This project focuses on identifying fraudulent insurance claims using advanced machine learning techniques. By leveraging detailed claim data and employing extensive preprocessing and feature engineering, we developed and compared models such as Random Forest, Logistic Regression, XGBoost, Naive Bayes, and KNN. The Random Forest model emerged as the most effective, achieving high accuracy and AUC scores. This work highlights the potential of data-driven approaches in addressing real-world challenges like fraud detection.

This project focuses on developing a diabetes prediction system using Google Cloud services, leveraging BigQuery for data preprocessing, AutoML for machine learning model training, Vertex AI for deployment, and Looker Studio for data visualization. It emphasizes early detection of diabetes, aiming to improve patient outcomes and resource efficiency. The project utilizes a dataset from Kaggle, cleans and transforms it into a structured format, trains models for predictions, and creates dashboards to present actionable insights, all while considering data privacy and ethical implications.

Developed a machine learning pipeline to automate the translation of Tunisian dialect sentences into French, addressing the operational inefficiencies of manual translation at Emrhod Consulting. The project utilized advanced NLP techniques, including fine-tuned MarianMT and mBART models, combined with semi-supervised learning and a transliteration module for converting alphanumeric inputs to Arabic script. This approach significantly reduced translation time, improved accuracy, and contributed to advancing NLP solutions for low-resource languages.

This project aims to develop an AI-powered exam generation system tailored for professors, using a Retrieval-Augmented Generation (RAG) pipeline. Professors can upload their teaching materials and past exams, enabling the system to generate new, personalized exams aligned with their teaching style and focused on specific chapters or concepts where students previously struggled.