Predictive Analytics for the Modern Enterprise: A Practitioner's Guide to Designing and Implementing Solutions

Preface
	Who Is This Book For?
	How This Book Is Organized
	Conventions Used in This Book
	Using Code Examples
	O’Reilly Online Learning
	How to Contact Us
	Acknowledgments
1. Data Analytics in the Modern Enterprise
	The Evolution of Data Analytics
	Different Types of Data Analytics
		Descriptive Analytics
		Diagnostic Analytics
		Predictive Analytics
		Prescriptive Analytics
	Knowledge Acquisition, Machine Learning, and the Role of Predictive Analytics
	Tools, Frameworks, and Platforms in the Predictive Analytics World
		Languages and Libraries
		Services
	Conclusion
2. Predictive Analytics: An Operational Necessity
	The Move from “Data Producing” to “Data Driven”
	Challenges to Using Predictive Analytics
		People
		Data
		Technology
	Vertical Industry Use Cases for Predictive Analytics
		Finance
		Healthcare
		Automotive
		Entertainment
	Conclusion
3. The Mathematics and Algorithms Behind Predictive Analytics
	Statistics and Linear Algebra
	Regression
		What Is Regression Analysis?
		Regression Techniques
		R-squared and P-value
		Selecting a Regression Model
	Decision Trees
		Training Decision Trees
		Using Decision Trees to Solve Regression Problems: Regression Trees
		Tuning Decision Trees
	Other Algorithms
		Random Forests
		Neural Networks
		Support Vector Machines
		Naive Bayes Classifier
	Other Learning Patterns in Machine Learning
	Conclusion
4. Working with Data
	Understanding Data
	Data Preprocessing and Feature Engineering
		Handling Missing Data
		Categorical Data Encoding
		Data Transformation
		Outlier Management
		Handling Imbalanced Data
		Combining Data
		Feature Selection
		Splitting Preprocessed Data
	Understanding Bias
	The Predictive Analytics Pipeline
		The Data Stage
		The Model Stage
		The Serving Stage
		Other Components
	Selecting the Right Model
	Conclusion
5. Python and scikit-learn for Predictive Analytics
	Anaconda and Jupyter Notebooks
	NumPy in Python
		Introduction to NumPy
		Generating Arrays
		Array Slicing
		Array Transformation
		Other Array Operations
		Exploring a Business Example Using Pandas
	Pandas in Python
		Import and View Data
		Visualize the Data
		Data Cleaning and Modification
		Reading from Different Data Sources
		Data Filtering and Grouping
	Scikit-learn
		Training and Predicting with a Linear Regression Model
		Using a Random Forest Classifier
		Training a Decision Tree
		A Clustering Example (Unsupervised Learning)
	Conclusion
6. TensorFlow and Keras for Predictive Analytics
	TensorFlow Fundamentals
	Linear Regression Using TensorFlow
		Data Preparation
		Model Creation and Training
		Predictions and Model Evaluation
	Deep Neural Networks in TensorFlow
	Conclusion
7. Predictive Analytics for Business Problem-Solving
	Prediction-Based Optimal Retail Price Recommendations
		Using a Simple Linear Regression Model
		Using a Polynomial Regression Model
		Using Multivariate Regression
	An Introduction to Recommender Systems
		Building Recommender Systems Using surprise scikit in Python
	Credit Card Fraud Classification
		Credit Card Fraud Baseline Analysis Using Artificial Neural Networks
		Credit Card Fraud Weighted Analysis Using Artificial Neural Networks
		Credit Card Analysis with Multiple Hidden Layers in the Artificial Neural Network
	Conclusion
8. Exploring AWS Cloud Provider Services for AI/ML
	To Cloud or Not to Cloud
	Exploring AWS SageMaker
		Prerequisites
		Data Ingest and Exploration
		Data Transformation
		Model Training and Prediction
		Cleanup
	Exploring Amazon Forecast
		Import Data
		Train the Predictor
		Create a Forecast
		What-If Analysis
		Cleanup
	Conclusion
9. Food for Thought
	A Few More Use Cases
		Navigation and Traffic Management
		Credit Scoring
	The Social Impact of Predictions
	Conclusion
Index
About the Author