My code for submissions to the Titanic Kaggle Competition.

My code for submissions to the Titanic Kaggle Competition.

Can competition scores be predicted using only weather data?

In this project, I scrapped historical scores from the World Surf League (WSL) and merged them with the corresponding wind and waves data for Oahu from NOAA to build regression models that predicted scores with RMSEs above baseline. A streamlit app allows for live evaluation of scoring potential.

Reddit users post content to different (or sometimes very similar) subreddits, and in this project, I collect posts using an API and accurately classify each post to its original subreddit.

The best part is how similar r/UnresolvedMysteries and r/UnsolvedMysteries are and how my models were still able to tell the difference!

There is digitized data from the 1900 US Census!

During a 6-hour hackathon, I worked with this data to sharpen my skills, and built models linking countries of origin for immigrants in 1900 to the most common household languages in each state today, excluding English and Spanish, of course.

Team effort where we gathered and cleaned hate crime and hate crime prosecution data from California. We built compelling visualizations of common biases and their relationships to county-level demographics and built models to predict the likelihood of prosecution for a hate crime, based on the crime's specific characteristics.

The classic Real Estate expression for property prices is: "Location, location, location." In this project, I look to evaluate the merits of this phrase. Specifically, if the property location is enough information to accurately predict its price.

SAT and ACT prep for high school students varies widely given different levels of access to materials, tutors, and time available for extra studying, among others. I will look at county-level data for California and seek to answer: How much are SAT and ACT scores in California related to median household income levels?

My first data project!

Inspired by the controversy on student loans in the US, I was interested in quantifying the most valuable college degrees. I used College Scorecard data to build a website that incorporates tuition costs and expected earnings after graduation to generate Net Present Values for loan-funded degrees and rank them nationally.

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A few weeks ago I started an immersive Data Science course at General Assembly. It took me weeks to actually decide to jump in and I’m so happy that I did! ... (continue reading)