In this notebook team ES2 analyzed 8 csv files containing information obtained from movies and users ratings of the movies. We then proceed to build 5 models to see which one would accurately be able to predict what a user would rate a movie that have not yet seen.
In today’s technology driven world, recommender systems are socially and economically critical for ensuring that individuals can make appropriate choices surrounding the content they engage with on a daily basis. One application where this is especially true surrounds movie content recommendations; where intelligent algorithms can help viewers find great titles from tens of thousands of options.…ever wondered how Netflix, Amazon Prime, Showmax, Disney and the likes somehow know what to recommend to you?…it's not just a guess drawn out of the hat. There is an algorithm behind it.
Data flix has been tasked by EDSA to construct a recommendation algorithm based on content or collaborative filtering, capable of accurately predicting how a user will rate a movie they have not yet viewed based on their historical preferences.
The data we were provided with consisted of several million 5-star ratings obtained from users of the online MovieLens movie recommendation service. The MovieLens data set has long been used by industry and academic researchers to improve the performance of explicitly-based recommender systems, and now you get to as well!