What is KNN imputation method?

In the context of data analytics, KNN imputation is a method used to fill in missing values in a dataset. KNN stands for K-Nearest Neighbors, a popular algorithm in machine learning. Here’s how KNN imputation works:

1. Identify Missing Values: First, you need to identify the missing values in your dataset. These are typically represented as NaN (Not a Number) or some other placeholder.
2. Calculate Distance: For each observation with missing values, calculate its distance to all other observations in the dataset. The distance metric used can vary, but commonly used metrics include Euclidean distance, Manhattan distance, or cosine similarity.
3. Select Neighbors: Once distances are calculated, select the K nearest neighbors to the observation with missing values. “K” is a parameter that you define, representing the number of nearest neighbors to consider.
4. Impute Missing Values: Finally, impute the missing values by taking the average (or weighted average) of the corresponding values from the selected neighbors. For numerical features, this typically means taking the mean of the neighboring values, while for categorical features, it might involve taking the mode.
5. Repeat: Repeat this process for all observations with missing values in your dataset.

KNN imputation is a simple and intuitive method for handling missing data, and it can be particularly useful when dealing with datasets where missing values are spatially or temporally related to the existing values. However, it’s important to note that KNN imputation can be computationally expensive, especially for large datasets, and the choice of the number of neighbors (K) can significantly impact the imputation results. Additionally, KNN imputation assumes that similar observations have similar values, which may not always hold true in practice.