Email filters classify new messages as either spam or not spam based on word frequency, syntax, and metadata. A "classifier" is an algorithm that maps input data into categories based on distinguishing characteristics, or "features." Features can be raw data or attributes derived from that data. "Feature engineering" is the process of identifying or extracting features that can be used to capture relevant patterns in data. The project that the author introduces in this article uses feature engineering with a dataset called the Modified National Institute of Standards and Technology dataset (MNIST). MNIST is a modified version of a NIST dataset that contains 70,000 grayscale images of handwritten digits, and the goal with this dataset is to build a classifier that can accurately recognize digits from those images. Students use feature engineering to build a classifier that can accurately recognize digits from images.