The field of computational social science classification provides potent instruments for extracting insights from extensive social datasets. This chapter delves into the algorithms, practical applications, validation methodologies, and real-world instances of classification within social science research. It begins by emphasizing the pivotal role of classification in uncovering patterns and interconnections within social phenomena. This chapter delves deeply into computational classification, exploring prevalent algorithms at its core. It introduces supervised learning methods like logistic regression and decision trees, as well as unsupervised techniques such as k-means clustering. The discussion also addresses the selection of the most appropriate algorithm for a given research question. Additionally, it explores a range of applications across social science disciplines, such as community detection in social networks, sentiment analysis of online opinions, and image classification to analyze social behavior. Furthermore, it emphasizes the pivotal aspect of model validation, presenting essential evaluation metrics to assess classification performance and discussing cross-validation techniques to ensure generalizability. This chapter also addresses the critical issue of bias and fairness in classification models. Furthermore, the chapter illustrates the potency of classification through real-world case studies, including detecting political polarization on social media, forecasting customer churn, and categorizing news articles by topic. These case studies vividly exhibit the tangible application of classification in addressing a spectrum of social science challenges. By delving into algorithms, applications, validation techniques, and concrete examples, this chapter empowers social scientists with the insights necessary to harness computational classification for impactful research endeavors.