R Data Mining Blueprints

Discover the versatility of R for data mining with this collect of real-world dataset analysis techniques

R Data Mining Blueprints

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Pradeepta Mishra

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Discover the versatility of R for data mining with this collect of real-world dataset analysis techniques
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Book Details

ISBN 139781783989683
Paperback260 pages

Book Description

The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to produce data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users.

This book explores data mining techniques and shows you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.

Table of Contents

Chapter 1: Data Manipulation Using In-built R Data
What is data mining?
Introduction to the R programming language
Data type conversion
Sorting and merging dataframes
Indexing or subsetting dataframes
Date and time formatting
Creating new functions
Loop concepts - the for loop
Loop concepts - the repeat loop
Loop concepts - while conditions
Apply concepts
String manipulation
NA and missing value management
Missing value imputation techniques
Summary
Chapter 2: Exploratory Data Analysis with Automobile Data
Univariate data analysis
Bivariate analysis
Multivariate analysis
Understanding distributions and transformation
Interpreting distributions
Variable binning or discretizing continuous data
Contingency tables, bivariate statistics, and checking for data normality
Hypothesis testing
Non-parametric methods
Summary
Chapter 3: Visualize Diamond Dataset
Data visualization using ggplot2
Using plotly
Creating geo mapping
Summary
Chapter 4: Regression with Automobile Data
Regression introduction
Linear regression
Stepwise regression method for variable selection
Logistic regression
Cubic regression
Penalized regression
Summary
Chapter 5: Market Basket Analysis with Groceries Data
Introduction to Market Basket Analysis
Practical project
Summary
Chapter 6: Clustering with E-commerce Data
Understanding customer segmentation
Various clustering methods available
References
Summary
Chapter 7: Building a Retail Recommendation Engine
What is recommendation?
Assumptions
What method to apply when
Limitations of collaborative filtering
Practical project
Summary
Chapter 8: Dimensionality Reduction
Why dimensionality reduction?
Practical project around dimensionality reduction
Parametric approach to dimension reduction
References
Summary
Chapter 9: Applying Neural Network to Healthcare Data
Introduction to neural networks
Understanding the math behind the neural network
Neural network implementation in R
Neural networks for prediction
Neural networks for classification
Neural networks for forecasting
Merits and demerits of neural networks
References
Summary

What You Will Learn

  • Make use of statistics and programming to learn data mining concepts and its applications
  • Use R Programming to apply statistical models on data
  • Create predictive models to be applied for performing classification, prediction, and recommendation
  • Use of various libraries available on R CRAN (comprehensive R archives network) in data mining
  • Apply data management steps in handling large datasets
  • Learn various data visualization libraries available in R for representing data
  • Implement various dimension reduction techniques to handle large datasets
  • Acquire knowledge about neural network concept drawn from computer science and its applications in data mining

Authors

Table of Contents

Chapter 1: Data Manipulation Using In-built R Data
What is data mining?
Introduction to the R programming language
Data type conversion
Sorting and merging dataframes
Indexing or subsetting dataframes
Date and time formatting
Creating new functions
Loop concepts - the for loop
Loop concepts - the repeat loop
Loop concepts - while conditions
Apply concepts
String manipulation
NA and missing value management
Missing value imputation techniques
Summary
Chapter 2: Exploratory Data Analysis with Automobile Data
Univariate data analysis
Bivariate analysis
Multivariate analysis
Understanding distributions and transformation
Interpreting distributions
Variable binning or discretizing continuous data
Contingency tables, bivariate statistics, and checking for data normality
Hypothesis testing
Non-parametric methods
Summary
Chapter 3: Visualize Diamond Dataset
Data visualization using ggplot2
Using plotly
Creating geo mapping
Summary
Chapter 4: Regression with Automobile Data
Regression introduction
Linear regression
Stepwise regression method for variable selection
Logistic regression
Cubic regression
Penalized regression
Summary
Chapter 5: Market Basket Analysis with Groceries Data
Introduction to Market Basket Analysis
Practical project
Summary
Chapter 6: Clustering with E-commerce Data
Understanding customer segmentation
Various clustering methods available
References
Summary
Chapter 7: Building a Retail Recommendation Engine
What is recommendation?
Assumptions
What method to apply when
Limitations of collaborative filtering
Practical project
Summary
Chapter 8: Dimensionality Reduction
Why dimensionality reduction?
Practical project around dimensionality reduction
Parametric approach to dimension reduction
References
Summary
Chapter 9: Applying Neural Network to Healthcare Data
Introduction to neural networks
Understanding the math behind the neural network
Neural network implementation in R
Neural networks for prediction
Neural networks for classification
Neural networks for forecasting
Merits and demerits of neural networks
References
Summary

Book Details

ISBN 139781783989683
Paperback260 pages
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