Use Python to check your data consistency and get rid of any missing or duplicate data
About This Video
Give me six hours to chop down a tree and I will spend the first four sharpening the axe"? Do you apply the same principle when doing Data Science?
Effective data cleaning is one of the most important aspects of good Data Science and involves acquiring raw data and preparing it for analysis, which, if not done effectively, will not give you the accuracy or results that you're looking to achieve, no matter how good your algorithm is.
Data Cleaning is the hardest part of big data and ML. To address this matter, this course will equip you with all the skills you need to clean your data in Python, using tried and tested techniques. You'll find a plethora of tips and tricks that will help you get the job done, in a smart, easy, and efficient way.
All the code and supporting files for this course are available on Github at https://github.com/PacktPublishing/Clean-Data-Tips-Tricks-and-Techniques