“K-nearest neighbours imputation” is a method for filling in missing values in a dataset by predicting those missing values based on the values of their K-nearest neighbours.
Steps for K-nearest Neighbours Imputation –
1. Identify Missing Values: First, you must identify the missing values in your dataset. These are often identified by “Nan” or another placeholder.
2. Select a Value for K: You must select a value for K, which is the number of nearest neighbours that will be used to impute the missing value. The value of K is a hyperparameter that you can change based on your data and problem.
3. Calculate Distances: For each missing value, calculate the distance between that missing point and all other data points in your dataset.
3. Calculate Distances: Calculate the distance between each missing value and all other data points in your dataset. Depending on your data and situation, common distance measures include Euclidean distance, Manhattan distance, and others.
4. Locate K-Nearest Neighbours: Choose the K data points that are closest to the missing value. These are the neighbours who are closest to K.
5. Impute the Missing Value: The average (for numerical data) or mode (for categorical data) of the appropriate characteristic from the K-nearest neighbours can be used to impute the missing value. Alternatively, you can utilise weighted averages depending on distances, giving closer neighbours more weight.
6. Repeat for All Missing Values: Go through steps 3-5 again for all missing values in your dataset.