Embedded Files
ETL Meets EDM:
San Francisco Concerts Spotify Playlist
ETL Meets EDM:
San Francisco Concerts Spotify Playlist
Tap into San Francisco's vibrant EDM scene with this Spotify playlist, refreshed nightly from a custom Python ETL process. Discover live EDM acts effortlessly, with tracks custom-ordered based on performance dates (sooner at the top of the playlist, later at the bottom).
Utilizes comprehensive web scraping to identify upcoming live EDM events in San Francisco.
Integrated with Spotify API to curate and update a concert-based playlist.
Received social media recognition from the 1015 Folsom concert venue.
Click HERE to explore the playlist
Chess GUI
Chess GUI
Experience chess in its purest form with Chess GUI, designed for intuitive play and rich features on Windows.
Follows Chess Rules: Includes all standard moves and scenarios.
Undo/Replay Moves: Flexibility in gameplay.
AI or Human Opponent: Play according to your preference.
PGN File Support: Save and load games in PGN format.
Game Details: Track names, ratings, and outcomes.
Propeller Level Editor Game
Propeller Level Editor Game
Propeller level editor game (similar to Super Mario Maker) created from Python.
Innovative Level Editor: Design your levels with an intuitive drag-and-drop interface.
Unique Gameplay: Engage with a special double jump mechanic that adds depth to your creations.
Mobile & Desktop Accessibility: Seamlessly create and play on your preferred platform with full functionality.
Mobile is without sound due to pygbag library limitation.
Click HERE to play
Fish Food Game
Fish Food Game
Created in Python, Fish Food is a similar game to Feeding Frenzy, where you (as the player) eats smaller fish, and your score increases and their fish grows larger. But, make sure to avoid the sharks, and any fish larger than you!
Includes mobile accessibility (without sound due to pygbag library limitation).
Click HERE to play
Genetic Algorithm Simulation
Genetic Algorithm Simulation
Inspired by my experience at haystagg, a programmatic advertising startup where I used a genetic algorithm with machine learning to characterize the top populations of internet users.
This simulation was coded in Python and uses the algorithm from PyWorld.
The goal is for the red fish to evolve by developing traits such as size and speed that will help them survive longer against hungry sharks.
The variables used were field of vision (the arc each fish can ‘see’ a shark), size, speed, intelligence (does the fish move away from a shark as it enters the arc), and frequency of switching direction.
Zip Text Droid App
Zip Text Droid App
Save time by making it easier to call or send texts to your closest contacts.
Customize the wording of the texts and change the contacts.
Decrease/increase the number of contacts/texts.
Select up to 10 contacts or 10 text messages.
WARNING: This app was created in 2011 in AppInventor and the format is not perfectly adjusted to the newer Droid OS. However, it still works, even if it's slower than it was originally.
Political Campaign Relational Database
Political Campaign Relational Database
Created a voter database in Microsoft SQL Server for a theoretical political candidate.
Built a targeted voter model through the relationships between voters, donors, and volunteers.
Wrote SQL queries to help the candidate such as:
Retrieve voters who have voted in all elections
Top donors for each candidate in the race
Volunteers from multiple previous campaigns
Integrated Interactive Graphics Dashboard
Integrated Interactive Graphics Dashboard
Built an online interactive graphics dashboard through Shiny app for R with an OKCupid dataset including each user's age, height, religion, ethnicity, etc.
Created a two-part visualization guide for R. To view properly, you'll need to download the HTML guides.
Simple graphics (bar charts, histograms, boxplots)
Advanced graphics (density plots, contour plots, heat maps)
OKCupid Dashboard in Shiny (Note: ~20 seconds to load)
Twitter Sentiment Analysis on Self-Driving Cars
Twitter Sentiment Analysis on Self-Driving Cars
Text mined a twitter dataset (from 2015) to analyze public opinion regarding self-driving cars.
Through statistical modeling (neural networks, support vector machines) in R, discovered the following themes:
Positive sentiment words that were highly ranked consisted of "cool", "exciting", "awesome", meaning that it can be branded as a status symbol.
Negative sentiment words that were highly ranked consisted of "safe", "problem", "trust", meaning that self-driving car companies should focus on reassuring the public.
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