Book Image

Statistical Application Development with R and Python - Second Edition

Book Image

Statistical Application Development with R and Python - Second Edition

Overview of this book

Statistical Analysis involves collecting and examining data to describe the nature of data that needs to be analyzed. It helps you explore the relation of data and build models to make better decisions. This book explores statistical concepts along with R and Python, which are well integrated from the word go. Almost every concept has an R code going with it which exemplifies the strength of R and applications. The R code and programs have been further strengthened with equivalent Python programs. Thus, you will first understand the data characteristics, descriptive statistics and the exploratory attitude, which will give you firm footing of data analysis. Statistical inference will complete the technical footing of statistical methods. Regression, linear, logistic modeling, and CART, builds the essential toolkit. This will help you complete complex problems in the real world. You will begin with a brief understanding of the nature of data and end with modern and advanced statistical models like CART. Every step is taken with DATA and R code, and further enhanced by Python. The data analysis journey begins with exploratory analysis, which is more than simple, descriptive, data summaries. You will then apply linear regression modeling, and end with logistic regression, CART, and spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects.

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Table of Contents (19 chapters)
Statistical Application Development with R and Python - Second Edition
Statistical Application Development with R and Python - Second Edition
Credits
Credits
About the Author
About the Author
Acknowledgment
Acknowledgment
About the Reviewers
About the Reviewers
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Data Characteristics
Data Characteristics
Questionnaire and its components
Experiments with uncertainty in computer science
Installing and setting up R
Using R packages
Python installation and setup
IDEs for R and Python
The companion code bundle
Discrete distributions
Continuous distributions
Summary
2
Import/Export Data
Import/Export Data
Packages and settings – R and Python
Understanding data.frame and other formats
Using utils and the foreign packages
Exporting data/graphs
Pop quiz
Summary
3
Data Visualization
Data Visualization
Packages and settings – R and Python
Visualization techniques for categorical data
Visualization techniques for continuous variable data
Pareto chart
A brief peek at ggplot2
Summary
4
Exploratory Analysis
Exploratory Analysis
Packages and settings – R and Python
Essential summary statistics
Techniques for exploratory analysis
Summary
5
Statistical Inference
Statistical Inference
Packages and settings – R and Python
Maximum likelihood estimator
Confidence intervals
Hypothesis testing
Summary
6
Linear Regression Analysis
Linear Regression Analysis
Packages and settings - R and Python
The essence of regression
The simple linear regression model
Multiple linear regression model
Regression diagnostics
Model selection
Summary
7
Logistic Regression Model
Logistic Regression Model
Packages and settings – R and Python
Model validation and diagnostics
Logistic regression for the German credit screening dataset
Summary
8
Regression Models with Regularization
Regression Models with Regularization
Packages and settings – R and Python
Regression spline
Ridge regression for linear models
Summary
9
Classification and Regression Trees
Classification and Regression Trees
Packages and settings – R and Python
Splitting the data
Summary
10
CART and Beyond
CART and Beyond
Packages and settings – R and Python
Understanding bagging
Summary
Index
Index

Packages and settings – R and Python

As this chapter reviews some of the techniques in the latter half of the book, we need lot of packages and functions:

  1. First, set the working directory:

    setwd("MyPath/R/Chapter_10")

    Load the required R package:

    library(boot)
library(RSADBE)
library(ipred)
library(randomForest)
library(rpart)
library(rattle)

    We will only develop the bagging and random forest in Python.

  2. A lot of functions are required to set up the bagging and random forest method in Python:

Improving the CART

In the Another look at model assessment section of Chapter 8, Regression Models with Regularization, we saw that the technique of train, validate, and test may be further enhanced by using the cross-validation technique. In the case of the linear regression model, we used the CVlm function from the DAAG package for the purpose of cross-validation of linear models. The cross-validation technique for the logistic regression models may be carried out by using the CVbinary function from the same...

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