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General article about feature engineering and selection (main reference): https://github.com/Yorko/mlcourse.ai/blob/master/jupyter_english/topic06_features_regression/topic6_feature_engineering_feature_selection.ipynb
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Feature engineering/preprocessing, using scikit-learn API (great code examples, but really brief explanation):
https://scikit-learn.org/stable/modules/preprocessing -
Feature scaling/normalization:
https://towardsdatascience.com/all-about-feature-scaling-bcc0ad75cb35 -
Log Transform/power transform:
https://medium.com/@kyawsawhtoon/log-transformation-purpose-and-interpretation-9444b4b049c9 -
Missing values preprocessing using scikit-learn API (great code examples, great explanation):
https://scikit-learn.org/stable/modules/impute.html -
Feature selection scikit-learn API (great code examples, great explanation):
https://scikit-learn.org/stable/modules/feature_selection.html -
Melbourne housing dataset source:
https://www.kaggle.com/anthonypino/melbourne-housing-market
See feature_engineering_selection.ipynb notebook.