Handling Missing Data | Part 1 | Complete Case Analysis
Channel: CampusX
Date: 2021-04-22

Handling missing data is an essential step in the data preprocessing pipeline, ensuring that ML models are trained on high-quality, representative datasets, leading to more accurate and reliable predictions Techniques like imputation, dropping missing values, or advanced methods such as Multiple Imputation can be employed based on the nature and impact of missing data. Choosing the right strategy ensures the reliability and accuracy of your models.

Code Used: https://github.com/campusx-official/100-days-of-machine-learning/tree/main/day35-complete-case-analysis

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⌚Time Stamps⌚

00:00 - Intro
00:58 - Handling Missing Data
05:50 - Complete Case Analysis [CCA]
07:09 - Assumption for CCA
09:38 - Advantages and Disadvantages of CCA
11:39 - When to use CCA?
13:24 - Code Example