Using the resources from the following repo: https://github.com/40Cakes/pokebot-gen3/blob/main/Readme.md
My proposed project explores multiple facets of data collection, interpretation, and optimization through a creative and engaging context: the analysis of a highly complex and stochastic challenge inspired by the mechanics of the Pokémon video game series. This project was motivated by an artificial intelligence agent developed to complete two notoriously difficult challenges—the Professor Oak Challenge and the Shiny Living Dex Challenge—in Pokémon Emerald. This AI agent plays the game 24/7 at human-like speed to achieve the goals of capturing every unique shiny Pokémon, offering a fascinating parallel to real-world problems solved through quantitative analysis and computing.
The core problem presented in this project is the optimization of approach and tactics in the face of random processes and probability. Though the game-based context is entertaining, the principles and methodologies explored—such as differentiating between probability and odds, data collection methods, statistical interpretation, and efficiency optimization—are directly applicable to real-world data science challenges. By experimenting with variables like encounter rate modifiers and game settings, the project aims to demonstrate how small optimizations in method can drastically impact time and resource efficiency. I am confident that this project will not only demonstrate my ability to handle a complex and unconventional dataset but also highlight my critical thinking and creativity in applying data science methodologies to novel contexts.