Housing affordability in the United States is a big deal—both in real life and in how people talk about it. You’ve probably heard comments like, “Buying a house was easier back in the day,” or “The housing market is tougher now.” These kinds of statements reflect what many people believe about how the economy and housing have changed over time. To dig deeper into these claims, this project looks at historical data to see how housing affordability has shifted from 2009 to 2023. Instead of relying on personal stories or opinions, this research uses real data to check if these beliefs are true and to uncover bigger trends in the housing market.

The main goal of this analysis is to measure how housing affordability has changed, both across the country and in individual states. To do this, the study uses a key measure of affordability: the percentage of a household’s median income needed to pay  for a mortgage. This method helps compare affordability across different states and years by accounting for differences in home prices, income levels, and mortgage rates. The project also highlights important trends and regional differences, showing how easy or hard it has become for people to afford a home over time.

The analysis is based on a lot of data from reliable sources. For example, median income and mortgage rate data come from the Federal Reserve Bank of St. Louis, which are two of the most important pieces of information for calculating affordability. Housing costs are based on median home sale prices from Zillow Research. To help create maps, a GeoJSON dataset with U.S. state borders was downloaded from Kaggle. The data was processed, cleaned, and organized using Python, with the help of tools like NumPy and pandas.

Github: link

Created a dashboard that highlighted areas of high and low in-securities in food, water, or electricity. The purpose of the project was to help volunteer organizations distribute resources more efficiently. In this project I used Plotly, Dash, Pandas, and SQLite.  

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Using data on a ticker previous market value from yfinance API, using a seen portion (one month) to predict a future unseen portion (past 4 months). The algorithm was able to buy and sell stocks on minimums (Buy) and maximums (Sell) determined from my regression model. Stock values, predicted values, and money profited if you used my model 4 months ago is all displayed as visuals in my Dashboard. 

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Made a Dashboard to practice web scrapping, data formatting, data analysis, and data visualization displaying different attributes or input in relation to the anime rating. Web scrapped this information using BeautifulSoup html parser on an anime website (MyAnimeList). After extracting the data, had to formatted it into rows of a PostgreSQL database. Finally, performed analysis on the data and made an interactive Dashboard using Dash to display the data in an easy-to-understand format. 

Github: Link 

This project is a desktop application used to display and organize any reminders you may need. This project helped me practice dealing with dates, working with PostgreSQL and sqlite databases (made two versions), and making GUIs (tkinter). Personally this appilcation in my day to day because it is more convenient than the one preinstalled on my computer.

Dark Blue: Reminder

Light Blue: Today Date

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This program can create and store your passwords and store them locally encrypted in a text file. This project allowed me to practice reading/writing text files, basic encrypting algorithm, and GUI development with tkinter. 

Github: Link 

The player is exploring a dungeon to get stronger and acquire treasure. As they defeat enemies there level increase. As the level increase the player have access to more abilities and have more mana, hp, and attack strength. 

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Developed a chemistry simulation of Na and Cl reactions using Unity and C#. User can change amount of atoms in the environment and the temperature to speed up or slow down reactions.

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