53 lines
1.8 KiB
Python
53 lines
1.8 KiB
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('ruchi798/movies-on-netflix-prime-video-hulu-and-disney', path='.', unzip=True)
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# odczyt danych
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film_data = pd.read_csv('MoviesOnStreamingPlatforms_updated.csv')
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# Czyszczenie wierszy z pustymi warościami.
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film_data.dropna(inplace=True)
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# Usunięcie zbędnych kolumn
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film_data.drop(film_data.columns[[0, 1]], axis = 1)
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# Normalizacja: Lowercase dla danych tekstowych, standaryzacja (0..1) dla wartości float, sortowanie danych w komórce.
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for col_name in ['Title', 'Directors', 'Genres', 'Country', 'Language']:
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film_data[col_name] = film_data[col_name].str.lower()
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for col_name in ['Directors', 'Genres', 'Country', 'Language']:
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film_data[col_name] = film_data[col_name].str.split(',').map(lambda x: ','.join(sorted(x)))
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scaler = preprocessing.MinMaxScaler()
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film_data[['IMDb', 'Runtime']] = scaler.fit_transform(film_data[['IMDb', 'Runtime']])
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# Podział zbioru na train, dev, test w proporcji 8:1:1
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train_ratio = 0.8
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validation_ratio = 0.1
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test_ratio = 0.1
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film_train, film_test = train_test_split(film_data, test_size=1 - train_ratio)
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film_valid, film_test = train_test_split(film_test, test_size=test_ratio/(test_ratio + validation_ratio))
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# Statystki głównego zbioru i podzbiorów
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for i, data_set in enumerate([film_data, film_train, film_valid, film_test]):
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if i == 0:
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print("Główny zbiór danych")
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elif i == 1:
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print("Zbiór trenujący")
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elif i == 2:
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print("Zbiór walidujący")
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if i == 3:
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print("Zbiór testowy")
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print(len(data_set))
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print(data_set.describe().loc[['count','mean', 'max', 'min', 'std', '50%']])
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[print(data_set[name].value_counts()) for idx, name in enumerate(data_set)]
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