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https://github.com/patriciavalentine/gym-membership-exploratory-data-analysis

This project analyzes a Gym Membership Dataset using Excel to uncover insights into member behavior and gym usage patterns. The analysis is visualized through charts and a dashboard.
https://github.com/patriciavalentine/gym-membership-exploratory-data-analysis

advanced-excel excel gym-membership

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This project analyzes a Gym Membership Dataset using Excel to uncover insights into member behavior and gym usage patterns. The analysis is visualized through charts and a dashboard.

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# 📊 GYM MEMBERSHIP EXPLORATORY DATA ANALYSIS



This project details the data analysis conducted on a Gym Membership Dataset, encompassing data cleaning, exploration, calculation of key metrics, and data visualization using `Microsoft Excel`. The dataset obtained from `Kaggle`, contains crucial member details such as membership types, personal training status, gym attendance, and gym time, among other details.

> The objective of this analysis was to extract meaningful insights regarding gym usage patterns, demographics, and member behavior, which will assist in decision-making and enhancing gym services.



The EDA involved answering key questions, such as:

- How many members have personal trainers? What percentage of members attend group lessons?

- What is the total gym time for premium members versus standard members?

- Is there a correlation between the number of visits per week and the average time spent in the gym?

- What is the median age of the gym members?

- What is the standard deviation of gym visits per week?

- How many members attend the gym more than three times per week?

  

*...among others!*



## 📂 Project Files

1. [The Raw Dataset](https://github.com/patriciavalentine/GYM-MEMBERSHIP-ANALYSIS/blob/main/Raw%20Gym%20Membership%20Dataset.xlsx)

2. [The Analysed Excel File](https://github.com/patriciavalentine/GYM-MEMBERSHIP-ANALYSIS/blob/main/Gym%20Membership%20Analysis.xlsx)

3. [The Project Report](https://github.com/patriciavalentine/GYM-MEMBERSHIP-ANALYSIS/blob/main/REPORT%20ON%20GYM%20MEMBERSHIP%20DATA%20ANALYSIS%20PROJECT.pdf)



## 🧑‍💻 Insights

Using `Excel formulas`, several key metrics were calculated to explore the dataset further and uncover trends in gym usage and membership characteristics.

Below are some of the insights gained:

- 518 members use personal trainers, showing strong demand for personalized fitness guidance.

- Premium and standard members have similar total gym time, with 49,589 hours and 52,011 hours, respectively.

- Both premium and standard members visit the gym about 2.68 times per week, indicating no significant difference in attendance based on membership type.

- The weak correlation (0.0659) between visit frequency and time spent in the gym suggests that frequent visitors don’t necessarily spend more time at the gym.

- 50.25% of members attend group lessons, making them a popular gym feature.

- The median age of gym members is 30 years, highlighting a predominantly young adult clientele.

- The standard deviation of 1.24 visits_per_week shows moderate variation in gym attendance frequency.

- 220 members visit the gym more than three times a week, indicating high engagement from a subset of the members.

- 145 members are under 18 years old, showing that the gym also attracts a younger demographic.

- Member Jamey visits the gym 3 times per week, slightly higher than the overall average attendance.

> For a detailed explanation of how these insights were arrived at, view the project report [here](https://github.com/patriciavalentine/GYM-MEMBERSHIP-ANALYSIS/blob/main/REPORT%20ON%20GYM%20MEMBERSHIP%20DATA%20ANALYSIS%20PROJECT.pdf)!



## 💻 Recommendations

1. The gym could enhance the value of premium memberships by offering more exclusive perks or incentivizing more frequent visits through loyalty programs or personalized experiences.

2. The gym should consider expanding the variety and schedule of group lessons to accommodate demand and attract more members to participate.

3. Promoting personal training services or offering discounted packages for new members could increase overall gym attendance and boost engagement.

4. Since the gym’s membership base skews young, with a median age of 30 and a significant portion of members under 35. The gym should continue tailoring its marketing strategies and services to appeal to this demographic, possibly incorporating more high-intensity workouts and fitness challenges that resonate with younger adults.



### Sample of the Dataset:

![Gym Membership Dataset Sample](https://github.com/user-attachments/assets/1807951f-2df5-4d51-b03d-0a914791ebdb)



*The dataset includes key details such as member demographics (id, first_name, gender, birthday, and age), subscription type (abonoment_type), gym attendance metrics (visit_per_week, days_per_week, avg_time_in_gym), and additional features such as personal training and group lesson participation.*



## 🧑‍💻 THE PROJECT WORKFLOW

### DATA CLEANING & PREPARATION

The first step in the project involved thoroughly cleaning and preparing the dataset to ensure the integrity and quality of the analysis.

Key data cleaning tasks included:

1. **Duplicate Handling**: No duplicate entries were found in the dataset, ensuring the uniqueness of each member's data.

2. **Missing Data/Blanks**: There were no unnecessary missing values or blank entries, further reinforcing the dataset's reliability.

3. **Column Formatting**: The following columns were appropriately formatted to match their data types:

- `birthday` was formatted into a date format to accurately reflect members' birthdates.

- `avg_time_check_in` and `avg_time_check_out` were formatted into time formats to properly display gym check-in and check-out times.

- `Age`, `visit_per_week`, and `avg_time_in_gym` were formatted into number formats with zero decimal places for clarity in analysis.

4. **Conditional Formatting**: Applied to highlight:

- Members with premium memberships.

- Members with personal trainers.

5. **Addition of a Necessary Column - `Age Groups`**: An additional column was created to classify members into age groups, with the following categories: *Below 18, 18-25, 26-35, 36-45, Above 45*.



### ⌨️ USE OF EXCEL FORMULAS

Key metrics related to gym usage and member behavior were derived using a variety of Excel formulas.

- The `COUNTIF` function was utilized to determine how many members have personal trainers and to identify specific member patterns, like those attending more than three gym sessions weekly or those under 18 years old.

- The `SUMIF` function was employed to calculate the total gym time for different membership types by summing the gym hours based on membership category.

- To understand average behaviors, the `AVERAGEIF` formula helped calculate the average number of weekly visits per member, categorized by membership type or individual member names.

- The `VLOOKUP` function was also used to match specific member details across datasets.

- Correlations between variables, such as the frequency of visits and time spent in the gym, were examined using the `CORREL` function. Excel’s `MEDIAN` and `STDEV.S` functions provided insights into the age distribution and the variability in gym attendance, while the percentage of members attending group lessons was derived by combining `COUNTIF` and `COUNTA` formulas.



> Each formula played a critical role in revealing different dimensions of gym member behavior and overall usage patterns.



### ⚖️ PIVOT TABLE ANALYSIS

Pivot tables were used to summarize and analyze key aspects of the dataset. The following pivot tables were created:

- Gym Attendance by Age Group: This analysis revealed that the 26-35 age group had the highest gym attendance, followed closely by the 18-25 and 36-45 groups.

- Total Gym Time by Membership Type: This breakdown demonstrated that both premium and standard members have almost similar total gym time, indicating no significant difference in gym usage between the two groups.

- Visits per Week by Personal Training Status: Members with personal trainers averaged higher gym visits, showing a positive impact of personal training on gym attendance.

- Gym Attendance by Gender: This analysis revealed that female attendees were slightly more than male attendees, ie 503 females and 497 males.

- Total Gym Time by Group Sessions and Personal Trainer: This breakdown demonstrated that both members with or without group sessions and personal trainers had almost similar total gym time, indicating no significant difference in gym usage between them.



### 📈 VISUALIZATIONS

The following visualizations were created to further communicate the insights:

1. **Bar Chart**: Distribution of Gym Membership by Gender - This chart illustrated that gym membership is relatively balanced between genders, with a slightly higher representation of male members.



2. **Line Graph**: Gym Member Count by Hour of the Day - This line graph showed the busiest times of the day for the gym, with peaks during the early morning and evening hours, reflecting typical workday schedules.



3. **Bar Chart**: Distribution by Membership Type (Premium vs. Standard) - A bar chart showing the breakdown of members by subscription type highlighted that the majority of members have standard memberships, with premium members representing a smaller portion.



4. **Doughnut Chart**: Distribution by Age Group - This chart revealed that the most common age groups are 18-25 and 26-35, indicating that the gym appeals largely to young adults.



## 🖥️ CONCLUSION

This project leverages Excel’s advanced functionalities to transform raw membership data into a comprehensive analysis of gym usage, member behavior, and operational efficiency

The analysis thus provided key insights that showed how the gym is attracting a young and engaged audience, with half of the members participating in group lessons and a large portion visiting regularly.

The findings are visualized in a dashboard, enabling data-driven decision-making to enhance the gym’s services and member engagement strategies.



## THE END!

### Thank you 😄.