Predictive Analytics for Dummies

  • 5h 55m
  • Anasse Bari, Mohamed Chaouchi, Tommy Jung
  • John Wiley & Sons (US)
  • 2014

Combine business sense, statistics, and computers in a new and intuitive way, thanks to Big Data

Predictive analytics is a branch of data mining that helps predict probabilities and trends. Predictive Analytics For Dummies explores the power of predictive analytics and how you can use it to make valuable predictions for your business, or in fields such as advertising, fraud detection, politics, and others. This practical book does not bog you down with loads of mathematical or scientific theory, but instead helps you quickly see how to use the right algorithms and tools to collect and analyze data and apply it to make predictions.

Topics include using structured and unstructured data, building models, creating a predictive analysis roadmap, setting realistic goals, budgeting, and much more.

  • Shows readers how to use Big Data and data mining to discover patterns and make predictions for tech-savvy businesses
  • Helps readers see how to shepherd predictive analytics projects through their companies
  • Explains just enough of the science and math, but also focuses on practical issues such as protecting project budgets, making good presentations, and more
  • Covers nuts-and-bolts topics including predictive analytics basics, using structured and unstructured data, data mining, and algorithms and techniques for analyzing data
  • Also covers clustering, association, and statistical models; creating a predictive analytics roadmap; and applying predictions to the web, marketing, finance, health care, and elsewhere

Propose, produce, and protect predictive analytics projects through your company with Predictive Analytics For Dummies.

About the Authors

Dr. Anasse Bari is a Fulbright scholar, a software engineer, and a data mining expert.

Mohamed Chaouchi has conducted extensive research using data mining methods in both health and financial domains.

Tommy Jung has worked extensively on natural language processing and algorithmic trading using machine learning.

In this Book

  • Introduction
  • Entering the Arena
  • Predictive Analytics in the Wild
  • Exploring Your Data Types and Associated Techniques
  • Complexities of Data
  • Applying Models
  • Identifying Similarities in Data
  • Predicting the Future Using Data Classification
  • Convincing Your Management to Adopt Predictive Analytics
  • Preparing Data
  • Building a Predictive Model
  • Visualization of Analytical Results
  • Creating Basic Prediction Examples
  • Creating Basic Examples of Unsupervised Predictions
  • Predictive Modeling with R
  • Avoiding Analysis Traps
  • Targeting Big Data
  • Ten Reasons to Implement Predictive Analytics
  • Ten Steps to Build a Predictive Analytic Model
  • Bonus Chapter—Ten Major Predictive Analytics Vendors


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