Thanks for visiting my personal website. I’m J Steven Raquel and am pursuing a Master of Science in Statistics at the University of California, Irvine.
Find out more about who I am and what I’ve done.
If you know anything about data science, statistics, machine learning, artificial intelligence, or computer science, feel free to get in touch with me! I have a lot to learn so I’m eager to touch base with folks with similar interests. You can find my email, GitHub and LinkedIn under Contact Me in the header.
These are some of the projects I’ve worked on throughout my time studying data science, and I am constantly learning and adding things to my repertoire.
For my final project for PSTAT 131 Data Mining, I used and compared the decision tree, randomForest and k-Nearest Neighbors algorithms using R to determine the quality of a wine and what the best algorithm was for classification. I also came to determine what attributes were most important towards a good wine.
This project utilizes various regression algorithms including ordinary least squares regression, decision trees, random forests, k-nearest neighbors, and gradient boosting in Python to create a regression model using various attributes of the housing market in Boston.
For my final project in PSTAT 147 Time Series, I attempted to apply the Box-Jenkins ARIMA model using R to several observations of the opening price of the cryptocurrency known as bitcoin. It turned out that the variability of the data was too sporadic and unpredictable, and ultimately I concluded that the GARCH model was most likely better because it accounts for the heteroskedasticity. While I did not fit that GARCH model I did fit a couple of ARIMA models and forecasted with them.
This is a simple visualization of film ratings on the Internet Movie Database in R, particularly focusing on the separate decades and the best and worst of each, as well as the trend of film ratings in general.
We later ported this game over to the Shiny framework in R, which allowed the game to be playable on the web without loading the package in R. It’s deployed on shinyapps.io. The source code for this Shiny app can be found on GitHub.