How To Automate Data Science Tasks Using Python (Part 3)

Author(s): Richard Warepam

Originally published on Towards AI.

Part 3: It is about “Outlier detection” and handling them.

This member-only story is on us. Upgrade to access all of Medium.

If you are not a member, read the full article here.

But as an appreciation, please 👏 clap on this article, if you had a good and informative read.

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.

#Ad: I would love it if you check out my eBooks later to support me:

Personal INTERVIEW Ready “SQL” CheatSheet

Top 50+ ChatGPT Personas for… Read the full blog for free on Medium.

Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.

Published via Towards AI