For a UK Government agency, I made a Comprehensive presentation titled " Feature Engineering for Data Engineers: Building Blocks for ML Success". I made an article of it in Linkedin together with the relevant GitHub code. In summary the code delves into the critical steps of feature engineering, demonstrating how to handle missing values, encode categorical data, and prepare numerical features for modelling. By employing techniques like mean imputation and one-hot encoding, we establish a solid foundation for training complex models such as Variational Autoencoders (VAEs). This comprehensive approach empowers data scientists and data engineers to extract meaningful insights and build high-performing machine learning pipelines.Hope you will find it useful.
The full post is here Feature Engineering for Data Engineers: Building Blocks for ML Success | LinkedIn <https://www.linkedin.com/pulse/feature-engineering-data-engineers-building-blocks-ml-mich-ektwe/> Mich Talebzadeh, Architect | Data Engineer | Data Science | Writer PhD <https://en.wikipedia.org/wiki/Doctor_of_Philosophy> Imperial College London <https://en.wikipedia.org/wiki/Imperial_College_London> London, United Kingdom view my Linkedin profile <https://www.linkedin.com/in/mich-talebzadeh-ph-d-5205b2/> https://en.everybodywiki.com/Mich_Talebzadeh *Disclaimer:* The information provided is correct to the best of my knowledge but of course cannot be guaranteed . It is essential to note that, as with any advice, quote "one test result is worth one-thousand expert opinions (Werner <https://en.wikipedia.org/wiki/Wernher_von_Braun>Von Braun <https://en.wikipedia.org/wiki/Wernher_von_Braun>)".
