Python Machine Learning Tutorial | Handling Missing Data | Databytes

DataCamp · Beginner ·📐 ML Fundamentals ·4y ago
This machine learning tutorial will take you through the different ways of dealing with missing data when building machine learning models in Python. The topics covered in this video are: 00:00 - 04:07 Missing data theory 04:08 - 06:50 Msleep data set 06:51 - 09:08 Standardizing missing data 09:09 - 10:01 Quantifying missing data 10:02 - 10:51 Dropping missing data 10:52 - 13:11 Separating data by column type 13:12 - 18:03 Replacing with mean or median 18:04 - 19:57 Replacing with mode 19:58 - 22:31 Iterative methods to find values 22:32 - 23:10 Next steps [Try it yourself!] Pre-prepared workspace: https://bit.ly/3PwDKvK Check out our Machine Learning Scientist in Python Career Track https://www.datacamp.com/tracks/machine-learning-scientist-with-python Find some interesting reads: Don’t Make Arrogant Models | DataCamp https://www.datacamp.com/blog/dont-make-arrogant-models A Survey Into Data Governance Tools | DataCamp https://www.datacamp.com/blog/a-survey-into-data-governance-tools DataCamp for Mobile: Mobile Coding at Its Best | DataCamp https://www.datacamp.com/blog/datacamp-for-mobile-mobile-coding-at-its-best [Connect with us!] Instagram: https://www.instagram.com/datacamp Twitter: https://twitter.com/DataCamp Facebook: https://www.facebook.com/datacampinc/ Youtube: https://www.youtube.com/channel/UC79Gv3mYp6zKiSwYemEik9A LinkedIn: https://www.linkedin.com/school/datacampinc/mycompany/ Website: https://www.datacamp.com/
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Related AI Lessons

Chapters (10)

04:07 Missing data theory
4:08 06:50 Msleep data set
6:51 09:08 Standardizing missing data
9:09 10:01 Quantifying missing data
10:02 10:51 Dropping missing data
10:52 13:11 Separating data by column type
13:12 18:03 Replacing with mean or median
18:04 19:57 Replacing with mode
19:58 22:31 Iterative methods to find values
22:32 23:10 Next steps
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