AI-Powered Diet Analysis & Nutrient Predictor
Personalized Nutritional Insights through Machine Learning
The AI-Powered Diet Analysis & Nutrient Predictor was designed to address the gap between general dietary advice and truly personalized nutrition. Led by Hridank Bhagath under the mentorship of Mr. Aman Kesarwani, the project applied regression-based models, particularly Random Forest Regressors, to predict daily nutrient requirements for individuals based on multi-variable health inputs such as age, weight, activity level, and lifestyle factors. The team built its own comprehensive dataset by collating and cleaning information through large-scale web scraping, ensuring diversity and reliability of training data. This project served as an early milestone in applying machine learning to healthcare, blending data science with real-world wellness applications. It provided practical learnings in regression modeling, dataset engineering, and the challenges of personalization in predictive health tools.
- Random Forest regression model for nutrient requirement prediction
- Dataset built through extensive web scraping and data curation
- Integration of multiple health and lifestyle variables for accurate insights
- Team-based development structure with defined roles across 3 members
- Python-based implementation with scikit-learn and data preprocessing libraries
- User-focused design for translating AI predictions into practical diet insights
- Building a diverse and clean dataset from scattered online sources
- Ensuring regression models generalized well across different body types and lifestyles
- Balancing model complexity with interpretability for practical diet planning
- Validating predictions against established nutritional standards
- Coordinating workflows across a small but specialized team
- Showcases how AI can democratize personalized nutrition planning
- Demonstrated early integration of regression models into healthcare applications
- Helped team members build applied skills in data science and machine learning
- Highlighted the power of combining mentorship with independent innovation
- Laid groundwork for more advanced AI-driven healthcare initiatives