Chadi Morsi

Collaborated with business teams, data engineers, and data scientists to align technical solutions with business needs. Analyzed and processed transaction and account data to build churn prediction models. Developed binary and sequential classification models (RNN and LSTM) to improve customer retention. Optimized credit card revenues and reduced customer acquisition costs by 50%, achieving a +20% improvement in retention efficiency.

Engineered features and applied decision trees, random forests, and logistic regression for predictive analysis. Designed and implemented ontologies, with storage and querying in SQL/SPARQL. Modeled Bayesian networks and graph neural networks (GNNs) to detect critical situations. Developed adaptive and transparent data architectures to improve scalability and clarity. Performed data cleaning, preprocessing, and visualization to ensure analytical quality. Applied granularity reduction techniques to optimize cognitive data analysis.

Preprocessed and analyzed physiological data to extract relevant features. Built predictive models of heart rate based on age using regression and machine learning techniques. Used Python, NumPy, Scikit-learn, and Matplotlib for data modeling and visualization.