Bike Sales Project

Here is another Project for Data Analyst which covers analyzing,cleaning and visualizing data.

→Cleaning Data

→Analyzing Data

→built interactive dashboard

→Summarize Findings

→ I removed the duplicated values by selecting the table, going to data to select remove duplicates, select all columns then press ok. 26 duplicates were removed in total leaving only the unique values.

2. Replace Marital Status Values

→ The Marital Status column values were replaced and denoted as follows, M=Married and S=Single. This was done by pressing CTRL +H which opens the Replace Menu that allows all values to be replaced at a go.

3. Replace Gender Values

→ The Gender column values were replaced and denoted as follows, M=Male and F=Female. This was done by pressing CTRL +H which opens the Replace Menu that allows all values to be replaced at a go.

4. Age Group

→ A new column (Age Group) was created to group the ages in ranges, Adolescent, Middle Age and Old using the IFS function.

→ Create a Bar Chart Shows Total Income by Gender.

2. Bike Purchase by Age Group

→ Create a Line Chart Shows Bike Purchase by Age Group.

3.Bike purchase per customers’ commute

→ Create a Line Chart Shows Bike Purchase per customers’ commute.

4. Purchase of Bike by Marital status

→ Create a Bar Chart Shows Purchase of Bike by Marital status.

5. Bike Purchase by Region

→ Create a Donut Chart Shows Purchase of Bike by Region.

Male Bike buyers with higher income purchased more bikes compared to females with slightly lower income who did not purchase.
Customers with the least commute (1 mile) own more bikes.

Middle aged people between 31–54 own more bikes than others.

North America has more customers purchase compared to Pacific and Europe region.

Married people purchased 7% more bikes than single people.

Based on the insights above, I would recommend that bike manufacturers and retailers focus their marketing efforts on male customers with higher income, as they are more likely to purchase bikes. Additionally, middle-aged people between 31–54 should also be targeted as they own more bikes compared to other age groups. Manufacturers and retailers should also consider the commuting distance of potential customers, as those with shorter commutes are more likely to own multiple bikes. Furthermore, the North American market should be a priority as it has more customers purchasing bikes compared to the Pacific and Europe regions. Finally, it is worth noting that married people purchased more bikes than single people, suggesting that family-oriented marketing strategies may be effective.

→You can give a look to dashboard and can have other insights too from it.

I hope you like this project for your data analyst journey and also helped you to know actual use of excel in data analysis .

Keep Learning and Keep Growing…

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