• Built a recommender system to recommend 10 beers for each user that he/she has not tried yet. • Built an API coded in Python to extract ratings and beer information from BeerAdvocate website with a preprocessed format. • Visualized data and use K-fold Cross Validation to find the best split. • Built the recommender system using model-based and memory-based collaborative filtering methods. • Visualized ROC curve, calculated MAE and RMSE to select the best algorithm. • Wrote a recommendation function using the selected algorithm.