KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5
Channel: CampusX
Date: 2021-04-27

The KNN Imputer is a technique used in multivariate imputation to fill in missing values by considering the values of their k-nearest neighbors. This method leverages similarities between data points to impute missing values effectively, offering a versatile approach to handling missing data in a multivariate context.

Code used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day39-knn-imputer

Documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html

https://scikit-learn.org/stable/modules/generated/sklearn.metrics.pairwise.nan_euclidean_distances.html

============================
Do you want to learn from me?
Check my affordable mentorship program at : https://learnwith.campusx.in/s/store
============================

📱 Grow with us:
CampusX' LinkedIn: https://www.linkedin.com/company/campusx-official
CampusX on Instagram for daily tips: https://www.instagram.com/campusx.official
My LinkedIn: https://www.linkedin.com/in/nitish-singh-03412789
Discord: https://discord.gg/PsWu8R87Z8
E-mail us at support@campusx.in

⌚Time Stamps⌚

00:00 - Intro
00:19 - Difference between Univariate and Multivariate Imputation
02:15 - How KNN Imputer works?
06:46 - NAN Euclidean Distance Documentation
14:17 - Advantages and Disadvantages of KNN Imputer
15:56 - Code Demo
20:00 - Concept of Uniform and Distance