Datasets
Models
Downloads: 1
Diff of
/AOZ/BMI Zahran 1.ipynb
[000000]
..
[1caa3f]
Switch to side-by-side view
--- a +++ b/AOZ/BMI Zahran 1.ipynb @@ -0,0 +1,550 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import the needed libiraries" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import scipy\n", + "from scipy import stats" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "read the file and give it a name" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"C:\\\\Users\\\\AOZ\\\\Desktop\\\\Statistics Project\\\\heart_data.csv\"\n", + "df = pd.read_csv(url)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "show info about all col" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "<class 'pandas.core.series.Series'>\n", + "RangeIndex: 319795 entries, 0 to 319794\n", + "Series name: BMI\n", + "Non-Null Count Dtype \n", + "-------------- ----- \n", + "319795 non-null float64\n", + "dtypes: float64(1)\n", + "memory usage: 2.4 MB\n" + ] + } + ], + "source": [ + "df['BMI'].info()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "descriptive stat of BMI" + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 319795.000000\n", + "mean 28.325399\n", + "std 6.356100\n", + "min 12.020000\n", + "25% 24.030000\n", + "50% 27.340000\n", + "75% 31.420000\n", + "max 94.850000\n", + "Name: BMI, dtype: float64" + ] + }, + "execution_count": 331, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['BMI'].describe(include=\"all\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "median" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "27.34" + ] + }, + "execution_count": 332, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['BMI'].median()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "boxplot of BMI" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.boxplot(df['BMI'])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "taking out the outliers" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(31.42, 24.03)" + ] + }, + "execution_count": 335, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "q75,q25=np.percentile(df['BMI'],[75,25])\n", + "q75,q25" + ] + }, + { + "cell_type": "code", + "execution_count": 349, + "metadata": {}, + "outputs": [], + "source": [ + "IQR=q75-q25" + ] + }, + { + "cell_type": "code", + "execution_count": 337, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.loc[df['BMI'] > (q25 - 1.5 * IQR)]\n", + "df = df.loc[df['BMI'] < (q75 + 1.5 * IQR)]" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mean of BMI of people having heart disease" + ] + }, + { + "cell_type": "code", + "execution_count": 339, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "28.574460099274535" + ] + }, + "execution_count": 339, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df[df['HeartDisease'] == 'Yes']['BMI'].mean()\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "percentage and plot of people having heart disease with bmi between 10 and 20" + ] + }, + { + "cell_type": "code", + "execution_count": 340, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.544288248942268\n" + ] + } + ], + "source": [ + "bmi = df[df['BMI'] <= 20]\n", + "bmiy = bmi[bmi['HeartDisease']== 'Yes']\n", + "percent_of_y_bmi1020 = bmiy.size/bmi.size*100\n", + "print(percent_of_y_bmi1020)" + ] + }, + { + "cell_type": "code", + "execution_count": 341, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\AOZ\\AppData\\Local\\Temp\\ipykernel_50748\\2261961746.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " plt.hist(df[df['BMI'] <= 20][df['BMI']>=10]['HeartDisease'])\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df[df['BMI'] <= 20][df['BMI']>=10]['HeartDisease'])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "percentage and plot of people having heart disease with bmi between 20 and 30" + ] + }, + { + "cell_type": "code", + "execution_count": 342, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.785808795939728\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\AOZ\\AppData\\Local\\Temp\\ipykernel_50748\\3236808134.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " bmi = df[df['BMI'] <= 30][df['BMI'] > 20]\n" + ] + } + ], + "source": [ + "bmi = df[df['BMI'] <= 30][df['BMI'] > 20]\n", + "bmiy = bmi[bmi['HeartDisease']== 'Yes']\n", + "percent_of_y_bmi2030 = bmiy.size/bmi.size*100\n", + "print(percent_of_y_bmi2030)" + ] + }, + { + "cell_type": "code", + "execution_count": 343, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\AOZ\\AppData\\Local\\Temp\\ipykernel_50748\\3412363346.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " plt.hist(df[df['BMI'] <= 30][df['BMI']>=20]['HeartDisease'])\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df[df['BMI'] <= 30][df['BMI']>=20]['HeartDisease'])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "percentage and plot of people having heart disease with bmi between 30 and 40" + ] + }, + { + "cell_type": "code", + "execution_count": 344, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.242697046335183\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\AOZ\\AppData\\Local\\Temp\\ipykernel_50748\\1193710883.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " bmi = df[df['BMI'] <= 40][df['BMI'] > 30]\n" + ] + } + ], + "source": [ + "bmi = df[df['BMI'] <= 40][df['BMI'] > 30]\n", + "bmiy = bmi[bmi['HeartDisease']== 'Yes']\n", + "percent_of_y_bmi3040 = bmiy.size/bmi.size*100\n", + "print(percent_of_y_bmi3040)" + ] + }, + { + "cell_type": "code", + "execution_count": 345, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\AOZ\\AppData\\Local\\Temp\\ipykernel_50748\\1118371564.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", + " plt.hist(df[df['BMI'] <= 40][df['BMI']>=30]['HeartDisease'])\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df[df['BMI'] <= 40][df['BMI']>=30]['HeartDisease'])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "percentage and plot of people having heart disease with bmi between 40 and 50" + ] + }, + { + "cell_type": "code", + "execution_count": 346, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10.535089181530255\n" + ] + } + ], + "source": [ + "bmi = df[df['BMI'] <= 50][df['BMI'] > 40]\n", + "bmiy = bmi[bmi['HeartDisease']== 'Yes']\n", + "percent_of_y_bmi4050 = bmiy.size/bmi.size*100\n", + "print(percent_of_y_bmi4050)" + ] + }, + { + "cell_type": "code", + "execution_count": 347, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df[df['BMI'] <= 50][df['BMI']>=40]['HeartDisease'])\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "corr between BMI and heart disease and plot between diffrent bmi levels and percentage of people having heart disease in it" + ] + }, + { + "cell_type": "code", + "execution_count": 348, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "corr = 0.9638133142871304\n" + ] + }, + { + "data": { + "image/png": "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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.array(['10-20','20-30','30-40','40-50'])\n", + "xtemp = np.array([1,2,3,4])\n", + "y = np.array([percent_of_y_bmi1020,percent_of_y_bmi2030,percent_of_y_bmi3040,percent_of_y_bmi4050])\n", + "a, b = np.polyfit(xtemp, y, 1)\n", + "plt.scatter(x, y)\n", + "plt.plot(x, a*xtemp+b)\n", + "\n", + "print(\"corr = \"+ str(np.corrcoef(xtemp,y)[0][1]))" + ] + } + ], + "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": "3.10.8" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "05c317967a6bcc6983b33dfc32d6a00017438325114854c9651b783a874b92d3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}