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Machine Learning · Major Project
Grocery Recommendation with Collaborative Filtering
Basket-aware product suggestions trained on Instacart order history
3-4 weeks build
Overview
Built on the public Instacart Market Basket Analysis dataset, this kit recommends groceries two ways: association rules mined with FP-Growth for items bought together, and implicit-feedback matrix factorisation for items a shopper is likely to reorder. A FastAPI service backs a React basket page that updates suggestions as items are added.
What makes it stand up
- 01FP-Growth association rules with support, confidence and lift
- 02Implicit-feedback ALS for personalised reorder suggestions
- 03Recall@k and MAP@k measured on held-out orders
- 04Popularity baseline included for honest comparison
- 05Live basket page that re-ranks suggestions as you shop
Module breakdown
- 01
Data loading
Joins orders, products, aisles and departments.
- 02
Rule mining
Frequent itemsets and association rules.
- 03
Factorisation
ALS model on the user-product interaction matrix.
- 04
Serving API
FastAPI endpoints for basket and user suggestions.
- 05
Storefront
React basket with live recommendations.
After this, you will be able to
- Explain support, confidence and lift with examples
- Defend implicit feedback over explicit ratings for groceries
- Demo suggestions changing as items are added to a basket
Technology stack
- Python
- Pandas
- scikit-learn
- FastAPI
- React
- PostgreSQL
- Level
- Major Project
- Domain
- Machine Learning
- Build time
- 3-4 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