Exercise

# 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.

Instructions

**100 XP**

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