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Hybrid Recommender System

This project is a hybrid recommender system that suggests movies, anime, and video games using a combination of content-based filtering (TF-IDF + cosine similarity) and popularity metrics (engagement, reviews, audience size).

It was built as part of my Data Research Analyst internship at Cooledtured, where I worked with over 170,000+ rows of data across multiple APIs and integrated them with Google Sheets + Python pipelines

Features

  • Scraped and processed 170,000+ rows of data from movies, anime, and video games APIs.
  • Built a hybrid recommender: TF-IDF similarity + popularity (owners, reviews, peak players).
  • Implemented an interactive Gradio dashboard to explore recommendations.
  • Integrated Google Sheets for collaborative data storage and updates.
  • Managed data pipelines: collection → processing → model → dashboard.