A pure Python implementation of an Item-Based Collaborative Filtering recommendation system. This project demonstrates the foundational machine learning techniques used by companies like Amazon and Netflix to drive revenue through personalized user experiences.
Instead of relying on black-box libraries, this engine manually constructs a user-item matrix and computes Cosine Similarity to discover organic relationships between products based entirely on user behavior.
- User-Item Matrix Construction: Transforms raw transactional data (users, items, ratings) into a mathematical vector space.
- Algorithmic Similarity Scoring: Calculates precise similarity metrics between items using Cosine Similarity, handling sparse datasets effectively.
- Dynamic Top-N Recommendations: Generates ranked, percentage-matched product recommendations for any given item in the database.
- Cold-Start Handling: Foundation laid for handling unrated items (NaN imputation).
- Language: Python 3.x
- Core Libraries: *
pandas(Pivot tables, data wrangling, matrix manipulation)numpy(Vector operations)scikit-learn(cosine_similaritymetric calculations)
- ML Concepts: Collaborative Filtering, Dimensionality structuring, Vector space modeling.
Ensure Python is installed along with the required data science libraries:
pip install pandas numpy scikit-learn