Exercise Performance and Health Dataset
Public Health & Epidemiology
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About
This dataset provides data that confirms the grade of physical performance based on age and various exercise performance metrics. The dataset is used for multi-class classification, where the goal is to classify individuals into performance grades.
Dataset Overview
- Data Shape: (13393, 12) – The dataset contains 13,393 records and 12 attributes.
- Age Range: 20 to 64 years.
- Gender: F (Female), M (Male).
- Target Variable (Class): A (best), B, C, D (worst). This represents the performance grade, with A being the best and D being the lowest.
Columns and Description
- Age: Age of the individual (in years).
- Gender: Gender of the individual (F for female, M for male).
- Height (cm): Height of the individual in centimeters (To convert to feet, divide by 30.48).
- Weight (kg): Weight of the individual in kilograms.
- Body Fat (%): Percentage of body fat in the individual.
- Diastolic (mmHg): Diastolic blood pressure (minimum pressure).
- Systolic (mmHg): Systolic blood pressure (maximum pressure).
- Grip Force (kg): Grip force in kilograms.
- Sit and Bend Forward (cm): The distance the individual can bend forward, measured in centimeters.
- Sit-ups Count: Number of sit-ups the individual can perform.
- Broad Jump (cm): The distance the individual can jump, measured in centimeters.
- Class: Performance grade classification (A, B, C, D).
Dataset Source
- Source: Korea Sports Promotion Foundation
- Data Processing: Some post-processing and filtering have been applied to the raw data.
Intended Use
This dataset can be used for:
- Multi-class classification tasks to predict an individual’s performance grade based on various physical and health-related factors.
- Exploring the relationship between physical metrics and overall performance classification.
- Building predictive models for fitness or health assessments using machine learning techniques.
License
- Data License: The dataset is provided for educational and non-commercial purposes.
Usage and Applications
- Health and Fitness Apps: To assess an individual’s physical performance and classify them into categories.
- Machine Learning Classification Models: Train models to predict physical performance grades (A, B, C, D) based on factors such as body fat percentage, blood pressure, grip force, and exercise performance.
- Sports Science Research: For studies on the relationship between age, gender, body measurements, and physical performance.
Data Insights
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The dataset provides insights into how different metrics such as body fat, blood pressure, and exercise performance can be related to overall physical performance classification.
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It allows exploration of physical health and performance across a wide range of ages (20 to 64 years) and can help in creating personalized fitness programs or assessments.