This repository explains Feature Engineering concepts in a simple, practical, and beginner-friendly way.
It is created for:
- Students learning Data Science & Machine Learning
- Interview and exam preparation
- Hands-on project reference
- MCAR, MAR, MNAR
- Complete Case Analysis (CCA)
- Mean / Median / Mode Imputation
- Arbitrary Value Imputation
- End of Distribution Imputation
- Random Sample Imputation
- Missing Indicator Method
- KNN Imputer
- MICE (Multiple Imputation by Chained Equations)
- Label Encoding
- One-Hot Encoding
- Normalization
- Standardization
- Feature Construction
- Domain-based feature engineering
- https://scikit-learn.org/stable/modules/impute.html
- https://machinelearningmastery.com/handle-missing-data-python/
- https://www.statisticssolutions.com/missing-data/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3668100/
- https://data.library.virginia.edu/understanding-missing-data/
- https://stefvanbuuren.name/fimd/
- https://www.statology.org/mcar-mar-mnar/
- https://scikit-learn.org/stable/modules/preprocessing.html#encoding-categorical-features
- https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/
- https://towardsdatascience.com/categorical-encoding-methods-d8eaf7e3c7f0
- https://scikit-learn.org/stable/modules/preprocessing.html
- https://machinelearningmastery.com/normalize-standardize-machine-learning-data-weka/
- https://towardsdatascience.com/feature-scaling-techniques-4e0f7c7f2c15
- https://developers.google.com/machine-learning/data-prep/transform/construct
- https://towardsdatascience.com/feature-engineering-for-machine-learning-3a5e293a5114
- https://machinelearningmastery.com/discover-feature-engineering-how-to-engineer-features-and-how-to-get-good-at-it/
📌 Note:
This repository is a learning-focused summary created from multiple trusted sources.
All reference links are provided for deeper understanding.
To make feature engineering easy to understand and easy to apply.
⭐ If this repo helped you, consider starring it!