Exercise Performance and Health Dataset

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

• 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

• 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.
• 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.