Hassan Eisa

Developed a research proposal applying machine learning (logistic regression, decision trees, topic modeling, and sentiment analysis) to study communication and data-driven collaboration in multidisciplinary teams. Conducted literature reviews, defined research questions/methodology, and proposed statistical validation tests including chi-square, ANOVA, and correlation.

Engineered an LSTM-based stock prediction model for Baladna using TensorFlow/Keras, managing the end-to-end data lifecycle from scraping and feature engineering through model training and evaluation. Built a Python regression-based sales forecasting solution for Apple to analyze advertising effectiveness and support marketing recommendations.

Supported web-based projects through data management and analytical problem-solving to improve system performance and user experience. Contributed to debugging, testing, and optimization of web applications to ensure reliable data flow and collaborated on data-driven improvements, including representing Tiactro at Web Summit 2025.

Performed exploratory data analysis and built machine learning models in Python using pandas, scikit-learn, and TensorFlow. Processed large datasets with Dask/Modin/Ray and deployed models through Flask and Django for interactive web integration using Agile workflows and version control.

Collaborated with the IT supervisor to understand key IT systems and standard departmental IT tasks, gaining exposure to network management and system troubleshooting. Reviewed the operation of enterprise systems (e.g., GIMA, Mantis, LDT, ATS, PCK, PABX, and Windows Servers) and gained insights into UPS and fire-fighting systems.

Bachelor of Arts in Management Information Systems (MIS) with a double minor in Statistics and Business Analytics, covering coursework such as systems analysis and design, business intelligence, data mining, and statistical/data-analytics methods.