Filling Missing Data with MICE Imputer: A Practical Approach When working with datasets, handling missing data effectively is critical for building robust machine learning models. In this blog, I will walk you through how to use Multiple Imputation by Chained Equations (MICE) for imputing missing values, along with alternative techniques such as the Simple Imputer and KNN Imputer . We'll focus on a dataset of startup expenses and profits and see how imputing the data affects the performance of a Linear Regression model. Dataset Overview For this tutorial, we will be using the "50 Startups" dataset. This dataset contains features such as R&D Spend , Administration , Marketing Spend , and the target variable Profit . Here’s what the dataset looks like: In real-world scenarios, missing data…