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Bike Store Performance Analysis

• A store who sells bikes in North America, Europe, and Pacific needed some help in analysing the raw data they have . The data is comprised of 1026 rows, and 13 attributes.

The owner wanted to acquire the answers for the following questions.

• How much per region are my buyers for male and female? Where should I focus, and who are my leads?

• Which profession has the highest and lowest average income for my buyers and non buyers? Who are my hot leads and cold leads?

• What is the average income of married and single customers? Should I target more married couples or single couples in our google ads or in marketing campaign?

• For our marketing campaign and ads campaign who should I target in facebook, twitter and tiktok ? Define the buyers quantity per age group and per region.

• I am interested to know the commute distance trendline of my buyers and non buyers. Who buys more? Is it those who travels more and travels less?

Data Cleaning

• Removal of 26 duplicate values

• There are data uncertainties in between Gender, and Marital Status.
• Under Marital Status, change M to Married and S to Single
• Under Gender, change M to Male and F to Female

Excel Pivot Data Anomalies and Issues Troubleshooting

• I have noticed that all my columns have blanks. Upon checking it turns out that these are the header columns of my OCCUPATION AND HOMEOWNER. Thus, I have to uncheck all blanks , so in turn it wouldn’t affect my pivot tables later.

• Checked the age and decided to create an age bracket to easily identify what category they fall into.
• Tried using AI to help me generate the proper formula for it. " =IF(AND($L1>=25,$L2<=35),"Adolescent",IF(AND($L2>=36,$L2<=65),"Middle Age",IF(AND($L2>=66,$L2<=89),"Old Age","Unknown Category"))) "

• Encountered a problem while creating my table through the pivot I have made. It might have changed after I did the transformation. Thus, I have to go back and filter it then change that row data to married as well.

• I also transformed the values of my data into whole numbers and placed a currency to avoid the following errors.

• This is the correct one.

• There is an issue after pivoting under the commute distance. The 10+ Miles is in the second row instead of last row.