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Body performance categories were predicted using health and physical metrics such as body fat, grip strength, BMI, sit-ups, and broad jump. 

After preprocessing and feature engineering (e.g., MAP, BMI), descriptive analysis revealed key patterns.

Gradient Boosting, XGBoost, and Random Forest models were tuned using Grid Search and cross-validation, with Gradient Boosting achieving the highest accuracy and strong generalization. 

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