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Machine Learning · Major Project
Personalised Diet & Nutrition Recommender
Meal plans tuned to BMI, goal, health conditions and food preference
2-3 weeks build
Overview
Takes age, height, weight, activity level, goals and conditions such as diabetes or hypertension, estimates daily energy needs, and assembles meal plans from a nutrition table built on public food-composition data. A content-based recommender learns from what the user swaps or skips.
What makes it stand up
- 01Energy targets from standard BMR and activity-factor equations
- 02Condition-aware rules that cap sugar, sodium or fat where needed
- 03Vegetarian, vegan, Jain and regional cuisine filters
- 04Content-based recommender that adapts to swaps and skips
- 05General wellness guidance that points users to a dietitian
Module breakdown
- 01
Profile intake
Form for body metrics, goals, conditions and preferences.
- 02
Nutrition table
Cleaned food items with macros and micronutrients.
- 03
Plan generator
Constraint-based daily plans that hit calorie and macro targets.
- 04
Recommender
Similarity model that re-ranks meals from feedback.
- 05
Dashboard
Weekly plan, macro charts and a shopping list export.
After this, you will be able to
- Explain how BMR and activity factors set a calorie target
- Defend your mix of hard rules and learned recommendations
- Demo a plan adapting after a user rejects several meals
Technology stack
- Python
- scikit-learn
- Pandas
- FastAPI
- React
- MongoDB
- Level
- Major Project
- Domain
- Machine Learning
- Build time
- 2-3 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