Not all Pokémon are created equal. Some are consigned to mediocrity, useless in battle until they reach their more evolved states. Others – like Zapdos, Articuno and Moltres – are so unique and powerful that they have officially been classified as legendary.
But what exactly makes a Pokémon the stuff of legend? In this project, we will answer that question with the help of a dataset that includes the base stats, height, weight and type of 801 Pokémon from all seven generations. Using the random forest algorithm, we will predict Pokemon status based on these characteristics and rank their importance in determining whether a Pokemon is classified as legendary.
Students should be familiar with the
tidyverse suite of packages, particularly
ggplot2 for data visualization and
dplyr for data manipulation. They should also have experience with classification problems and tree-based methods, as taught through Supervised Learning in R: Classification and Machine Learning with Tree-Based Models in R.
This project uses a subset of The Complete Pokemon Dataset published on Kaggle.
Data Scientist at BBC
Joshua Feldman is a data scientist at the BBC, where he uses a host of machine learning techniques to answer business problems and help the organization better understand its audiences. He mainly codes in R and SQL, taking a specialist interest in computational text analysis and data visualization. He holds an MSc in quantitative research methodology from the London School of Economics.See More