Outliers Detection and Removal using IQR (Inter Quartile) method - Part 2

This blog is in continuation of Outliers post

There is a interesting term , percentile which looks very similar to percentage but its we can say a advance version of %.

What is 25 percentile?

Lets say I got 25% marks which means out of 100 I scored 25 marks but when it comes to 25th percentile which means I am standing ahead of 24th position or there are 24 person behind me. Percentile has a very significant importance in marks calculation.

So in a dataset we try plot a column using a box plot and try to find the outliers. We will a result like this after implementation using seaborn library and boxplot method.

This method gives us the outliers on both the sides as it may happen with a dataset that outliers exist in both upper limit and lower limit.

Now we can apply TRIMMING or CAPPING.

Implementation of both the methods using IQR method on this link Github