Solutions of applied exercises contained in "An Introduction to Statistical Learning with Applications in Python", by Tibshirani et al, edition 2023

Jupyter Notebook solutions to the exercises in the book Introduction to Statistical Learning with Python.

📘 Python solutions to ISLR (2nd Edition) exercises & labs 📊 Covers regression, classification, resampling & trees 🧑‍💻 Jupyter notebooks with datasets, code & visualizations 🎯 Great for learning statistical learning methods hands-on

Solution to exercises for ISLP "An Introduction to Statistical Learning with application in Python"

Introduction to Statistical Learning with Python

This project is for ISLP

My solutions to Introduction to Statistical Learning in Python

Modeling and predicting credit card debt using linear and polynomial regression, with model fitting, coefficient comparisons, p-value evaluation, and visualization of regression results. All data is provided via ISLP. This project was done in Python.

Exploration of model performance, bias-variance tradeoffs, and dataset effects on classification accuracy with fully reproducible simulated data. This project was done in Python.

Modeling and predicting carseats sales using decision trees and random forests, with feature importance analysis, test performance evaluation, and reproducible visualizations. This project was done in Python.

My Jupyter notebook collection