FlexField Fitness Partnership Data Analysis
In this project, myself, Lok To Ho and Dany Raihan attempt to provide the best partnership solution for FlexField Fitness who has been lacing in brand loyalty and popularity. The code for this project can be found here.
Introduction to The Challenge
You are part of a Data Analytics and Consulting team, and Anastasia has reached out requesting immediate assistance in advising her company. She has already begun to compile some helpful information (see Industry Information) but needs your support. You recall that the dataset was created by a former employee who left the company. Later, data was added by a new hire, resulting in errors during the process. Anastasia also indicates that the Board provided her with a detailed list of expectations. They would like the presentation to include the following:
- Filtered data, removing any errors.
- A summary of key findings from the dataset (3–5 visualizations) to better understand employee attrition.
- A clear statement identifying the subset of individuals most likely to impact employee turnover, derived from the dataset.
- Realistic and creative recommendations on how the company can address the lack of sales in identified products and increase profits.
- An outline of what success looks like and how to measure the efficacy of your recommendations.
Nice to have: An explanation of your data analysis steps and how you arrived at your conclusions and outcomes. You will assist the CEO with each of the above points, including preparing the presentation that she will deliver to the Board. State any assumptions clearly.
Datasets
The Datasets are from 4 distinct companies including our own (FlexField Fitness). Congregating all of them gives us the columns:
- Customer ID: String
- Gender: String
- Age: Integer
- Hours at Gym (per week): Integer
- Fitness Goal: CString
- Preferred Sports Drink Type: String
- Average Weekly Consumption (Bottles): Integer
- Calorie Intake: Integer
- Dietary Preferences: String
- Average Spend per Meal Order: Float
- Gym Membership Length (years): Integer
- Average Spend on Apparel ($/year): Float
- Type of Apparel Purchased: String
- Primary Apparel Purchase Channel: String