From 1166a9124a6d3b796f8e08156358826e51c9d6a0 Mon Sep 17 00:00:00 2001 From: Bouchaira Neirouz <neirouz.bouchaira@etu.ec-lyon.fr> Date: Thu, 23 Jan 2025 12:56:00 +0000 Subject: [PATCH] Edit README.md --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 8d8911d..8e86765 100644 --- a/README.md +++ b/README.md @@ -32,13 +32,13 @@ This repository contains reports from various research projects focusing mostly * [**6- Enhancing Fraud Detection with Machine Learning: A Case Study for BNP Paribas Personal Finance**](https://gitlab.ec-lyon.fr/nbouchai/data-challenge-bnp-parisbas-finance) - * **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. + * **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 + * **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. + * 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. ## Usage -- GitLab