Project 2

This web application is designed to facilitate the sale of materials and automatically generate invoices with total amounts. The system allows users to add products, calculate totals, and produce printable invoices efficiently. The platform combines an easy-to-use interface with dynamic back-end functionality, ensuring accurate transaction management and improving the overall sales process.

Project 3

A large-scale web application developed with React.js and Laravel to fully digitalize Dar Atfal’s administrative processes. I handled backend development and collaborated on frontend tasks, integrating over 30 interfaces for efficient management of records and workflows. This project showcases my ability to manage complex systems and bridge backend and frontend functionalities.

Project 4

This web application features an intelligent chatbot developed with Python, React.js, and NLP techniques. I created a custom database based on information gathered from experts to address various technical problems. The system provides accurate, automated responses and demonstrates my ability to integrate backend AI functionalities with a dynamic frontend interface, creating a smart and interactive user experience.

Project 5

This project is a desktop and web-based application for managing library operations, developed using Java Swing and Servlets. It allows users to track books, manage borrowings and returns, and maintain member records efficiently. The system combines a user-friendly interface with robust backend functionality, showcasing my ability to develop Java-based applications that handle real-time data management.

Project 6

This project collects tweets using the Twitter API, processes the data with Apache Spark, and stores it in MongoDB for display on a dynamic web interface. The application demonstrates my skills in data collection, large-scale data processing, database management, and integrating processed data into an interactive web platform.

Project 7

MediRisk is an AI-powered health risk assessment system that uses a Linear Regression model to predict high or low medical risk from personal and health data. It integrates a Large Language Model (LLM) assistant to explain predictions, answer questions, and provide personalized lifestyle recommendations. The system highlights the most influential health factors affecting risk, helping users make informed decisions. By combining predictive modeling with conversational AI, MediRisk makes health management more accessible and proactive.