Netflix Movie Recommendation System Check out my blog on building a Recommender System at the following link: https://medium.com/@gauravsharma2656/how-to-built-a-recommender-system-rs-616c988d64b2

Business Problem Netflix is all about connecting people to the movies they love. To help customers find those movies, they developed world-class movie recommendation system: CinematchSM. Its job is to predict whether someone will enjoy a movie based on how much they liked or disliked other movies. Netflix use those predictions to make personal movie recommendations based on each customer’s unique tastes. And while Cinematch is doing pretty well, it can always be made better. Now there are a lot of interesting alternative approaches to how Cinematch works that netflix haven’t tried. Some are described in the literature, some aren’t. We’re curious whether any of these can beat Cinematch by making better predictions. Because, frankly, if there is a much better approach it could make a big difference to our customers and our business. Credits: https://www.netflixprize.com/rules.html

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 Netflix blog: https://medium.com/netflix-techblog/netflix-recommendations-beyond-the-5-stars-part-1-55838468f429 (very nice blog) surprise library: http://surpriselib.com/ (we use many models from this library) surprise library doc: http://surprise.readthedocs.io/en/stable/getting_started.html (we use many models from this library) installing surprise: https://github.com/NicolasHug/Surprise#installation Research paper: http://courses.ischool.berkeley.edu/i290-dm/s11/SECURE/a1-koren.pdf (most of our work was inspired by this paper) SVD Decomposition : https://www.youtube.com/watch?v=P5mlg91as1c Real world/Business Objectives and constraints Objectives: Predict the rating that a user would give to a movie that he has not yet rated. Minimize the difference between predicted and actual rating (RMSE and MAPE) Constraints: Some form of interpretability. There is no low latency requirement as the recommended movies can be precomputed earlier. Type of Data: There are 17770 unique movie IDs. There are 480189 unique user IDs. There are ratings. Ratings are on a five star (integral) scale from 1 to 5. There is a date on which the movie is watched by the user in the format YYYY-MM-DD. Getting Started Start by downloading the project and run "NetflixMoviesRecommendation.ipynb" file in ipython-notebook.

Prerequisites You need to have installed following softwares and libraries in your machine before running this project.

Python 3 Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy, scipy. XGBoost Surprise Installing Python 3: https://www.python.org/downloads/ Anaconda: https://www.anaconda.com/download/ XGBoost: conda install -c conda-forge xgboost Surprise: pip install surprise Built With ipython-notebook - Python Text Editor sklearn - Machine learning library seaborn, matplotlib.pyplot, - Visualization libraries numpy, scipy- number python library pandas - data handling library XGBoost - Used for making regression models Surprise - used for making recommendation system models