My solutions to Chapter 9 ('Support Vector Machines') of the book 'An Introduction to Statistical Learning, with Applications in R'.

My solutions to Chapter 8 ('Tree-Based Methods') of the book 'An Introduction to Statistical Learning, with Applications in R'.

My solutions to Chapter 7 ('Moving Beyond Linearity') of the book 'An Introduction to Statistical Learning, with Applications in R'.

An overview of the MissForest algorithm for predictive imputation of missing data. Covers various topics including the algorithm itself, implementation, measuring its effectiveness w.r.t a modelling objective, tips to increase imputation speed & accuracy.

My solutions to Chapter 6 ('Linear Model Selection and Regularization') of the book 'An Introduction to Statistical Learning, with Applications in R'.

My solutions to Chapter 5 ('Resampling Methods') of the book 'An Introduction to Statistical Learning, with Applications in R'.

In Linear Regression, reducing the computation time of Leave-One-Out Cross-Validation MSE to that of a single model fit.

My solutions to Chapter 4 ('Classification') of the book 'An Introduction to Statistical Learning, with Applications in R'.

My solutions to Chapter 2 ('Statistical Learning') of the book 'An Introduction to Statistical Learning, with Applications in R'.

My solutions to Chapter 3 ('Linear Regression') of the book 'An Introduction to Statistical Learning, with Applications in R'.