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Synthetic Obesity Classification Dataset
Nutrition, Preventive Health & Policy
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£79.99
About
This synthetic Obesity Classification Dataset is designed to support educational and research applications in data science, machine learning, and health analytics. The dataset provides detailed information about various physical factors such as age, gender, height, weight, and BMI (Body Mass Index) to help users analyze relationships between these factors and obesity classification. It is useful for building predictive models, conducting health assessments, and exploring trends in obesity-related health conditions.
Dataset Features:
- ID: Unique identifier for each individual.
- Age: Age of the individual (in years).
- Gender: Gender of the individual (Male, Female).
- Height: Height of the individual (in cm).
- Weight: Weight of the individual (in kg).
- BMI (Body Mass Index): A calculated value based on height and weight, used to categorize obesity status.
- Label: Obesity classification label (Obese, Normal Weight, Underweight).
Usage:
This dataset is valuable for various applications, including:
- Obesity Classification: To explore the relationship between physical characteristics and obesity classification, and to build models that categorize individuals based on BMI.
- Health Risk Assessment: To assess the health risks associated with different obesity categories and analyze contributing factors to body weight and health conditions.
- Predictive Modeling: To develop models that predict obesity status (Obese, Normal Weight, Underweight) based on individual physical data.
- Healthcare Research: To identify key factors influencing obesity and to explore trends in obesity across different demographics.
- Public Health Policy: To analyze trends and guide health interventions aimed at managing and preventing obesity.
Distribution:
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Coverage:
This dataset is synthetic and anonymised, ensuring it is suitable for experimentation and learning without concerns about real patient data.
License:
CC0 (Public Domain)
Who Can Use It:
- Data Science Learners: Ideal for practising data cleaning, manipulation, and building classification models.
- Healthcare Professionals and Researchers: Useful for studying obesity-related trends and contributing to health and weight management research.
- Medical Analysts: For developing models to predict obesity and evaluate treatment strategies.
- Public Health Officials: For analyzing obesity trends and making data-driven decisions to address public health concerns related to weight.
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