Improving massively imbalanced datasets in machine learning with synthetic data

This text discusses the use of synthetic data to improve model accuracy for fraud detection, cyber security, or any classification task with an extremely limited minority class. It highlights the challenge of handling imbalanced datasets in machine learning and presents a solution using gretel-synthetics, which generates additional samples of fraudulent records by incorporating features from both fraudulent records and their nearest neighbors labeled as non-fraudulent but close enough to be "shady." The text provides an example using the Credit Card Fraud Detection dataset on Kaggle and demonstrates how synthetic data can improve model performance. It also encourages readers to try running the notebooks provided with their own datasets.