Gym-Member-Exercise-Analysis

Project Overview

This project analyzes gym member data to uncover trends in attendance, workout preferences, and health habits. The dataset includes demographic details, session statistics, and health metrics.

Tools Used

• Python Libraries: pandas, numpy, seaborn, matplotlib
• Dataset: gym_members.csv

Key Insights

1. Most frequent gym-goers are below 43 years old.
2. Males are more active than females.
3. Cardio and strength training are the most popular workouts.
4. Around 80% of members exercise for more than 1 hour per session.
5. Only 30% of members meet the recommended daily water intake of 3 liters.
6. Most of people workout atleast 3 to 4 day of weeks.
7. Most Pepole who workout are beginner and intermediate.
8. Beats per minutes is highest around 35 year olds Humans who workout.

How to Run the Analysis

1. Clone this repository.
2. Install required Python libraries: pip install -r requirements.txt.
3. Place gym_members.csv in the project directory.
4. Run gym_analysis.py for visualizations and insights.

Future Enhancements

• Add clustering for member segmentation.
• Develop predictive models for calorie burn estimation.
• Visualize trends using dashboards (e.g., Power BI or Tableau).

Author

Swatantra Yadav

E-Mail:swatantra.yadav2027@gmail.com