score.py

# 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)