Youtor
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http://youtube.com/watch?v=AHBHMQyD75U
#knn #imputer #algorithm • In this tutorial, we'll understand KNN Imputation algorithm using a interactive approach, which will clear all your doubts regarding this topic, if any. • 0:00 Intro • 1:15 Problem Statement • 2:35 Algorithm steps • 4:56 nan_euclidean distance • 5:59 Imputations • KNN Imputer is a technique by which we can effortlessly impute missing values in a dataset by looking at neighboring values. • 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. • 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. • 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/ma... • If you like my content, please don not forget to upvote this video and subscribe to my channel. • If you have any qualms regarding any of the content here, please feel free to comment below and I'll be happy to assist you in whatever capacity possible. • Thank you!
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