predictive modeling

Integrating machine learning techniques into automotive quality control improves responsiveness, accuracy, and efficiency, thereby reducing costs and increasing customer satisfaction.  The study focuses on the cutting process of a mechanical transmission shaft manufactured in the Moroccan automotive industry.  Two models, Decision Tree and Random Forest, are used to analyze the impact of parameters on the conformity of the parts, including length and diameter parameters.  The results show that diameter is the key factor influencing quality, and the Random Forest model,

This study addresses the lack of region-specific tools for academic counseling in Morocco by proposing a machine learning framework to predict student performance across secondary education pathways.  Using academic records of students from the Greater Casablanca region, we evaluate four models – Random Forest, Support Vector Machine (SVM), Decision Tree, and Linear Regression – following a methodology that integrates data preprocessing, feature selection, and synthetic data enrichment to address class imbalance.  The Random Forest algorithm achieved an accuracy rate of