An Introduction to Machine Learning in R

All code and resources for these projects can be found in the GitHub Repository

K-Nearest Neighbor

Predicting Heart Disease in Patients using K-Nearest Neighbor




Decision Trees and Random Forests

Predicting the Quality of Wine using Decision Trees and Random Forests




Naïve Bayes

Filtering SMS Spam with Naïve Bayes




Artificial Neural Networks

Black Box Method 1: Predicting the Edibility of Mushrooms using Artificial Neural Networks




Support Vector Machines

Black Box Method 2: Predicting the Edibility of Mushrooms using Support Vector Machines




Evaluation Metrics

Classifying Abalone Age and Investigating Different Ways to Evaluate Classifier Performance




K-Means and Hierarchical Cluster Analysis

Exploring Wholesale Data with K-Means and Hierarchical Cluster Analysis




Reinforcement Learning

Solving the Tower of Hanoi and Playing Tic-Tac-Toe with Reinforcement Learning