obesity-classification

The purpose of this project's design, development, and structure is to create an end-to-end Machine Learning Operations (MLOps) lifecycle to classify an individual's level of obesity based on their physical characteristics and eating habits.

Obesity Level Classification using Random Forest with SHAP and LIME Explainable AI. Predicts obesity categories from lifestyle and health attributes while providing transparent, interpretable model explanations through feature importance analysis and local prediction explanations.

Pediatric growth analyzer using Belarusian centile tables. Calculates SDS, centile corridors, BMI, and detects growth disorders