Predictive Modeling of Teenager Obesity Risk Factors In Indonesia Using Supervised Machine Learning Techniques Dataset

The dataset used in this study was collected between 7 November 2024 and 31 May 2025, comprising a total of 351 adolescent records. Each record consists of demographic, physical, lifestyle, and behavioral attributes related to obesity risk factors. The dataset includes 19 variables: Anthropometric Variables: Gender, Age, Height, Weight, Body Mass Index (BMI), and Label (obesity category). Food Consumption Patterns: History of obesity (HOO), high-calorie food consumption (HFC), daily vegetable consumption (DVC), main meal frequency (MMF), and snack consumption frequency (SCF). Physical Activities: Smoking habit (SKH), drinking water frequency (DWF), calorie consumption monitoring (CCM), alcohol consumption (ABC), exercise habits (EXC), exercise frequency (EHF), Technology usage frequency (TUF) and type of transportation used (TPU). This dataset provides a comprehensive representation of both anthropometric variables and physical activities, making it suitable for predictive modeling of teenage obesity risk factors in Indonesia.