Hello 👋 my name is Hassan. I’m a third-year Computer Science student at Western University with a strong passion for programming and a curiosity for exploring new technologies. Eager to tackle complex challenges and contribute to innovative software projects. I am always happy to chat, feel free to reach out!

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Developed an end-to-end NBA game outcome prediction pipeline using data scraping, feature engineering, and machine learning

• Data Scraping: Used Playwright and BeautifulSoup to extract NBA box scores (2019-2024).
• Data Processing: Parsed statistics into CSV files, incorporating rolling averages for better predictions.
• Model Training: Built and trained a Ridge Classifier for game outcome predictions.
• Prediction Interface: Designed a CLI for easy input and winner predictions.
• Automation: Provided pre-processed data and trained models for streamlined use.
• Tools: Python, numpy, scikit-learn, Playwright, BeautifulSoup.

Developed an automated system to extract due dates from course syllabi and add them to Google Calendar, simplifying academic planning.

• PDF Parsing: Extracted text using pdfminer and identified dates with regular expressions.
• Date Processing: Converted dates into ISO-compliant formats.
• Calendar Integration: Added events with automatic 1-day popup and 2-day email reminders.
• Event Categorization: Classified events (e.g., assignments, quizzes, midterms) based on keywords.
• Tools: Python, pdfminer, google-auth, google-api-python-client, dateutil.

My team and I developed a secure marketplace for AI models during UofTHacks 12, enabling developers to upload, manage, and sell their work while protecting data privacy and intellectual property.

• Purpose: Addressed challenges in securely sharing and selling AI models while promoting responsible usage.
• Key Features: Streamlined platform for showcasing, purchasing, and integrating AI models securely.
• Technologies:
  • Frontend: HTML, CSS, Tailwind CSS.
  • Backend: Node.js, JavaScript, Express.js.
  • Database: MongoDB.
• Next Steps: Integrate Midnight’s security protocols for enhanced transaction protection and data privacy.

Developed a Java program to find optimal bike paths in a National Park using custom data structures and algorithms.

• Pathfinding Logic: Designed an algorithm to compute safe paths from the park's entrance to treasure chambers while avoiding hazards.
• Data Structures: Implemented a custom stack (DLStack) using doubly linked lists for efficient path management.
• Chamber Prioritization: Prioritized treasure chambers, lighted chambers, and dim chambers during navigation.
• Algorithm Implementation: Used stack-based traversal to track visited chambers and maintain path constraints.
• Tools: Java, doubly linked lists, stacks.

(In progress, Private Repository)

• Developing a system with a team of 5 to identify the closest hospital for users by factoring in wait times, ensuring the most efficient access to treatment.
• Utilizing Supabase as the database to securely store user and hospital information, ensuring data integrity and scalability.
• Integrating Mapbox to calculate hospital distances and identify the nearest facilities based on user location.

(Private Repository)

• Developed machine learning models to analyze and process large datasets for real-time predictive analytics, ensuring data accuracy and reliability for decision-making.
• Integrated predictive algorithms into a mobile app with a focus on functionality and UX.
• Refined AI algorithms to maximize accuracy and efficiency, achieving an 88% accuracy score while ensuring compliance with medical industry standards.

• Languages: Java, Python, HTML, CSS, JavaScript, C, ARM7 Assembly, C#
• Databases: Supabase, SQL
• Tools & Platforms: Git, Linux, UNIX, Bash, Figma, Android Studio, Jupyter
• Other: Object-Oriented Programming (OOP), RESTful APIs, Playwright, NumPy, Scikit-learn, Unit Testing, Debugging, Documentation Writing