Exploratory Analysis
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In this section, I Conducted exploratory Analysis on the datasets to a have better understanding of my datasets and check for outliers.
Explored Total Amount Spent Column
Explored Total Amount Spent Column
Explored the Total Amount Spent column using boxplot
Detected outlier using boxplot
Handled outlier by filtering the Total Amount Spent column by lower bound and upper bound
Explored the Income column using boxplot
Explored the Income column using boxplot
Explored the Income column using boxplot and no outlier was detected
Explored Income Column Using Histogram
Explored Income Column Using Histogram
Age group by Count
Age group by Count
Grouped Customers in the range of 19-31 as young adults, 32-41 as Middle aged adults, 42-51 as Adults, and 51+ as Senior.
The dataset has more distribution of customers in the senior and adult categories.
Income group by Count
Income group by Count
Grouped the Income column into four quartile.
The datasets has more distribution of customer in the lower and middle Income group.
Marital Status By Count
Marital Status By Count
Grouped the customers into two main categories.
The dataset has more distribution of customers who are partnered
Marital Status vs Total Amount Spent
Marital Status vs Total Amount Spent
Single Customers have more purchase power.
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