Practical Predictive Analytics

Key Features

• A unique book that focuses on developing practical skills to make informed business decisions using predictive analytics
• Apply the principles and techniques of predictive analytics to effectively interpret big data
• Solve real-world analytical problems with the help of practical case studies and real-world scenarios

Book Description

This is a go-to book for anyone interested in predicting actions of people, businesses, and more. With this book, you will learn the entire process of predictive analytics and modeling techniques to practically implement them.

You’ll get started with the basics of predictive analytics and its applications along with the installation and set up of the tools. Once you have completed the installation, get ready for an exciting journey to uncover answers to hidden questions. You will learn about entering the data (or should I say dirty data), cleaning the data, and apply modeling techniques to this data. When you have done the crucial bit and cleaned the data, we’ll tell you stories from within and let you visit the future.

“You” – the fortune teller now can predict the number of expected re-admissions in a hospital or even the place where a virus may hit next. Wouldn’t it be great if you could predict the injury and insurance claim payments based on the characteristics of the insured’s vehicle or predict load defaults? Are you going to acquire those 10k customers for your start-up? Build your own model and answer this crucial question yourselves in the later part of the book.

The journey does not end here, and you will learn to present your results with fantastic visualizations to showcase your results to the world. By the end of this book, you’ll have learned about, implemented, and mastered predictive analytic techniques.

What you will learn

• Find out how predictive analytics work
• Identify, model, and prioritize the decisions you need to optimize
• Classify the right algorithm for your requirements
• Use and apply predictive analytics to research problems in healthcare
• Implement predictive analytics to retain and acquire your customers
• Use text mining to understand unstructured data

Table of Contents

Chapter 1. Getting Started with Predictive Analytics
Chapter 2. The Modeling Process
Chapter 3. Inputting and Exploring Data
Chapter 4. Introduction to Regression Algorithms
Chapter 5. Introduction to Decision Trees, Clustering, and SVM
Chapter 6. Using Survival Analysis to Predict and Analyze Customer Churn
Chapter 7. Using Market Basket Analysis as a Recommender Engine
Chapter 8. Exploring Health Care Enrollment Data as a Time Series
Chapter 9. Introduction to Spark Using R
Chapter 10. Exploring Large Datasets Using Spark
Chapter 11. Spark Machine Learning – Regression and Cluster Models
Chapter 12. Spark Models – Rule-Based Learning