The K-Nearest Neighbors (KNN) imputation method is a technique used in data analysis and machine learning to fill in missing values in data sets. It works on the principle of finding a predefined number (K) of the nearest neighbors of the data point with the missing value. In the context of imputation, KNN calculates the distance between each data point with missing values and its neighbors (other data points with complete information). The missing value is then replaced by the average (mean or median) of the nearest neighbor values. The choice of K determines how many neighbors are taken into account, which affects the accuracy and performance of the imputation. KNN imputation is effective when the dataset exhibits local structure or when the missing values are not completely random, but rather related to neighboring values. However, it may not work well with high-dimensional data or datasets where the underlying distribution of missing values is complex. KNN imputation is widely used in data preprocessing tasks to prepare datasets for analysis or machine learning models, preserving valuable information and improving the robustness of data-driven analyses.

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