__STYLES__
The goal of this project is to gain insight into bike sales demographics. The final Dashboard compares whether a bike was purchased, based on Income, Age, Occupation, or Commute Time. These results can also be filtered by Marital Status, Region, and/or Education.
Raw Data
I converted all the "M" entries in the Marital column to "Married", using Find and Replace.
I converted all the "S" entries in the Marital column to "Single", using Find and Replace.
I used the same process for the Gender column to convert "M" >>> "Male" and "F" >>> "Female".
I created a new column, called "Age Brackets".
In this column, I took the value from the "Age" column and converted it into either "Adolescent" (Age<31), "Middle Aged" (31 <= Age <=54), or "Old" (Age > 54).
After creating a Pivot Table using the data in the "Commute Distance" column, I realized that I needed to go back and change "10+ Miles" >>> "More Than 10 Miles" for the resulting Chart to be organized in a logical order.
This completed my Data Cleaning Process.
Cleaned Data
I created 4 Pivot Tables.
The first Pivot Table outputs the Average Income of Customers, separated by Gender and whether they bought a bike.
The second Pivot Table outputs the number of Customers, separated by Commute Distance and whether they bought a bike.
The third Piot Table outputs the number of Customers, separated by Age Brackets and whether they bought a bike.
The fourth Pivot Table outputs the number of Customers, separated by Occupation and whether they bought a bike.
Pivot Table Data
After I created the Pivot Tables above, I created a Visualization Chart for each.
I added these Visualization Charts to a Dashboard, adding a Title and aligning the Charts to look orderly.
Dashboard - Default
Dashboard - Interactive