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--- a +++ b/HIMA/activity.ipynb @@ -0,0 +1,405 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "heart_data = pd.read_csv('../heart_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "heart_data['HeartDisease'] = heart_data['HeartDisease']\n", + "allactive = heart_data['PhysicalActivity']\n", + "Active = heart_data[heart_data['PhysicalActivity']=='Yes']\n", + "notActive = heart_data[heart_data['PhysicalActivity']=='No']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 319795\n", + "unique 2\n", + "top Yes\n", + "freq 247957\n", + "Name: PhysicalActivity, dtype: object" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "allactive.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "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>BMI</th>\n", + " <th>PhysicalHealth</th>\n", + " <th>MentalHealth</th>\n", + " <th>SleepTime</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>247957.00000</td>\n", + " <td>247957.000000</td>\n", + " <td>247957.000000</td>\n", + " <td>247957.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>27.81011</td>\n", + " <td>2.377634</td>\n", + " <td>3.488121</td>\n", + " <td>7.100050</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>5.90583</td>\n", + " <td>6.479349</td>\n", + " <td>7.353138</td>\n", + " <td>1.320686</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>12.02000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>1.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>23.74000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>6.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>26.79000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>7.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>30.79000</td>\n", + " <td>1.000000</td>\n", + " <td>3.000000</td>\n", + " <td>8.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>94.85000</td>\n", + " <td>30.000000</td>\n", + " <td>30.000000</td>\n", + " <td>24.000000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " BMI PhysicalHealth MentalHealth SleepTime\n", + "count 247957.00000 247957.000000 247957.000000 247957.000000\n", + "mean 27.81011 2.377634 3.488121 7.100050\n", + "std 5.90583 6.479349 7.353138 1.320686\n", + "min 12.02000 0.000000 0.000000 1.000000\n", + "25% 23.74000 0.000000 0.000000 6.000000\n", + "50% 26.79000 0.000000 0.000000 7.000000\n", + "75% 30.79000 1.000000 3.000000 8.000000\n", + "max 94.85000 30.000000 30.000000 24.000000" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Active.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of active people that don't have heart disease 92.9467609303226\n", + "Percentage of active people that have heart disease 7.053239069677404\n" + ] + } + ], + "source": [ + "print(\"Percentage of active people that don't have heart disease\", 100 * Active[Active[\"HeartDisease\"] == \"No\"].size / Active.size )\n", + "print(\"Percentage of active people that have heart disease\", 100 * Active[Active[\"HeartDisease\"] == \"Yes\"].size / Active.size )" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "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>BMI</th>\n", + " <th>PhysicalHealth</th>\n", + " <th>MentalHealth</th>\n", + " <th>SleepTime</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>71838.000000</td>\n", + " <td>71838.000000</td>\n", + " <td>71838.000000</td>\n", + " <td>71838.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>30.103974</td>\n", + " <td>6.802876</td>\n", + " <td>5.314374</td>\n", + " <td>7.086806</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>7.441630</td>\n", + " <td>11.014781</td>\n", + " <td>9.618466</td>\n", + " <td>1.777442</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>12.020000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>1.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>25.020000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>6.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>29.050000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>7.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>33.910000</td>\n", + " <td>10.000000</td>\n", + " <td>5.000000</td>\n", + " <td>8.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>94.660000</td>\n", + " <td>30.000000</td>\n", + " <td>30.000000</td>\n", + " <td>24.000000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " BMI PhysicalHealth MentalHealth SleepTime\n", + "count 71838.000000 71838.000000 71838.000000 71838.000000\n", + "mean 30.103974 6.802876 5.314374 7.086806\n", + "std 7.441630 11.014781 9.618466 1.777442\n", + "min 12.020000 0.000000 0.000000 1.000000\n", + "25% 25.020000 0.000000 0.000000 6.000000\n", + "50% 29.050000 0.000000 0.000000 7.000000\n", + "75% 33.910000 10.000000 5.000000 8.000000\n", + "max 94.660000 30.000000 30.000000 24.000000" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "notActive.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of non active people that don't have heart disease 86.24126506862663\n", + "Percentage of non active people that have heart disease 13.758734931373368\n" + ] + } + ], + "source": [ + "print(\"Percentage of non active people that don't have heart disease\", 100 * notActive[notActive[\"HeartDisease\"] == \"No\"].size / notActive.size )\n", + "print(\"Percentage of non active people that have heart disease\", 100 * notActive[notActive[\"HeartDisease\"] == \"Yes\"].size / notActive.size )" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<AxesSubplot: >" + ] + }, + "execution_count": 17, + "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": [ + "Active['HeartDisease'].hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<AxesSubplot: >" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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