{ "cells": [ { "cell_type": "code", "execution_count": 19, "id": "50eddcda", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n", "0 No 16.60 Yes No No 3.0 \n", "1 No 20.34 No No Yes 0.0 \n", "2 No 26.58 Yes No No 20.0 \n", "3 No 24.21 No No No 0.0 \n", "4 No 23.71 No No No 28.0 \n", "\n", " MentalHealth DiffWalking Sex AgeCategory Race Diabetic \\\n", "0 30.0 No Female 55-59 White Yes \n", "1 0.0 No Female 80 or older White No \n", "2 30.0 No Male 65-69 White Yes \n", "3 0.0 No Female 75-79 White No \n", "4 0.0 Yes Female 40-44 White No \n", "\n", " PhysicalActivity GenHealth SleepTime Asthma KidneyDisease SkinCancer \n", "0 Yes Very good 5.0 Yes No Yes \n", "1 Yes Very good 7.0 No No No \n", "2 Yes Fair 8.0 Yes No No \n", "3 No Good 6.0 No No Yes \n", "4 Yes Very good 8.0 No No No \n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "cardio_data_2 = pd.read_csv('heart_data.csv')\n", "print(cardio_data_2.head())" ] }, { "cell_type": "code", "execution_count": 20, "id": "34be506a", "metadata": {}, "outputs": [], "source": [ "cardio_data_2['HeartDisease'] = cardio_data_2['HeartDisease'].map({'Yes': 1, 'No': 0})\n", "diabetes = cardio_data_2[cardio_data_2['Diabetic']=='Yes']\n", "non_diabetes = cardio_data_2[cardio_data_2['Diabetic']=='No']\n", "diabetes.describe()\n", "diabetes_stdev = diabetes['HeartDisease'].std()\n" ] }, { "cell_type": "code", "execution_count": 21, "id": "f7d4a6e3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
HeartDiseaseBMIPhysicalHealthMentalHealthSleepTime
count269653.000000269653.000000269653.000000269653.000000269653.000000
mean0.06496927.7546612.8455353.7873827.096769
std0.2464716.0304787.2617707.7747131.379846
min0.00000012.0200000.0000000.0000001.000000
25%0.00000023.6700000.0000000.0000006.000000
50%0.00000026.6300000.0000000.0000007.000000
75%0.00000030.6800001.0000003.0000008.000000
max1.00000094.85000030.00000030.00000024.000000
\n", "
" ], "text/plain": [ " HeartDisease BMI PhysicalHealth MentalHealth \\\n", "count 269653.000000 269653.000000 269653.000000 269653.000000 \n", "mean 0.064969 27.754661 2.845535 3.787382 \n", "std 0.246471 6.030478 7.261770 7.774713 \n", "min 0.000000 12.020000 0.000000 0.000000 \n", "25% 0.000000 23.670000 0.000000 0.000000 \n", "50% 0.000000 26.630000 0.000000 0.000000 \n", "75% 0.000000 30.680000 1.000000 3.000000 \n", "max 1.000000 94.850000 30.000000 30.000000 \n", "\n", " SleepTime \n", "count 269653.000000 \n", "mean 7.096769 \n", "std 1.379846 \n", "min 1.000000 \n", "25% 6.000000 \n", "50% 7.000000 \n", "75% 8.000000 \n", "max 24.000000 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "non_diabetes.describe()" ] }, { "cell_type": "code", "execution_count": 22, "id": "a361496e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "diabetes['HeartDisease'].hist()" ] }, { "cell_type": "code", "execution_count": 23, "id": "d1cc0119", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "non_diabetes['HeartDisease'].hist()" ] }, { "cell_type": "code", "execution_count": 24, "id": "d137624b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The percent of people that have diabetes and heart diseases 21.952355276702125\n", "The percent of people that don't have diabetes but have heart diseases 6.4968681972757585\n" ] } ], "source": [ "diabetes_heart = diabetes[diabetes['HeartDisease']==1]\n", "non_diabetes_heart = non_diabetes[non_diabetes['HeartDisease']==1]\n", "percent_diabetes = diabetes_heart.size/ diabetes.size*100\n", "percent_non_diabetes = non_diabetes_heart.size / non_diabetes.size*100\n", "print('The percent of people that have diabetes and heart diseases',percent_diabetes)\n", "print('The percent of people that don\\'t have diabetes but have heart diseases',percent_non_diabetes)" ] }, { "attachments": {}, "cell_type": "markdown", "id": "6fa769f0", "metadata": {}, "source": [ "We will assume that the confidence interval is 95 % and then we will start calculating the error" ] }, { "cell_type": "code", "execution_count": 25, "id": "df583c52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.0009466836515954478\n" ] } ], "source": [ "n = diabetes.size\n", "z95 = 1.96\n", "error_diabetes_heart = diabetes_stdev / np.sqrt(n) * z95\n", "print(error_diabetes_heart)" ] }, { "cell_type": "code", "execution_count": null, "id": "440e82ce", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "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": "3.10.7" }, "vscode": { "interpreter": { "hash": "3bd8617b38ef4fa5b1c1552c94826c5d43d8082c101bf117576360b493384e0f" } } }, "nbformat": 4, "nbformat_minor": 5 }