Below I have showcased two technical projects that I programmed: A snake/trivia game based on the popular influencer, David Dobrik, and a random anime (Japanese animation) recommender system.
Snake/trivia Game
For the group project in my CPSC 100 course at UBC, I collaborated with other students throughout the semester to program a snake/trivia game, using the visual programming language, Snap! The game is based around the popular social media influencer, David Dobrik.
Rules: Playing as a David Dobrik ‘snake’, Youtube, Snapchat, and Instagram icons are collected to grow the snake and gain followers. The hazard icon should be avoided as it causes you to lose followers. There are also 7 question mark icons, which temporarily pauses the snake game for you to answer a true or false trivia question about David Dobrik. A correct answer leads to a gain in followers, while an incorrect answer leads a loss in followers.
Initiation: The starting screen displays a list of rules and prompts the player to begin the game by clicking the green flag on the Snap! website.
How to Lose: The game can be lost in two ways: By hitting one of the walls, or by losing all of your followers. The above video shows some gameplay and demonstrates losing by hitting one of the walls.
How to Win: The game can be won by reaching 1 million followers, which the above video demonstrates. The follower count can be seen in the top left-hand corner of the screen.
Click here to play the snake/trivia game.
Random Anime Recommender System
In my CPSC 103 course at UBC, I learned a programming design recipe known as ‘How to Design Analysis Programs’, and this recipe centers around analyzing a dataset. Using this recipe, I analyzed an anime (Japanese animation) dataset and programmed a random anime recommender system in Python.
The recommender system consists of three inputs: Genre of the anime (Action, Comedy, Adventure, etc.), minimum user rating of the anime out of 10, and form of the anime (TV show or movie). The system will then output a random anime that fits all three of these criteria. The above video shows 4 demonstrations of the recommender system. To showcase the system’s random nature, the first 2 demonstrations input the same criteria (Genre: Action; Minimum Rating: 7; Form: TV), and the system outputs a different anime each time. The last 2 demonstrations input different criteria to showcase the system’s diversity.
Click here for a PDF of my work using the ‘How to Design Analysis Programs’ design recipe, which includes the full Python code for the recommender system.