This project explores factors affecting student performance across math, reading, and writing scores. Visualizations were created using Base R, ggplot2, TrelliscopeJS, and Plotly. Key variables analyzed include test preparation, parental education, sibling count, and sports participation. Each graph includes an investigation statement and analysis discussing how these variables relate to academic outcomes.

For my BAS 474 final project, I analyzed Airbnb listings in Austin to uncover meaningful insights for both Airbnb and its users. The report is split into two main parts: a cluster analysis to segment listings into distinct groups, and a predictive modeling section to estimate listing prices based on various features. In the cluster analysis, I used K-means to group similar listings and interpreted each cluster based on factors like room type, number of reviews, and price range. This could help Airbnb identify different types of listings on their platform or give travelers better ways to filter options. The second half focuses on price prediction. I tested several machine learning models and selected the one that offered the best balance of accuracy and interpretability. The final model can predict listing prices within roughly $1000 of the actual price on average. I also highlighted a few key factors, like number of bedrooms and host experience, that influence pricing, and suggested ways to improve accuracy with more data. The goal was to keep the report concise, visually clear, and useful to a non-technical audience, while still reflecting the technical decisions behind the analysis.

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