BIKE-SALES-ANALYSIS
The objective of this project is to analyze bike sales data across various regions, occupation, age, gender etc. using it to analyze sales trends and identify patterns of Purchased bikes among different Age groups, Regions, Occupation etc., to gain insights for well informed decisions and also to understand customer behavior.
DATA PREPARATION AND PROCESSES
This project consists of four sheet: The main dataset sheet, The Working sheet - a copy of the original data to perform all the cleaning procedures with no worries of loosing the main data, The pivot tables sheet and lastly the Dashboard sheet. During the cleaning procedures, duplicates were removed, values were replaced using the FIND and REPLACE function (Marital Status and Gender for clarity), another column was created for Age groups(grouping the ages into Young, Middle age and Old using formula) and value was modified(changed "10+ Miles" to "More than 10 Miles" in Commute Distance column)
DATA ANALYSIS AND VISUALIZATION
Four pivot tables,charts and graphs were created to summarise the data and help identify patterns and trends focusing on relationship between Bike Purchase and other factors such as Average Income of both gender, Commute distance,Age group and Region. An interactive dashboard was built with additional three slicers for more interactions to help the user understand more trends and patterns.
INSIGHTS
- Both Male and female customers who purchased bikes had higher average income than those who did not.
- People with a 0-1 Mile commute distance purchased bikes more than others followed by 2-5 Miles.That means, people with shorter distances purchased more bikes.
- North America region had a higher proportion of bike purchases compared to the Pacific and Europe.
- A significant number of Middle Age group individuals purchased more bikes compared to Young and Old people.