ium_s487178/.ipynb_checkpoints/body_performance-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74524ede",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0177f243",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.duplicated().sum()\n",
    "print(f'with duplicates:{df.shape}')\n",
    "df.drop_duplicates(inplace=True)\n",
    "print(f'without duplicates:{df.shape}')\n",
    "df_copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8abefe6e",
   "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,
   "id": "0f3ad57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(df.describe(include='all'))\n",
    "#sit and bend forward_cm jest na minusie!!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b694be50",
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd376cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2375b677",
   "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": "781b7e0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "225a3cd0",
   "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": []
  }
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