The workflow reads the input dataset, tokenizes the text into individual words, and converts it into numerical feature vectors using HashingTF. A Logistic Regression model is then built and combined into a pipeline for efficient processing. The model’s performance is evaluated using BinaryClassificationEvaluator and CrossValidator to estimate accuracy on unseen data by training and testing on different data subsets. Finally, predictions are generated and displayed, enabling comprehensive evaluation of model performance and generalization capability.