Machine Learning

Body Fat Percentage Prediction Using Machine Learning

The Body Fat Percentage Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts body fat percentage using anthropometric measurements. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Ridge Regression and Lasso Regression algorithms, achieving 74.6% R² score with an RMSE of 4.23%.

The system utilizes the Body Fat Percentage Using Anthropometric Measurements dataset comprising 252 samples with 14 features including Age, Weight, Height, and various circumference measurements (Neck, Chest, Abdomen, Hip, Thigh, Knee, Ankle, Biceps, Forearm, Wrist). It provides an accessible, web-based solution for predicting body fat percentage from simple physical measurements, enabling health professionals and individuals to assess obesity-related health risks conveniently and cost-effectively.

• Multi-Model Body Fat Prediction
• Ridge Regression (74.6% R²)
• Lasso Regression (73.8% R²)
• Feature Engineering (BMI, WHR)
• Interactive Visualizations
• Real-time Body Fat Predictions
• Model Performance Comparison
• Feature Importance Analysis
• 5-Fold Cross-Validation
• Flask Web Application