Movie Recommendation System
Completed2024
Content-based filtering for personalized movie recommendations. Utilizes user ratings and movie metadata to generate tailored suggestions. Implemented both TF-IDF and Count Vectorization methods for feature extraction and similarity scoring. Built with Python, Pandas, and Scikit-learn, and supports scalable batch processing.
About This Project
Content-based filtering for personalized movie recommendations. Utilizes user ratings and movie metadata to generate tailored suggestions. Implemented both TF-IDF and Count Vectorization methods for feature extraction and similarity scoring. Built with Python, Pandas, and Scikit-learn, and supports scalable batch processing.
Technologies Used
PythonPandasScikit-learn
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