Project Title:

Bike Sales Project

Overview:

The aim of this project is to analyze the following questions:

1. Which Gender earns the most?
2. Which age range purchase bikes the most?
3. Does the customer commute, marital status, education, and region affect the way customers purchase bikes?

Data Source:

The data for this project was sourced from kaggle, which is stored in an Excel spreadsheet. The data includes customers information on Marital status, Gender, income, Children, Education, Occupation, Home owners, Cars, Commute distance, Region, Age and Purchased Bike.

Data Cleaning and Preparation:

The following steps were taken to clean and prepare the data for analysis in excel;

1. Removed duplicates and incomplete column
2. The "Marital Status" column inputs was edited from "M" and "S" to "Married" and "Single" respectively.
3. The "Gender" column was edited from "F" and "M" to "Female" and "Male" respectively.
4. New column for "Age Range" was created from the "Age" column splitting them into three categories. Adolescent(Age less than 31), Middle Age(Greater than or equal to 31), and Old(Greater than 54).
5. Created pivot tables to summarize the data.

Exploratory data analysis:

Interactive dashboard and visualization was created to explore the data. The key findings include;

1. The "Male" gender earns more than the "Female"
2. "Middle Age" individuals purchase bikes the most
3. Customers within 0-1 miles to work place purchase bikes the most
4. Customers with Single "Marital Status" purchase bikes the most.
5. North America is a key market
6. Individuals with Bachelor degree purchase bikes the most.

Results:

Based on the analysis, the following insights were obtained:

1. The "Male" gender earns more than the "Female", hence should be considered in future marketing campaigns.
2. "Middle Age" individuals purchase bikes the most and should be targeted in future marketing efforts.
3. Customers within 0-1 mile commute distance purchase bikes the most and should be prioritised in future marketing campaigns.
4. Single individuals purchase bikes the most, hence should be targeted in future marketing campaings.
5. North America is a key market and should be targeted for increased sales effort.
6. Individuals with Bachelor degree purchase bikes the most and should be targerted in future marketing campaigns.

Conclusion:

This analysis provide a detailed insight into the customer of the bike company. By tailoring thier marketing campaings towards the "Male" gender, "Middle Age" individuals, Customers with commute distance of 0-1 mile, Single individuals, Customers in North America, and individuals with bachelor degree, the company can improve sales profitability.