Applied-Predictive-Modeling-with-Python

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Applied-Predictive-Modeling-with-Python

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A collection of notebook to learn the Applied Predictive Modeling using Python.

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- Chapter 1.ipynb
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- Chapter 2.ipynb
- Chapter 3.ipynb
- Chapter 4.ipynb
- Chapter 5.ipynb
- Chapter 6.ipynb
- Chapter 7.ipynb
- Chapter 8.ipynb
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Applied-Predictive-Modeling
===========================

This is the study notes of [Applied Predictive Modeling (Kuhn and Johnson (2013))](http://www.amazon.com/Applied-Predictive-Modeling-Max-Kuhn/dp/1461468485/ref=sr_1_1?ie=UTF8&qid=1415171098&sr=8-1&keywords=applied+predictive+modeling) 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](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%201.ipynb)

**Part I General Strategies**
- [Ch.2 A short tour of the predictive modeling process](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%202.ipynb)
- [Ch.3 Data pre-processing](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%203.ipynb)
- [Ch.4 Over-fitting and model tuning](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%204.ipynb)

**Part II Regression Models**
- [Ch.5 Measuring performance in regression models](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%205.ipynb)
- [Ch.6 Linear regression and its cousins](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%206.ipynb)
- [Ch.7 Nonlinear regression models](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%207.ipynb)
- [Ch.8 Regression trees and rule-based models](http://nbviewer.ipython.org/github/leig/Applied-Predictive-Modeling-with-Python/blob/master/notebooks/Chapter%208.ipynb)
- 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]
<!---
- [Ch.12 Discriminant analysis and other linear classification models]
- [Ch.13 Nonlinear classification models]
- [Ch.14 Classification trees and rule-based models]
- [Ch.15 A summary of grant application models]
- [Ch.16 Remedies for severe class imbalance]
- [Ch.17 Case study: job scheduling]

**Part IV Other Considerations**
- [Ch.18 Measuring predictor importance]
- [Ch.19 An introduction to feature selection]
- [Ch.20 Factors that can affect model performance]
-->

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