netflix-movie-recommendation

Deployed app: https://movie-recommendation-ucb.herokuapp.com/
(demo version, due to file storage and processing constraints)

Project Proposal:

Team members:

• Girija Ghali
• Kevin McCurdy
• Manel Mahroug

Objective:

Create a recommendation engine for movies using the publicly-available Netflix data: https://www.kaggle.com/netflix-inc/netflix-prize-data.

The goal is to create movie recommendations to users based on the movies they rated highly. The data contain over 100 million ratings from 480 thousand randomly-chosen, anonymous Netflix customers over 17 thousand movie titles.
The data were collected between October, 1998 and December, 2005 and reflect the distribution of all ratings received during this period. The ratings are on a scale from 1 to 5 (integral) stars. To protect customer privacy, each customer id has been replaced with a randomly-assigned id. The date of each rating and the title and year of release for each movie id are also provided.

Methods:

Data cleaning and exploration:

• Jupyter notebook
• Pandas
• Matplotlib
• Seaborn

Recommendation engine:

Machine learning library used:

• Scikit-surprise