M2 - Data Science Project - Universite Paris Saclay
Recommender systems are among the most popular applications of data science today. They are used to predict the "rating" or "preference" that a user would give to an item. Almost every major tech company has applied them in some form. Amazon uses it to suggest products to customers, YouTube uses it to decide which video to play next on autoplay, and Facebook uses it to recommend pages to like and people to follow.
Collaborative filtering engines: these systems are widely used, and they try to predict the rating or preference that a user would give an item-based on past ratings and preferences of other users. Collaborative filters do not require item metadata like its content-based counterparts.
I used Collaborative filtering - Item Based approach, In this code I applied two approaches:
- Movies prediction on the basis of KNN
- Movie Rating prediction using Pearson similarity and then Movies prediction.
DataSet : MovieLens Data