Our analysis revolves around the MovieLens 1M Dataset, a rich collection of movie ratings by numerous users. We started by loading the dataset and conducting preliminary checks for data integrity.
Following a brief exploration, we split the data into training (80%) and testing (20%) sets. By transforming this data into user-item matrices, we're now set to build and refine our recommendation system. As we delve deeper, we'll uncover insights and methodologies to recommend top movies tailored for each user's preferences with the help of Collaborative Filtering.