Consider the 2013 declined loan data from LendingClub titled “RejectStatsB2013.” Similar to the analysis done in the chapter, let’s scrub the debt-to-income data. Because our analysis requires risk scores, debt-to-income data, and employment length, we need to make sure each of them has valid data.
Sort the file based on debt-to-income and remove those observations (the complete row or record) that have a missing score, a score of zero, or a negative score.
Assign each valid debt-to-income ratio into three buckets (labeled DTI bucket) by classifying each debt-to-income ratio into high (>20 percent), medium (10–20 percent), and low (<10 percent) buckets. Consider using if-then statements to complete this. Or sort the row and manually input.
Run a PivotTable analysis that shows the number of loans in each DTI bucket. Any interpretation of why these loans were declined based on debt-to-income ratios?