Adil Eddarif

Conducted advanced exploratory data analysis on large-scale financial datasets to understand the performance dynamics of banking agencies. Developed supervised machine learning models (regression, classification) to predict annual commercial targets for agency networks. Built a predictive model for conquest/business acquisition targets based on historical production indicators (Prod N-2, Prod N-1, Prod N, RAF), removing dependency on legacy business rules. Integrated the predictive model into an MLOps pipeline (training workflow, metric tracking, version control, .pkl model export, and logging with MLflow). Evaluated model performance using R², MAE, cross-validation, and performed visual correlation and residual analysis. Contributed to the transition from a manual, heuristic-based business process to a scalable, data-driven decision-making approach.

Designed and developed advanced computer vision detection algorithms (OpenCV, Python). Implemented medical data augmentation techniques (cancer cell images) using image generation models based on GANs (Generative Adversarial Networks). Analyzed and processed complex medical imaging data to improve model performance. Applied predictive modeling and anomaly detection techniques. Actively collaborated with the R&D team in an agile workflow. Contributed to a Proof of Concept (PoC) for an automated diagnostic assistance solution.

Developed A face recognition model for internal employees