upgrade of python script
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.ipynb_checkpoints/body_performance-checkpoint.ipynb
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.ipynb_checkpoints/body_performance-checkpoint.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "74524ede",
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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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" age gender height_cm weight_kg body fat_% diastolic systolic \\\n",
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"0 27.0 M 172.3 75.24 21.3 80.0 130.0 \n",
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"1 25.0 M 165.0 55.80 15.7 77.0 126.0 \n",
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"2 31.0 M 179.6 78.00 20.1 92.0 152.0 \n",
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"3 32.0 M 174.5 71.10 18.4 76.0 147.0 \n",
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"4 28.0 M 173.8 67.70 17.1 70.0 127.0 \n",
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"\n",
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" gripForce sit and bend forward_cm sit-ups counts broad jump_cm class \\\n",
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"0 54.9 18.4 60.0 217.0 C \n",
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"1 36.4 16.3 53.0 229.0 A \n",
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"2 44.8 12.0 49.0 181.0 C \n",
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"3 41.4 15.2 53.0 219.0 B \n",
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"4 43.5 27.1 45.0 217.0 B \n",
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"\n",
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" BMI \n",
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"0 25.344179 \n",
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"1 20.495868 \n",
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"2 24.181428 \n",
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"3 23.349562 \n",
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"4 22.412439 \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 plotly.express as px\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.preprocessing import MinMaxScaler\n",
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"\n",
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"df = pd.read_csv(r'.\\body_performance.csv')\n",
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"\n",
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"df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n",
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"print(df.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": null,
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"id": "0177f243",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.duplicated().sum()\n",
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"print(f'with duplicates:{df.shape}')\n",
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"df.drop_duplicates(inplace=True)\n",
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"print(f'without duplicates:{df.shape}')\n",
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"df_copy = df.copy()"
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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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"id": "05f9442a",
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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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"number of elements in data frame: 13393\n",
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"train: 10715\n",
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"test: 1339\n",
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"valid: 1339\n"
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]
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}
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],
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"source": [
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"body_train, body_test = train_test_split(df, test_size=int(df[\"age\"].count()*0.2), random_state=1)\n",
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"body_test, body_valid = train_test_split(body_test, test_size=int(body_test[\"age\"].count()*0.5), random_state=1)\n",
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"\n",
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"print(\"number of elements in data frame: {}\".format(df['age'].count()))\n",
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"print(\"train: {}\".format(body_train[\"age\"].count()))\n",
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"print(\"test: {}\".format(body_test[\"age\"].count()))\n",
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"print(\"valid: {}\".format(body_valid[\"age\"].count()))"
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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": 12,
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"id": "0f3ad57a",
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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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" age gender height_cm weight_kg body fat_% \\\n",
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"count 13393.000000 13393 13393.000000 13393.000000 13393.000000 \n",
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"unique NaN 2 NaN NaN NaN \n",
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"top NaN M NaN NaN NaN \n",
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"freq NaN 8467 NaN NaN NaN \n",
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"mean 36.775106 NaN 168.559807 67.447316 23.240165 \n",
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"std 13.625639 NaN 8.426583 11.949666 7.256844 \n",
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"min 21.000000 NaN 125.000000 26.300000 3.000000 \n",
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"25% 25.000000 NaN 162.400000 58.200000 18.000000 \n",
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"50% 32.000000 NaN 169.200000 67.400000 22.800000 \n",
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"75% 48.000000 NaN 174.800000 75.300000 28.000000 \n",
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"max 64.000000 NaN 193.800000 138.100000 78.400000 \n",
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"\n",
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" diastolic systolic gripForce sit and bend forward_cm \\\n",
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"count 13393.000000 13393.000000 13393.000000 13393.000000 \n",
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"unique NaN NaN NaN NaN \n",
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"top NaN NaN NaN NaN \n",
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"freq NaN NaN NaN NaN \n",
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"mean 78.796842 130.234817 36.963877 15.209268 \n",
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"std 10.742033 14.713954 10.624864 8.456677 \n",
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"min 0.000000 0.000000 0.000000 -25.000000 \n",
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"25% 71.000000 120.000000 27.500000 10.900000 \n",
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"50% 79.000000 130.000000 37.900000 16.200000 \n",
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"75% 86.000000 141.000000 45.200000 20.700000 \n",
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"max 156.200000 201.000000 70.500000 213.000000 \n",
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"\n",
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" sit-ups counts broad jump_cm class BMI \n",
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"count 13393.000000 13393.000000 13393 13393.000000 \n",
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"unique NaN NaN 4 NaN \n",
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"top NaN NaN C NaN \n",
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"freq NaN NaN 3349 NaN \n",
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"mean 39.771224 190.129627 NaN 23.606014 \n",
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"std 14.276698 39.868000 NaN 2.940936 \n",
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"min 0.000000 0.000000 NaN 11.103976 \n",
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"25% 30.000000 162.000000 NaN 21.612812 \n",
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"50% 41.000000 193.000000 NaN 23.463513 \n",
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"75% 50.000000 221.000000 NaN 25.341367 \n",
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"max 80.000000 303.000000 NaN 42.906509 \n"
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]
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}
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],
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"source": [
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"print(df.describe(include='all'))\n",
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"#sit and bend forward_cm jest na minusie!!!"
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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": 15,
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"id": "dacdd816",
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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>age</th>\n",
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" <th>gender</th>\n",
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" <th>height_cm</th>\n",
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" <th>weight_kg</th>\n",
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" <th>body fat_%</th>\n",
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" <th>diastolic</th>\n",
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" <th>systolic</th>\n",
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" <th>gripForce</th>\n",
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" <th>sit and bend forward_cm</th>\n",
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" <th>sit-ups counts</th>\n",
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" <th>broad jump_cm</th>\n",
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" <th>class</th>\n",
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" <th>BMI</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>13393.000000</td>\n",
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" <td>13393</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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" <td>13393.000000</td>\n",
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||||
" <td>13393</td>\n",
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" <td>13393.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>unique</th>\n",
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" <td>NaN</td>\n",
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" <td>2</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>4</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>top</th>\n",
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" <td>NaN</td>\n",
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" <td>M</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>C</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>freq</th>\n",
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" <td>NaN</td>\n",
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" <td>8467</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>3349</td>\n",
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" <td>NaN</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>0.366863</td>\n",
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" <td>NaN</td>\n",
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" <td>0.633137</td>\n",
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" <td>0.368044</td>\n",
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" <td>0.268437</td>\n",
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" <td>0.504461</td>\n",
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||||
" <td>0.647934</td>\n",
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||||
" <td>0.524310</td>\n",
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" <td>-0.662107</td>\n",
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" <td>0.497140</td>\n",
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" <td>0.627491</td>\n",
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" <td>NaN</td>\n",
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" <td>0.393115</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>0.316875</td>\n",
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" <td>NaN</td>\n",
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" <td>0.122479</td>\n",
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" <td>0.106884</td>\n",
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" <td>0.096245</td>\n",
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" <td>0.068771</td>\n",
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" <td>0.073204</td>\n",
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" <td>0.150707</td>\n",
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" <td>0.071065</td>\n",
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" <td>0.178459</td>\n",
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" <td>0.131578</td>\n",
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" <td>NaN</td>\n",
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" <td>0.092475</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>NaN</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>0.000000</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>0.000000</td>\n",
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" <td>-1.000000</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>NaN</td>\n",
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" <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",
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" <td>0.093023</td>\n",
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" <td>NaN</td>\n",
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" <td>0.543605</td>\n",
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" <td>0.285331</td>\n",
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" <td>0.198939</td>\n",
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" <td>0.454545</td>\n",
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" <td>0.597015</td>\n",
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" <td>0.390071</td>\n",
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" <td>-0.698319</td>\n",
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" <td>0.375000</td>\n",
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" <td>0.534653</td>\n",
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" <td>NaN</td>\n",
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" <td>0.330440</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>0.255814</td>\n",
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" <td>NaN</td>\n",
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" <td>0.642442</td>\n",
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" <td>0.367621</td>\n",
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" <td>0.262599</td>\n",
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" <td>0.505762</td>\n",
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" <td>0.646766</td>\n",
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" <td>0.537589</td>\n",
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||||
" <td>-0.653782</td>\n",
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" <td>0.512500</td>\n",
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" <td>0.636964</td>\n",
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" <td>NaN</td>\n",
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" <td>0.388634</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>0.627907</td>\n",
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" <td>NaN</td>\n",
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" <td>0.723837</td>\n",
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" <td>0.438283</td>\n",
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" <td>0.331565</td>\n",
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" <td>0.550576</td>\n",
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||||
" <td>0.701493</td>\n",
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" <td>0.641135</td>\n",
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" <td>-0.615966</td>\n",
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" <td>0.625000</td>\n",
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" <td>0.729373</td>\n",
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" <td>NaN</td>\n",
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" <td>0.447681</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>NaN</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>1.000000</td>\n",
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" <td>NaN</td>\n",
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" <td>1.000000</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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" age gender height_cm weight_kg body fat_% \\\n",
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"count 13393.000000 13393 13393.000000 13393.000000 13393.000000 \n",
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"unique NaN 2 NaN NaN NaN \n",
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"top NaN M NaN NaN NaN \n",
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"freq NaN 8467 NaN NaN NaN \n",
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"mean 0.366863 NaN 0.633137 0.368044 0.268437 \n",
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"std 0.316875 NaN 0.122479 0.106884 0.096245 \n",
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"min 0.000000 NaN 0.000000 0.000000 0.000000 \n",
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"25% 0.093023 NaN 0.543605 0.285331 0.198939 \n",
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"50% 0.255814 NaN 0.642442 0.367621 0.262599 \n",
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"75% 0.627907 NaN 0.723837 0.438283 0.331565 \n",
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"max 1.000000 NaN 1.000000 1.000000 1.000000 \n",
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"\n",
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" diastolic systolic gripForce sit and bend forward_cm \\\n",
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"count 13393.000000 13393.000000 13393.000000 13393.000000 \n",
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"unique NaN NaN NaN NaN \n",
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"top NaN NaN NaN NaN \n",
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"freq NaN NaN NaN NaN \n",
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"mean 0.504461 0.647934 0.524310 -0.662107 \n",
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"std 0.068771 0.073204 0.150707 0.071065 \n",
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||||
"min 0.000000 0.000000 0.000000 -1.000000 \n",
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||||
"25% 0.454545 0.597015 0.390071 -0.698319 \n",
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||||
"50% 0.505762 0.646766 0.537589 -0.653782 \n",
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"75% 0.550576 0.701493 0.641135 -0.615966 \n",
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"max 1.000000 1.000000 1.000000 1.000000 \n",
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"\n",
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" sit-ups counts broad jump_cm class BMI \n",
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"count 13393.000000 13393.000000 13393 13393.000000 \n",
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"unique NaN NaN 4 NaN \n",
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"top NaN NaN C NaN \n",
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"freq NaN NaN 3349 NaN \n",
|
||||
"mean 0.497140 0.627491 NaN 0.393115 \n",
|
||||
"std 0.178459 0.131578 NaN 0.092475 \n",
|
||||
"min 0.000000 0.000000 NaN 0.000000 \n",
|
||||
"25% 0.375000 0.534653 NaN 0.330440 \n",
|
||||
"50% 0.512500 0.636964 NaN 0.388634 \n",
|
||||
"75% 0.625000 0.729373 NaN 0.447681 \n",
|
||||
"max 1.000000 1.000000 NaN 1.000000 "
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"scaler = MinMaxScaler()\n",
|
||||
"df[['age', 'height_cm', 'weight_kg','body fat_%',\n",
|
||||
" 'diastolic','systolic','gripForce','sit-ups counts',\n",
|
||||
" 'broad jump_cm','BMI']] = scaler.fit_transform(df[[\n",
|
||||
" 'age', 'height_cm', 'weight_kg','body fat_%',\n",
|
||||
" 'diastolic','systolic','gripForce','sit-ups counts',\n",
|
||||
" 'broad jump_cm','BMI']])\n",
|
||||
"\n",
|
||||
"scaler = MinMaxScaler(feature_range=(-1, 1))\n",
|
||||
"df['sit and bend forward_cm'] = scaler.fit_transform(df[['sit and bend forward_cm']])\n",
|
||||
"df.describe(include='all')\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"id": "5cd376cf",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"<class 'pandas.core.frame.DataFrame'>\n",
|
||||
"RangeIndex: 13393 entries, 0 to 13392\n",
|
||||
"Data columns (total 13 columns):\n",
|
||||
" # Column Non-Null Count Dtype \n",
|
||||
"--- ------ -------------- ----- \n",
|
||||
" 0 age 13393 non-null float64\n",
|
||||
" 1 gender 13393 non-null object \n",
|
||||
" 2 height_cm 13393 non-null float64\n",
|
||||
" 3 weight_kg 13393 non-null float64\n",
|
||||
" 4 body fat_% 13393 non-null float64\n",
|
||||
" 5 diastolic 13393 non-null float64\n",
|
||||
" 6 systolic 13393 non-null float64\n",
|
||||
" 7 gripForce 13393 non-null float64\n",
|
||||
" 8 sit and bend forward_cm 13393 non-null float64\n",
|
||||
" 9 sit-ups counts 13393 non-null float64\n",
|
||||
" 10 broad jump_cm 13393 non-null float64\n",
|
||||
" 11 class 13393 non-null object \n",
|
||||
" 12 BMI 13393 non-null float64\n",
|
||||
"dtypes: float64(11), object(2)\n",
|
||||
"memory usage: 1.3+ MB\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "93dcf330",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Each class in data frame: \n",
|
||||
"C 3349\n",
|
||||
"D 3349\n",
|
||||
"A 3348\n",
|
||||
"B 3347\n",
|
||||
"Name: class, dtype: int64\n",
|
||||
"Each class in train data: \n",
|
||||
"A 2703\n",
|
||||
"B 2681\n",
|
||||
"C 2671\n",
|
||||
"D 2660\n",
|
||||
"Name: class, dtype: int64\n",
|
||||
"Each class in test data: \n",
|
||||
"D 353\n",
|
||||
"C 332\n",
|
||||
"B 328\n",
|
||||
"A 326\n",
|
||||
"Name: class, dtype: int64\n",
|
||||
"Each class in valid data: \n",
|
||||
"C 346\n",
|
||||
"B 338\n",
|
||||
"D 336\n",
|
||||
"A 319\n",
|
||||
"Name: class, dtype: int64\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print('Each class in data frame: \\n{}'.format(df['class'].value_counts()))\n",
|
||||
"print('Each class in train data: \\n{}'.format(body_train['class'].value_counts()))\n",
|
||||
"print('Each class in test data: \\n{}'.format(body_test['class'].value_counts()))\n",
|
||||
"print('Each class in valid data: \\n{}'.format(body_valid['class'].value_counts()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b5620509",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3e9bbbe7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "4857a167",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df[\"class\"].value_counts().plot(kind=\"bar\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "779157c0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#df[[\"class\",\"body fat_%\"]].groupby(\"class\").mean().plot(kind=\"bar\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "da14bf43",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#sns.set_theme()\n",
|
||||
"\n",
|
||||
"#sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "6597e57c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "957e1b2e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9f0394f0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#px.box(df, y=['height_cm',\n",
|
||||
"# 'weight_kg',\n",
|
||||
"# 'body fat_%',\n",
|
||||
"# 'diastolic',\n",
|
||||
"# 'systolic',\n",
|
||||
"# 'gripForce',\n",
|
||||
"# 'sit and bend forward_cm',\n",
|
||||
"# 'sit-ups counts',\n",
|
||||
"# 'broad jump_cm',\n",
|
||||
"# 'BMI'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "22542bba",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# this is taking too long time\n",
|
||||
"#sns.pairplot(data=df.drop(columns=[\"gender\"]).head(500), hue=\"class\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "29730d20",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "dc21a9cb",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"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": "3.10.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
@ -10,13 +10,13 @@
|
||||
"import pandas as pd\n",
|
||||
"import plotly.express as px\n",
|
||||
"import seaborn as sns\n",
|
||||
"from sklearn.model_selection import train_test_split\n",
|
||||
"from sklearn.preprocessing import MinMaxScaler\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(r'.\\body_performance.csv')\n",
|
||||
"\n",
|
||||
"#print(df.dtypes)\n",
|
||||
"#df['BMI'] = float(df['weight_kg'])/(float(df['height_cm'])*0.01)^2\n",
|
||||
"df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n",
|
||||
"df.head()"
|
||||
"print(df.head())"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -33,6 +33,22 @@
|
||||
"df_copy = df.copy()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "56212519",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"body_train, body_test = train_test_split(df, test_size=int(df[\"age\"].count()*0.2), random_state=1)\n",
|
||||
"body_test, body_valid = train_test_split(body_test, test_size=int(body_test[\"age\"].count()*0.5), random_state=1)\n",
|
||||
"\n",
|
||||
"print(\"number of elements in data frame: {}\".format(df['age'].count()))\n",
|
||||
"print(\"train: {}\".format(body_train[\"age\"].count()))\n",
|
||||
"print(\"test: {}\".format(body_test[\"age\"].count()))\n",
|
||||
"print(\"valid: {}\".format(body_valid[\"age\"].count()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -40,7 +56,29 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.describe(include='all')"
|
||||
"print(df.describe(include='all'))\n",
|
||||
"#sit and bend forward_cm jest na minusie!!!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b7df6cfc",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"scaler = MinMaxScaler()\n",
|
||||
"df[['age', 'height_cm', 'weight_kg','body fat_%',\n",
|
||||
" 'diastolic','systolic','gripForce','sit-ups counts',\n",
|
||||
" 'broad jump_cm','BMI']] = scaler.fit_transform(df[[\n",
|
||||
" 'age', 'height_cm', 'weight_kg','body fat_%',\n",
|
||||
" 'diastolic','systolic','gripForce','sit-ups counts',\n",
|
||||
" 'broad jump_cm','BMI']])\n",
|
||||
"\n",
|
||||
"scaler = MinMaxScaler(feature_range=(-1, 1))\n",
|
||||
"df['sit and bend forward_cm'] = scaler.fit_transform(df[['sit and bend forward_cm']])\n",
|
||||
"df.describe(include='all')\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -53,6 +91,38 @@
|
||||
"df.info()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3be91a92",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"print('Each class in data frame: \\n{}'.format(df['class'].value_counts()))\n",
|
||||
"print('Each class in train data: \\n{}'.format(body_train['class'].value_counts()))\n",
|
||||
"print('Each class in test data: \\n{}'.format(body_test['class'].value_counts()))\n",
|
||||
"print('Each class in valid data: \\n{}'.format(body_valid['class'].value_counts()))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ab230559",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "b7cb3ebf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
@ -60,7 +130,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[\"class\"].value_counts().plot(kind=\"bar\")"
|
||||
"#df[\"class\"].value_counts().plot(kind=\"bar\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -70,7 +140,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df[[\"class\",\"body fat_%\"]].groupby(\"class\").mean().plot(kind=\"bar\")"
|
||||
"#df[[\"class\",\"body fat_%\"]].groupby(\"class\").mean().plot(kind=\"bar\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -80,9 +150,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sns.set_theme()\n",
|
||||
"#sns.set_theme()\n",
|
||||
"\n",
|
||||
"sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')"
|
||||
"#sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -92,7 +162,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
|
||||
"#sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -102,7 +172,7 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
|
||||
"#sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
|
||||
]
|
||||
},
|
||||
{
|
||||
@ -112,16 +182,16 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"px.box(df, y=['height_cm',\n",
|
||||
" 'weight_kg',\n",
|
||||
" 'body fat_%',\n",
|
||||
" 'diastolic',\n",
|
||||
" 'systolic',\n",
|
||||
" 'gripForce',\n",
|
||||
" 'sit and bend forward_cm',\n",
|
||||
" 'sit-ups counts',\n",
|
||||
" 'broad jump_cm',\n",
|
||||
" 'BMI'])"
|
||||
"#px.box(df, y=['height_cm',\n",
|
||||
"# 'weight_kg',\n",
|
||||
"# 'body fat_%',\n",
|
||||
"# 'diastolic',\n",
|
||||
"# 'systolic',\n",
|
||||
"# 'gripForce',\n",
|
||||
"# 'sit and bend forward_cm',\n",
|
||||
"# 'sit-ups counts',\n",
|
||||
"# 'broad jump_cm',\n",
|
||||
"# 'BMI'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -8,7 +8,7 @@ RUN apt-get update \
|
||||
&& pip install --user kaggle \
|
||||
&& pip install --user pandas \
|
||||
&& pip install --user plotly \
|
||||
&& pip install --user seaborn
|
||||
&& pip install --user seaborn \
|
||||
|
||||
ENV PATH="/root/.local/bin:$PATH"
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user