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Health and Lifestyle Obesity Dataset
Patient Health Records & Digital Health
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About
This dataset is designed for the estimation of obesity levels in individuals, providing insights into the correlation between eating habits, physical condition, and obesity. It serves as a valuable resource for studies in public health, nutrition, and machine learning applications aimed at predicting and understanding obesity.
Columns
The dataset contains 17 attributes, including:
- Gender: The individual's gender.
- Age: The individual's age.
- Height: The individual's height in metres.
- Weight: The individual's weight in kgs.
- family_history: Indicates whether a family member has suffered or suffers from overweight.
- FAVC: Reflects the frequency of consuming high caloric food.
- FCVC: Describes how often vegetables are consumed in meals.
- NCP: The number of main meals consumed daily.
- CAEC: Indicates whether food is eaten between meals.
- SMOKE: Denotes if the individual smokes.
- CH2O: The daily water intake in litres.
- SCC: Whether calories are monitored daily.
- FAF: The frequency of physical activity.
- TUE: The amount of time spent using technological devices (e.g., mobile phone, video games, television, computer).
- CALC: The frequency of alcohol consumption.
- MTRANS: The usual mode of transportation.
- Obesity_level: The target column, classifying obesity into levels such as Insufficient Weight, Normal Weight, Overweight Level I, Overweight Level II, Obesity Type I, Obesity Type II, and Obesity Type III.
Distribution
The dataset is provided as a CSV file and contains 2111 records. The file size is approximately 263.63 kB.
Usage
This dataset is ideal for a variety of applications, including:
- Obesity Prediction Models: Developing machine learning models to predict an individual's obesity level.
- Exploratory Data Analysis: Analysing relationships between lifestyle factors, eating habits, and obesity.
- Public Health Research: Investigating obesity trends and risk factors within populations.
- Educational Purposes: Serving as a practical dataset for classification, regression, and clustering exercises in data science and statistics courses.
Coverage
The data encompasses individuals from Mexico, Peru, and Colombia. It focuses on various aspects of eating habits and physical condition relevant to obesity estimation. The dataset does not have expected updates and is considered a static resource.
License
Attribution 4.0 International (CC BY 4.0)
Who Can Use It
This dataset is particularly useful for:
- Data Scientists and Machine Learning Engineers: For building and testing predictive models.
- Public Health Researchers: To understand epidemiological patterns of obesity.
- Nutritionists and Dietitians: To identify contributing factors to weight management challenges.
- Students and Educators: For learning and teaching data analysis, statistics, and machine learning techniques, especially at a beginner level.
Dataset Name Suggestions
- Obesity Level Prediction Dataset
- Eating Habits and Physical Condition for Obesity Estimation
- Global Obesity Risk Factors Data
- Health and Lifestyle Obesity Dataset
- BMI Prediction Features
Attributes
Original Data Source: Health and Lifestyle Obesity Dataset
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