About Me

My name is Nate Diaz-Santana, and I recently completed my Master of Science in Data Science, along with a Bachelor of Science in Computer Science.

Projects

Childcare Affordability in the US

Analyzed county-level childcare cost and income data to measure affordability across the United States. Built visualizations and an interactive map to highlight regional disparities and households exceeding recommended affordability thresholds.

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Predicting Diabetic Patient Readmissions

Developed a classification model using hospital encounter data to predict 30-day readmissions for diabetic patients. Evaluated model performance with an emphasis on recall to better identify high-risk patients.

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Philadelphia Mobility, Weather & Unemployment Analysis

Explored relationships between mobility trends, weather patterns, and unemployment in Philadelphia. Combined multiple datasets to identify how environmental and economic factors influence movement patterns.

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Fair Pricing Predictions in the Used Car Market

Built regression models to estimate fair market prices for used vehicles based on features such as mileage, model year, and vehicle characteristics. The analysis demonstrates how data-driven pricing models can improve transparency in online car marketplaces.

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Predicting Sleep Quality from Lifestyle Factors

Developed machine learning models to predict sleep quality using lifestyle and health variables such as physical activity, stress levels, and daily habits. The project highlights how behavioral patterns influence sleep outcomes.

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Airport Complaint Trends and Operational Insights

Analyzed passenger complaint data to identify recurring operational issues at airports. Used data visualization and trend analysis to uncover patterns in service disruptions and customer dissatisfaction.

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Kia & Hyundai Theft Trend Analysis Across U.S. Cities

Investigated theft trends involving Kia and Hyundai vehicles across multiple U.S. cities. Time-series analysis and visualizations highlight how theft patterns changed following viral social media trends and security vulnerabilities.

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Optimizing Sponsored Content on Twitch: Game and Viewership Trends

Analyzed Twitch streaming data to identify game categories and time periods with the highest viewer engagement. The analysis provides insights for optimizing sponsored content placement and promotional campaigns.

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Modeling Steam Game Prices for Indie Developers

Built predictive models to estimate appropriate pricing for indie games on Steam using features such as genre, reviews, and gameplay characteristics. The project demonstrates how data can guide pricing strategy in digital game marketplaces.

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Predicting Obesity Levels in Latin American Cities

Developed machine learning classifiers to predict obesity levels using lifestyle, dietary, and demographic data from cities in Colombia, Peru, and Mexico. The models illustrate how behavioral patterns can help identify obesity risk and support public health research.

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