This project explores a traditional machine learning-based approach to intent classification for a simple personal assistant chatbot use case. We use the CLINC-150 dataset, a text classification dataset covering many intent classes. We explore and augment the data, train several different models using various algorithms, tune their hyperparameters, and evaluate their output. The focus is on simpler algorithms that are relatively fast and easy to train and optimize yet still perform well on the data.