obesity {SLmetrics} | R Documentation |
Obesity levels dataset
Description
This dataset is used to estimate obesity levels based on eating habits and physical condition. The data originates from the UCI Machine Learning Repository and has been preprocessed to include both predictors and a target variable.
The dataset is provided as a list with two components:
- features
A data frame containing various predictors related to lifestyle, eating habits, and physical condition. The variables include:
- age
The age of the individual in years.
- height
The height of the individual in meters.
- family_history_with_overweight
Binary variable indicating whether the individual has a family history of overweight (1 = yes, 0 = no).
- favc
Binary variable indicating whether the individual frequently consumes high-calorie foods (1 = yes, 0 = no).
- fcvc
The frequency of consumption of vegetables in meals.
- ncp
The number of main meals consumed per day.
- caec
Categorical variable indicating the frequency of consumption of food between meals. Typical levels include
"no"
,"sometimes"
,"frequently"
, and"always"
.- smoke
Binary variable indicating whether the individual smokes (1 = yes, 0 = no).
- ch2o
Daily water consumption (typically in liters).
- scc
Binary variable indicating whether the individual monitors calorie consumption (1 = yes, 0 = no).
- faf
The frequency of physical activity.
- tue
The time spent using electronic devices (e.g., screen time in hours).
- calc
Categorical variable indicating the frequency of alcohol consumption. Typical levels include
"no"
,"sometimes"
,"frequently"
, and"always"
.- male
Binary variable indicating the gender of the individual (1 = male, 0 = female).
- target
A list containing two elements:
- regression
A numeric vector representing the weight of the individual (used as the regression target).
- class
A factor indicating the obesity level classification. The levels are derived from the original
nobeyesdad
variable in the dataset.
Usage
data(obesity)
Format
A list with two components:
- features
A data frame containing various predictors related to eating habits, physical condition, and lifestyle.
- target
A list with two elements:
regression
(weight in kilograms) andclass
(obesity level classification).
References
Palechor, Fabio Mendoza, and Alexis De la Hoz Manotas. "Dataset for estimation of obesity levels based on eating habits and physical condition in individuals from Colombia, Peru and Mexico." Data in brief 25 (2019): 104344.