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Machine Learning · Mini Project
Movie Recommendation System
Content-based and collaborative filtering on MovieLens with TMDB posters
1-2 weeks build
Overview
Two recommenders built on the MovieLens dataset: a content-based model that matches genres, cast and overview text with cosine similarity, and a collaborative model using matrix factorisation on user ratings. The official TMDB API supplies posters and metadata, and a Streamlit app lets you pick a film or a user and compare both sets of suggestions.
What makes it stand up
- 01Content-based similarity over genres, cast, crew and plot text
- 02SVD matrix factorisation for collaborative filtering
- 03RMSE and precision@k evaluation on held-out ratings
- 04Posters and details fetched through the official TMDB API
- 05Side-by-side view of both recommenders for the same input
Module breakdown
- 01
Data merge
Joins MovieLens ratings with TMDB metadata.
- 02
Content model
Tag soup vectorisation and cosine similarity.
- 03
Collaborative model
SVD trained with the Surprise library.
- 04
Evaluation
Rating error and top-k ranking metrics.
- 05
App
Streamlit interface with poster grids.
After this, you will be able to
- Explain content-based versus collaborative filtering
- Defend how you handle the cold-start problem
- Demo recommendations for a film the examiner picks
Technology stack
- Python
- Pandas
- scikit-learn
- Surprise
- Streamlit
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support