How To Automate Data Science Tasks Using Python (Part 3)
Last Updated on September 17, 2024 by Editorial Team
Author(s): Richard Warepam
Originally published on Towards AI.
Part 3: It is about “Outlier detection” and handling them.
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Here we go again! Have you read the previous two parts of this series?
In those articles, I demonstrated how to use Python to automate tasks such as data loading, basic summarization, missing value handling, and data transformation.
If you haven’t already, consider reading these articles.
2 stories · Learn automation using python with Richard Warepam
warepam.medium.com
As a result, you should have an easier time following this part of the guide.
Do you recall the main motto of this series?
If you do, I would appreciate it if you could comment below.
Here’s a reminder of the motto:
If any tasks in your project appear repetitive or redundant. Always define a function and automate your task.
So in this article, we’ll look at another important aspect of data preprocessing. Here, we will learn how to automate both the process of detecting and handling outliers.
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