Developed a Jupyter Notebook for the flipoyo/MOLONARI1D repository, focusing on polynomial regression analysis and synthetic data generation for hydraulic head differentials. The work introduced a reproducible workflow for generating synthetic datasets, supporting robust testing and experimentation. Implemented in Python, the notebook included functions for coefficient retrieval and enhanced plotting options to improve regression visualization. Leveraging skills in data analysis and machine learning, the solution enabled users to efficiently analyze and visualize polynomial relationships within hydraulic data. The project emphasized reproducibility and clarity, providing a practical tool for researchers and engineers working with hydraulic head differential modeling and regression techniques.