Get startedGet started for free

Non-aggressive methods of dealing with outliers

Trimming a distribution is one of the easiest ways of dealing with an outlier without dropping it. If you know the natural range of values a distribution must take related to your business problem, you can use that knowledge to trim the distribution.

Or, if you have some previous knowledge about which specific values are considered outliers based on domain knowledge or experience, you can also replace them with hard-coded values.

This exercise is a chance to practice these two techniques on the apple stocks dataset.

This exercise is part of the course

Anomaly Detection in Python

View Course

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Find the first percentile of Volume
percentile_first = ____
Edit and Run Code