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Machine Learning · Major Project
Ayurveda Remedy Recommender
Suggests traditional wellness practices from symptoms and dosha
2-3 weeks build
Overview
A questionnaire estimates a user's dosha profile, then a content-based recommender matches reported symptoms to traditional Ayurvedic wellness practices, herbs and diet habits from a curated knowledge base. It is framed throughout as wellness information, never treatment, and tells users to see a doctor for anything serious.
What makes it stand up
- 01Dosha questionnaire scored into a Vata, Pitta and Kapha profile
- 02Curated knowledge base with sources cited per remedy
- 03Content-based matching of symptoms, dosha and contraindications
- 04Red-flag symptoms route straight to a see-a-doctor message
- 05Presented as wellness information, not medical treatment
Module breakdown
- 01
Questionnaire
Weighted prakriti questions producing a dosha profile.
- 02
Knowledge base
Structured remedies with uses, cautions and sources.
- 03
Recommender
Similarity scoring over symptoms, dosha and cautions.
- 04
Safety layer
Red-flag rules and interaction warnings before any result.
- 05
Web app
React flow from questionnaire to saved suggestions.
After this, you will be able to
- Explain how content-based recommendation works on a knowledge base
- Defend your safety framing and red-flag rules in a viva
- Demo a questionnaire run producing tailored suggestions
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