(Code) KNN Imputer for imputing missing values | Machine Learning

#knn #imputer #python

In this tutorial, we'll will be implementing KNN Imputer in Python, a technique by which we can effortlessly impute missing values in a dataset by looking at neighboring values.

Machine Learning models can't inherently work with missing data, and hence it becomes imperative to learn how to properly decide between different kinds of imputation techniques to achieve the best possible model for our use case.

KNN works on the intuition that to fill a missing value, it is better to impute with values that are more likely to be like that row, or mathematically, it tries to find points (other rows in the dataset) in space which are the closest to it, and uses them as a benchmark for figuring out a value to impute.

I've uploaded all the relevant code and datasets used here (and all other tutorials for that matter) on my github page which is accessible here:

Link:

https://github.com/rachittoshniwal/machineLearning

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Видео (Code) KNN Imputer for imputing missing values | Machine Learning канала Rachit Toshniwal