Goal

For the project "Bike Purchase Analysis", my main goal was to gain insights into bike purchasing behavior based on different demographics such as age, marital status, education, and commute distance. The dummy dataset provided had 12121 rows and columns such as ID, Marital Status, Gender, Income, Children, Education, Occupation, Home Owner, Cars, Commute Distance, Region, Age, Age Bracket, and Purchased Bike.

Data Cleaning and Processing

To start my analysis, I first cleaned the data by standardizing the marital status and gender columns. I replaced "M" with "Married" and "U" with "Unmarried" in the marital status column, and "M" with "Male" and "F" with "Female" in the gender column. This made the data more readable and user-friendly. I also check for null values as well as duplicates values.

Next, I created an age bracket column using the formula "=IF(L2>51,"Old(52+)",(IF(L2>=31,"Middle Age(31-51)","Adolescent(0-30)")))". This column helped me segment the data by age brackets and gain insights into which age groups were purchasing the most bikes.

I then created a pivot table containing the purchased bike column as the value, the age bracket as the row, and the count of purchased bikes as the value. This pivot table provided a clear picture of which age groups were purchasing the most bikes and allowed me to draw insights about bike purchasing behavior among different age groups.

Data Visualization

To present my findings in a visually appealing and user-friendly manner, I created a dashboard containing three slicers - marital status, education, and children. I also created three charts - average income per purchase, number of bike purchases by age bracket, and number of bike purchases by age bracket by the commute distance. These charts helped me present the insights I discovered in a meaningful and easy-to-understand way.

Insights

Through my analysis, I discovered that middle-aged individuals with higher income and education levels tend to purchase more bikes. I also found that those who commute distances of around 2-5 miles are more likely to purchase a bike. These insights can help businesses tailor their sales and marketing strategies to target specific demographics and improve their overall sales performance.