What is the KNN imputation method?

KNN (K-nearest neighbour) is an algorithm that is used for matching a point with its closest k neighbours in a multi-dimensional space.

In data analytics, KNN 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:

1. Identify missing values: First, identify the missing values in the dataset that need to be imputed.
2. Calculate distances: Calculate the distances between the data point with the missing value and all other data points in the dataset. Common distance metrics used include Euclidean distance, Manhattan distance, or cosine similarity.
3. Find nearest neighbors: Select the k nearest neighbors to the data point with the missing value based on the calculated distances.
4. Impute missing value: Take the average (for numerical variables) or mode (for categorical variables) of the values of the selected nearest neighbors and use it to fill in the missing value.
5. Repeat for all missing values: Repeat steps 2-4 for all missing values in the dataset.

KNN imputation is useful when dealing with datasets containing missing values, especially when the missingness is not completely at random. It leverages the information from similar data points to estimate missing values, thereby preserving the underlying structure of the data. However, it’s important to choose an appropriate value for the parameter k, which determines the number of neighbors considered during imputation, as it can impact the imputation results.