Treat / Tranform extreme values of LoanAmount and ApplicantIncome

Let’s analyze LoanAmount first. Since the extreme values are practically possible, i.e. some people might apply for high-value loans due to specific needs.

train ['LoanAmount'].hist(bins=20)

So instead of treating them as outliers, let’s try a log transformation to nullify their effect:

import numpy as np
train ['LoanAmount_log'] = np.log(train['LoanAmount'])
train ['LoanAmount_log'].hist(bins=20)

Now the distribution looks much closer to normal and effect of extreme values has been significantly subsided.

Add both ApplicantIncome and CoapplicantIncome as TotalIncome
Take log transformation of TotalIncome to deal with extreme values

Exercise

Treat / Tranform extreme values of LoanAmount and ApplicantIncome

Instructions