This project focuses on using Machine Learning to automate test case generation from user stories. It involves pre-processing user stories with NLP to extract actions and expectations. The next step is training a machine learning model to classify the generated test cases as either unique or generic. Labeled tokens are used to classify new test data, and the test cases are saved in an Excel workbook. The approach aims to reduce manual effort in test case creation and improve the accuracy of testing.