The number of members of fitness centers and health clubs within the United States has experienced a near continual increase between 2000 and 2024. In 2024, there were found to be around ** million members of fitness centers and health clubs within the U.S., the greatest number during the period of observation.

README - shylee presenty The project is based on the “Gym Members Exercise Tracking Synthetic Data” dataset from Kaggle, which contains approximately 1,800 records. Each record represents an individual gym member and includes around 15 physical and behavioral attributes. The physiological features include: Age Gender Weight Height BMI Body fat percentage Heart rate measurements (Resting / Avg / Max BPM) In addition, the dataset includes workout-related features such as: Session duration… See the full description on the dataset page: https://huggingface.co/datasets/shylee1/gym_members_exercise_tracking_synthetic_data.

This dataset contains the predicted prices of the asset GO GYM over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

This fitness dataset provides personalized exercise plans tailored to individuals' physical features, activity levels, and gender. It is designed to assist individuals in achieving their fitness goals by offering customized workout routines that optimize effectiveness and safety.

Key Features:

Physical Features: The dataset includes physical attributes such as height (h), weight (w), body mass index (BMI), body fat percentage, muscle mass, and other relevant metrics. These features are crucial for determining an individual's baseline fitness level and guiding exercise recommendations. Gender: Gender is an essential factor in designing personalized exercise plans. The dataset categorizes individuals into different gender groups to account for physiological differences and tailor workouts accordingly. Activity Levels: The dataset captures information about individuals' activity levels, including their daily physical activity, exercise frequency, intensity, and duration. Understanding activity levels helps in prescribing appropriate workout regimens that align with individuals' lifestyles and fitness goals. Exercise Preferences: Individuals may have preferences for specific types of exercises, such as cardio, strength training, flexibility, or endurance activities. The dataset includes information about exercise preferences to ensure that recommended workout plans are enjoyable and sustainable. Fitness Goals: The dataset allows individuals to set personalized fitness goals, such as weight loss, muscle gain, improved endurance, or overall health and wellness. Exercise plans are tailored to help individuals achieve their specific objectives effectively and efficiently.