Advanced Feature Engineering
Code and instructions for techniques to creating new features, detecting outliers, handling imbalanced data, and impute missing values.
In this repo, you will find the code and instructions for this article. It is advised to read through the article whilst coding along using the Engineering Tips.ipynb notebook.
This repo and the corresponding article describe several methods for advanced feature engineering including:
- Resampling Imbalanced Data using SMOTE
- Creating New Features with Deep Feature Synthesis
- Handling Missing Values with the Iterative Imputer and CatBoost
- Outlier Detection with IsolationForest