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A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
| Date | Stars |
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| 2026-07-31 | 598 |
| 2026-08-05 | 598 |
| 2026-08-06 | 598 |
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# Awesome Feature Engineering for Machine Learning [](https://github.com/sindresorhus/awesome) A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning Maintainers - [Andrei Khobnia](https://github.com/aikho) This page is licensed under [Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported License](https://creativecommons.org/licenses/by-nc-sa/3.0/) Please feel free to create [pull requests](https://github.com/aikho/awesome-feature-engineering/pulls). ## Contents - [Numeric Data](#numeric-data) - [Scaling](#scaling) - [Ranking](#ranking) - [Quantization and Binning](#quantization-and-binning) - [Box-Cox Transformation](#box-cox-transformation) - [Yeo-Johnson Transformation](#yeo-johnson-transformation) - [Feature Interactions](#feature-interactions) - [Clustering Features](#clustering-features) - [t-SNE Features](#t-sne-features) - [PCA Features](#pca-features) - [Textual Data](#textual-data) - [Bag of Words](#bag-of-words) - [Phrase Detection Features](#phrase-detection-features) - [TFIDF](#tfidf) - [Word Embeddings](#word-embeddings) - [Subword Embeddings](#subword-embeddings) - [Pattern Features](#pattern-features) - [Lexicon Features](#lexicon-features) - [PoS Features](#pos-features) - [Image Data](#image-data) - [Computer Vision Algorithm Features](#computer-vision-algorithm-features) - [Image Statistics Features](#image-statistics-features) - [OCR Features](#ocr-features) - [Deep Learning Features](#deep-learning-features) - [Categorical Data](#categorical-data) - [One Hot Encoding](#one-hot-encoding) - [Count Encoding](#count-encoding) - [Label Encoding](#label-encoding) - [Dummy Encoding](#dummy-encoding) - [Mean Encoding](#mean-encoding) - [Hashing](#hashing) - [Time Series Data](#time-series-data) - [Rolling Window Features](#rolling-window-features) - [Lag Features](#lag-features) - [Geospatial Data](#geospatial-data) ## Numeric Data * [Understanding Feature Engineering (Part 1) -- Continuous Numeric Data](https://towardsdatascience.com/understanding-feature-engineering-part-1-continuous-numeric-data-da4e47099a7b) ### Scaling * [sklearn.preprocessing.MinMaxScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html) * [sklearn.preprocessing.StandartScaler](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html) ### Ranking * [Ranking](https://en.wikipedia.org/wiki/Ranking) * [scipy.stats.rankdata](https://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.stats.rankdata.html) ### Quantization and Binning * [Data Binning](https://en.wikipedia.org/wiki/Data_binning) * [Bucketing Continuous Variables in pandas](http://benalexkeen.com/bucketing-continuous-variables-in-pandas/) * [pandas.cat](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.cut.html) ### Box-Cox Transformation * [scipy.stats.boxcox](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.boxcox.html) * `np.log (x + const)` ### Yeo-Johnson Transformation * [Yeo-Johnson Transformation](https://gist.github.com/mesgarpour/f24769cd186e2db853957b10ff6b7a95) ### Feature Interactions * [Featuretools](https://docs.featuretools.com/) * [sklearn.preprocessing.PolynomialFeatures](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.PolynomialFeatures.html) * Divisions * Other interactions ### Clustering Features * [How to create New Features using Clustering!!](https://towardsdatascience.com/how-to-create-new-features-using-clustering-4ae772387290) ### t-SNE Features * [t-SNE](https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding) * [Automatic feature extraction with t-SNE](https://medium.com/jungle-book/automatic-feature-extraction-with-t-sne-62826ce09268) ### PCA F
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matched fp:8d3ad73e89dc4aec, llm:Repository description and README: 'A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning'; topics include feature-engineering, feature-extraction, machine-learning, data-science, ai.
matched fp:8d3ad73e89dc4aec, llm:Repository description and README: 'A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning'; topics include feature-engineering, feature-extraction, machine-learning, data-science, ai.
matched fp:8d3ad73e89dc4aec, llm:Repository description and README: 'A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning'; topics include feature-engineering, feature-extraction, machine-learning, data-science, ai.