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- # My own recommender system
- # half/half recommendation based on scaled weighted average & popularity score
- from sklearn import preprocessing
- min_max_scaler = preprocessing.MinMaxScaler()
- movies_scaled = min_max_scaler.fit_transform(movies_clean[['weighted_average', 'popularity']])
- movies_norm = pd.DataFrame(movies_scaled, columns=['weighted_average', 'popularity'])
- movies_norm.head()
- movies_clean[['norm_weighted_average', 'norm_popularity']] = movies_norm
- movies_clean['score'] = movies_clean['norm_weighted_average'] * 0.5 + movies_clean['norm_popularity'] * 0.5
- movies_scored = movies_clean.sort_values(['score'], ascending=False)
- movies_scored[['original_title', 'norm_weighted_average', 'norm_popularity', 'score']].head(20)
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