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feature engineering for time series forecasting

Course: Feature engineering for time series forecasting


Python feature engineering cookbook

Book: Second edition




Our courses


Feature Engineering for Machine Learning

Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.

Soledad Galli

Soledad Galli

$19.99

Feature Selection for Machine Learning

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

Soledad Galli

Soledad Galli

$19.99

Hyperparameter Optimization for Machine Learning

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.

Soledad Galli

Soledad Galli

$19.99

Machine Learning with Imbalanced Data

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.

Soledad Galli

Soledad Galli

$19.99

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☻ Almost 10k student reviews.



☻ Average courses rating: 4.6 out of 5.



Students, reviews and rating of our courses

Our Python Open-Source


Feature-engine, a python open source library for feature engineering

➊ Transformers for feature engineering and creation.


feature selection techniques and tools➋ Algorithms for feature selection.


feature engineering tutorials and demos➌ Extensive docs and demos.



Our books

Python feature engineering cookbook

Feature engineering for machine learning


Feature selection in machine learning

Feature selection in machine learning with Python



Our Instructors


Soledad Galli, data scientist, instructor, open source developer

Soledad Galli, PhD

Lead Data Scientist


Kishan Manani, data scientist, instructor

Kishan Manani, PhD

Lead Data Scientist




Chris Samiullah, software developer, instructor

Chris Samiullah

Senior Software Developer


Feature selection in machine learning with Feature-engine

Get a copy of our eBook when you subscribe.


Feature Selection in Machine Learning with Feature-engine covers feature selection methods available in the open-source Python library Feature-engine:

  • Drop constant and duplicated features.
  • Single feature classifiers.
  • Target encoding.
  • Feature permutation.
  • Recursive feature addition.
  • Recursive feature elimination.