Handling Missing Data (Part - 2)

Handling missing data is an important step in data cleaning and preparation. Missing data can occur due to a variety of reasons, including errors in data collection, non-response, or data loss during transfer. Failure to handle missing data can lead to biased results and inaccurate conclusions.

There are several techniques for handling missing data, including:
Deleting missing data, Imputing missing data, Ignoring missing data

Demo session Part - 1:

For Handling Missing Data theoretical part, refer this vedio:

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