KNN Imputation: An Effective Approach for Handling Missing Data

Introduction:

Missing data is a common challenge in data analysis and modeling. It can introduce bias and affect the accuracy of results. K-Nearest Neighbors (KNN) imputation is a widely used method to handle missing data by estimating the missing values based on the characteristics of neighboring data points. In this blog post, we will explore KNN imputation, discuss when to use it, learn the formulas used (uniform and distance-based), highlight its advantages and disadvantages, and provide a Python code example using the Titanic dataset.

What is KNN Imputation?

KNN imputation is a technique for filling in missing values by estimating them based on the characteristics of similar neighboring data points. It is referred to as multivariate because it considers multiple variables or features in the dataset to estimate the missing values. By leveraging the values of other variables, KNN imputation takes into account the relationships and patterns present in the data to impute missing values.

When to Use KNN Imputation?

KNN imputation is particularly suitable when the missing data exhibits the “Missing Completely at Random” (MCAR) or “Missing at Random” (MAR) patterns. MCAR refers to missing data…