Practical Business Analytics Using R and Python: Solve Business Problems Using a Data-driven Approach, Second Edition

Table of Contents
About the Authors
Preface
Foreword
Part I: Introduction to Analytics
	Chapter 1: An Overview of Business Analytics
		1.1 Introduction
		1.2 Objectives of This Book
		1.3 Confusing Terminology
		1.4 Drivers for Business Analytics
			1.4.1 Growth of Computer Packages and Applications
			1.4.2 Feasibility to Consolidate Data from Various Sources
			1.4.3 Growth of Infinite Storage and Computing Capability
			1.4.4 Survival and Growth in the Highly Competitive World
			1.4.5 Business Complexity Growing Out of Globalization
			1.4.6 Easy-to-Use Programming Tools and Platforms
		1.5 Applications of Business Analytics
			1.5.1 Marketing and Sales
			1.5.2 Human Resources
			1.5.3 Product Design
			1.5.4 Service Design
			1.5.5 Customer Service and Support Areas
		1.6 Skills Required for an Analytics Job
		1.7 Process of an Analytics Project
		1.8 Chapter Summary
	Chapter 2: The Foundations of Business Analytics
		2.1 Introduction
		2.2 Population and Sample
			2.2.1 Population
			2.2.2 Sample
		2.3 Statistical Parameters of Interest
			2.3.1 Mean
			2.3.2 Median
			2.3.3 Mode
			2.3.4 Range
			2.3.5 Quantiles
			2.3.6 Standard Deviation
			2.3.7 Variance
			2.3.8 Summary Command in R
		2.4 Probability
			2.4.1 Rules of Probability
				2.4.1.1 Probability of Mutually Exclusive Events
				2.4.1.2 Probability of Mutually Nonexclusive Events
				2.4.1.3 Probability of Mutually Independent Events
				2.4.1.4 The Probability of the Complement
			2.4.2 Probability Distributions
				2.4.2.1 Normal Distribution
				2.4.2.2 Binomial Distribution
				2.4.2.3 Poisson Distribution
			2.4.3 Conditional Probability
		2.5 Computations on Data Frames
		2.6 Scatter Plot
		2.7 Chapter Summary
	Chapter 3: Structured Query Language Analytics
		3.1 Introduction
		3.2 Data Used by Us
		3.3 Steps for Business Analytics
			3.3.1 Initial Exploration and Understanding of the Data
			3.3.2 Understanding Incorrect and Missing Data, and Correcting Such Data
			3.3.3 Further Exploration and Reporting on the Data
				3.3.3.1 Additional Examples of the Useful SELECT Statements
		3.4 Chapter Summary
	Chapter 4: Business Analytics Process
		4.1 Business Analytics Life Cycle
			4.1.1 Phase 1: Understand the Business Problem
			4.1.2 Phase 2: Data Collection
				4.1.2.1 Sampling
			4.1.3 Phase 3: Data Preprocessing and Preparation
				4.1.3.1 Data Types
				4.1.3.2 Data Preparation
					Handling Missing Values
					Handling Duplicates, Junk, and Null Values
				4.1.3.3 Data Transformation
					Normalization
			4.1.4 Phase 4: Explore and Visualize the Data
			4.1.5 Phase 5: Choose Modeling Techniques and Algorithms
				4.1.5.1 Descriptive Analytics
				4.1.5.2 Predictive Analytics
				4.1.5.3 Machine Learning
					Supervised Machine Learning
					Unsupervised Machine Learning
			4.1.6 Phase 6: Evaluate the Model
			4.1.7 Phase 7: Report to Management and Review
				4.1.7.1 Problem Description
				4.1.7.2 Data Set Used
				4.1.7.3 Data Cleaning Steps Carried Out
				4.1.7.4 Method Used to Create the Model
				4.1.7.5 Model Deployment Prerequisites
				4.1.7.6 Model Deployment and Usage
				4.1.7.7 Handling Production Problems
			4.1.8 Phase 8: Deploy the Model
		4.2 Chapter Summary
	Chapter 5: Exploratory Data Analysis
		5.1 Exploring and Visualizing the Data
			5.1.1 Tables
			5.1.2 Describing Data: Summary Tables
			5.1.3 Graphs
				5.1.3.1 Histogram
				5.1.3.2 Box Plots
					Parts of Box Plots
					Box Plots Using Python
				5.1.3.3 Bivariate Analysis
				5.1.3.4 Scatter Plots
			5.1.4 Scatter Plot Matrices
				5.1.4.1 Correlation Plot
				5.1.4.2 Density Plots
		5.2 Plotting Categorical Data
		5.3 Chapter Summary
	Chapter 6: Evaluating Analytics Model Performance
		6.1 Introduction
		6.2 Regression Model Evaluation
			6.2.1 Root-Mean-Square Error
			6.2.2 Mean Absolute Percentage Error
			6.2.3 Mean Absolute Error (MAE) or Mean Absolute Deviation (MAD)
			6.2.4 Sum of Squared Errors (SSE)
			6.2.5 R2 (R-Squared)
			6.2.6 Adjusted R2
		6.3 Classification Model Evaluation
			6.3.1 Classification Error Matrix
			6.3.2 Sensitivity Analysis in Classification
		6.4 ROC Chart
		6.5 Overfitting and Underfitting
			6.5.1 Bias and Variance
		6.6 Cross-Validation
		6.7 Measuring the Performance of Clustering
		6.8 Chapter Summary
Part II: Supervised Learning and Predictive Analytics
	Chapter 7: Simple Linear Regression
		7.1 Introduction
		7.2 Correlation
			7.2.1 Correlation Coefficient
		7.3 Hypothesis Testing
		7.4 Simple Linear Regression
			7.4.1 Assumptions of Regression
			7.4.2 Simple Linear Regression Equation
			7.4.3 Creating a Simple Regression Equation in R
			7.4.4 Testing the Assumptions of Regression
				7.4.4.1 Test of Linearity
				7.4.4.2 Test of Independence of Errors Around the Regression Line
				7.4.4.3 Test of Normality
				7.4.4.4 Equal Variance of the Distribution of the Response Variable
				7.4.4.5 Other Ways of Validating the Assumptions to Be Fulfilled by a Regression Model
					Using the gvlma Library
					Using the Scale-Location Plot
					Using the crPlots(model name) Function from library(car)
			7.4.5 Conclusion
			7.4.6 Predicting the Response Variable
			7.4.7 Additional Notes
		7.5 Using Python to Generate the Model and Validating the Assumptions
			7.5.1 Load Important Packages and Import the Data
			7.5.2 Generate a Simple Linear Regression Model
			7.5.3 Alternative Way for Generation of the Model
			7.5.4 Validation of the Significance of the Generated Model
			7.5.5 Validating the Assumptions of Linear Regression
			7.5.6 Predict Using the Model Generated
		7.6 Chapter Summary
	Chapter 8: Multiple Linear Regression
		8.1 Using Multiple Linear Regression
			8.1.1 The Data
			8.1.2 Correlation
			8.1.3 Arriving at the Model
			8.1.4 Validation of the Assumptions of Regression
			8.1.5 Multicollinearity
			8.1.6 Stepwise Multiple Linear Regression
			8.1.7 All Subsets Approach to Multiple Linear Regression
			8.1.8 Multiple Linear Regression Equation
			8.1.9 Conclusion
		8.2 Using an Alternative Method in R
		8.3 Predicting the Response Variable
		8.4 Training and Testing the Model
		8.5 Cross Validation
		8.6 Using Python to Generate the Model and Validating the Assumptions
			8.6.1 Load the Necessary Packages and Import the Data
			8.6.2 Generate Multiple Linear Regression Model
			8.6.3 Alternative Way to Generate the Model
			8.6.4 Validating the Assumptions of Linear Regression
			8.6.5 Predict Using the Model Generated
		8.7 Chapter Summary
	Chapter 9: Classification
		9.1 What Are Classification and Prediction?
			9.1.1 K-Nearest Neighbor
			9.1.2 KNN Algorithm
			9.1.3 KNN Using R
			9.1.4 KNN Using Python
		9.2 Naïve Bayes Models for Classification
			9.2.1 Naïve Bayes Classifier Model Example
			9.2.2 Naïve Bayes Classifier Using R (Use Same Data Set as KNN)
			9.2.3 Advantages and Limitations of the Naïve Bayes Classifier
		9.3 Decision Trees
			9.3.1 Decision Tree Algorithm
				9.3.1.1 Entropy
				9.3.1.2 Information Gain
			9.3.2 Building a Decision Tree
			9.3.3 Classification Rules from Tree
				9.3.3.1 Limiting Tree Growth and Pruning the Tree
		9.4 Advantages and Disadvantages of Decision Trees
		9.5 Ensemble Methods and Random Forests
		9.6 Decision Tree Model Using R
		9.7 Decision Tree Model Using Python
			9.7.1 Creating the Decision Tree Model
			9.7.2 Making Predictions
			9.7.3 Measuring the Accuracy of the Model
			9.7.4 Creating a Pruned Tree
		9.8 Chapter Summary
	Chapter 10: Neural Networks
		10.1 What Is an Artificial Neural Network?
		10.2 Concept and Structure of Neural Networks
			10.2.1 Perceptrons
			10.2.2 The Architecture of Neural Networks
		10.3 Learning Algorithms
			10.3.1 Predicting Attrition Using a Neural Network
			10.3.2 Classification and Prediction Using a Neural Network
			10.3.3 Training the Model
			10.3.4 Backpropagation
		10.4 Activation Functions
			10.4.1 Linear Function
			10.4.2 Sigmoid Activation Function
			10.4.3 Tanh Function
			10.4.4 ReLU Activation Function
			10.4.5 Softmax Activation Function
			10.4.6 Selecting an Activation Function
		10.5 Practical Example of Predicting Using a Neural Network
			10.5.1 Implementing a Neural Network Model Using R
				10.5.1.1 Exploring Data
				10.5.1.2 Preprocessing Data
				10.5.1.3 Preparing the Train and Test Data
				10.5.1.4 Creating a Neural Network Model Using the Neuralnet() Package
				10.5.1.5 Predicting Test Data
				10.5.1.6 Summary Report
				10.5.1.7 Model Sensitivity Analysis and Performance
				10.5.1.8 ROC and AUC
		10.6 Implementation of a Neural Network Model Using Python
		10.7 Strengths and Weaknesses of Neural Network Models
		10.8 Deep Learning and Neural Networks
		10.9 Chapter Summary
	Chapter 11: Logistic Regression
		11.1 Logistic Regression
			11.1.1 The Data
			11.1.2 Creating the Model
			11.1.3 Model Fit Verification
			11.1.4 General Words of Caution
			11.1.5 Multicollinearity
			11.1.6 Dispersion
			11.1.7 Conclusion for Logistic Regression
		11.2 Training and Testing the Model
			11.2.1 Example of Prediction
			11.2.2 Validating the Logistic Regression Model on Test Data
		11.3 Multinomial Logistic Regression
		11.4 Regularization
		11.5 Using Python to Generate Logistic Regression
			11.5.1 Loading the Required Packages and Importing the Data
			11.5.2 Understanding the Dataframe
			11.5.3 Getting the Data Ready for the Generation of the Logistic Regression Model
			11.5.4 Splitting the Data into Training Data and Test Data
			11.5.5 Generating the Logistic Regression Model
			11.5.6 Predicting the Test Data
			11.5.7 Fine-Tuning the Logistic Regression Model
			11.5.8 Logistic Regression Model Using the statsmodel() Library
		11.6 Chapter Summary
Part III: Time-Series Models
	Chapter 12: Time Series: Forecasting
		12.1 Introduction
		12.2 Characteristics of Time-Series Data
		12.3 Decomposition of a Time Series
		12.4 Important Forecasting Models
			12.4.1 Exponential Forecasting Models
			12.4.2 ARMA and ARIMA Forecasting Models
			12.4.3 Assumptions for ARMA and ARIMA
		12.5 Forecasting in Python
			12.5.1 Loading the Base Packages
			12.5.2 Reading the Time-Series Data and Creating a Dataframe
			12.5.3 Trying to Understand the Data in More Detail
			12.5.4 Decomposition of the Time Series
			12.5.5 Test Whether the Time Series Is “Stationary”
			12.5.6 The Process of “Differencing”
			12.5.7 Model Generation
			12.5.8 ACF and PACF Plots to Check the Model Hyperparameters and the Residuals
			12.5.9 Forecasting
		12.6 Chapter Summary
Part IV: Unsupervised Models and Text Mining
	Chapter 13: Cluster Analysis
		13.1 Overview of Clustering
			13.1.1 Distance Measure
			13.1.2 Euclidean Distance
			13.1.3 Manhattan Distance
			13.1.4 Distance Measures for Categorical Variables
		13.2 Distance Between Two Clusters
		13.3 Types of Clustering
			13.3.1 Hierarchical Clustering
			13.3.2 Dendrograms
			13.3.3 Nonhierarchical Method
			13.3.4 K-Means Algorithm
			13.3.5 Other Clustering Methods
			13.3.6 Evaluating Clustering
		13.4 Limitations of Clustering
		13.5 Clustering Using R
			13.5.1 Hierarchical Clustering Using R
		13.6 Clustering Using Python sklearn()
		13.7 Chapter Summary
	Chapter 14: Relationship Data Mining
		14.1 Introduction
		14.2 Metrics to Measure Association: Support, Confidence, and Lift
			14.2.1 Support
			14.2.2 Confidence
			14.2.3 Lift
		14.3 Generating Association Rules
		14.4 Association Rule (Market Basket Analysis) Using R
		14.5 Association Rule (Market Basket Analysis) Using Python
		14.6 Chapter Summary
	Chapter 15: Introduction to Natural Language Processing
		15.1 Overview
		15.2 Applications of NLP
			15.2.1 Chatbots
			15.2.2 Sentiment Analysis
			15.2.3 Machine Translation
		15.3 What Is Language?
			15.3.1 Phonemes
			15.3.2 Lexeme
			15.3.3 Morpheme
			15.3.4 Syntax
			15.3.5 Context
		15.4 What Is Natural Language Processing?
			15.4.1 Why Is NLP Challenging?
		15.5 Approaches to NLP
			15.5.1 WordNet Corpus
			15.5.2 Brown Corpus
			15.5.3 Reuters Corpus
			15.5.4 Processing Text Using Regular Expressions
				15.5.4.1 re.search() Method
				15.5.4.2 re.findall()
				15.5.4.3 re.sub()
		15.6 Important NLP Python Libraries
		15.7 Important NLP R Libraries
		15.8 NLP Tasks Using Python
			15.8.1 Text Normalization
			15.8.2 Tokenization
			15.8.3 Lemmatization
			15.8.4 Stemming
			15.8.5 Stop Word Removal
			15.8.6 Part-of-Speech Tagging
			15.8.7 Probabilistic Language Model
			15.8.8 N-gram Language Model
		15.9 Representing Words as Vectors
			15.9.1 Bag-of-Words Modeling
			15.9.2 TF-IDF Vectors
			15.9.3 Term Frequency
			15.9.4 Inverse Document Frequency
			15.9.5 TF-IDF
		15.10 Text Classifications
		15.11 Word2vec Models
		15.12 Text Analytics and NLP
		15.13 Deep Learning and NLP
		15.14 Case Study: Building a Chatbot
		15.15 Chapter Summary
	Chapter 16: Big Data Analytics and Future Trends
		16.1 Introduction
		16.2 Big Data Ecosystem
		16.3 Future Trends in Big Data Analytics
			16.3.1 Growth of Social Media
			16.3.2 Creation of Data Lakes
			16.3.3 Visualization Tools at the Hands of Business Users
			16.3.4 Prescriptive Analytics
			16.3.5 Internet of Things
			16.3.6 Artificial Intelligence
			16.3.7 Whole Data Processing
			16.3.8 Vertical and Horizontal Applications
			16.3.9 Real-Time Analytics
		16.4 Putting the Analytics in the Hands of Business Users
		16.5 Migration of Solutions from One Tool to Another
		16.6 Cloud Analytics
		16.7 In-Database Analytics
		16.8 In-Memory Analytics
		16.9 Autonomous Services for Machine Learning
		16.10 Addressing Security and Compliance
		16.11 Big data Applications
		16.12 Chapter Summary
Part V: Business Analytics Tools
	Chapter 17: R for Analytics
		17.1 Data Analytics Tools
		17.2 Data Wrangling and Data Preprocessing Using R
			17.2.1 Handling NAs and NULL Values in the Data Set
			17.2.2 Apply() Functions in R
			17.2.3 lapply()
			17.2.4 sapply()
		17.3 Removing Duplicate Records in the Data Set
		17.4 split()
		17.5 Writing Your Own Functions in R
		17.6 Chapter Summary
	Chapter 18: Python Programming for Analytics
		18.1 Introduction
		18.2 pandas for Data Analytics
			18.2.1 Data Slicing Using pandas
			18.2.2 Statistical Data Analysis Using pandas
			18.2.3 Pandas Database Functions
			18.2.4 Data Preprocessing Using pandas
			18.2.5 Handling Data Types
			18.2.6 Handling Dates Variables
			18.2.7 Feature Engineering
			18.2.8 Data Preprocessing Using the apply() Function
			18.2.9 Plots Using pandas
		18.3 NumPy for Data Analytics
			18.3.1 Creating NumPy Arrays with Zeros and Ones
			18.3.2 Random Number Generation and Statistical Analysis
			18.3.3 Indexing, Slicing, and Iterating
			18.3.4 Stacking Two Arrays
		18.4 Chapter Summary
References
	Dataset CITATION
Index