OUNANE, AminaMESSABIHI, MeriemBOUHALOUAN, Djamila2024-12-102024-12-102023http://dspace.univ-temouchent.edu.dz/handle/123456789/5881Our work involves combining the fields of knowledge extraction and artificial intelligence to develop a sophisticated system for detecting fraud related to electricity and gas consumption. To achieve this, we leveraged the online database of STEG and implemented two state-of-the-art classifiers, XGBoost and LightGBM, which are among the top machine learning algorithms for solving such problems. After conducting a comparative evaluation, we selected the most performant model, LightGBM, taking into account several metrics demonstrating its superiority. Using this model, we achieved a precision rate of 95.00% and a score of 88.71%, resulting in an excellent 19th position out of 295 participants in the Zindi challenge. These results showcase the effectiveness of our innovative approach and our dedication to tackling challenges in energy fraud detection.frExtraction de connaissances, intelligence artificielle, apprentissage automatique détection de fraudes, consommation d'électricité et de gaz, Base de données révérencielle STEG, classificateurs XGBoost et LightGBM, challenge ZindiKnowledge extraction, artificial intelligence, machine learning fraud detection, electricity and gas consumption, Rev.STEG database, XGBoost and LightGBM classifiers, Zindi challengeThesis