Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst | 1 Edition

Details about Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst:

Predictive Analytics shows tech-savvy business managers and data analysts how to use the techniques of predictive analytics to solve practical business problems.  It teaches readers the methods, principles, and techniques for conducting predictive analytics projects, from start to finish.  The author focuses on best practices---including tips and tricks---that are essential for successful predictive modeling. The author explains the theory behind the principles of predictive analytics in plain English; readers don't need an extensive background in math and statistics, which makes it ideal for most tech-savvy business and data analysts. Each of the techniques chapters will begin with a description of the specific technique and how it relates to the overall process model for predictive analytics.  The depth of the description of a technique will match the complexity of the approach; the intent is to describe the techniques in enough depth for a practitioner to understand the effect of the major parameters needed to effectively use the technique and interpret the results.  For example, with decision trees, the primary algorithms (C5, CART and CHAID) will be described in qualitative terms (what are trees, what is a split), how they are similar and different (Gini vs. Entropy vs. chi-square tests), why one might use one technique over another, how one can be fooled by the models built using each algorithm (i.e., their weaknesses), what knobs one can adjust (depth, complexity penalties, priors, costs, etc.), and how to interpret the results. Each of the techniques is illustrated by hands-on examples, either unique to the task or as part of a more comprehensive case study.   The companion website will provide all of the data sets used to generate these examples, along with a free trial version of software, so that readers can recreate and explore the examples and case studies.   The book concludes with a series of in-depth case studies that apply predictive analytics to common types of business scenarios.