Handling Structural Error using Python | Data Cleaning Tutorial 3

During the Machine Learning Data Cleaning process, you will often need to figure out whether you have irrelevant data, and if so, how to deal with it. In this video, I have demonstrated the methods for finding and removing irrelevant data, as well as how to modify their behavior to suit your specific needs.
Structural errors are those that arise during measurement, data transfer, or other types of "poor housekeeping.", such as:
Data Type Conversion
Syntax Errors (Remove white spaces)
Fix Typos

#DataScience #MachineLearning #DataCleaning

Used Python Notebook :

[escapepeterpan]

Thanks Atul, sir, That value counts trick will be very helpful!