In addition, the system includes an interactive user interface built with Streamlit, allowing users to explore recommendations through genre filters, keyword search, and similarity-based results. The project also involved data preprocessing, feature selection, and combining textual and numerical data to improve recommendation quality, with results ranked based on similarity scores, ratings, and popularity.
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Anime Recommendation System
Developed a content-based filtering engine in Python using TF-IDF Vectorization and Cosine Similarity to recommend media based on user preferences.
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