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

Table of Contents
About the Authors
Preface
Foreword
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
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
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
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
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
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