Compression/Decompression program using Huffman Tree encoding/decoding to turn input files into binary encrypted files, and decoding encrypted files using the saved Huffman Trees.

Alexandra Hernandez

This C++ program implements a sophisticated file compression and decompression system built around a custom HCTree (Huffman Coding Tree) class. It utilizes C++ libraries like FancyInputStream and FancyOutputStream in its transformation of input files into efficiently encoded binary representations. The primary class of the programs, `HCTree` class serves as the core mechanism for encoding and decoding, enabling precise file compression that reduces data size while preserving original content integrity. Through carefully implemented functions within the `HCTree` class, the program can both compress files into compact binary formats and accurately decompress them back to their original state, demonstrating a robust approach to data handling and file transformation.


Analyzing and predicting California housing prices using census data from 2009-2020, alongside machine learning models such as Linear Regression and Random Forest to examine the differences in accuracy/variation, as well as predicted prices of a potential house in California.

Conducted an in-depth analysis of California housing prices by leveraging federal census data spanning from 2009 to 2020. We developed predictive models that explored the intricate factors influencing real estate valuations across the state by employing both Linear Regression and Random Forest modeling approaches. We examined critical variables including coastal proximity, average household income, and median house prices to assess their impact on housing market dynamics; research meticulously compared the accuracy and variation between these two modeling techniques, providing nuanced insights into the complex mechanisms driving California's housing market. The study not only offered a sophisticated statistical examination of price determinants but also demonstrated the potential for predictive modeling to generate precise estimates for potential house values in different regions of California.

Report is accessible [here](https://alexandrarh.github.io/cogs_137_reports/final_project.html).


Predicting Housing Prices in California

Case study analyzing air pollution in the United States with tidymodels. Using data from EPA's pollution monitors, it examines factors such as poverty levels, road lengths, and education levels around monitor to determine the average levels of pollution per zip code.

Conducted an in-depth analysis of air pollution patterns across the United States using advanced machine learning techniques in R and RStudio. Drawing on comprehensive data from the EPA's nationwide pollution monitoring network, our group developed a sophisticated predictive model that examines the complex interplay of environmental and socioeconomic factors influencing air quality. By leveraging packages like `tidyverse`, `ggplot2`, and `tidymodels`, we employed linear regression modeling to investigate how characteristics such as road lengths, zip code demographics, education status, and poverty levels correlate with local pollution levels. Our study meticulously mapped and analyzed pollution concentrations. Through rigorous statistical analysis and tidymodels machine learning algorithms, we generated insights that illuminate the multifaceted nature of air pollution distribution and its relationship to community characteristics.

Report is accessible [here](https://alexandrarh.github.io/cogs_137_reports/cs02.html).
