This dataset helps estimate obesity levels based on eating habits, family history and physical condition. It includes data from individuals in Mexico, Peru, and Colombia, covering 16 lifestyle and health-related features with 2111 records. The labels classify obesity levels, ranging from underweight to different obesity types. Most of the data was generated using synthetic techniques, while some were collected directly from users through a web platform. It is useful for classification, regression, and clustering tasks.
Gender – Male or Female.
Age – The person’s age in years.
Height – Height in meters.
Weight – Weight in kilograms.
family_history_with_overweight – Whether the person has a family history of being overweight (yes/no).
FAVC – If the person frequently consumes high-calorie foods (yes/no).
FCVC – Frequency of vegetable consumption (scale from 1 to 3).
NCP – Number of main meals per day.
CAEC – Frequency of consuming food between meals (Never, Sometimes, Frequently, Always).
SMOKE – Whether the person smokes (yes/no).
CH2O – Daily water intake (scale from 1 to 3).
SCC – If the person monitors their calorie intake (yes/no).
FAF – Physical activity frequency (scale from 0 to 3).
TUE – Time spent using technology (scale from 0 to 3).
CALC – Frequency of alcohol consumption (Never, Sometimes, Frequently, Always).
MTRANS – Main mode of transportation (Automobile, Bike, Motorbike, Public Transportation, Walking).
NObeyesdad – Obesity level (Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II, Obesity Type III).
This dataset provides a structured view of how different lifestyle factors relate to obesity levels, making it useful for healthcare analysis and decision-making.