Data Imputation Techniques: Handling Missing Data in Machine Learning

 Missing data is one of the biggest challenges in machine learning, and handling it the right way can significantly improve model performance. This article explores essential data imputation techniques—from basic methods like mean, median, and mode to advanced approaches such as KNN, regression, and model-based imputation. Learn how to choose the right strategy, reduce bias, and build accurate, reliable, and production-ready machine learning models.