netflix_recommender_system

Predicting user ratings on unseen movie which can be used in recommending movies to users

Problem Statement

Netflix provided a lot of anonymous rating data, and a prediction accuracy bar that is 10% better than what Cinematch can do on the same training data set. (Accuracy is a measurement of how closely predicted ratings of movies match subsequent actual ratings.)

Sources https://www.netflixprize.com/rules.html https://www.kaggle.com/netflix-inc/netflix-prize-data

Business Objectives and constraints

Objectives: Predict the rating that a user would give to a movie that he ahs not yet rated. Minimize the difference between predicted and actual rating (RMSE and MAPE)

Constraints: Some form of interpretability.

Data files :

combined_data_1.txt combined_data_2.txt combined_data_3.txt combined_data_4.txt movie_titles.csv

Refer : https://www.kaggle.com/netflix-inc/netflix-prize-data/data

Prerequisites

1. Sparse matrices (https://docs.scipy.org/doc/scipy/reference/sparse.html)
2. SVD and Truncated SVD (https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.TruncatedSVD.html)
3. Surprise baseline models http://surpriselib.com/

There is a single ipynb file with the name Netflix_Movie.ipynb