In KNN imputation, the missing attribute values are imputed by using the attributes value that are most similar to the attribute whose values are missing. By using a distance function, the similarity of two attributes is determined.

KNN imputation, or k-nearest neighbors imputation, is a technique used to fill in missing values in a dataset based on the values of its nearest neighbors. Here’s how it works:

Identify missing values: First, identify the missing values in the dataset that need to be imputed.
Calculate distances: For each observation with missing values, calculate its distance to all other observations in the dataset. The distance can be measured using various metrics such as Euclidean distance, Manhattan distance, etc.
Select neighbors: Choose the k nearest neighbors to the observation with missing values based on the calculated distances. “k” is a predefined number chosen by the user.
Impute missing values: Once the nearest neighbors are identified, impute the missing values by taking the average (for numerical variables) or the mode (for categorical variables) of the corresponding values from the selected neighbors.
Repeat for all missing values: Repeat the process for all observations with missing values in the dataset.

KNN imputation is a simple and intuitive method for handling missing data, especially in cases where missing values are assumed to be similar to the values of nearby observations. However, it can be computationally expensive, especially for large datasets, and the choice of the value of “k” can significantly impact the imputation results. Additionally, it may not perform well if the dataset has high dimensionality or if the underlying data distribution is not well-suited for the k-nearest neighbors approach.