This dataset provides a detailed overview of gym members' exercise routines, physical attributes, and fitness metrics. It contains 973 samples of gym data, including key performance indicators such as heart rate, calories burned, and workout duration. Each entry also includes demographic data and experience levels, allowing for comprehensive analysis of fitness patterns, athlete progression, and health trends.

Key Features:

• Age: Age of the gym member.
• Gender: Gender of the gym member (Male or Female).
• Weight (kg): Member’s weight in kilograms.
• Height (m): Member’s height in meters.
• Max_BPM: Maximum heart rate (beats per minute) during workout sessions.
• Avg_BPM: Average heart rate during workout sessions.
• Resting_BPM: Heart rate at rest before workout.
• Session_Duration (hours): Duration of each workout session in hours.
• Calories_Burned: Total calories burned during each session.
• Workout_Type: Type of workout performed (e.g., Cardio, Strength, Yoga, HIIT).
• Fat_Percentage: Body fat percentage of the member.
• Water_Intake (liters): Daily water intake during workouts.
• Workout_Frequency (days/week): Number of workout sessions per week.
• Experience_Level: Level of experience, from beginner (1) to expert (3).
• BMI: Body Mass Index, calculated from height and weight.

This dataset is ideal for data scientists, health researchers, and fitness enthusiasts interested in studying exercise habits, modeling fitness progression, or analyzing the relationship between demographic and physiological data. With a wide range of variables, it offers insights into how different factors affect workout intensity, endurance, and overall health.

1) Data Introduction • The Gym Members Exercise Dataset is a dataset built to systematically collect gym members' exercise routines, body information, exercise habits, and fitness indicators to analyze individual exercise patterns and health conditions.

2) Data Utilization (1) Gym Members Exercise Dataset has characteristics that: • This dataset contains various body and exercise related numerical and categorical variables such as age, gender, weight, height, body fat percentage, BMI, exercise type (e.g., aerobic, muscular, yoga, HIIT), exercise frequency, session time, heart rate (maximum, average, rest), calorie consumption, water intake, and experience level. (2) Gym Members Exercise Dataset can be used to: • Exercise effect analysis and customized fitness strategy: Various variables such as exercise type, frequency, session time and heart rate, calorie burn, body fat percentage, etc. can be analyzed and used to establish customized exercise plans for each member and optimize exercise effectiveness. • Healthcare and Member Characteristics Based Marketing: Based on demographics and exercise habit data such as age, gender, and experience level, it can be used to develop healthcare programs, segment members, and establish targeted marketing strategies.