An Introduction to Statistical Learning: With Applications in R is a great book to learn data science. The associated code is R which is a nice programming language for statisticians. However, R is not fast and in my opinion, it does not have nice syntax. There is a project named ISLR-python which ports the book to Python. I was inspired by the Python project and try to implement the introduced materials of this book in Julialang. Julia is a new language which is faster and more friendly to scientific computing than Python. Hopefully, this code is helpful for Julia lovers when they read the book.
In this project, I use Julia v1.5.1 and the following library
- Plots.jl for visualization
- CSV.jl for reading csv file
- Clustering.jl for clustering algorithms
- LIBSVM.jl for LibSVM implementation
- StatsPlots.jl for plotting statistic
- DataFrames.jl for data frame
- GLM.jl for generalized linear model
- Distributions.jl for stat distributions
I write a blog post to summarize the lessons that I learn after doing this project.
This repo is for 2nd Edition of ISLR: https://github.com/compleathorseplayer/ISLR-labs-in-Julia. I am not the author of this repo. The author sent it to me.