Feature Engineering For Machine Learning

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

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

Unravel the art and science behind effective data analysis with this comprehensive guide to feature engineering. Crafted for beginners, this book is your gateway to understanding the pivotal role of features in extracting meaningful insights from data.

This book discusses various applications of machine learning using a new approach, the dynamic wavelet fingerprint technique, to identify features for machine learning and pattern classification in time-domain signals. Whether for medical imaging or structural health monitoring, it develops analysis techniques and measurement technologies for the quantitative characterization of materials, tissues and structures by non-invasive means.

Become expert in Data Cleaning and Feature Engineering for Machine Learning Data Science and Data Analytics

Data Science Projects with Linear Regression, Logistic Regression, Random Forest, SVM, KNN, KMeans, XGBoost, PCA etc