Virtual Booth

The present research uses Python and machine learning on UCI datasets to investigate obesity-related factors in Mexico, Peru, and Colombia. Through EDA and advanced techniques, it classifies and clusters individuals to reveal obesity patterns. Unsupervised learning segments populations, while supervised methods like logistic regression predict obesity levels. The study uses GUIs like SweetViz and PandaGui to analyze data using a combination of Python and Excel. This study contributes to the understanding of obesity classification using machine-learning techniques and offering actionable insights for public health interventions.