Unveiling the Secrets to Calorie Burn: A Data-Driven Approach

As an avid physical fitness enthusiast, I've often wondered which demographic and performance factors are most significant in determining calorie burn during exercise sessions.

To answer this question, I searched for exercise datasets and came across a dataset offered by Kaggle which contains gym data on 973 gym members.

Gym members performed 1 of 4 workout types: yoga, high-intensity interval training (HIIT), cardio, and strength training.

I then used Pandas and Matplotlib libraries to visualize the relationship between each feature and my target variable, calories burned.

The scatter plot charts revealed the following:

- Body Weight has a weak positive relationship to calories burned. As weight increases, there is a slight tendency for more calories burned.

- BMI has a weak positive relationship to calories burned. As BMI increases, calories burned tend to increase slightly.

- Average Beats Per Minute (BPM) has a weak positive relationship to calories burned. Not surprisingly, the higher the AVG BPM, the more calories burned.

- Session Duration has a very strong linear relationship to calories burned and is by far the most important driver of determining calories burned.

- Body Fat Percentage has a negative relationship to calories burned, meaning the higher the member's body fat, there is a tendency for calories burned to decrease.

- Workout Frequency has a weak positive relationship to calories burned. Those who work out 4 to 5x per week are burning anywhere from 200 - 500 more calories per week than those who work out 2 to 3x per week.

- Experience Level has a weak positive relationship to calories burned. The most experienced members are burning anywhere from 200 - 300 more calories per week.

When it comes to interpreting the charts, data points that group up and to the right, as seen in the "Session_Duration" chart, indicate that as the X value increases, so does the Y value, implying a positive correlation.

Data points that group down and to the right, like the "Fat Percentage" chart, indicate that as the X value increases, the Y value decreases, implying a negative correlation.

Charts with data points scattered in no discernable direction or cluster indicate that there is likely no correlation between your features and your target variable.

These visualizations helped me understand the key to burning calories is really about session duration, regardless of the type of workout! Additionally, increasing workout frequency and experience level can lead to higher calorie burn.

Interestingly, lower body weight and body fat percentage likely result in higher calorie burn. One hypothesis to explain this could be due to increased metabolic efficiency at lower weights and BMIs, as lean muscle mass is more metabolically active than fat mass.

Here's a sampling of the charts I created:

From there I used scikit-learn's Linear Regression algorithm to build and train a model using this data, allowing me to generate predictions on calories burned based on the features (inputs) I've described above.

As I reflect on the charts and the results of the model, I see three key takeaways that are critical to increasing calorie burn rates for those seeking to transform their physique:

1. Time - The dataset unequivocally demonstrates that the more time spent exercising the more calories will be burned. Interestingly, the type of workout didn't significantly impact calories burned; the time spent performing the exercise appears to be the primary factor.
2. Experience level - The data demonstrated a minor correlation between a higher experience level and increased calorie burn over newer members. Although Kaggle didn't provide details about how the experience level is acquired or developed, it's plausible that experienced members have longer tenure and may have worked with a personal trainer.
3. Frequency - Although not surprising, the data demonstrates those who work out 4x or more a week are burning far more calories.

Frequency is like the multiplier symbol in this equation:

• time * frequency + experience level = potential number of calories burned

So, why is understanding the number of calories burned important? The key to transforming one's body composition, where weight reduction is the goal, is understanding the number of calories burned vs. the number of calories consumed. Those who can create a caloric deficit through exercise and smart dieting will see significant changes in their body composition - especially with proper nutrition.

Thanks for reading.

** Google Gemini was used to generate the article title. It also assisted in classifying the charts in terms of strong/weak and negative/positive correlations.

#datascience #data #gym #workouts #python #matplotlib #sklearn #linearregression #kaggle