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
- 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.