Group member: Yuchen Yao, Qixin Wang, Margaux Tian, Shuangning Yang, Chenyu Zhang
We choose Pokemon Dataset (https://www.kaggle.com/abcsds/pokemon) to be our dataset which includes 721 Pokemons with 18 different types: Normal, Fighting, Flying, Poison, Ground, Rock, Bug, Ghost, Steel, Fire, Water, Grass, Electric, Psychic, Ice, Dragon, Dark and Fairy. The dataset has 13 features(attributes) described as:
- ID for each pokemon
- Name (Name of each pokemon)
- Type 1 (Each pokemon has a type,which determines weakness/resistance to attacks)
- Type 2 (Some pokemon are dual type)
- Total (Sum of all HP, Attach, Defense, SP Atk, SP Def, Speed)
- HP (Health points of each pokemon)
- Attack (The base modifier for physical attacks)
- Defense (the base damage resistance against normal attacks)
- SP Atk (special attack)
- SP Def (the base damage resistance against special attacks)
- Speed (determines which pokemon attacks first each round)
- Generation (when did the pokemon came out)
- Legendary (special, rare pokemon, usually with high total)
We also use Pokemon- Weedle's Cave Dataset (https://www.kaggle.com/terminus7/pokemon-challenge) which contains the results of previous combats, and the combination of those two datasets help develop Machine Learning models able to predict the result of future pokemon combats.
Special thanks 🙏 for the advice from
[Dr. Christian Kuemmerle (https://pages.jh.edu/kuemmerle/) & Dr. Jinchao Feng (https://www.linkedin.com/in/jinchao-feng-01660870/)] (Johns Hopkins University)
also for the cooperation and joint efforts from the co-authors (Margaux Tian, Shuangning Yang, Yuchen Yao and Qixin Wang) of this project.
If you got any enquiries or suggestions, I'm all ears 😎
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Institution: Johns Hopkins University 🎓 Graduate
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Personal E-mail: zhangchenyu555@gmail.com
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Academic E-mail: czhan146@jhu.edu
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Insterested Area:
- Machine Learning
- Data Analysis
- Business Analytics