Case Study1: Develop analysis and a dashboard using a comparison of bike customers with non-customers by establishing trends and patterns across their relationships to other various fields in the dataset below.
Cleaned and processed data set in the Working_Sheet;
Removing Duplicates.
Notice the difference from 1027 in the Original_Data_Set to 1001 observations in the Working_sheet. 26 duplicates removed.
Finding and Replacing M and S in the Marital Status column for Married and Single then M and F in Gender column for Male and Female respectively.
Validating Marital Status, Gender.
Highlighting customers and non-customers with green for the former and red for the latter, using Conditional Formatting
Changing Income column fomart to currency.
Creating new colum Age groups to reduce the cluttering in the Age column.
The age groups include (0-35: Youth), (36-55: Middle Age), (56+: Senior)
Using Pivot Tables to summarize the data being visualized on the dashboard.
-A table summarizing data on average income and it's respective totals of the two gender categories who are and aren't customers. This is to determine difference in income for various genders of customers and non-customers.
- A table summarizing the bikes purchased per commuting distance range to discern which range has most customers and least.
- A table summarizing the bike customers and non customers among the various age groups.
- A table summarizing bike customers and non customers who are car owners as categorized by gender.
https://docs.google.com/spreadsheets/d/1n9DIlyed-eBVHmJUJEByyoGpxlMBW-NRkig1wdsb9Fw/edit?usp=sharing