This chapter uses the Home Credit Default Risk competition as an example to introduce practical solutions for structured data competitions. First, an overview of the competition problem is presented. This is followed by an introduction to data exploration, which is an open-ended process designed to help identify trends, anomalies, patterns, and relationships within the data. These discoveries serve as guidance for subsequent modeling processes, including how to perform data preprocessing, which features to construct, and which appropriate models to select. The data for the competition problem is examined from four aspects: label distribution, missing values, outliers, and correlation. Finally, an interpretation of excellent competition solutions is provided.