Description: Worked as part of a team to analyze large scale book rating data and build a recommender system using collaborative filtering techniques. The project focused on generating personalized book recommendations based on user similarity and rating patterns. Key Contributions: 1.Cleaned and preprocessed user-book rating datasets using Python (Pandas, NumPy). 2.Reduced dataset to the top active users and books to improve performance. 3.Built a user-based collaborative filtering model using Nearest Neighbors with cosine similarity. 4.Generated top 5 personalized book recommendations for each user. 5.Merged recommendation results with book title data and exported final results to CSV. 6.Applied sparse matrix techniques for efficient big data processing.