upd 5
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@ -17,9 +17,9 @@ y = data['Rating']
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# Preprocess the data
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# Preprocess the data
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# Convert the categorical columns into numerical representations
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# Convert the categorical columns into numerical representations
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mlb = MultiLabelBinarizer()
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mlb = MultiLabelBinarizer()
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X['Generes'] = mlb.fit_transform(X['Generes'])
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X['Generes'] = mlb.fit_transform(X['Generes']).tolist()
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X['Plot Kyeword'] = mlb.fit_transform(X['Plot Kyeword'])
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X['Plot Kyeword'] = mlb.fit_transform(X['Plot Kyeword']).tolist()
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X['Top 5 Casts'] = mlb.fit_transform(X['Top 5 Casts'].astype(str))
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X['Top 5 Casts'] = mlb.fit_transform(X['Top 5 Casts'].astype(str)).tolist()
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# Split the data into training and testing sets
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# Split the data into training and testing sets
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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