartDisease']==1]\n",





  
  

  271
  
    "non_diabetes_heart = non_diabetes[non_diabetes['HeartDisease']==1]\n",





  
  

  272
  
    "percent_diabetes = diabetes_heart.size/ diabetes.size*100\n",





  
  

  273
  
    "percent_non_diabetes = non_diabetes_heart.size / non_diabetes.size*100\n",





  
  

  274
  
    "print('The percent of people that have diabetes and heart diseases',percent_diabetes)\n",





  
  

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    "print('The percent of people that don\\'t have diabetes but have heart diseases',percent_non_diabetes)"





  
  

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   ]





  
  

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  },





  
  

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  {





  
  

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   "attachments": {},





  
  

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   "id": "6fa769f0",





  
  

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   "metadata": {},





  
  

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   "source": [





  
  

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    "We will assume that the confidence interval is 95 % and then we will start calculating the error"





  
  

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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": 25,





  
  

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   "id": "df583c52",





  
  

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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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      "0.0009466836515954478\n"





  
  

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     ]





  
  

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    }





  
  

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   ],





  
  

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   "source": [





  
  

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    "n = diabetes.size\n",





  
  

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    "z95 = 1.96\n",





  
  

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    "error_diabetes_heart = diabetes_stdev / np.sqrt(n) * z95\n",





  
  

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    "print(error_diabetes_heart)"





  
  

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   ]





  
  

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  },





  
  

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  {





  
  

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