How To Handle Missing Values — The Guide Every Data Scientist Must Have By Their Bedside — Part 1

Explore various techniques to efficiently handle missing values and their implementations in Python

Zoumana Keita
Artificial Corner

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Photo by Ryoji Iwata on Unsplash

Dealing with missing data is a common and inherent issue in data collection, especially when working with large datasets.

There are various reasons for missing data, such as :

  • Incomplete information provided by participants of a survey
  • Non-response from those who decline to share information
  • Poorly designed surveys, or removal of confidential information.

When not appropriately handled, missing data can introduce bias into the overall data, and lead analysts and stakeholders to make wrong decisions.

This article will focus on four approaches to efficiently deal with missing values. The benefits and drawbacks of each technic will be illustrated for better decision-making.

This first part of the article will focus on the first two approaches.

Different Types of Missing Data

Before diving into the technics to handle missing, it is important to know more about the different…

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Zoumana Keita
Artificial Corner

Senior Data Scientist/IT Analyst @OXY || Videos about AI, Data Science, Programming & Tech 👉 https://www.youtube.com/@zoumdatascience