This project analyzed 2022–2023 college degree completions across 16,000+ U.S. institutions to uncover the strongest demographic predictors of success.

• Key Findings:
  • Female completions were the most influential factor.
  • Non-traditional students (ages 25–39) play a critical role in completions.
  • Random Forest achieved ~99% accuracy, outperforming logistic regression and decision trees.

🔗 View Full Repository on GitHub

This project analyzed Fall 2023 U.S. college enrollment data (115K records, 5,900+ institutions) to uncover demographic patterns and predict graduate enrollment.

• Key Findings:
  • Women consistently outnumber men in enrollment, with the gap widening at the graduate level (60.6% vs. 39.4%).
  • Hispanic student representation drops sharply from undergrad (25.6%) to graduate (15.2%).
  • Institutional size distribution is highly skewed (median 588 vs. mean 3,332).
• **Models Utilized: Linear Regression, Decision Trees, and Random Forest.
  • Best performer: Random Forest (R² ≈ 0.78, MAE ~631).
  • Strongest predictors of graduate enrollment: female enrollment and Asian student representation.

🔗 View Full Repository on GitHub

Utilized Python and Plotly to explore and analyze the 2019 AustinISD TEA accountability ratings as my final project for the course Data Visualization and Communication. Additionally, a website houses the analysis report, the visualizations and the video with the presentation of the results.

This project transforms complex Texas Education Agency district performance data into an accessible, interactive web-based comparison tool. Users can easily compare multiple districts side-by-side, making informed decisions about educational institutions through clean data visualization and intuitive design.

• Key Features:
  • Side-by-Side Comparison: Compare multiple districts simultaneously with a clean, organized layout
  • Interactive Search & Filter: Quickly find district performance metrics
  • Responsive Design: Optimized for desktop and mobile viewing experiences
  • Interactive Data Display: Instant updates as users modify their selections
  • Performance Metrics Visualization: Clear presentation of key educational indicators
  • Clean User Interface: Minimalist design focused on usability and data clarity
• Technologies Used:
  • HTML5: Semantic markup and accessibility-focused structure
  • CSS3: Modern styling with responsive design principles
  • Vanilla JavaScript: Dynamic functionality and DOM manipulation
  • Data Processing and Preparation: Utilized various file formats and processes for cleaning and structuring TEA dataset

This project transforms complex Texas Education Agency school performance data into an accessible, interactive web-based comparison tool. Users can easily compare multiple schools side-by-side, making informed decisions about educational institutions through clean data visualization and intuitive design.

• Key Features:
  • Side-by-Side Comparison: Compare multiple schools simultaneously with a clean, organized layout
  • Interactive Search & Filter: Quickly find school performance metrics
  • Interactive Data Display: Instant updates as users modify their selections
  • Performance Metrics Visualization: Clear presentation of key educational indicators
  • Clean User Interface: Minimalist design focused on usability and data clarity
• Technologies Used:
  • HTML5: Semantic markup and accessibility-focused structure
  • CSS3: Modern styling with responsive design principles
  • Vanilla JavaScript: Dynamic functionality and DOM manipulation
  • Data Processing and Preparation: Utilized various file formats and processes for cleaning and structuring TEA dataset

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