275 lines
7.3 KiB
Plaintext
275 lines
7.3 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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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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"import os\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(os.path.join('.', '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": null,
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"id": "8abefe6e",
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"metadata": {},
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"outputs": [],
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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": null,
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"id": "0f3ad57a",
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"metadata": {},
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"outputs": [],
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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": null,
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"id": "b694be50",
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"metadata": {},
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"outputs": [],
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"source": [
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"scaler = MinMaxScaler()\n",
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"df[['age', 'height_cm', 'weight_kg','body fat_%',\n",
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" 'diastolic','systolic','gripForce','sit-ups counts',\n",
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" 'broad jump_cm','BMI']] = scaler.fit_transform(df[[\n",
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" 'age', 'height_cm', 'weight_kg','body fat_%',\n",
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" 'diastolic','systolic','gripForce','sit-ups counts',\n",
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" 'broad jump_cm','BMI']])\n",
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"\n",
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"scaler = MinMaxScaler(feature_range=(-1, 1))\n",
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"df['sit and bend forward_cm'] = scaler.fit_transform(df[['sit and bend forward_cm']])\n",
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"df.describe(include='all')\n",
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"\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": null,
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"id": "5cd376cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.info()"
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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": "2375b677",
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"metadata": {},
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"outputs": [],
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"source": [
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"print('Each class in data frame: \\n{}'.format(df['class'].value_counts()))\n",
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"print('Each class in train data: \\n{}'.format(body_train['class'].value_counts()))\n",
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"print('Each class in test data: \\n{}'.format(body_test['class'].value_counts()))\n",
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"print('Each class in valid data: \\n{}'.format(body_valid['class'].value_counts()))"
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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": "781b7e0b",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "225a3cd0",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"\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": null,
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"id": "4857a167",
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"metadata": {},
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"outputs": [],
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"source": [
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"#df[\"class\"].value_counts().plot(kind=\"bar\")"
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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": "779157c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"#df[[\"class\",\"body fat_%\"]].groupby(\"class\").mean().plot(kind=\"bar\")"
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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": "da14bf43",
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"metadata": {},
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"outputs": [],
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"source": [
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"#sns.set_theme()\n",
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"\n",
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"#sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')"
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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": "6597e57c",
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"metadata": {},
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"outputs": [],
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"source": [
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"#sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
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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": "957e1b2e",
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"metadata": {},
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"outputs": [],
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"source": [
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"#sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
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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": "9f0394f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"#px.box(df, y=['height_cm',\n",
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"# 'weight_kg',\n",
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"# 'body fat_%',\n",
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"# 'diastolic',\n",
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"# 'systolic',\n",
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"# 'gripForce',\n",
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"# 'sit and bend forward_cm',\n",
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"# 'sit-ups counts',\n",
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"# 'broad jump_cm',\n",
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"# 'BMI'])"
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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": "22542bba",
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"metadata": {},
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"outputs": [],
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"source": [
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"# this is taking too long time\n",
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"#sns.pairplot(data=df.drop(columns=[\"gender\"]).head(500), hue=\"class\")"
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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": "29730d20",
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"metadata": {},
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"outputs": [],
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"source": []
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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": "dc21a9cb",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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