Great British Accent Challenge
Great British Accent Challenge is a project inspired by GeoGuessr. Users listen to a short audio clip of a speaker from
Great Britain and attempt to guess where the speaker is from. After guessing, the results display how many users correctly
select the speaker's location.
using AWS S3. The site was deployed using AWS Elastic Beanstalk.
Lyric Guesser is a simple game of guessing which word appears in more hit songs.
Users can submit guesses for comparisons submitted by other users or create their own comparisons.
After a user has voted, the site displays data about the pair of words as a venn diagram. Clicking on a section
of the venn diagram displays a list of songs that contain the selected word. The database also tracks how other users guessed
on the same comparison.
The site is a MERN stack application with the front-end created with React and the back-end with Express and MongoDB.
The database contains the lyrics for songs from the Billboard
Hot 100 top ten singles from 1958 to 2020 and the
Year End Hot 100 from 1946 to 2020. The lyrics were sourced using Python and the Genius API.
To help me avoid reading too much news focused on Donald Trump and to continue learning about making Chrome extensions,
I decided to make an extension that previews links and displays a popup to warn you if Trump's name is mentioned.
When the cursor hovers over a link a Ajax call to the page is made and the number of Trump mentions is displayed in the popup
next to the link. The photos inside the popup correspond to the number of mentions.
Click on the title to download the Chrome extension. It can be added to Chrome by accessing your Chrome extensions, turning on
developer mode and loading the download as an unpacked extension.
Audio Visualizer- Chrome Extension
My audio visualizer Chrome extension creates a 3D visualizer for the audio from a tab. When the chrome extension icon is clicked,
the tab's audio stream is sent to the visualizer, using tab capture.
I decided to use the visualizer part of the project to learn some Three.js. The audio stream is analyzed by the Web Audio API
and the data is used to animate objects in Three.js.
NBA Predictor takes in a NBA player's name and an opponent team and makes a prediction for the player's
points, assists, and rebounds. It also includes sliders to adjust the weight given to different variables
used to make an estimate. Player and defense stats are collected using Ajax calls to ESPN and TeamRankings.com.
I collected the birthplaces of professsionial athletes from the NFL, MLB, NBA, and NHL using ESPN's API and Wikipedia. Each birthplace was
were born. When zoomed in close, the map changes to a simple point map so that points can be clicked to find out which athlete they represent.
The coding game is a simple puzzle game in which you solve a puzzle by scripting the movements of a robot.
The script written to move the robot is similar to Python with some modifications.
The goal of the game is to move
the robot to the finish square and after acquiring the correct sum. When the robot passes over a red square, the red
square's value is added to the robot's value. There are three levels to solve in any order.
I used Python to gather addresses and denominations of churches in the United States and then geocoded them using GeoPy.
into a heat map. Layers can be toggled on and off to view each denomination seperately.
I used data from the Census Bureau and then cleaned it up using Python.
The map displays each cities share of the total national population as well as its rank. I also added info popups
that appear when the base of the graphic is clicked.
The scene is lit using diffuse lighting and includes shadows. I implemented signed distance
functions for a sphere, a box, and a plane. The lightsource rotates slowly around the scene,
but with three objects, the scene runs very slowly.
Before rendering a full 3D scene using ray marching, I created a 2D visualization of sphere tracing.
The canvas displays the circles that represent the ray marching steps leaving the "camera". The
"camera" turns red when it detects a collision.
Left and right arrow keys rotate the "camera" and the up and down arrow keys move the "camera" forward
and back. Clicking on the scene adds a random shape to the scene.
This project allows you to create a crossword grid and then attempts to fill it using words taken from Dictionary.com and
Wikipedia article titles. Custom words can be filled into the crossword before the rest of the crossword is filled.
Unfortunately, pulling from such a large pool of possible words creates many crosswords with very obscure words and acronyms.
Words were collected using Python and Selenium, and the crossword is created and filled using Java.
Mapping Google Trends
Google Trends limits the number of comparisons to 5 search terms. I wanted to make a visualization of sports teams based on
Google search popularity, so I used Python and the Pytrends API to compare all teams in a league to each other.
I used this project to create maps of NFL, NBA, NHL, MLB, and MLS teams in the United States.
This project creates a visualization of binary trees. Every binary tree has a unique signature created by a pre-order traversal.
Each node is represented by two decimals. The first digit represents a left child and the second represents the right child. for
example, 01 represents a node with a right child, but no left child.
Trees can be constructed by clicking on the nodes or by entering a valid binary tree signature in either
decimal or binary representation.
Harry Potter Sentiment Analysis
I used Python and VADER to analyze the sentiment of each of the Harry Potter books. I collected the average sentiment for
each chapter and plotted the results using Matplotlib. I labeled the highest and lowest sentiment chapters of each book.
I wanted to compare the sentiment of different subreddits and see what kind of comments attracted upvotes. I thought it would
be interesting to compare subreddits focused on the same subject so I looked at r/Seattle and r/SeattleWA as well as r/Canada
and r/OnGuardForThee. For more of a contrast, I also tried comparing r/Politics to r/Sports.
I used the Reddit API to collect comments from top posts on a subreddit and used VADER to analyze the sentiment of each comment.
Using Matplotlib, I made histograms for different subreddits based on the frequency of comments with similar sentiments and
colored each segment based on the average number of upvotes for a comment within that segment. The dark green segments are the least
upvoted, and the yellow segments are the most upvoted.
map the college football champions since 1869. I used the data from the College Football Data Warehouse's list
of recognized winners.
Each school's bar grows based on its weight and its color fades to red based on the number of years since
its last win. The green dot represents the weighted center for national champions. I assigned each year a
weight of one and evenly divided it between the year's winners.
This project was created after seeing that a skilled solitaire player wins around 43% of games. I attempted to see what percentage
of games of solitaire a simple AI could win. The AI I created is capable of winning games, but it is no where near a 43% win rate.
Twitch Clip Compiler
This project uses Python and Selenium to collect the top clips from the last week from a specified category on Twitch. It then downloads
ten minutes worth of clips and edits them into one video along with titles. Finally, the edited video is uploaded to YouTube using the
YouTube Data API. An edited video can also be created and uploaded from a list of handpicked links.
After seeing that the Instagram API would be deprecated in 2020, I decided to use Python and Selenium to create an Instagram
bot that analyzes the likes on recent Instagram posts. It looks at the previous four posts and collects the lists of likes for each
photo. It then looks at the most recent post and displays likes from people who did not like many of the previous posts,
and people who liked many of the previous posts, but not the most recent one.
Topographic Map Experiments
In this project, I created a few experiments using screenshots of topographic maps. One of the more interesting looking experiments
was looking at the relative heights of the terrain. The Java program converts the topographic map into a height map and then
displays parts of the terrain that would appear higher or lower from the perspective of someone standing at the mouse cursors location.