Applied Analytics through Case Studies Using SAS and R: Implementing Predictive Models and Machine Learning Techniques
by Deepti Gupta (Author)

About the Author
Deepti Gupta completed her MBA in Finance and PGPM in operation research in 2010. She has worked with KPMG and IBM private limited as Data Scientist and is currently working as a data science freelancer. Deepti has extensive experience in predictive modeling and machine learning with an expertise in SAS and R. Deepti has developed data science courses, delivered data science trainings, and conducted workshops for both corporate and academic institutions. She has written multiple blogs and white papers. Deepti has a passion for mentoring budding data scientists.

About this book
Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language.  
This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data.  The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms.
Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills.
What You'll Learn  

• Understand analytics and basic data concepts
• Use an analytical approach to solve Industrial business problems
• Build predictive model with machine learning techniques
• Create and apply analytical strategies
  

Who This Book Is For
Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.

Table of contents
Chapter 1: Data Analytics and Its Application in Various Industries 1
    What Is Data Analytics?  2
    Data Collection 3
    Data Preparation 4
    Data Analysis 4
    Model Building 5
    Results 5
    Put into Use 5
    Types of Analytics 6
    Understanding Data and Its Types 7
    What Is Big Data Analytics?  8
    Big Data Analytics Challenges 10
    Data Analytics and Big Data Tools 11
    Role of Analytics in Various Industries 14
    Who Are Analytical Competitors?  18
    Key Models and Their Applications in Various Industries 18
    Summary 21
    References 21
Chapter 2: Banking Case Study 27
    Applications of Analytics in the Banking Sector 28
    Increasing Revenue by Cross-Selling and Up-Selling 29
    Minimizing Customer Churn 30
    Increase in Customer Acquisition 30
    Predicting Bank-Loan Default 31
    Predicting Fraudulent Activity 32
    Case Study: Predicting Bank-Loan Defaults with Logistic Regression Model 34
    Logistic Regression Equation 35
    Odds 36
    Logistic Regression Curve 37
    Logistic Regression Assumptions 38
    Logistic Regression Model Fitting and Evaluation 39
    Statistical Test for Individual Independent Variable in Logistic 40
    Regression Model 40
    Predictive Value Validation in Logistic Regression Model 41
    Logistic Regression Model Using R 46
    About Data 47
    Performing Data Exploration 47
    Model Building and Interpretation of Full Data 52
    Model Building and Interpretation of Training and Testing Data 56
    Predictive Value Validation 61
    Logistic Regression Model Using SAS 65
    Model Building and Interpretation of Full Data 74
    Summary 92
    References 92
Chapter 3: Retail Case Study 97
    Supply Chain in the Retail Industry 98
    Types of Retail Stores 99
    Role of Analytics in the Retail Sector 100
    Customer Engagement 100
    Supply Chain Optimization 101
    Price Optimization 103
    Space Optimization and Assortment Planning 103
    Case Study: Sales Forecasting for Gen Retailers with SARIMA Model 105
    Overview of ARIMA Model 107
    Three Steps of ARIMA Modeling 111
    Identification Stage 111
    Estimation and Diagnostic Checking Stage 113
    Forecasting Stage 114
    Seasonal ARIMA Models or SARIMA 115
    Evaluating Predictive Accuracy of Time Series Model 117
    Seasonal ARIMA Model Using R 118
    About Data 119
    Performing Data Exploration for Time Series Data 119
    Seasonal ARIMA Model Using SAS 133
    Summary 158
    References 159
Chapter 4: Telecommunicatio n Case Study 161
    Types of Telecommunicatio ns Networks 162
    Role of Analytics in the Telecommunicatio ns Industry 163
    Predicting Customer Churn 163
    Network Analysis and Optimization 165
    Fraud Detection and Prevention 166
    Price Optimization 166
    Case Study: Predicting Customer Churn with Decision Tree Model 168
    Advantages and Limitations of the Decision Tree 169
    Handling Missing Values in the Decision Tree 170
    Handling Model Overfitting in Decision Tree 170
    How the Decision Tree Works 171
    Measures of Choosing the Best Split Criteria in Decision Tree 172
    Decision Tree Model Using R 179
    About Data 179
    Performing Data Exploration 180
    Splitting Data Set into Training and Testing 183
    Model Building & Interpretation on Training and Testing Data 184
    Decision Tree Model Using SAS 193
    Model Building and Interpretation of Full Data 200
    Model Building and Interpretation on Training and Testing Data 208
    Summary 217
    References 217
Chapter 5: Healthcare Case Study 221
    Application of Analytics in the Healthcare Industry 224
    Predicting the Outbreak of Disease and Preventative Management 225
    Predicting the Readmission Rate of the Patients 225
    Healthcare Fraud Detection 227
    Improve Patient Outcomes & Lower Costs 228
    Case Study: Predicting Probability of Malignant and Benign Breast Cancer with Random Forest     Model 230
    Working of Random Forest Algorithm 230
    Random Forests Model Using R 238
    Random Forests Model Using SAS 249
    Summary 271
    References 271
Chapter 6: Airline Case Study 277
    Application of Analytics in the Airline Industry 280
    Personalized Offers and Passenger Experience 281
    Safer Flights 282
    Airline Fraud Detection 283
    Predicting Flight Delays 284
    Case Study: Predicting Flight Delays with Multiple Linear Regression Model 286
    Multiple Linear Regression Equation 287
    Multiple Linear Regression Assumptions and Checking for Violation of Model Assumptions 287
    Variables Selection in Multiple Linear Regression Model 290
    Evaluating the Multiple Linear Regression Model 290
    Multiple Linear Regression Model Using R 292
    About Data 293
    Performing Data Exploration 293
    Model Building & Interpretation on Training and Testing Data 299
    Multiple Linear Regression Model Using SAS 311
    Summary 340
    References 340
Chapter 7: FMCG Case Study 345
    Application of Analytics in FMCG Industry 346
    Customer Experience & Engagement 347
    Sales and Marketing 347
    Logistics Management 348
    Markdown Optimization 349
    Case Study: Customer Segmentation with RFM Model and K-means Clustering 350
    Overview of RFM Model 351
    Overview of K-means Clustering 355
    RFM Model & K-means Clustering Using R 358
    About Data 358
    Performing Data Exploration 359
    RFM Model & K-means Clustering Using SAS 376
    Summary 393
    References 394

Length: 404 pages
Publisher: Apress; 1st ed. edition (September 22, 2018)
Language: English
ISBN-10: 148423524X
ISBN-13: 978-1484235249

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