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Lifestyle and Obesity Estimation Data
Public Health & Epidemiology
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About
Data provides detailed variables intended for the estimation of obesity levels in individuals. Various factors are considered to analyse the health and lifestyle of respondents, including demographics, physical activity, and detailed dietary habits. The dataset examines the interplay of lifestyle choices, health-related behaviours, and specific consumption patterns, enabling detailed analysis of factors influencing weight status. The central purpose is to provide a crucial health indicator that classifies individuals as obese or not.
Columns
- Gender: A binary variable indicating the sex of the respondent (1 for males, 0 for females).
- Age: Represents the respondent's age measured in years.
- family_history_with_overweight: Denotes the presence (1) or absence (0) of a family history related to overweight individuals.
- FAVC: Indicates the frequent consumption of high-caloric foods.
- FCVC: Reflects the regular intake of vegetables.
- NCP: Captures the number of main meals consumed daily, scaled (0 for 1-2 meals, 1 for 3 meals, 2 for more than 3 meals).
- CAEC: Quantifies the amount of food consumed between main meals on a scale ranging from 0 to 3.
- SMOKE: Indicates smoking habits.
- CH2O: Measures daily water intake on a scale from 0 to 2.
- SCC: Signifies adherence to monitoring caloric intake, where 1 means adherence.
- FAF: Expresses physical activity levels, scaled from 0 to 3.
- TUE: Captures the time spent looking at screens, offering insights into sedentary behaviours, scaled from 0 to 2.
- CALC: Codes the frequency of alcohol consumption, with values ranging from 0 to 3.
- Automobile, Bike, Motorbike, Public_Transportation, Walking: These variables illustrate the respondent's primary mode of transportation, with a value of 1 for the primary mode and 0 for others.
- NObeyesdad: The target variable, which categorises respondents as obese (1) or not (0).
Distribution
The data is available in a standard tabular format, typically referenced as
obesity new.csv. The dataset contains 19 columns and includes 2,111 valid records. Data integrity is robust, with all sampled columns demonstrating 100% validity and no missing values recorded. The structure is suitable for detailed statistical analysis and machine learning applications.Usage
This data is suitable for several advanced analytical applications. It can be used for developing and training machine learning models intended to predict obesity risk. Statistical analysis can be performed to isolate key drivers relating dietary choices and activity levels to health outcomes. Furthermore, researchers and healthcare professionals can use the data to gain valuable insights into population patterns contributing to obesity, informing public health strategies.
Coverage
The dataset focuses on diverse aspects of individual lifestyle, dietary choices, and physical activity levels across 2,111 respondents. Demographic variables include Gender and Age. The variables provide a detailed snapshot of health-related behaviours and consumption patterns. The expected update frequency for this data is Annually.
License
CC0: Public Domain
Who Can Use It
- Academic Researchers: To study the relationship between lifestyle, environment, and physical health, particularly weight management.
- Data Scientists: For advanced predictive modelling, correlation analysis, and feature engineering related to health outcomes.
- Healthcare Professionals and Planners: For understanding specific risk factors and informing targeted patient interventions or community health campaigns.
Dataset Name Suggestions
- Obesity_worlds
- Lifestyle and Obesity Estimation Data
- Health Behavioural Indicators for Weight Prediction
- Diet, Activity, and Obesity Predictors
Attributes
Original Data Source: Lifestyle and Obesity Estimation Data
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