Missing Values Explained (MCAR, MAR, MNAR) + KNN & Iterative Imputer | Data Science Tutorial

In this video, you’ll learn everything about handling missing values in Data Science from basics to advanced techniques. Study material: https://github.com/datasciencekibaatein/Handling-Missing-Values We cover: What are missing values? - Types of missing data: MCAR, MAR, MNAR - Univariate vs Multivariate imputation - Handling numerical & categorical data Advanced techniques: - KNN Imputer - Iterative Imputer (MICE) This tutorial is perfect for beginners as well as intermediate learners who want to build a strong foundation in data preprocessing and feature engineering. 🚀 By the end of this video, you’ll be able to confidently handle missing data in real-world datasets. #DataScience #MachineLearning #MissingValues #MCAR #MAR #MNAR handling missing values missing data in data science MCAR MAR MNAR explained types of missing data univariate imputation multivariate imputation knn imputer sklearn iterative imputer sklearn mice algorithm data science data preprocessing tutorial feature engineering techniques python data analysis missing values machine learning data cleaning how to handle null values data science full tutorial missing values