C4.5 is an algorithm used for generating decision trees, which are a popular method in machine learning for classification tasks. Developed by Ross Quinlan in 1993, it builds upon its predecessor, ID3, by improving how it handles both continuous and categorical data. C4.5 uses a concept called information gain to determine the best attribute for splitting the data at each node of the tree. The algorithm also includes features like pruning, which helps to reduce overfitting by removing branches that have little importance. Additionally, C4.5 can handle missing values and generate rules from the decision tree, making it a versatile tool for data analysis and predictive modeling.