This video gives us a detailed explanation of Hot Deck Imputation and how to code the KNN algorithm using Python modules.
This video is presented by Dr. Nitin Misra Sr VP and Chief Data Scientist of 360digiTMG - the training arm of INNODATATICS ( US). He has a rich experience of almost 17 years and is a successful data scientist with abilities in Business Analytics, Project Management, and Quality Management.
Missing data are defined as values that are not available and that would be meaningful if they are observed. Missing data can be missing sequences, incomplete features, missing files, incomplete information, and data entry errors. Whenever a research scholar conducts a survey the problem of missing data surfaces. It is very important to handle missing values for machine learning. There is a need to replace this missing data to make the survey more reliable. A better strategy is to infer them from the known part of the data- also called imputing values.
Imputation is the process of replacing missing data with substituted values from the known data collected.
This tutorial explains Imputation and the various imputation techniques such as
a) Hot Deck Imputation
b) Substitution
c) Cold Deck Imputation
d) Regression Imputation
e) Stochastic Regression Imputation
f) Interpolation and Extrapolation.
The prime focus of this tutorial is Hot Deck Imputation and developing modules for the KNN algorithm in Python. HotDeck Imputation is a method for handling missing data in which each missing value is replaced with an observed value from a similar unit. So in a survey of respondents, hot-deck imputation typically replaces each missing value with a random draw from a subsample of respondents that scored similarly on a data set of matching variables.
The tutorial contains live coding in Python of the KNN Algorithm. KNN is an algorithm that enables Hot Deck Imputation. It matches a point with it's closest " k "neighbors in a multidimensional space.
This tutorial is a boon for all students of data science who want to understand Hot Deck Imputation and Python coding of the KNN Algorithm.

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