Global Bike Sales Insights
In this excel project, analysis is done on global bike sales data and various insights developed. Here is a step to step walk through of the project.
- An Overview of the Data
- Data Cleaning
- Removing duplicates
- Replacing M with Married and S with Single in the Marital Status column
- Replacing F with Female and M with Male in the Gender Column
- Creating a new column Age Bracket from the Age column using a nested excel formula
- Pivot Tables
- Average Income of Buyers
- Commuting distance of Buyers
- Purchases by Age Bracket
- Creating a Dashboard
A brief overview of the data that we are going to use for this project.
After creating a working sheet, I then cleaned the data to make it more usable. This involved:
Using the cleaned data, I now created a pivot table from the working sheet data. From this pivot table we will create various analysis, insights and visualizations.
The first insight was to determine the average income of bike buyers. This might help answer questions like: Does income level affect bike sales? Is the average income of bike buyers greater than their fellow non-buyers? We also break it down to gender and try to see whether there is a difference in average income between the male and female buyers and non-buyers.
From the analysis, we see that on average, bike buyers earn higher than their fellow non-buyers. Also male respondents, from the data, are earning higher than their female counterparts.
I then created a bar graph to visualize the average income of buyers and non-buyers by gender.
Another analysis might be trying to find out whether the commuting distance of the buyers was affected by their commuting distance. Do people who commute short distances have a likelihood of buying more bikes than those who commute for longer distances?
Doing this analysis, we find out that those who commute shorter distance bought more bikes than than those who commuted for longer distance. This might indicate that as distance of commuting increases, people prefer other means of transport.
I then created a line graph that better visualizes this.
We then analyse the data to determine which of the three age brackets: youth, middle age and old purchased the bikes the most. The data table and the visualization showed that middle age bracket bought more bikes than the other age groups.
Finally I create a dashboard where I put all the visualizations together. I add slicers to the dashboard that can be used to draw various insights and analysis based on various filters.
To download and view the full project on GitHub, click here.