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Machine Learning · Mini Project
Book Recommendation System
Personalised book suggestions from ratings and descriptions
1-2 weeks build
Overview
Recommends books using the public Goodbooks-10k dataset. A popularity model handles new visitors, item-based collaborative filtering suggests titles similar to ones a reader liked, and a TF-IDF model over tags covers books with few ratings. A Flask site lets a reader rate a few titles and get a personalised list.
What makes it stand up
- 01Popularity, item-based and content-based recommenders
- 02Cold-start handling for new readers and rarely rated books
- 03Precision@k and coverage measured on held-out ratings
- 04Similar-books panel on every book page
- 05Rate-a-few onboarding flow in the Flask site
Module breakdown
- 01
Data prep
Filters sparse users and books and builds the rating matrix.
- 02
Popularity model
Weighted rating score for new visitors.
- 03
Collaborative model
Item-item similarity on user ratings.
- 04
Content model
TF-IDF over tags and titles.
- 05
Web app
Flask pages for browsing, rating and suggestions.
After this, you will be able to
- Explain item-based collaborative filtering step by step
- Defend how the system treats a brand new user
- Demo a personalised list after rating five books
Technology stack
- Python
- Pandas
- scikit-learn
- Surprise
- Flask
- 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