Introduction
In the bustling world of fitness, GoalZone Fitness Club, a leading chain in Canada, faced a challenge that many fitness centers encounter – the eternal battle for space in popular classes.
As they strive to meet the ever-growing demand for their fitness classes, GoalZone sought to maximize class availability and attendance. This is where data science entered the picture.
The Challenge
GoalZone Fitness Club aimed to optimize their class schedules by predicting which members were likely to miss their booked classes.
By identifying members who might not attend, they could open up spaces for other eager fitness enthusiasts and, at the same time, ensure a better attendance rate.
The Data
To tackle this challenge, I embarked on a data science journey armed with data collected from GoalZone’s fitness clubs across Canada.
The dataset included various features such as weight, days before class registration, day of the week, time of the class, and category (including cycling, HIIT, and others). The target variable we aimed to predict was named ‘attended.’
Data Exploration:
My journey began with data exploration. I delved into the dataset to understand its characteristics, checking for missing values, outliers, and relationships between variables. This phase was crucial in preparing the data for modeling.
Data Preprocessing:
Cleaning the data was the next step. I handled missing values, encoded categorical variables, and normalized numerical features. This preparation ensured the data was in top shape for model training.
EDA
Key Points
Midweek Busiest Days: Wednesday and Thursday are the busiest days at the fitness club, which is somewhat unexpected as weekends are traditionally considered the busiest days in the fitness industry. This suggests that members may prefer to work out during the middle of the week, possibly due to work schedules or other commitments.
Low Attendance on Saturdays: Saturday, which is typically considered a prime workout day, experiences lower attendance. This could be due to various factors, such as members prioritizing weekend leisure or having other weekend plans.
Weight and Attendance: There appears to be a correlation between lower weight and higher attendance. Members with lower weights are more likely to attend classes, indicating that they may be more committed to their fitness goals or find classes more beneficial.
Cycling vs. Strength Classes: Cycling classes have the highest attendance, while Strength classes have the lowest attendance. This suggests that the club may want to explore ways to promote Strength classes or consider adjusting class schedules to better suit member preferences.
Intermediate Members Most Active: Intermediate members are more active than both new and veteran members. This suggests that members who have been with the club for a moderate duration are more engaged and dedicated to attending classes.
Veteran and Long-Term Members’ Attendance: Surprisingly, veteran and long-term members attend classes less frequently. This may be due to complacency or the need for new and engaging class offerings to keep long-term members motivated.
Evening Sessions Preferred: Members tend to prefer attending sessions in the evening. This information can help the club optimize class schedules to accommodate member preferences.
Recommendations
Optimize Class Schedules: Given that Wednesday and Thursday are the busiest days, consider offering more classes or extending class hours on these days to accommodate the higher demand. On Saturdays, explore ways to attract more members, such as special weekend-themed classes or events.
Promote Strength Classes: Since Strength classes have the lowest attendance, develop marketing strategies to increase interest and participation in these classes. Highlight the benefits of strength training and offer trial sessions to encourage attendance.
Engage Long-Term and Veteran Members: Implement programs or incentives to re-engage long-term and veteran members. Special loyalty rewards, personalized workouts, or introducing new class formats may help retain these members.
Weight-Based Promotions: Consider weight-based promotions or challenges to encourage members to achieve their fitness goals. Recognize and reward members who achieve their target weights.
Evening Class Expansion: Given the preference for evening sessions, expand the range of classes available in the evening hours. Ensure that the class schedule aligns with members’ availability after work.
Member Feedback: Collect feedback from members to better understand their preferences and reasons for attending or missing classes. Use this feedback to continuously improve class offerings and the overall member experience.
Modeling
For this classification problem, I experimented with various machine learning algorithms, including logistic regression, decision trees, random forests, and gradient boosting.
After rigorous testing and fine-tuning, I found that RandomForest performed the best.
Results
The model achieved impressive results. During training, it boasted an accuracy of around 87%, showcasing its ability to learn from the data. The real test, however, came during the evaluation phase. On the test dataset, the model maintained a commendable accuracy of 77%.
The Impact
The true measure of success lay in the impact on GoalZone Fitness Club. Armed with the predictive model, the club could proactively identify members likely to miss classes.
By freeing up spaces in advance, they not only satisfied more fitness enthusiasts but also significantly improved class attendance rates. It was a win-win situation!
Conclusion
In the world of fitness, where every inch of space matters, data science provided GoalZone Fitness Club with a solution to optimize class schedules and increase attendance rates.
This project was a testament to the power of data-driven decision-making in addressing real-world challenges. As GoalZone continues to grow, the data-driven approach ensures that every fitness enthusiast can find their spot in the classes they love.
Closing Thoughts
The GoalZone Fitness Club project was an exhilarating journey into the world of data science and its practical applications.
It showcased the potential for data to transform not just businesses but also the experiences of customers. It’s a reminder that even in the realm of fitness, data-driven insights can make a significant impact.
