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This notebook presents a concise analysis for predicting obesity risk using machine learning models like Random Forest and XGBoost. Focused on identifying key factors influencing obesity through exploratory data analysis (EDA) and predictive modeling, the notebook offers insights into effective prevention strategies.
This repository presents a project focused on predicting obesity levels using machine learning models based on dietary habits, physical activity, and genetic factors. It includes data querying scripts, preprocessing guidelines, and detailed analysis notebooks to explore and model obesity risk factors effectively.
This repository presents a project focused on predicting obesity levels using machine learning models based on dietary habits, physical activity, and genetic factors. It includes data querying scripts, preprocessing guidelines, and detailed analysis notebooks to explore and model obesity risk factors effectively.