Missing Indicator | Random Sample Imputation | Handling Missing Data Part 4
Channel: CampusX
Date: 2021-04-26

The Missing Indicator method involves creating a binary indicator for missing values in a dataset, providing additional information on missing patterns. Random Sample Imputation, on the other hand, fills missing values with random samples from the observed data. These techniques offer alternative strategies for handling missing data in a dataset.

Code Used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day38-missing-indicator

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⌚Time Stamps⌚

00:00 - Intro
00:12 - Revision
02:12 - What is Random Imputation?
08:35 - Code Demo using Titanic Dataset
21:33 - Missing Indicator
30:17 - Automatically selecting value for Imputation
36:36 - Outro