32 lines
919 B
Python
32 lines
919 B
Python
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn import preprocessing
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import kaggle
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kaggle.api.authenticate()
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kaggle.api.dataset_download_files('martj42/international-football-results-from-1872-to-2017', path='.', unzip=True)
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results = pd.read_csv('results.csv')
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#brak wierszy z NaN
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results.dropna()
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#normalizacja itp
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for collumn in ['home_team', 'away_team', 'tournament', 'city', 'country']:
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results[collumn] = results[collumn].str.lower()
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# Podział zbioru 6:1:1
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train, test = train_test_split(results, test_size= 1 - 0.6)
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valid, test = train_test_split(test, test_size=0.5)
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print("All data: ", results.size)
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print("Train size: ", train.size)
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print("Test size: ", test.size)
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print("Validate size: ", valid.size)
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print(results.describe(include='all'))
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# sprawdzenie czy cały dataset oraz podział na podzbiory jest równy
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print(train.size+test.size+valid.size)
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