The article explores the use of synthetic data, generated by Gretel's ACTGAN model, in training machine learning classifiers for downstream tasks, with a focus on predicting customer purchases, such as frozen pizza. Synthetic data is highlighted as a solution to avoid linkage attacks and maintain data privacy while still enabling the training of accurate machine learning models. The data preparation involves splitting the dataset into training, test, and validation sets to evaluate the model's performance on unseen real data, ensuring no data leakage. The process includes the use of the PyCaret library to automate model selection and validation, discovering that while models trained on synthetic data may show slightly lower performance metrics compared to those trained on original data, their performance is generally comparable. This finding is significant as it implies that synthetic data can be used effectively to maintain privacy and reduce costs without compromising the effectiveness of machine learning models in real-world applications.