159 lines
2.9 KiB
Python
159 lines
2.9 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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import pandas as pd
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import plotly.express as px
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import seaborn as sns
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import os
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from sklearn.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler
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df = pd.read_csv(os.path.join('.', 'body_performance.csv'))
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df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])
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print(df.head())
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# In[ ]:
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df.duplicated().sum()
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print(f'with duplicates:{df.shape}')
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df.drop_duplicates(inplace=True)
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print(f'without duplicates:{df.shape}')
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df_copy = df.copy()
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# In[ ]:
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body_train, body_test = train_test_split(df, test_size=int(df["age"].count()*0.2), random_state=1)
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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)
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print("number of elements in data frame: {}".format(df['age'].count()))
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print("train: {}".format(body_train["age"].count()))
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print("test: {}".format(body_test["age"].count()))
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print("valid: {}".format(body_valid["age"].count()))
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# In[ ]:
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print(df.describe(include='all'))
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#sit and bend forward_cm jest na minusie!!!
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# In[ ]:
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scaler = MinMaxScaler()
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df[['age', 'height_cm', 'weight_kg','body fat_%',
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'diastolic','systolic','gripForce','sit-ups counts',
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'broad jump_cm','BMI']] = scaler.fit_transform(df[[
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'age', 'height_cm', 'weight_kg','body fat_%',
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'diastolic','systolic','gripForce','sit-ups counts',
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'broad jump_cm','BMI']])
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scaler = MinMaxScaler(feature_range=(-1, 1))
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df['sit and bend forward_cm'] = scaler.fit_transform(df[['sit and bend forward_cm']])
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df.describe(include='all')
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# In[ ]:
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df.info()
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# In[ ]:
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print('Each class in data frame: \n{}'.format(df['class'].value_counts()))
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print('Each class in train data: \n{}'.format(body_train['class'].value_counts()))
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print('Each class in test data: \n{}'.format(body_test['class'].value_counts()))
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print('Each class in valid data: \n{}'.format(body_valid['class'].value_counts()))
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# In[ ]:
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# In[ ]:
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# In[ ]:
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#df["class"].value_counts().plot(kind="bar")
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# In[ ]:
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#df[["class","body fat_%"]].groupby("class").mean().plot(kind="bar")
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# In[ ]:
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#sns.set_theme()
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#sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')
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# In[ ]:
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#sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')
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# In[ ]:
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#sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')
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# In[ ]:
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#px.box(df, y=['height_cm',
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# 'weight_kg',
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# 'body fat_%',
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# 'diastolic',
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# 'systolic',
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# 'gripForce',
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# 'sit and bend forward_cm',
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# 'sit-ups counts',
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# 'broad jump_cm',
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# 'BMI'])
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# In[ ]:
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# this is taking too long time
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#sns.pairplot(data=df.drop(columns=["gender"]).head(500), hue="class")
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# In[ ]:
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# In[ ]:
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print("no elo mordeczko, która tu dotarła")
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