aniruddha-panwar / islr

From Data Analyst to Data Scientist Using 'An Introduction to Statistical Learning with Applications in R'

INTRODUCTION TO STATISTICAL LEARNING - NOTES AND EXERCISES

INTRO

The repository holds my work in taking a self taught approach towards my dip into the world of data science. As of early 2019, I work as a data analyst and hope to become a data scientist by 2021.

The main source material on this learning Journey comes from An Introduction to Statistical Learning with Applications in R by the authors

• Gareth James
• Daniela Witten
• Trevor Hastie
• Robert Tibshirani

The contents of this project come directly from the book and thus would include -

• Statistical Learning
• Linear Regression
• Classification
• Resampling Methods
• Linear Model Selection and Regularization
• Moving Beyond Linearity
• Tree-Based Methods
• Support Vector Machines
• Unsupervised Learning

Each topic would contain a Lab file that has notes and lab code and an Applied file that contains answers to exercises from the books.

PRE-REQUISITES

The following pre-requisites cover installations and packages ->

Software

• R

R Packages

• tidyverse
• ISLR
• MASS
• boot
• ggplot2
• dplyr

BUILT WITH

• R
• R Markdown

SOURCES

1. An Introduction to Statistical Learning with Applications in R - Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani ; 2013

2. Statistical Learning Notes (Series) - Ankit Rathi