Get personalized book recommendations based on your reading history and preferences. Our algorithm analyzes your favorite books and reading patterns to suggest your next great read.

"Feature engineering" as a book category covers the principles, techniques, tools, and best practices for transforming raw data into informative inputs for machine learning and statistical models. Titles in this genre typically explain how to create, select, encode, scale, and extract features across different data types (tabular, time series, text, images), how to handle missing values and outliers, and how to incorporate domain knowledge, feature interactions, and temporal or hierarchical structure. Books may range from practical, hands-on guides and case studies showing pipelines, feature stores, and automation tools (including AutoML and feature synthesis) to deeper treatments of theoretical foundations, performance trade-offs, and interpretability. Intended readers include data scientists, ML engineers, analysts, and students who want systematic approaches to boost model performance, reduce overfitting, and make models more robust and explainable.

Filter books by their publication year. Enter the earliest year (Start) and latest year (End) to find books published within that period. Leave either field empty to search from the beginning of time or up to the present day.