Why and How to deal with missing values | #aikachilla #eda
Channel: Codanics
Date: 08/08/2023

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#datascience 
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Video Description:
Missing data is a common challenge in data science. Learn what missing values are, why they matter, and different techniques to properly handle them in this practical video. We'll go over simple imputation methods like mean, median, and mode imputation, as well as more advanced methods like MICE and matrix factorization.

Key topics covered:

What are missing values and why do they matter?
Simple imputation methods (mean, median, mode)
Advanced imputation with MICE
Matrix factorization for imputation
When to delete missing value rows
Principles for handling missing data
Follow along as we use Python to demonstrate different approaches to dealing with missing values on sample datasets. This video is perfect for students, data analysts, aspiring data scientists or anyone wanting to learn proper techniques for handling missing data. Learn to identify, analyze and fill gaps in your datasets with the right imputation strategy.
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