Machine Learning for Business Analytics Concepts, Techniques, and Applications in R by Galit Shmueli, Peter C. Bruce, Peter Gedeck, Inbal Yahav, Nitin R. Patel Publisher: Wiley; 2nd edition (February, 2023) ISBN: 978-1-118-83517-2 Buy at Amazon or Wiley

Description

Machine learning —also known as data mining or data analytics— is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.

Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.

Source code, datasets, and instructors material

This repository contains:

• Rmd: R code of individual chapters as R markdown files - download all as mlba-Rmd.zip
• R: R code of individual chapters as plain R R files - download all as mlba-R.zip

The datasets are distributed using the mlba package; see below for installation instructions. To find instructors material go to www.dataminingbook.com.

Installation of R packages used in the book

R and most packages can be installed directly from CRAN. Go there for instructions on how to install R and individual packages. The RStudio IDE is a

MLBA

The mlba package is available from . You can install this package using the following commands:

if (!require(mlba)) {
  library(devtools)
  install_github("gedeck/mlba/mlba", force=TRUE)
}

Note that this requires the installation of the devtools package

DiscriMiner

The DiscriMiner package is currently not available from CRAN. You can install it directly from Github as described in https://github.com/gastonstat/DiscriMiner

if (!require(DiscriMiner)) {
    library(devtools)
    install_github('DiscriMiner’, username='gastonstat')
}

Setting up an environment for deep learning applications

In order to run the code for the deep learning applications, you will need to create a Python environment with the required packages. A convenient way to do this is to use Anaconda. See installPython.md for instructions on how to install Anaconda and create the required environment.