07118nam a22002537a 450000500170000000800410001702000180005804000180007608200210009410000190011524501170013430000320025150005610028350457780084450500400662265000400666265000270670265000340672970000200676370000180678370000200680170000280682170000150684920260306133414.0260223b |||||||| |||| 00| 0 eng d a9789390421701 cAIMIT LIBRARY 21a005.762bSHMG aShmueli, Galit aData mining for business analytics :bconcepts techniques and applications in R /cBy Galit Shmueli ...[et.al.]. axxxii,552 p.;bPBc23.5 cm. aData Mining for Business Analytics: Concepts, Techniques, and Applications in R is a comprehensive resource for learners pursuing graduate and undergraduate level courses in data mining, business analytics, and related courses within domain of AI. This is an excellent reference for analysts, researchers, and practitioners working in various domains of business like finance, marketing, human resource, operations, information services, consultancy etc., who want to tap more opportunities for insights in variety of data driven decision-making scenarios. aPART I PRELIMINARIES CHAPTER 1 Introduction 1.1 What Is Business Analytics? 1.2 What Is Data Mining? 1.3 Data Mining 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 Order of Topics CHAPTER 2 Overview of the Data Mining Process 2.1 Introduction 2.2 Core Ideas in Data Mining 2.3 The Steps in Data Mining 2.4 Preliminary Steps 2.5 Predictive Power and Overfitting 2.6 Building a Predictive Model 2.7 Using R for Data Mining on a Local Machine 2.8 Automating Data Mining Solutions PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 3.1 Uses of Data Visualization 3.2 Data Examples 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatter Plots 3.4 Multidimensional Visualization 3.5 Specialized Visualizations 3.6 Summary: Major Visualizations and Operations, by Data Mining Goal 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 Variables 4.7 Converting a Categorical Variable to a Numerical Variable 4.8 Principal Components Analysis 4.9 Dimension Reduction Using Regression Models 4.10 Dimension Reduction Using Classification and Regression Trees 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 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 CHAPTER 7 k-Nearest Neighbors (kNN) 7.1 The k-NN Classifier (Categorical Outcome) 7.2 k-NN for a Numerical Outcome 7.3 Advantages and Shortcomings of k-NN Algorithms CHAPTER 8 The Naive Bayes Classifier 8.1 Introduction 8.2 Applying the Full (Exact) Bayesian Classifier 8.3 Advantages and Shortcomings of the Naive Bayes Classifier 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 CHAPTER 10 Logistic Regression 10.1 Introduction 10.2 The Logistic Regression Model 10.3 Example: Rating Behavior Towards Online Quick Food Service Providers In Indian Cities 10.4 Evaluating Classification Performance 10.5 Example of Complete Analysis: Predicting Delayed Flights 10.6 Appendix: Logistic Regression for Profiling CHAPTER 11 Neural Nets 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 Outcome 11.6 Advantages and Weaknesses of Neural Networks 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 CHAPTER 13 Combining Methods: Ensembles and Uplift Modeling 13.1 Ensembles 13.2 Uplift (Persuasion) Modeling 13.3 Summary PART V MINING RELATIONSHIPS AMONG RECORDS CHAPTER 14 Association Rules and Collaborative Filtering 14.1 Association Rules 14.2 Collaborative Filtering 14.3 Summary CHAPTER 15 Cluster Analysis 15.1 Introduction 15.2 Measuring Distance Between Two Records 15.3 Measuring Distance Between Two Clusters 15.4 Hierarchical (Agglomerative) Clustering 15.5 Non-Hierarchical Clustering: The k-Means Algorithm PART VI FORECASTING TIME SERIES CHAPTER 16 Handling Time Series 16.1 Introduction 16.2 Descriptive vs. Predictive Modeling 16.3 Popular Forecasting Methods in Business 16.4 Time Series Components 16.5 Data-Partitioning and Performance Evaluation CHAPTER 17 Regression-Based Forecasting 17.1 A Model with Trend 17.2 A Model with Seasonality 17.3 A Model with Trend and Seasonality 17.4 Autocorrelation and ARIMA Models CHAPTER 18 Smoothing Methods 18.1 Introduction 18.2 Moving Average 18.3 Simple Exponential Smoothing 18.4 Advanced Exponential Smoothing PARTVII DATA ANALYTICS CHAPTER 19 Social Network Analytics 19.1 Introduction 19.2 Directed vs. Undirected Networks 19.3 Visualizing and Analyzing Networks 19.4 Social Data Metrics and Taxonomy 19.5 Using Network Metrics in Prediction and Classification 19.6 Collecting Social Network Data with R 19.7 Advantages and Disadvantages CHAPTER 20 Text Mining 20.1 Introduction 20.2 The Tabular Representation of Text: Term-Document Matrix and “Bag-of-Words” 20.3 Bag-of-Words vs. Meaning Extraction at Document Level 20.4 Preprocessing the Text 20.5 Implementing Data Mining Methods 20.6 Example: Online Discussions on Autos and Electronics 20.7 Summary PART VIII CASES CHAPTER 21 Cases 21.1 Charles Book Club 21.2 German Credit 21.3 Tayko Software Cataloger 21.4 Political Persuasion 21.5 Taxi Cancellations 21.6 Segmenting Consumers of Bath Soap 21.7 Direct-Mail Fundraising 21.8 Predicting Tourist Travel Packages 21.9 Predicting Bankruptcy 21.10 Time Series Case: Forecasting Public Transportation Demand 21.11 Predicting Attrition 21.12 Attitude Towards Therapy Suggestions In Covid-19 Times References Data Files Used in the Book Index rAbout the Author Galit Shmueli, PhD aOverview of the data mining process aPerformance evaluation aClassification and regression aBruce, Peter C. aYahav, Inbal. aPatel, Nitin R. aLichtendahl, Kenneth C. aWali, O.P.