We've been given a dataset from OKCoin that contains records of card transactions, each with ten different fields. Our task is to create a supervised model for fraud detection using a systematic approach. This involves cleaning the data, performing feature engineering, and conducting out-of-time (OOT) validation to ensure the model's effectiveness over time. We need to carefully select appropriate algorithms and address time-related factors within the data. Beyond the technical aspects, we're required to prepare a concise report summarizing our methodology, quantitative assessments of model performance, and our key findings and recommendations. Ultimately, our goal is to develop a robust fraud detection system that can enhance security and reduce the risks associated with card transactions