Comprehensive Guide: Handling Missing Values in Machine Learning — A-Z Crash Course

Author(s): Aleti Adarsh

Originally published on Towards AI.

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Before we dive into the intricacies of handling missing values and improving your dataset, here are a few things I want to share with you:

➜ Resources at Your Fingertips:All the resources you’ll need, including code snippets, the Colab notebook link, dataset links, and references, are provided at the end of this article. Everything is organized to ensure you have the tools to practice and master the concepts.

➜ Why You Should Stick Around:I know your time is valuable, but trust me — this article is worth every second. It might take you 10 minutes to read through, but it will equip you with a comprehensive understanding of handling missing data in machine learning, a skill that can significantly boost your projects.

➜ A Quick Request:Learning is best done hands-on. As you go through this article, I encourage you to open an editor, load a dataset, and code along with the examples. This practical approach will help you internalize the techniques better and make them a part of your data preprocessing workflow.

In this guide, we’ll:

Explore why handling missing values is important.Dive into multiple strategies… Read the full blog for free on Medium.

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Published via Towards AI