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https://github.com/aikho/awesome-feature-engineering

A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
https://github.com/aikho/awesome-feature-engineering

List: awesome-feature-engineering

ai data-science feature-engineering feature-extraction machine-learning

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A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning

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README 

        
# Awesome Feature Engineering for Machine Learning



[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](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 Features

* [Principal component analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis)

* [sklearn.decomposition.PCA](http://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html)



## Textual Data

* [Understanding Feature Engineering (Part 3) -- Traditional Methods for Text Data](https://towardsdatascience.com/understanding-feature-engineering-part-3-traditional-methods-for-text-data-f6f7d70acd41)

### Bag of Words

* [Bag-of-words model](https://en.wikipedia.org/wiki/Bag-of-words_model)

* [A Gentle Introduction to the Bag-of-Words Model](https://machinelearningmastery.com/gentle-introduction-bag-words-model/)

* [sklearn.feature_extraction.text.CountVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.CountVectorizer.html)

* [sklearn.feature_extraction.DictVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.DictVectorizer.html)

* [sklearn.feature_extraction.FeatureHasher](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html)

### Phrase Detection Features

* [sklearn_api.phrases – Scikit learn wrapper for phrase (collocation) detection](https://radimrehurek.com/gensim/models/phrases.html)

### TFIDF

* [tf-idf](https://en.wikipedia.org/wiki/Tf%E2%80%93idf)

* [sklearn.feature_extraction.text.TfidfVectorizer](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html)

### Word Embeddings

* [Word embedding](https://en.wikipedia.org/wiki/Word_embedding)

* [GloVe: Global Vectors for Word Representation](https://nlp.stanford.edu/projects/glove/)

* [Gensim: models.word2vec – Word2vec embeddings](https://radimrehurek.com/gensim/models/word2vec.html)

* [fastText](https://fasttext.cc/)

* [Word2Vec and FastText Word Embedding with Gensim](https://towardsdatascience.com/word-embedding-with-word2vec-and-fasttext-a209c1d3e12c)

* [Do Pretrained Embeddings Give You The Extra Edge?](https://www.kaggle.com/sbongo/do-pretrained-embeddings-give-you-the-extra-edge)

### Subword Embeddings

* [Pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE)](https://github.com/bheinzerling/bpemb)

### Pattern Features

* [ClearTK - Feature Extraction Tutorial](https://cleartk.github.io/cleartk/docs/tutorial/feature_extraction.html)

* Regular Expressions

### Lexicon Features

* [Named Entity Recognition with Bidirectional LSTM-CNNs (arXiv:1511.08308)](https://arxiv.org/abs/1511.08308v4)

### PoS Features

* [Part-of-Speech_Tagging](https://en.wikipedia.org/wiki/Part-of-speech_tagging)

* [NLTK Categorizing and Tagging Words](https://www.nltk.org/book/ch05.html)

* [How to use PoS features in scikit learn classfiers](https://stackoverflow.com/questions/24002485/python-how-to-use-pos-part-of-speech-features-in-scikit-learn-classfiers-svm)



## Image Data

### Computer Vision Algorithm Features

* [Feature extraction and similar image search with OpenCV for newbies](https://medium.com/machine-learning-world/feature-extraction-and-similar-image-search-with-opencv-for-newbies-3c59796bf774)

* [OpenCV -- Feature Detection and Description](https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_table_of_contents_feature2d/py_table_of_contents_feature2d.html)

* [SimpleCV.Features package](http://simplecv.readthedocs.io/en/latest/SimpleCV.Features.html)

* [Scikit-image feature module](http://scikit-image.org/docs/stable/api/skimage.feature.html)

### Image Statistics Features

* [ImageStat Module -- Pillow](http://pillow.readthedocs.io/en/3.1.x/reference/ImageStat.html)

### OCR Features

* [A Python wrapper for Google Tesseract](https://github.com/madmaze/pytesseract)

### Deep Learning Features

* [Keras pre-trained models feature extraction](https://keras.io/applications/)

* [Using Keras’ Pre-trained Models for Feature Extraction in Image Clustering](https://medium.com/@franky07724_57962/using-keras-pre-trained-models-for-feature-extraction-in-image-clustering-a142c6cdf5b1)



## Categorical Data

* [Understanding Feature Engineering (Part 2) -- Categorical Data](https://towardsdatascience.com/understanding-feature-engineering-part-2-categorical-data-f54324193e63)

### One Hot Encoding

* [Why One-Hot Encode Data in Machine Learning?](https://machinelearningmastery.com/why-one-hot-encode-data-in-machine-learning/)

* [How to One Hot Encode Sequence Data in Python](https://machinelearningmastery.com/how-to-one-hot-encode-sequence-data-in-python/)

* [sklearn.preprocessing.OneHotEncoder](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html)

* [Keras - to_categorical](https://keras.io/utils/#to_categorical)

### Count Encoding

* [Feature engineering: Count encoding](https://www.slideshare.net/HJvanVeen/feature-engineering-72376750/11)

### Label Encoding

* [Label encoding in scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html)

* [Feature engineering: Label encoding](https://www.slideshare.net/HJvanVeen/feature-engineering-72376750/9)

### Dummy Encoding

* [Dummy Coding: The how and why](http://www.statisticssolutions.com/dummy-coding-the-how-and-why/)

* [pandas.get_dummies](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html)

* [One-Hot vs Dummy encoding](https://stats.stackexchange.com/questions/224051/one-hot-vs-dummy-encoding-in-scikit-learn)

### Mean Encoding

* [Likelihood encoding of categorical features](https://www.kaggle.com/tnarik/likelihood-encoding-of-categorical-features)

* [Python target encoding for categorical features](https://www.kaggle.com/ogrellier/python-target-encoding-for-categorical-features)

* [Adding variance column when mean encoding](https://www.kaggle.com/general/16927#95887)

### Hashing

* [Feature Hashing on Wikipedia](https://en.wikipedia.org/wiki/Feature_hashing)

* [Feature hashing and Extraction in VowpalWabbit](https://github.com/VowpalWabbit/vowpal_wabbit/wiki/Feature-Hashing-and-Extraction)

* [Feature hashing in scikit-learn](http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html)



## Time Series Data

* [Automatic extraction of relevant features from time series](http://tsfresh.readthedocs.io)

* [Basic Feature Engineering With Time Series Data in Python](https://machinelearningmastery.com/basic-feature-engineering-time-series-data-python/)

### Rolling Window Features

* [pandas.DataFrame.rolling](https://pandas.pydata.org/pandas-docs/version/0.22/generated/pandas.DataFrame.rolling.html)

### Lag Features

* [Use pandas to lag your timeseries data in order to examine causal relationships](https://medium.com/@NatalieOlivo/use-pandas-to-lag-your-timeseries-data-in-order-to-examine-causal-relationships-f8186451b3a9)



## Geospatial Data

* [Geospatial Feature Engineering and Visualization](https://www.kaggle.com/camnugent/geospatial-feature-engineering-and-visualization)

* [Intro to Geospatial Data using Python](https://github.com/SocialDataSci/Geospatial_Data_with_Python/blob/master/Intro%20to%20Geospatial%20Data%20with%20Python.ipynb)



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