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awesome-feature-engineering
A curated list of resources dedicated to Feature Engineering Techniques for Machine Learning
https://github.com/aikho/awesome-feature-engineering
- Understanding Feature Engineering (Part 1) -- Continuous Numeric Data
- sklearn.preprocessing.MinMaxScaler
- sklearn.preprocessing.StandartScaler
- Ranking
- scipy.stats.rankdata
- Data Binning
- Bucketing Continuous Variables in pandas
- pandas.cat
- scipy.stats.boxcox
- Yeo-Johnson Transformation
- Featuretools
- sklearn.preprocessing.PolynomialFeatures
- How to create New Features using Clustering!!
- t-SNE
- Automatic feature extraction with t-SNE
- Principal component analysis (PCA)
- sklearn.decomposition.PCA
- Understanding Feature Engineering (Part 3) -- Traditional Methods for Text Data
- Bag-of-words model
- A Gentle Introduction to the Bag-of-Words Model
- sklearn.feature_extraction.text.CountVectorizer
- sklearn.feature_extraction.DictVectorizer
- sklearn.feature_extraction.FeatureHasher
- sklearn_api.phrases – Scikit learn wrapper for phrase (collocation) detection
- tf-idf
- sklearn.feature_extraction.text.TfidfVectorizer
- Word embedding
- GloVe: Global Vectors for Word Representation
- Gensim: models.word2vec – Word2vec embeddings
- fastText
- Word2Vec and FastText Word Embedding with Gensim
- Do Pretrained Embeddings Give You The Extra Edge?
- Pre-trained subword embeddings in 275 languages, based on Byte-Pair Encoding (BPE)
- ClearTK - Feature Extraction Tutorial
- Named Entity Recognition with Bidirectional LSTM-CNNs (arXiv:1511.08308)
- Part-of-Speech_Tagging
- NLTK Categorizing and Tagging Words
- How to use PoS features in scikit learn classfiers
- Feature extraction and similar image search with OpenCV for newbies
- OpenCV -- Feature Detection and Description
- SimpleCV.Features package
- Scikit-image feature module
- ImageStat Module -- Pillow
- A Python wrapper for Google Tesseract
- Keras pre-trained models feature extraction
- Using Keras’ Pre-trained Models for Feature Extraction in Image Clustering
- Understanding Feature Engineering (Part 2) -- Categorical Data
- Why One-Hot Encode Data in Machine Learning?
- How to One Hot Encode Sequence Data in Python
- sklearn.preprocessing.OneHotEncoder
- Keras - to_categorical
- Feature engineering: Count encoding
- Label encoding in scikit-learn
- Feature engineering: Label encoding
- Dummy Coding: The how and why
- pandas.get_dummies
- One-Hot vs Dummy encoding
- Likelihood encoding of categorical features
- Python target encoding for categorical features
- Adding variance column when mean encoding
- Feature Hashing on Wikipedia
- Feature hashing and Extraction in VowpalWabbit
- Feature hashing in scikit-learn
- Automatic extraction of relevant features from time series
- Basic Feature Engineering With Time Series Data in Python
- pandas.DataFrame.rolling
- Use pandas to lag your timeseries data in order to examine causal relationships
- Geospatial Feature Engineering and Visualization
- Intro to Geospatial Data using Python
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