Yelp Restaurant Recommendation Project

Objective

To build different types of recommendation systems using the yelp training data to predict the ratings/stars for given user ids and business ids. You can make any improvement to your recommendation system in terms of speed and accuracy.

Models Used

Item-based CF
XGBoost + Catboost
Catboost Regression
Linear Regression

Final Results

Train RMSE: 0.9722, Validation RMSE: 0.9742, Test RMSE: Ranked #3 out of 300 (RMSE undisclosed).
The final model was mixed using XGBoost, Catboost, and CF with user friends, then refined with grid-search on many different parameters.