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Repository for final group project for ECS 171 Fall 2023

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ECS 171 Final Project: Pokemon Rank Classifier

Getting Started

To run the website, first run the development server:

flask --app pokemon_website run -p 3000

Then, open http://localhost:3000 with your browser to see the result.

Background Information

Pokémon is one of the most popular franchises in the world, with various console games, mobile apps, and a very passionate trading card collecting community. There are over 8 million daily Pokémon Go players, over 480 million lifetime console game sales, and over 3.7 billion pokemon cards sold in the 2020-2021 fiscal year. The biggest Pokémon tournament is the Pokémon World Championship with over 110,000 USD in prize money, so every advantage a player can get is extremely valuable. A big advantage that players can get is being able to categorize Pokémon with regards to their strength level.

Some reasons this is beneficial to players are that:

  1. Accurately measure the overall strength of a team and allow players to strategically choose their Pokémon teams at tournaments
  2. Efficiently determine which Pokémon to train more based on Effort Values (EVs)
  3. Predict opponent's moves during battles

Dataset

Our dataset was obtained through Kaggle, which was generated by scraping pokeapi.co. It comprises 1017 rows and 18 columns, with the following attributes:

  • ID of the Pokémon
  • Name
  • Rank (strength level)
  • Generation
  • Evoluntion chain
  • Primary type
  • Secondary type
  • HP
  • Attack
  • Defense
  • Special Attack
  • Special Defense
  • Speed
  • Total Stats
  • Height
  • Weight
  • Abilities
  • Description of Pokémon in English

https://www.kaggle.com/datasets/hamzacyberpatcher/data-of-1010-pokemons

Contributors

Alex D'Souza

Catherine Chen

Varun Wadha

Shane Kim

Fall 2023 Group 8

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Repository for final group project for ECS 171 Fall 2023

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