This method is used to impute the missing attribute values which are imputed by the attribute values that are most similar to the attribute whose values are missing. The similarity of the two attributes is determined by using the distance functions.

The KNN (K-Nearest Neighbors) imputation method is a technique used to fill in missing values in a dataset by considering the values of neighboring data points. Here’s how it works:

Identify missing values: First, identify the missing values in your dataset.
Select a value for ‘k’: Determine the number of nearest neighbors (k) to consider when imputing missing values. The choice of ‘k’ can significantly impact the imputation results.
Calculate distances: Calculate the distance between each observation with missing values and all other observations in the dataset. Common distance metrics used include Euclidean distance, Manhattan distance, or cosine similarity.
Find nearest neighbors: Select the ‘k’ nearest neighbors based on the calculated distances.
Impute missing values: For each missing value, take the average (or weighted average) of the corresponding values from its ‘k’ nearest neighbors and use this average as the imputed value.
Repeat for all missing values: Iterate through all missing values in the dataset and repeat the imputation process.
Post-processing: After imputing missing values, you may need to perform additional data cleaning or preprocessing steps depending on your specific analysis requirements.

KNN imputation is particularly useful when dealing with datasets with missing values and can help preserve the underlying structure of the data. However, it’s essential to carefully select the value of ‘k’ and consider the appropriate distance metric for your dataset to achieve accurate imputation results. Additionally, KNN imputation can be computationally expensive, especially for large datasets, so it may not be suitable for all scenarios.