Feature Engineering for Data Engineers: Building Blocks for ML Success

[MichTalebzadeh]

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 Linkedlin 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.

The full post is here

Feature Engineering for Data Engineers: Building Blocks for ML Success | LinkedIn

[Anushree_Tatode · Accepted solution]

Hi,
Excellent presentation and article! Your insights on feature engineering and practical code examples are incredibly useful for building strong ML models. Thanks for sharing!

Thanks,
Anushree

[MichTalebzadeh]

Many thanks for your kind words Anushree. Much appreciated.