100 Days of Machine Learning | CampusX - KNN Imputer | Multivariate Imputation | Handling Missing Data Part 5

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 [email protected] ⌚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