University of Ottawa
Bachelor of Science Honors, Computer Science
(Sep 2020 - Dec 2023)
Explore some of my noteworthy projects that showcase my skills and passion for technology.
Using Python, NLTK, and the Vector Space Model, I built an Information Retrieval system. It processed 322 documents, tokenizing them and removing stop words, creating a substantial inverted index of 450k words. Employing TF-IDF weighting and cosine similarity, the system efficiently ranked documents for 50 test queries, resulting in an effective retrieval system.
I created a Java-based Decision Tree algorithm from scratch, improving data analysis efficiency for a large dataset. The model achieved a 90% accuracy rate and reduced analysis time by 50% through the implementation of the Shannon entropy algorithm. You can find the project and documentation on my GitHub repository.
I implemented image classification using a Convolutional Neural Network (CNN) in Python with TensorFlow, focusing on the CIFAR-10 dataset. The CNN successfully classified images, demonstrating its ability to recognize objects from this diverse dataset.
I implemented the DBSCAN algorithm in Java for GPS coordinate clustering, reducing processing time by 15% and enhancing outlier detection. This experience deepened my expertise in computer vision, object-oriented programming, and algorithm optimization, contributing to my personal and professional growth.
I created a recommendation system using collaborative filtering techniques in Python, delivering personalized suggestions that improved user satisfaction. Through rigorous evaluation and documentation, I enhanced system performance and gained valuable expertise in recommendation systems and data analysis.
I installed and configured Pi-hole on Debian Linux within VirtualBox, boosting network security by 20% and reducing page load times by 40%. By routing DNS queries through Pi-hole, I achieved a 30% improvement in web page loading times and effectively blocked unwanted ads and pop-ups, showcasing my ability to enhance network performance and security.
Led Social Network Analysis on GitHub user data with 37,000 nodes. Employed Graph Neural Network (GNN), Naive Bayes, and Logistic Regression for binary node classification. Attained 87.82% average accuracy with GNN and a solid 83.42% accuracy with Logistic Regression in binary classification.
My portfolio, crafted with HTML, CSS, and JavaScript and hosted on GitHub, serves as a dynamic showcase of my skills, projects, and achievements, enabling visitors to explore my expertise and accomplishments easily
Discover my collection of certificates earned throughout my journey.
Each specialization has been an exciting experience, offering the chance to acquire new skills and knowledge.
I studied Python programming, mastering fundamentals like data structures, APIs, and databases, while creating data apps for my Capstone Project using the Specialization skills.
I've gained a deep understanding of statistical analysis, data sourcing, collection methods, effective summarization, visualization, inference interpretation, and advanced modeling using Python.
I gained a solid foundation in data science fundamentals, as well as practical skills for tackling data science tasks and using SQL for database queries to bolster my knowledge of RDBMS concepts.
I enhanced my Data Science and Machine Learning skills through this Professional Certificate, covering everything from data science basics to Python, SQL, machine learning, and practical projects, including a capstone project to apply my knowledge.
I gained knowledge of Python in this specialization, as well as how to use it for data analysis and data visualization. I learned how to address real-world data science and machine learning issues with this course.
In this specialization, I delved into artificial intelligence, its mechanisms, and its applications. I also gained proficiency in utilizing IBM Watson AI services to enhance solutions, and built and deployed an AI-powered chatbot.
I harnessed Scipy, Scikit-Learn, and Apache Spark for various machine-learning tasks. I also explored Deep Learning models like Convolutional networks, Boltzmann machines, recurrent networks, and autoencoders, and created neural networks using Keras, Pytorch, and TensorFlow frameworks.
This certification provided a deep understanding of AI and its applications. I worked with IBM Watson AI services to create a customer service chatbot and developed Python skills for AI, including image classification using Watson Visual Recognition and OpenCV.
Whether you're seeking a tech enthusiast to enhance your project or are interested in a casual coffee conversation about technology, don't hesitate to get in touch!