Feature engineering is the process of using data domain knowledge to create features or variables that make Machine Learning (ML) algorithms work more efficiently. It’s a fundamental task for improving Machine Learning model performance and prediction accuracy. However, feature engineering can be very time-consuming, as it takes quite a bit of effort to effectively process variables in our datasets before using them in a model. The reader of this book should have some familiarity with Machine Learning. We’ll be using Python as a programming language, as well as data science libraries like: Pandas, NumPy, Matplotlib, SciPy, Scikit-learn.