Machine Learning for Business Analytics Concepts Techniques and Applications in RapidMiner 1st edition by Galit Shmueli 1119828815 9781119828815

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ISBN-10 ‏ : ‎1119828815 

ISBN-13 ‏ : ‎9781119828815

Author:   Galit Shmueli 

Machine Learning for Business Analytics
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 RapidMiner 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.

Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner 1st Table of contents:

PART I: Preliminaries
CHAPTER 1: Introduction
1.1 WHAT IS BUSINESS ANALYTICS?
1.2 WHAT IS MACHINE LEARNING?
1.3 MACHINE LEARNING, AI, AND RELATED TERMS
1.4 BIG DATA
1.5 DATA SCIENCE
1.6 WHY ARE THERE SO MANY DIFFERENT METHODS?
1.7 TERMINOLOGY AND NOTATION
1.8 ROAD MAPS TO THIS BOOK
1.9 USING RAPIDMINER STUDIO
CHAPTER 2: Overview of the Machine Learning Process
2.1 INTRODUCTION
2.2 CORE IDEAS IN MACHINE LEARNING
2.3 THE STEPS IN A MACHINE LEARNING PROJECT
2.4 PRELIMINARY STEPS
2.5 PREDICTIVE POWER AND OVERFITTING
2.6 BUILDING A PREDICTIVE MODEL WITH RAPIDMINER
2.7 USING RAPIDMINER FOR MACHINE LEARNING
2.8 AUTOMATING MACHINE LEARNING SOLUTIONS
2.9 ETHICAL PRACTICE IN MACHINE LEARNING
PROBLEMS
NOTES
PART II: Data Exploration and Dimension Reduction
CHAPTER 3: Data Visualization
3.1 INTRODUCTION
3.2 DATA EXAMPLES
3.3 BASIC CHARTS: BAR CHARTS, LINE CHARTS, AND SCATTER PLOTS
3.4 MULTIDIMENSIONAL VISUALIZATION
3.5 SPECIALIZED VISUALIZATIONS
3.6 SUMMARY: MAJOR VISUALIZATIONS AND OPERATIONS, BY MACHINE LEARNING GOAL
PROBLEMS
NOTES
CHAPTER 4: Dimension Reduction
4.1 INTRODUCTION
4.2 CURSE OF DIMENSIONALITY
4.3 PRACTICAL CONSIDERATIONS
4.4 DATA SUMMARIES
4.5 CORRELATION ANALYSIS
4.6 REDUCING THE NUMBER OF CATEGORIES IN CATEGORICAL ATTRIBUTES
4.7 CONVERTING A CATEGORICAL ATTRIBUTE TO A NUMERICAL ATTRIBUTE
4.8 PRINCIPAL COMPONENT ANALYSIS
4.9 DIMENSION REDUCTION USING REGRESSION MODELS
4.10 DIMENSION REDUCTION USING CLASSIFICATION AND REGRESSION TREES
PROBLEMS
NOTES
PART III: Performance Evaluation
CHAPTER 5: Evaluating Predictive Performance
5.1 INTRODUCTION
5.2 EVALUATING PREDICTIVE PERFORMANCE
5.3 JUDGING CLASSIFIER PERFORMANCE
5.4 JUDGING RANKING PERFORMANCE
5.5 OVERSAMPLING
PROBLEMS
NOTES
PART IV: Prediction and Classification Methods
CHAPTER 6: Multiple Linear Regression
6.1 INTRODUCTION
6.2 EXPLANATORY VS. PREDICTIVE MODELING
6.3 ESTIMATING THE REGRESSION EQUATION AND PREDICTION
6.4 VARIABLE SELECTION IN LINEAR REGRESSION
PROBLEMS
NOTES
CHAPTER 7: k‐Nearest Neighbors (k‐NN)
7.1 THE k‐NN CLASSIFIER (CATEGORICAL LABEL)
7.2 k‐NN FOR A NUMERICAL LABEL
7.3 ADVANTAGES AND SHORTCOMINGS OF k‐NN ALGORITHMS
APPENDIX: COMPUTING DISTANCES BETWEEN RECORDS IN RAPIDMINER
PROBLEMS
NOTES
CHAPTER 8: The Naive Bayes Classifier
8.1 INTRODUCTION
8.2 APPLYING THE FULL (EXACT) BAYESIAN CLASSIFIER
8.3 SOLUTION: NAIVE BAYES
8.4 ADVANTAGES AND SHORTCOMINGS OF THE NAIVE BAYES CLASSIFIER
PROBLEMS
NOTE
CHAPTER 9: Classification and Regression Trees
9.1 INTRODUCTION
9.2 CLASSIFICATION TREES
9.3 EVALUATING THE PERFORMANCE OF A CLASSIFICATION TREE
9.4 AVOIDING OVERFITTING
9.5 CLASSIFICATION RULES FROM TREES
9.6 CLASSIFICATION TREES FOR MORE THAN TWO CLASSES
9.7 REGRESSION TREES
9.8 IMPROVING PREDICTION: RANDOM FORESTS AND BOOSTED TREES
9.9 ADVANTAGES AND WEAKNESSES OF A TREE
PROBLEMS
NOTES
CHAPTER 10: Logistic Regression
10.1 INTRODUCTION
10.2 THE LOGISTIC REGRESSION MODEL
10.3 EXAMPLE: ACCEPTANCE OF PERSONAL LOAN
10.4 LOGISTIC REGRESSION FOR MULTI‐CLASS CLASSIFICATION
10.5 EXAMPLE OF COMPLETE ANALYSIS: PREDICTING DELAYED FLIGHTS
APPENDIX: LOGISTIC REGRESSION FOR ORDINAL CLASSES
PROBLEMS
NOTES
CHAPTER 11: Neural Networks
11.1 INTRODUCTION
11.2 CONCEPT AND STRUCTURE OF A NEURAL NETWORK
11.3 FITTING A NETWORK TO DATA
11.4 REQUIRED USER INPUT
11.5 EXPLORING THE RELATIONSHIP BETWEEN PREDICTORS AND TARGET ATTRIBUTE
11.6 DEEP LEARNING
11.7 ADVANTAGES AND WEAKNESSES OF NEURAL NETWORKS
PROBLEMS
NOTES
CHAPTER 12: Discriminant Analysis
12.1 INTRODUCTION
12.2 DISTANCE OF A RECORD FROM A CLASS
12.3 FISHER’S LINEAR CLASSIFICATION FUNCTIONS
12.4 CLASSIFICATION PERFORMANCE OF DISCRIMINANT ANALYSIS
12.5 PRIOR PROBABILITIES
12.6 UNEQUAL MISCLASSIFICATION COSTS
12.7 CLASSIFYING MORE THAN TWO CLASSES
12.8 ADVANTAGES AND WEAKNESSES
PROBLEMS
NOTES
CHAPTER 13: Generating, Comparing, and Combining Multiple Models
13.1 AUTOMATED MACHINE LEARNING (AUTOML)
13.2 EXPLAINING MODEL PREDICTIONS
13.3 ENSEMBLES
13.4 SUMMARY
PROBLEMS
NOTES
PART V: Intervention and User Feedback
CHAPTER 14: Interventions: Experiments, Uplift Models, and Reinforcement Learning
14.1 A/B TESTING
14.2 UPLIFT (PERSUASION) MODELING
14.3 REINFORCEMENT LEARNING
14.4 SUMMARY
PROBLEMS
NOTES
PART VI: Mining Relationships Among Records
CHAPTER 15: Association Rules and Collaborative Filtering
15.1 ASSOCIATION RULES
15.2 COLLABORATIVE FILTERING
15.3 SUMMARY
PROBLEMS
NOTES
CHAPTER 16: Cluster Analysis
16.1 INTRODUCTION
16.2 MEASURING DISTANCE BETWEEN TWO RECORDS
16.3 MEASURING DISTANCE BETWEEN TWO CLUSTERS
16.4 HIERARCHICAL (AGGLOMERATIVE) CLUSTERING
16.5 NON‐HIERARCHICAL CLUSTERING: THE k‐MEANS ALGORITHM
PROBLEMS
NOTES
PART VII: Forecasting Time Series
CHAPTER 17: Handling Time Series
17.1 INTRODUCTION
17.2 DESCRIPTIVE VS. PREDICTIVE MODELING
17.3 POPULAR FORECASTING METHODS IN BUSINESS
17.4 TIME SERIES COMPONENTS
17.5 DATA PARTITIONING AND PERFORMANCE EVALUATION
PROBLEMS
NOTES
CHAPTER 18: Regression‐Based Forecasting
18.1 A MODEL WITH TREND
18.2 A MODEL WITH SEASONALITY
18.3 A MODEL WITH TREND AND SEASONALITY
18.4 AUTOCORRELATION AND ARIMA MODELS
PROBLEMS
NOTES
CHAPTER 19: Smoothing and Deep Learning Methods for Forecasting
19.1 SMOOTHING METHODS: INTRODUCTION
19.2 MOVING AVERAGE
19.3 SIMPLE EXPONENTIAL SMOOTHING
19.4 ADVANCED EXPONENTIAL SMOOTHING
19.5 DEEP LEARNING FOR FORECASTING
PROBLEMS
NOTES
PART VIII: Data Analytics
CHAPTER 20: Social Network Analytics
20.1 INTRODUCTION
20.2 DIRECTED VS. UNDIRECTED NETWORKS
20.3 VISUALIZING AND ANALYZING NETWORKS
20.4 SOCIAL DATA METRICS AND TAXONOMY
20.5 USING NETWORK METRICS IN PREDICTION AND CLASSIFICATION
20.6 COLLECTING SOCIAL NETWORK DATA WITH RAPIDMINER
20.7 ADVANTAGES AND DISADVANTAGES
PROBLEMS
NOTES
CHAPTER 21: Text Mining
21.1 INTRODUCTION
21.2 THE TABULAR REPRESENTATION OF TEXT: TERM–DOCUMENT MATRIX AND “BAG‐OF‐WORDS”
21.3 BAG‐OF‐WORDS VS. MEANING EXTRACTION AT DOCUMENT LEVEL
21.4 PREPROCESSING THE TEXT
21.5 IMPLEMENTING MACHINE LEARNING METHODS
21.6 EXAMPLE: ONLINE DISCUSSIONS ON AUTOS AND ELECTRONICS
21.7 EXAMPLE: SENTIMENT ANALYSIS OF MOVIE REVIEWS
21.8 SUMMARY
PROBLEMS
NOTES
CHAPTER 22: Responsible Data Science
22.1 INTRODUCTION
22.2 UNINTENTIONAL HARM
22.3 LEGAL CONSIDERATIONS
22.4 PRINCIPLES OF RESPONSIBLE DATA SCIENCE
22.5 A RESPONSIBLE DATA SCIENCE FRAMEWORK
22.6 DOCUMENTATION TOOLS
22.7 EXAMPLE: APPLYING THE RDS FRAMEWORK TO THE COMPAS EXAMPLE
22.8 SUMMARY
PROBLEMS
NOTES
PART IX: Cases
CHAPTER 23: Cases
23.1 CHARLES BOOK CLUB
23.2 GERMAN CREDIT
23.3 TAYKO SOFTWARE CATALOGER
23.4 POLITICAL PERSUASION
23.5 TAXI CANCELLATIONS
23.6 SEGMENTING CONSUMERS OF BATH SOAP
23.7 DIRECT‐MAIL FUNDRAISING
23.8 CATALOG CROSS‐SELLING
23.9 TIME SERIES CASE: FORECASTING PUBLIC TRANSPORTATION DEMAND
23.10 LOAN APPROVAL

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Tags: Machine Learning, Business Analytics, Concepts, Techniques, Applications, RapidMiner, Galit Shmueli