R Data Mining Blueprints by Mishra, Pradeepta -- Read -- Imperial Library of Trantor

Index

R Data Mining Blueprints

R Data Mining Blueprints Credits About the Author About the Reviewer www.PacktPub.com

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Preface

What this book covers What you need for this book Who this book is for Conventions Reader feedback Customer support

Downloading the example code Downloading the color images of this book Errata Piracy Questions

1. Data Manipulation Using In-built R Data

What is data mining?

How is it related to data science, analytics, and statistical modeling?

Introduction to the R programming language

Getting started with R Data types, vectors, arrays, and matrices List management, factors, and sequences Import and export of data types

Data type conversion Sorting and merging dataframes Indexing or subsetting dataframes Date and time formatting Creating new functions

User-defined functions Built-in 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

2. Exploratory Data Analysis with Automobile Data

Univariate data analysis Bivariate analysis Multivariate analysis Understanding distributions and transformation

Normal probability distribution Binomial probability distribution Poisson probability distribution

Interpreting distributions

Interpreting continuous data

Variable binning or discretizing continuous data Contingency tables, bivariate statistics, and checking for data normality Hypothesis testing

Test of the population mean

One tail test of mean with known variance One tail and two tail test of proportions

Two sample variance test

Non-parametric methods

Wilcoxon signed-rank test Mann-Whitney-Wilcoxon test Kruskal-Wallis test

Summary

3. Visualize Diamond Dataset

Data visualization using ggplot2

Bar chart Boxplot Bubble chart Donut chart Geo mapping Histogram Line chart Pie chart Scatterplot Stacked bar chart Stem and leaf plot Word cloud Coxcomb plot

Using plotly

Bubble plot Bar charts using plotly Scatterplot using plotly Boxplots using plotly Polar charts using plotly Polar scatterplot using plotly Polar area chart

Creating geo mapping Summary

4. Regression with Automobile Data

Regression introduction

Formulation of regression problem Case study

Linear regression Stepwise regression method for variable selection Logistic regression Cubic regression Penalized regression Summary

5. Market Basket Analysis with Groceries Data

Introduction to Market Basket Analysis

What is MBA? Where to apply MBA? Data requirement Assumptions/prerequisites Modeling techniques Limitations

Practical project

Apriori algorithm Eclat algorithm Visualizing association rules Implementation of arules

Summary

6. Clustering with E-commerce Data

Understanding customer segmentation

Why understanding customer segmentation is important How to perform customer segmentation?

Various clustering methods available

K-means clustering Hierarchical clustering Model-based clustering Other cluster algorithms Comparing clustering methods

References Summary

7. Building a Retail Recommendation Engine

What is recommendation?

Types of product recommendation Techniques to perform recommendation

Assumptions What method to apply when Limitations of collaborative filtering Practical project Summary

8. Dimensionality Reduction

Why dimensionality reduction?

Techniques available for dimensionality reduction

Which technique to apply where?

Principal component analysis

Practical project around dimensionality reduction

Attribute description

Parametric approach to dimension reduction References Summary

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

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