Applied Supervised Learning with R

Learn the ropes of supervised machine learning with R by studying popular real-world use cases, and understand how it drives object detection in driverless cars, customer churn, and loan default prediction.

Key Features

• Study supervised learning algorithms by using real-world datasets
• Fine-tune optimal parameters with hyperparameter optimization
• Select the best algorithm using the model evaluation framework

Book Description

What you will learn

• Develop analytical thinking to precisely identify a business problem
• Wrangle data with dplyr, tidyr, and reshape2
• Visualize data with ggplot2
• Validate your supervised machine learning model using k-fold
• Optimize hyperparameters with grid and random search, and Bayesian optimization
• Deploy your model on Amazon Web Services (AWS) Lambda with plumber
• Improve your model's performance with feature selection and dimensionality reduction

Who this book is for

This book is specially designed for beginner and intermediate-level data analysts, data scientists, and data engineers who want to explore different methods of supervised machine learning and its use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the concepts covered in this book.