Bad features slow your models down, muddy your results, and make your work harder to explain to stakeholders. Feature selection fixes that - and this book shows you exactly how.
Feature selection is the process of identifying the variables that actually matter in your data, so your machine learning models run faster, generalise better, and produce outputs that make sense to the people using them. Done well, it's one of the highest-leverage skills in a data scientist's toolkit.
This book covers the full landscape of feature selection methods - filter, wrapper, and embedded approaches - plus techniques developed specifically for applied predictive modelling and data science competitions. You'll learn not just what works, but why it works, and when to reach for one method over another.
You'll start by eliminating useless and redundant features through variability and correlation analysis. From there, you'll explore statistical tests like ANOVA, chi-square, and mutual information to identify what's worth keeping. Then you'll move into embedded methods like Lasso regularisation and decision tree feature importance, before tackling more advanced techniques like recursive feature elimination and value permutation.
Each chapter pairs clear conceptual explanations with hands-on Python implementations. A companion GitHub repository provides the full source code, ready to adapt for your own projects.
If you work with predictive models and want cleaner data, faster training, and results you can actually stand behind - this book is for you.
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