Applied-Predictive-Modeling

This is the study notes of Applied Predictive Modeling (Kuhn and Johnson (2013)) using IPython notebook. This text, written in R, is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. The notebook reproduces book examples, provides exercise solutions and study notes for interested readers who wants to study the book using Python.

Table of Contents (in progress)

• Ch.1 Introduction

Part I General Strategies

• Ch.2 A short tour of the predictive modeling process
• Ch.3 Data pre-processing
• Ch.4 Over-fitting and model tuning

Part II Regression Models

• Ch.5 Measuring performance in regression models
• Ch.6 Linear regression and its cousins
• Ch.7 Nonlinear regression models
• Ch.8 Regression trees and rule-based models
• Ch.9 A summary of solubility models
• Ch.10 Case study: compressive strength of concrete

Part III Classification Models

• [Ch.11 Measuring performance in classification models]