Hi, I'm Ruby.

About Me

Hello! I'm Ruby Nunez, a recent graduate with expertise in cognitive neuroscience, data analysis, and software development supported by a BA in Psychology and Computer Science from Colby College. I am adept at leading creative projects and research initiatives. I am able to leverage technical skills in Python, Java, SQL, and data visualization to enhance stakeholder engagement and software solutions for analyzing and interpreting complex data sets.

Projects

Principal Component Analysis (PCA) with Covariance Matrix Approach

A high-performance Python library that simplifies Principal Component Analysis through the covariance matrix approach. Designed for data analysts and data scientists, this tool enhances dimensionality reduction, boosts data visualization, and streamlines exploratory data analysis. With features like variance analysis, elbow plot generation, and data reconstruction.

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Custom K-Means Clustering Algorithm for Data Analysis & Image Compression

Developed a flexible, Python-based K-Means clustering implementation for unsupervised learning tasks, including data clustering and image compression. Utilized libraries like NumPy, Pandas, and Matplotlib for data manipulation and visualization. Demonstrated practical applications in reducing image complexity and optimizing clustering processes for real-world datasets.

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Zomato Customer Segmentation: Insights for Targeted Marketing

Performed demographic and behavioral segmentation of Zomato's users by analyzing customer orders, spending habits, and cuisine preferences. Delivered actionable insights through Tableau visualizations, enabling targeted marketing strategies and enhancing user engagement.

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Custom Radial Basis Function (RBF) Neural Network for Function Approximation & Pattern Recognition

A specialized implementation of an RBF neural network utilizing Gaussian kernels and K-means clustering for efficient nonlinear data modeling. Ideal for regression, classification, and signal processing tasks, this project demonstrates how RBF networks can be applied to solve complex problems, including handwritten digit recognition with 80% accuracy on the MNIST dataset. With a focus on modularity and performance, this network showcases skills in Python, NumPy, machine learning, and data visualization.

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Data Projects

A portfolio of diverse projects showcasing practical applications of business analytics using SQL, Google Sheets, Tableau, and Power BI. Each project highlights my ability to analyze data, create interactive dashboards, and present actionable insights, reflecting my expertise in transforming complex datasets into meaningful narratives.

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Contact Me

rubyn0620@gmail.com

+1 (321) 295-9169

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