/FeatureEngineering

Feature Engineering for Machine Learning, Soledad Galli

Primary LanguageJupyter Notebook

Feature Engineering for Machine Learning

-- From beginner to advanced Soledad Galli light version on udemy, full version on Train in Data

  1. Introduction
  2. Types of variables
  3. Types of problems in variables
  4. Machine learning model requirements
  5. Engineering missing values(NA) in numerical variables
  6. Engineering missing values (NA) in categorical variables
  7. Bonus: More on engineering missing values
  8. Engineering outliers in numerical variables
  9. Engineering rare values in categorical variables
  10. Engineer labels of categorical variables
  11. Engineering mixed variables
  12. Engineering dates
  13. Feature Scaling
  14. Gaussian Transformation
  15. Discretisation
  16. NEW: Engineering features with Feature_Engine
  17. Putting it all together