A python notebook for building collaborative, content-based, and ml-based recommender systems with Sklearn and Surprise
A movie recommendation system built using Jupyter Notebook, leveraging vectorization techniques for content-based filtering. The frontend is developed with Streamlit, allowing users to interactively input preferences and receive personalized movie suggestions.
A hybrid content-based and collaborative filtering movie recommender system built using Python and Jupyter. Notebook. Designed for showcasing recommender system skills and end-to-end ML workflow.
Movie Recommendation System using Unsupervised Learning A Python-based recommendation engine built with K-Means Clustering that groups movies by genres, ratings, and popularity to suggest similar titles. Developed in a Jupyter Notebook, it demonstrates content-based filtering using real-world movie metadata.
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