My Portfolio
Welcome to my portfolio. Here you’ll find a selection of my work. Explore my projects to learn more about what I do.
Some of my Work on Rust
Amazon Co- Purchasing Network
I analyzed Amazon's product co-purchasing network, consisting of 334,863 nodes and 925,872 edges, to identify influential products and uncover clustering patterns. Applied graph theory techniques, including degree centrality, betweenness centrality, and clustering coefficients, to evaluate product relationships and group dynamics. Leveraged Rust for optimizing large-scale graph data processing, ensuring high efficiency and scalability. Insights were visualized to inform product recommendation strategies, providing actionable guidance for enhancing customer purchasing experiences.
Some of my Work with Python
Study on Sales Data
Used linear regression to analyze the impact of advertising budgets across different channels (TV, radio, and newspapers) on sales. The dataset includes three independent variables—TV, radio, and newspaper ad budgets—and a dependent variable, net sales, to quantify the relationship and assess the effectiveness of each channel.
Digits Data Set Study
In this study I explored the digits dataset from sklearn, containing 1797 grayscale images of handwritten digits (0–9), each represented as 8x8 matrices. The dataset includes flattened vector representations (data) and labels (target). My initial visualizations showcase the individual images and their corresponding labels. This dataset was analyzed for centering and processed using Principal Component Analysis (PCA) to reduce dimensionality. I included a 2D scatter plot (using the top 2 principal components) to highlight the clustering of digit categories, showcasing PCA’s effectiveness in visualizing high-dimensional data.
Some of my Work with Tableau
This was a project from my QM222 Class Modeling Business Decisions and Market Outcomes. We where assigned employee data from a company called HT, Inc and tasked with creating regression models and using Tableau to visualize our outcomes.
Always Learning, Always Exploring: What's Next?
This semester, I'm delving deep into the world of machine learning and data science. Through courses like Machine Learning for Businesses, Data Mechanics, and Deep Learning with PyTorch, I'm building hands-on skills to extract insights, optimize systems, and create transformative solutions for complex challenges.
