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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
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
PDF version
Apress__Applied Analytics through Case Studies Using SAS and R.pdf
(7.97 MB, 需要: 15 个论坛币)
EPUB version
Apress__Applied Analytics through Case Studies Using SAS and R.epub
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