Fuad Hassan's Projects
This course aims to provide a comprehensive introduction to the practice of data science, equipping students with practical skills essential for real-world applications.
This course will cover the fundamental concepts of database systems, with an emphasis on relational database systems. It will cover both the theory and practice of relational databases, including Codd's original relational model, the relational algebra and calculus, and normalization.
The goal of the field of machine learning is to build computer systems that learn from experience and are able to adapt to their environments. This introductory machine learning course will present modern machine learning algorithms from supervised and unsupervised learning.
Computer Systems and Low-Level Programming
Data Structures
This is my public facing repo. You can find code, assignments, and in class examples in here. What you will NOT find are my answers or exams.
Final exam for 4331
Credit card debt prediction is important for managing risk, aiding financial planning, retaining customers, complying with regulations, and making informed investment decisions.
Credit scoring is a crucial task in financial institutions to assess the creditworthiness of individuals or businesses. This project focuses on building classification models to predict credit scores based on various features such as income, debt, and credit history.
My website Repo
GitGenius is a powerful tool designed to clone a GitHub repository, read all files, and create a document retrieval system using FAISS and OpenAI embeddings. This project leverages LangChain for natural language query processing to provide relevant information from the cloned repository.
Global Insights: Country Clustering Analysis Using Unsupervised Machine Learning
Java
This repository contains machine learning algorithms and models implemented in Python for various tasks, providing a comprehensive resource for learning and experimentation in the field of machine learning.
This repository hosts a collection of scripts and resources aimed at building a robust recommendation system for movies. Leveraging advanced algorithms and data analysis techniques, this project offers a seamless experience for users to discover movies tailored to their tastes.