@@ -12,7 +12,7 @@ This repository contains reports from various research projects focusing on Arti
*[**2- Data Science for Electric Battery Performance (August 2023)**](https://gitlab.ec-lyon.fr/nbouchai/data-and-ai-research-projects/-/blob/main/2-%20Data%20Science%20for%20Electric%20Battery%20Performance.pdf?ref_type=heads)
* **Abstract:** This report details an internship project within the Materials Center of Excellence (CoE) that explored using machine learning algorithms to simulate battery performance. The project aimed to identify new business opportunities for Hexagon through battery innovation. This exploratory phase focused on research using available databases. The report summarizes the approach, challenges, simulation results, and their potential implications. The incubation studies suggest the viability of using AI and machine learning for battery performance research at Hexagon. The simulations show promise as a cost-effective way to test innovative concepts for advanced battery materials.
* **Keywords:** ML, Data pre-processing, Electric battery performance
* **Keywords:** ML, Data preprocessing, Electric battery performance
*[**3- Planes and Fluid Forces (March 2024)**](https://gitlab.ec-lyon.fr/nbouchai/data-and-ai-research-projects/-/blob/main/3-%20Planes%20and%20Fluid%20Forces.pdf?ref_type=heads)
* **Abstract:** This report characterizes the forces acting on an aircraft, focusing on optimizing operation and fuel consumption. Key findings include the stall angle of the studied wing profile (12°), the importance of flaps during takeoff, and the optimal wing angle of incidence (1° to 6°). The report also analyzes pressure contours, wake creation, and the relationship between fuel flow and thrust in turbojets.
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@@ -29,6 +29,13 @@ This repository contains reports from various research projects focusing on Arti
* **Description:** This project investigates how web scraping and data enrichment techniques can improve the efficiency and scope of startup sourcing and evaluation for Private Equity (PE) and Venture Capital (VC) firms, overcoming the limitations of traditional, manual methods.
* **Keywords:** Webscraping, Venture Capital
*[**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.
***Keywords:** Fraud detection, Machine learning, Neural networks, Class imbalance, Data preprocessing, BNP Paribas
## Usage
Each report is contained within its own directory (or file). You can navigate to the specific report you are interested in to view the full document.
