Business analytics using R: a practical approach
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York
Apress
[2017]
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| Subjects: | |
| Online Access: | Inhaltstext Inhaltsverzeichnis |
| Physical Description: | xvii, 280 Seiten Diagramme 23.5 cm x 15.5 cm |
| ISBN: | 1484225139 9781484225134 |
Staff View
MARC
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| 264 | 1 | |a New York |b Apress |c [2017] | |
| 264 | 4 | |c © 2017 | |
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Record in the Search Index
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Contents at a Glance
About the Authors. xv
About the Technical Reviewer. xvii
Chapter 1: Overview of Business Analytics.1
KiChapter 2: Introduction to R.17
felChapter 3: R for Data Analysis . 37
^Chapter 4: Introduction to descriptive analytics.59
Chapter 5: Business Analytics Process and Data Exploration.91
it Chapter 6: Supervised Machine Learning—Classification. 131
iiChapter 7: Unsupervised Machine Learning.161
1 Chapter 8: Simple Linear Regression.187
iiChapter 9: Multiple Linear Regression.207
ii Chapter 10: Logistic Regression.233
iiChapter 11: Big Data Analysis—Introduction and
Future Trends. 257
References. .267
Index. 273
iii
Contents
About the Authors. xv
About the Technical Reviewer.xvii
^Chapter 1: Overview of Business Analytics.1
1.1 Objectives of This Book.3
1.2 Confusing Terminology.4
1.3 Drivers for Business Analytics.5
1.3.1 Growth of Computer Packages and Applications.6
1.3.2 Feasibility to Consolidate Data from Various Sources.7
1.3.3 Growth of Infinite Storage and Computing Capability.7
1.3.4 Easy-to-Use Programming Tools and Platforms .7
1.3.5 Survival and Growth in the Highly Competitive World.7
1.3.6 Business Complexity Growing out of Globalization.8
1.4 Applications of Business Analytics.8
1.4.1 Marketing and Sales.8
1.4.2 Human Resources. 9
1.4.3 Product Design.9
1.4.4 Service Design. 9
1.4.5 Customer Service and Support Areas.9
1.5 Skills Required for a Business Analyst. 10
1.5.1 Understanding the Business and Business Problems. 10
1.5.2 Understanding Data Analysis Techniques and Algorithms.10
1.5.3 Having Good Computer Programming Knowledge.11
V
CONTENTS
1.5.4 Understanding Data Structures and Data Storage/Warehousing
Techniques.11
1.5.5 Knowing Relevant Statistical and Mathematical Concepts.11
1.6 Life Cycle of a Business Analytics Project.11
1.7 The Framework for Business Analytics.14
1.8 Summary.15
: Chapter 2: Introduction to R. 17
2.1 Data Analysis Tools.17
2.2 R Installation. 21
2.2.1 Installing R.21
2.2.2 Installing RStudio.23
2.2.3 Exploring the RStudio Interface.23
2.3 Basics of R Programming.25
2.3.1 Assigning Values.26
2.3.2 Creating Vectors. 27
2.4 R Object Types. 27
2.5 Data Structures in R. 29
2.5.1 Matrices . 30
2.5.2 Arrays.31
2.5.3 Data Frames.32
2.5.4 Lists. 34
2.5.5 Factors.35
2.6 Summary. 36
Chapter 3: R for Data Analysis.37
3.1 Reading and Writing Data.37
3.1.1 Reading Data from a Text File.38
3.1.2 Reading Data from a Microsoft Excel File.42
3.1.3 Reading Data from the Web.44
vi
CONTENTS
3.2 Using Control Structures in R. 45
3.2.1 if-eise.46
3.2.2 for loops. 46
3.2.3 while loops.47
3.2.4 Looping Functions.48
3.2.5 Writing Your Own Functions in R. 55
3.3 Working with R Packages and Libraries.56
3.4 Summary. 58
Chapter 4: Introduction to descriptive analytics. 59
4.1 Descriptive analytics.62
4.2 Population and sample.62
4.3 Statistical parameters of interest. 63
4.3.1 Mean. 64
4.3.2 Median.66
4.3.3 Mode. 68
4.3.4 Range.68
4.3.5 Quantiles.69
4.3.6 Standard deviation.70
4.3.7 Variance. 73
4.3.8 “Summary” command in R.73
4.4 Graphical description of the data. 74
4.4.1 Plots in R. 74
4.4.2 Histogram. 77
4.4.3 Bar plot.77
4.4.4 Boxplots.78
vii
CONTENTS
4.5 Computations on data frames.79
4.5.1 Scatter plot. .81
4.6 Probability.84
4.6.1 Probability of mutually exclusive events.85
4.6.2 Probability of mutually independent events.85
4.6.3 Probability of mutually non-exclusive events:.86
4.6.4 Probability distributions.86
4.7 Chapter summary. 88
K Chapter 5: Business Analytics Process and Data Exploration. 91
5.1 Business Analytics Life Cycle.91
5.1.1 Phase 1: Understand the Business Problem. 91
5.1.2 Phase 2: Collect and Integrate the Data.92
5.1.3 Phase 3: Preprocess the Data. 92
5.1.4 Phase 4: Explore and Visualize the Data.92
5.1.5 Phase 5: Choose Modeling Techniques and Algorithms.93
5.1.6 Phase 6: Evaluate the Model.93
5.1.7 Phase 7: Report to Management and Review.94
5.1.8 Phase 8: Deploy the Model.94
5.2 Understanding the Business Problem.94
5.3 Collecting and Integrating the Data. 95
5.3.1 Sampling.96
5.3.2 Variable Selection.97
5.4 Preprocessing the Data.97
5.4.1 Data Types.97
5.4.2 Data Preparation.99
5.4.3 Data Preprocessing with R.100
viii
CONTENTS
5.5 Exploring and Visualizing the Data.104
5.5.1 Tables. 105
5.5.2 Summary Tables. 106
5.5.3 Graphs.106
5.5.4 Scatter Plot Matrices. 112
5.5.5 Data Transformation.117
5.6 Using Modeling Techniques and Algorithms.118
5.6.1 Descriptive Analytics. 118
5.6.2 Predictive Analytics.118
5.6.3 Machine Learning.119
5.7 Evaluating the Model.122
5.7.1 Training Data Partition. 122
5.7.2 Test Data Partition.122
5.7.3 Validation Data Partition.122
5.7.4 Cross-Validation.123
5.7.5 Classification Model Evaluation.123
5.7.6 Regression Model Evaluation.127
5.8 Presenting a Management Report and Review.128
5.8.1 Problem Description.128
5.8.2 Data Set Used.128
5.8.3 Data Cleaning Carried Out.128
5.8.4 Method Used to Create the Model.128
5.8.5 Model Deployment Prerequisites.128
5.8.6 Model Deployment and Usage.129
5.8.7 Issues Handling.129
5.9 Deploying the Model.129
5.10 Summary.130
ix
CONTENTS
Chapter 6: Supervised Machine Learning—Classification .131
6.1 What Is Classification? What Is Prediction?.131
6.2 Probabilistic Models for Classification.132
6.2.1 Example.133
6.2.2 Naïve Bayes Classifier Using R.134
6.2.3 Advantages and Limitations of the Naïve Bayes Classifier.136
6.3 Decision Trees.136
6.3.1 Recursive Partitioning Decision-Tree Algorithm.138
6.3.2 Information Gain.138
6.3.3 Example of a Decision Tree.140
6.3.4 Induction of a Decision Tree.142
6.3.5 Classification Rules from Tree.145
6.3.6 Overfitting and Underfitting.145
6.3.7 Bias and Variance.147
6.3.8 Avoiding Overfitting Errors and Setting the Size of Tree Growth.148
6.4 Other Classifier Types.150
6.4.1 K-Nearest Neighbor.150
6.4.2 Random Forests.152
6.5 Classification Example Using R. 153
6.6 Summary.160
Chapter 7: Unsupervised Machine Learning.161
7.1 Clustering - Overview. 161
7.2 What Is Clustering?.163
7.2.1 Measures Between Two Records.163
7.2.2 Distance Measures for Categorical Variables.164
7.2.3 Distance Measures for Mixed Data Types.165
7.2.4 Distance Between Two Clusters.166
CONTENTS
7.3 Hierarchical Clustering.168
7.3.1 Dendrograms.169
7.3.2 Limitations of Hierarchical Clustering.169
7.4 Nonhierarchical Clustering.169
7.4.1 K-Means Algorithm. 170
7.4.2 Limitations of K-Means Clustering.172
7.5 Clustering Case Study.172
7.5.1 Retain Only Relevant Variables in the Data Set.173
7.5.2 Remove Any Outliers from the Data Set. 173
7.5.3 Standardize the Data. 174
7.5.4 Calculate the Distance Between the Data Points.175
7.6 Association Rule.182
7.6.1 Choosing Rules.183
7.6.2 Example of Generating Association Rules.185
7.6.3 Interpreting Results.186
7.7 Summary.186
¡^Chapter 8: Simple Linear Regression. 187
8.1 Introduction.187
8.2 Correlation.188
8.2.1 Correlation Coefficient.189
8.3 Hypothesis Testing.192
8.4 Simple Linear Regression.193
8.4.1 Assumptions of Regression.193
8.4.2 Simple Linear Regression Equation.193
8.4.3 Creating Simple Regression Equation in R.194
8.4.4 Testing the Assumptions of Regression:.197
xi
CONTENTS
8.4.5 Conclusion.203
8.4.6 Predicting the Response Variable.203
8.4.7 Additional Notes.204
8.5 Chapter Summary.204
Chapter 9: Multiple Linear Regression. 207
9.1 Using Multiple Linear Regression.209
9.1.1 The Data. 209
9.1.2 Correlation.210
9.1.3 Arriving at the Model.212
9.1.4 Validation of the Assumptions of Regression.213
9.1.5 Multicollinearity. 218
9.1.6 Stepwise Multiple Linear Regression.221
9.1.7 All Subsets Approach to Multiple Linear Regression.221
9.1.8 Multiple Linear Regression Equation.223
9.1.9 Conclusion. 224
9.2 Using an Alternative Method in R. 224
9.3 Predicting the Response Variable. 225
9.4 Training and Testing the Model . 225
9.5 Cross Validation. 227
9.6 Summary. 230
' Chapter 10: Logistic Regression.233
10.1 Logistic Regression.235
10.1.1 The Data.235
10.1.2 Creating the Model.236
10.1.3 Model Fit Verification.240
10.1.4 General Words of Caution.241
xii
CONTENTS
10.1.5 Multicollinearity.242
10.1.6 Dispersion.242
10.1.7 Conclusion for Logistic Regression.242
10.2 Training and Testing the Model .243
10.2.1 Predicting the Response Variable.245
10.2.2 Alternative Way of Validating the Logistic Regression Model.245
10.3 Multinomial Logistic Regression.248
10.4 Regularization. 248
10.5 Summary.254
^Chapter 11: Big Data Analysis—Introduction and
Future Itends.257
11.1 Big Data Ecosystem.259
11.2 Future Trends in Big Data Analytics.261
11.2.1 Growth of Social Media .261
11.2.2 Creation of Data Lakes .262
11.2.3 Visualization Tools at the Hands of Business Users.262
11.2.4 Prescriptive Analytics.262
11.2.5 Internet of Things .262
11.2.6 Artificial Intelligence.262
11.2.7 Whole Data Processing.263
11.2.8 Vertical and Horizontal Applications.263
11.2.9 Real-Time Analytics.263
11.2.10 Putting the Analytics in the Hands of Business Users.263
11.2.11 Migration of Solutions from One Tool to Another.263
11.2.12 Cloud, Cloud, Everywhere the Cloud.264
11.2.13 In-Database Analytics.264
xiii
1
CONTENTS
11.2.14 In-Memory Analytics.264
11.2.15 Autonomous Services for Machine Learning.264
11.2.16 Addressing Security and Compliance.264
11.2.17 Healthcare.265
References.267
Index.273
XIV |
| any_adam_object | 1 |
| author | Rao, Umesh Hodeghatta Nayak, Umesha |
| author_GND | (DE-588)1059143925 (DE-588)1059144298 |
| author_facet | Rao, Umesh Hodeghatta Nayak, Umesha |
| author_role | aut aut |
| author_sort | Rao, Umesh Hodeghatta |
| author_variant | u h r uh uhr u n un |
| building | Verbundindex |
| bvnumber | BV043996855 |
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| dewey-full | 004 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004 |
| dewey-search | 004 |
| dewey-sort | 14 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| indexdate | 2024-09-10T02:09:04Z |
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| institution_GND | (DE-588)1065538766 |
| isbn | 1484225139 9781484225134 |
| language | English |
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| owner_facet | DE-945 DE-1050 DE-739 DE-1043 DE-1102 DE-20 |
| physical | xvii, 280 Seiten Diagramme 23.5 cm x 15.5 cm |
| publishDate | 2017 |
| publishDateSearch | 2017 |
| publishDateSort | 2017 |
| publisher | Apress |
| record_format | marc |
| spelling | Rao, Umesh Hodeghatta Verfasser (DE-588)1059143925 aut Business analytics using R a practical approach Dr. Umesh R. Hodeghatta, Umesha Nayak New York Apress [2017] © 2017 xvii, 280 Seiten Diagramme 23.5 cm x 15.5 cm txt rdacontent n rdamedia nc rdacarrier Big Data (DE-588)4802620-7 gnd rswk-swf Data Mining (DE-588)4428654-5 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf Wirtschaftsinformatik (DE-588)4112736-5 gnd rswk-swf R Programm (DE-588)4705956-4 s Wirtschaftsinformatik (DE-588)4112736-5 s Big Data (DE-588)4802620-7 s Datenanalyse (DE-588)4123037-1 s Data Mining (DE-588)4428654-5 s DE-604 Nayak, Umesha Verfasser (DE-588)1059144298 aut Apress L.P. (DE-588)1065538766 pbl Erscheint auch als Online-Ausgabe 978-1-4842-2514-1 X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=23172ba45a334085ba2cd1ca1e92cb0c&prov=M&dok_var=1&dok_ext=htm Inhaltstext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029404907&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Rao, Umesh Hodeghatta Nayak, Umesha Business analytics using R a practical approach Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd R Programm (DE-588)4705956-4 gnd Wirtschaftsinformatik (DE-588)4112736-5 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4428654-5 (DE-588)4123037-1 (DE-588)4705956-4 (DE-588)4112736-5 |
| title | Business analytics using R a practical approach |
| title_auth | Business analytics using R a practical approach |
| title_exact_search | Business analytics using R a practical approach |
| title_full | Business analytics using R a practical approach Dr. Umesh R. Hodeghatta, Umesha Nayak |
| title_fullStr | Business analytics using R a practical approach Dr. Umesh R. Hodeghatta, Umesha Nayak |
| title_full_unstemmed | Business analytics using R a practical approach Dr. Umesh R. Hodeghatta, Umesha Nayak |
| title_short | Business analytics using R |
| title_sort | business analytics using r a practical approach |
| title_sub | a practical approach |
| topic | Big Data (DE-588)4802620-7 gnd Data Mining (DE-588)4428654-5 gnd Datenanalyse (DE-588)4123037-1 gnd R Programm (DE-588)4705956-4 gnd Wirtschaftsinformatik (DE-588)4112736-5 gnd |
| topic_facet | Big Data Data Mining Datenanalyse R Programm Wirtschaftsinformatik |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=23172ba45a334085ba2cd1ca1e92cb0c&prov=M&dok_var=1&dok_ext=htm http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=029404907&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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