From 8bb42de895ae871ec2495618029d1e874710ebbf Mon Sep 17 00:00:00 2001
From: Bouchaira Neirouz <neirouz.bouchaira@etu.ec-lyon.fr>
Date: Thu, 23 Jan 2025 12:55:27 +0000
Subject: [PATCH] Edit README.md

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 README.md | 5 +++++
 1 file changed, 5 insertions(+)

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  *   **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
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+ *   [**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)
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+ *   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.
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 ## Usage
 
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GitLab