C++ program that creates a graph using customized classes with basic properties as well as returning the shortest paths from parameterized nodes using different algorithms.

Alexandra Hernandez

This program reads a provided `.csv` file, a creates a comprehensive graph using C++'s data structure library (`tuple` , `vector` , `queue` , etc.). Utilizing custom classes to implement the graph, it helps represent a complex network of relationships using node and edge management, eventually going beyond basic representation and implementing traversal algorithms to return the shortest defined paths. With algorithms such as Breadth-First Search (BFS), Depth-First Search (DFS), and Dijkstra's algorithm, each traversal returns the specific path based on the provided node. Additionally, the implementation incorporates disjoint set operations with union functionality, providing a robust toolkit for graph analysis and manipulation. Through carefully designed classes and methods, the program enables users to explore graph properties, perform sophisticated network traversals, and solve complex pathfinding problems with efficiency and precision.

Original picture is accessible [here](https://www.google.com/url?sa=i\&url=https%3A%2F%2Fpython.plainenglish.io%2Fgraph-theory-23468908321d\&psig=AOvVaw3fSJb5_xGU00FXbapEXVrz\&ust=1734233876001000\&source=images\&cd=vfe\&opi=89978449\&ved=0CBcQjhxqFwoTCICN2eOqpooDFQAAAAAdAAAAABAZ).


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).
