@@ -35,6 +35,11 @@ This repository contains reports from various research projects focusing mostly
***Abstract:** This project focuses on developing a machine learning-based fraud detection system to address financial losses caused by fraudulent activities within BNP Paribas Personal Finance. The problem is treated as a highly imbalanced binary classification task, with only ~1.45% of data representing fraudulent cases. Three primary methods are explored: boosted tree algorithms (XGBoost, LightGBM, CatBoost), neural networks, and preprocessing techniques, aiming to enhance detection accuracy. Model evaluation uses the area under the Precision-Recall curve (PR-AUC), with benchmark scores guiding improvement. Key innovations include addressing class imbalance via parameter adjustments and leveraging ensemble techniques. The best-performing model achieves a PR-AUC score of 0.238, outperforming existing benchmarks.
***Keywords:** Fraud detection, Machine learning, Neural networks, Class imbalance, Data preprocessing, BNP Paribas
*[**7- Use of solar sails to deorbit satellites in low-Earth orbit**](https://gitlab.ec-lyon.fr/nbouchai/data-and-ai-research-projects/-/blob/main/7-%20solar%20sail%20to%20deorbit%20satellites%20in%20low-Earth%20orbit.pdf?ref_type=heads)
* This was one of my first research projects, which I have kept to document my progress. The slides were translated from French, hence the presence of some French words in the figures.
