eBook - Machine Learning for Business Analytics, 2ed : Concepts, Techniques, and Applications in R
Available Exclusively as an eBook | Part of the AI eBook Collection | Only for Institutional Purchase | Publication Year: 2023
Description
Machine Learning for Business Analytics: Concepts, Techniques, and Applications in R, 2nd Edition provides a comprehensive introduction to statistical and machine learning algorithms for business data analytics. It covers prediction, classification, visualization, dimension reduction, rule mining, clustering, text mining, and network analytics with real-life case studies, ethical considerations, and R implementations.
Table of Contents
Part I Preliminaries
Chapter 1 Introduction
Chapter 2 Overview of the Machine Learning Process
Part II Data Exploration and Dimension Reduction
Chapter 3 Data Visualization
Chapter 4 Dimension Reduction
Part III Performance Evaluation
Chapter 5 Evaluating Predictive Performance
Part IV Prediction and Classification Methods
Chapter 6 Multiple Linear Regression
Chapter 7 k-Nearest Neighbors (kNN)
Chapter 8 The Naive Bayes Classifier
Chapter 9 Classification and Regression Trees
Chapter 10 Logistic Regression
Chapter 11 Neural Nets
Chapter 12 Discriminant Analysis
Chapter 13 Generating, Comparing, and Combining Multiple Models
Part V Intervention and User Feedback
Chapter 14 Interventions: Experiments, Uplift Models, and Reinforcement Learning
Part VI Mining Relationships Among Records
Chapter 15 Association Rules and Collaborative Filtering
Chapter 16 Cluster Analysis
Part VII Forecasting Time Series
Chapter 17 Handling Time Series
Chapter 18 Regression-Based Forecasting
Chapter 19 Smoothing and Deep Learning Methods for Forecasting
Part VIII Data Analytics
Chapter 20 Social Network Analytics
Chapter 21 Text Mining
Chapter 22 Responsible Data Science
Part IX Cases
Chapter 23 Cases