Laha Advances in Analytics and Applications

1. Auflage 2018
ISBN: 978-981-13-1208-3
Verlag: Springer Nature Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 289 Seiten

Reihe: Springer Proceedings in Business and Economics

This book includes selected papers submitted to the ICADABAI-2017 conference, offering an overview of the new methodologies and presenting innovative applications that are of interest to both academicians and practitioners working in the area of analytics. It discusses predictive analytics applications, machine learning applications, human resource analytics, operations analytics, analytics in finance, methodology and econometric applications. The papers in the predictive analytics applications section discuss web analytics, email marketing, customer churn prediction, retail analytics and sports analytics. The section on machine learning applications then examines healthcare analytics, insurance analytics and machine analytics using different innovative machine learning techniques. Human resource analytics addresses important issues relating to talent acquisition and employability using analytics, while a paper in the section on operations analytics describe an innovative application in oil and gas industry. The papers in the analytics in finance part discuss the use of analytical tools in banking and commodity markets, and lastly the econometric applications part presents interesting banking and insurance applications.

Prof. Arnab K Laha takes keen interest in understanding how analytics, machine learning and artificial intelligence can be leveraged to solve complex problems of business and society.  His areas of research and teaching interest include Advanced Data Analytics, Quality Management and Risk Modeling.  He has published papers in national and international journals of repute in these areas and has served on the editorial board of several journals including Statistical Analysis and Data Mining: The ASA Data Science Journal. He was featured among India's best business school professors by Business Today in 2006 and Business India in 2012 and was named as one of the '10 Most Prominent Analytics Academicians in India' by Analytics India Magazine in 2014 and 2017. He is the convener of the biennial IIMA series of conferences on Advanced Data Analysis, Business Analytics and Intelligence. He is the author of the popular book on analytics entitled 'How to Make the Right Decision' published by Penguin-Random House.  He has conducted large number of training programmes and undertaken consultancy work in the fields of business analytics, quality management and risk management.

Autoren/Hrsg.

Laha, Arnab Kumar

Inhaltsverzeichnis

1;Preface;5
2;Contents;7
3;About the Editor;10
4;Brief Overviews;11
5;Machine Learning: An Introduction;12
5.1;1 Introduction;12
5.2;2 Supervised Learning;13
5.2.1;2.1 K-Nearest Neighbor (KNN);14
5.2.2;2.2 Artificial Neural Networks (ANN);14
5.2.3;2.3 Support Vector Machines (SVM);15
5.2.4;2.4 Random Forest;16
5.3;3 Unsupervised Learning;17
5.3.1;3.1 Clustering;17
5.4;4 Deep Learning;18
5.5;5 Conclusion;19
5.6;References;19
6;Linear Regression for Predictive Analytics;21
6.1;1 Introduction;21
6.2;2 Linear Regression Model;22
6.3;3 Prediction Using Linear Regression Model;23
6.4;4 Hidden Extrapolation;23
6.5;5 Prediction Accuracy;24
6.6;6 Use of Validation and Test Data;24
6.7;7 Tracking Model Performance;26
6.8;References;27
7;Directional Data Analysis;28
7.1;1 Introduction;28
7.2;2 Descriptive Statistics on the Circle;29
7.3;3 Probability Models on the Circle;30
7.4;4 Inference on the Circle;32
7.5;5 Robustness with Circular Data;34
7.6;6 Conclusion;35
7.7;References;35
8;Branching Processes;38
8.1;1 Introduction and History;38
8.2;2 Simple Branching Process;39
8.3;3 Variants of Simple Branching Process;42
8.3.1;3.1 Bisexual Branching Processes;42
8.3.2;3.2 Varying Environments;43
8.3.3;3.3 Multi-type Branching Processes;44
8.4;4 Applications;47
8.5;References;48
9;Predictive Analytics Applications;49
10;Click-Through Rate Estimation Using CHAID Classification Tree Model;50
10.1;1 Introduction;50
10.1.1;1.1 Need for CTR;51
10.1.2;1.2 Factors Impacting CTR;52
10.1.3;1.3 Issues with CTR;54
10.1.4;1.4 Research Purpose;55
10.2;2 Literature Review;55
10.3;3 Methodology;58
10.4;4 Results;59
10.5;5 Conclusion;60
10.6;References;61
11;Predicting Success Probability in Professional Tennis Tournaments Using a Logistic Regression Model;64
11.1;1 Introduction;64
11.2;2 Literature Review;65
11.3;3 Model, Data and Results;66
11.4;4 Conclusion;70
11.5;References;70
12;Hausdorff Path Clustering and Hidden Markov Model Applied to Person Movement Prediction in Retail Spaces;71
12.1;1 Introduction;71
12.2;2 Data Description;71
12.3;3 Preliminaries;72
12.4;4 Part 1: Movement Prediction;72
12.4.1;4.1 Room Assignment;72
12.4.2;4.2 Movement Modeling Using Hidden Markov Models;74
12.5;5 Part 2: Path Clustering;75
12.6;6 Experimental Results;75
12.7;7 Part 2: Path Clustering (Experimental Results);77
12.8;8 Experimental Results of Path Clustering Using Hausdorff Distance;78
12.9;9 Conclusions and Further Work;79
12.10;References;80
13;Improving Email Marketing Campaign Success Rate Using Personalization;81
13.1;1 Introduction;81
13.2;2 Language Modelling;83
13.3;3 Two-Step Personalization Process;85
13.3.1;3.1 Step 1;85
13.3.2;3.2 Step 2;85
13.4;4 Results and Future Scope;86
13.5;References;87
14;Predicting Customer Churn for DTH: Building Churn Score Card for DTH;88
14.1;1 Introduction and Motivation;89
14.2;2 Literature Survey;89
14.3;3 Dataset;90
14.4;4 Methodology;91
14.4.1;4.1 Sampling Strategy and Base Creation;91
14.4.2;4.2 Database Creation;92
14.4.3;4.3 Character Variable Generation;93
14.4.4;4.4 Segmentation Analysis;93
14.4.5;4.5 Modelling Methodology;94
14.5;5 Results and Findings;95
14.5.1;5.1 Comparison of Result at Population Level;95
14.5.2;5.2 Segment Level Model Result;97
14.6;6 Validation Result;100
14.6.1;6.1 Circlewise;100
14.6.2;6.2 Monthwise;100
14.7;7 Conclusion;101
14.8;Appendix;102
14.9;References;107
15;Applying Predictive Analytics in a Continuous Process Industry;108
15.1;1 Introduction and Literature Review;109
15.2;2 Data;111
15.3;3 Data Analysis and Results;111
15.4;4 Findings and Interpretation of Results;115
15.5;5 Conclusion;118
15.6;References;118
16;Machine Learning Applications;119
17;Automatic Detection of Tuberculosis Using Deep Learning Methods;120
17.1;1 Introduction;121
17.2;2 Related Work;122
17.3;3 Methodology;123
17.3.1;3.1 Data, Software and Hardware;123
17.3.2;3.2 TB Diagnosis;124
17.4;4 Results and Discussion;127
17.4.1;4.1 Results;127
17.4.2;4.2 Discussion;128
17.5;5 Conclusion;129
17.6;References;129
18;Connected Cars and Driving Pattern: An Analytical Approach to Risk-Based Insurance;131
18.1;1 Introduction;131
18.2;2 Our Approach;132
18.2.1;2.1 Data;132
18.2.2;2.2 Clustering;133
18.2.3;2.3 Cluster Labelling;133
18.2.4;2.4 Sampling;134
18.2.5;2.5 Feature Selection;134
18.2.6;2.6 Building Classifier;134
18.2.7;2.7 Analytical Results;135
18.2.8;2.8 Performance Monitoring;136
18.3;3 Conclusion;136
18.4;References;136
19;Human Resource Analytics;138
20;Analytics-Led Talent Acquisition for Improving Efficiency and Effectiveness;139
20.1;1 Introduction;140
20.2;2 Analytics for Talent Acquisition;141
20.3;3 Data Mining and Text Mining-Based Solution Components;144
20.3.1;3.1 Resume Information EXtractor (RINX);144
20.3.2;3.2 RINX Search Engine (RINX SE);149
20.3.3;3.3 Skill Similarity Computation;150
20.3.4;3.4 JD Extraction;151
20.3.5;3.5 JD Completion;154
20.4;4 Conclusion and Future Work;157
20.5;References;158
21;Assessing Student Employability to Help Recruiters Find the Right Candidates;159
21.1;1 Introduction;159
21.2;2 Research Objectives;160
21.3;3 Data Analysis;160
21.3.1;3.1 Approach;160
21.3.2;3.2 Data Collection;161
21.3.3;3.3 Assumptions, Constraints, and Mitigation;161
21.3.4;3.4 Exploratory Data Analysis;162
21.3.5;3.5 Data Modeling and Methodology;163
21.3.6;3.6 Results;167
21.4;4 Discussion;172
21.5;References;172
22;Operations Analytics;173
23;Estimation of Fluid Flow Rate and Mixture Composition;174
23.1;1 Introduction;174
23.2;2 Materials and Methods;175
23.2.1;2.1 Signal Description;175
23.2.2;2.2 Signal Filtering and Down-Sampling;175
23.2.3;2.3 Auto-regression Model: AR(1) Process;176
23.2.4;2.4 Hidden Markov Model;176
23.2.5;2.5 Mel-Frequency Cepstral Coefficients (MFCC);177
23.2.6;2.6 Relating HMM and MFCC to Flow Rate Classification/Estimation;178
23.2.7;2.7 Choice of Hidden Markov Model Parameters and Training the HMM;178
23.2.8;2.8 Log-Likelihood Estimation for the AR(1) Process;179
23.3;3 Multiclass AUC Computation (In-House Developed);180
23.4;4 Results;180
23.5;5 Conclusion;181
23.6;References;182
24;Analytics in Finance;183
25;Loan Loss Provisioning Practices in Indian Banks;184
25.1;1 Introduction;185
25.2;2 Literature;189
25.3;3 Research Methodology;190
25.3.1;3.1 Sample;190
25.3.2;3.2 Statistical Tool for Analysis of Data;191
25.3.3;3.3 Descriptive Study of the Sample;191
25.4;4 Analysis;192
25.4.1;4.1 OLS Method;192
25.4.2;4.2 Dynamic GMM (Generalized Method of Moments);193
25.4.3;4.3 Impact of GDP and Earnings on LLP;194
25.5;5 Conclusion;194
25.6;References;195
26;Modeling Commodity Market Returns: The Challenge of Leptokurtic Distributions;197
26.1;1 Introduction;197
26.1.1;1.1 Generalized Secant Hyperbolic Distribution;198
26.1.2;1.2 Mixture of Normal Distributions;199
26.1.3;1.3 Sampling Importance Resampling (SIR) Algorithm;200
26.1.4;1.4 The Variance Gamma Model;200
26.2;2 Modeling Daily Gold Returns;201
26.2.1;2.1 Mixture of Normal Model;202
26.2.2;2.2 Variance Gamma Distribution Model;204
26.2.3;2.3 Generalized Secant Hyperbolic Distribution Model;205
26.3;3 Modeling Daily Silver Returns;206
26.3.1;3.1 Mixture of Normal Distributions Model;207
26.3.2;3.2 Variance Gamma Distribution Model;209
26.3.3;3.3 Generalized Secant Hyperbolic Distribution Model;210
26.4;4 Modeling Daily Crude Oil Returns;211
26.4.1;4.1 Mixture of Normal Model;211
26.4.2;4.2 Variance Gamma Distribution Model;215
26.4.3;4.3 Generalized Secant Hyperbolic Distribution Model;216
26.5;5 Summary and Conclusions;216
26.6;References;217
27;Methodology;219
28;OLS: Is That So Useless for Regression with Categorical Data?;220
28.1;1 Introduction;221
28.2;2 Modeling Categorical Data;223
28.2.1;2.1 Logistic Regression;223
28.2.2;2.2 Proposed Ordinary Least Square (OLS) Based Methodology;225
28.3;3 Simulations;227
28.4;4 Relative Entropy Based Assessment;232
28.5;5 Concluding Remarks;234
28.6;References;235
29;Estimation of Parameters of Misclassified Size Biased Borel Tanner Distribution;236
29.1;1 Introduction;236
29.2;2 Size Biased Borel–Tanner Distribution (SBBTD);238
29.3;3 Misclassified Size Biased Borel–Tanner Distribution (MSBBTD);240
29.4;4 Methods of Estimation of the Parameters of MSBBTD;242
29.5;5 Simulation Study;250
29.6;References;252
30;A Stochastic Feedback Queuing Model with Encouraged Arrivals and Retention of Impatient Customers;254
30.1;1 Introduction;255
30.2;2 Formulation of Stochastic Model;256
30.3;3 Steady-State Equations;256
30.4;4 Steady-State Solution;257
30.5;5 Measures of Performance;257
30.5.1;5.1 Expected System Size (Ls);257
30.5.2;5.2 Expected Queue Length (Lq);258
30.5.3;5.3 Average Rate of Reneging Is Given by (Rr);258
30.5.4;5.4 Average Rate of Retention Is Given by (RR);258
30.6;6 Numerical Illustration;258
30.7;7 Economic Analysis of the System;261
30.8;8 Conclusion and Future Scope;264
30.9;References;265
31;Econometric Applications;266
32;Banking Competition and Banking Stability in SEM Countries: The Causal Nexus;267
32.1;1 Introduction;267
32.2;2 Review of Literature and Rationale of Analysis;269
32.2.1;2.1 Hypotheses Tested;271
32.3;3 Data, Variables, and Econometric Model;271
32.4;4 Empirical Results;275
32.4.1;4.1 Short-Run Causality Results Between Banking Competition and Banking Stability;275
32.4.2;4.2 Long-Run Causality Results Between the Variables;282
32.4.3;4.3 Results from Innovation Accounting;282
32.5;5 Concluding Comments and Policy Implications;283
32.6;Appendix 1: Description of Variables;284
32.7;Appendix 2: Devising of Composite Index of Financial Stability by Using PCA;285
32.8;References;286