R Data Analysis Solutions - Machine Learning Techniques [Video]

Book Image

R Data Analysis Solutions - Machine Learning Techniques [Video]

By : Shanthi Viswanathan, Viswa Viswanathan

Book Image

R Data Analysis Solutions - Machine Learning Techniques [Video]

By: Shanthi Viswanathan, Viswa Viswanathan

Overview of this book

<p>Data analysis has recently emerged as a very important focus for a huge range of organizations and businesses. R makes detailed data analysis easier, making advanced data exploration and insight accessible to anyone interested in learning it. This video empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. You will learn to carry out different tasks on the data to bring it into action.By the end of this course, you will be able to carry out different analyzing techniques, apply classification and regression, and also reduce data.</p> <h1>Style and Approach</h1> <p>This course follows a recipe-based approach. Here each video presents you with a step-by-step approach to performing many important data analytics tasks.</p>

Free Chapter

Acquire and Prepare the Ingredients – Your Data

The Course Overview

Reading Data from CSV Files

Reading XML and JSON Data

Reading Data from Fixed-Width Formatted Files, R Files, and R Libraries

Removing and Replacing Missing Values

Removing Duplicate Cases

Rescaling a Variable

Normalizing or Standardizing Data in a Data Frame

Binning Numerical Data

Creating Dummies for Categorical Variables

What's in There? – Exploratory Data Analysis

Creating Standard Data Summaries

Extracting Subset of a Dataset

Splitting a Dataset

Creating Random Data Partitions

Generating Standard Plots

Generating Multiple Plots

Selecting a Graphics Device

Creating Plots with the Lattice and ggplot2package

Creating Charts that Facilitate Comparisons

Creating Charts that Visualize Possible Causality

Creating Multivariate Plots

Where Does It Belong? – Classification

Generating Error/Classification-Confusion Matrices

Generating ROC Charts

Building, Plotting, and Evaluating – Classification Trees

Using random Forest Models for Classification

Classifying Using the Support Vector Machine Approach

Classifying Using the Naïve Bayes Approach

Classifying Using the KNN Approach

Using Neural Networks for Classification

Classifying Using Linear Discriminant Function Analysis

Classifying Using Logistic Regression

Using AdaBoost to Combine Classification Tree Models

Give Me a Number – Regression

Computing the Root Mean Squared Error

Building KNN Models for Regression

Performing Linear Regression

Performing Variable Selection in Linear Regression

Building Regression Trees

Building Random Forest Models for Regression

Using Neural Networks for Regression

Performing k-Fold Cross-Validation and Leave-One-Out-Cross-Validation

Can You Simplify That? – Data Reduction Techniques

Performing Cluster Analysis Using K-Means Clustering

Performing Cluster Analysis Using Hierarchical Clustering

Reducing Dimensionality with Principal Component Analysis

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Chapter 2

What's in There? – Exploratory Data Analysis

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Section 2

Extracting Subset of a Dataset

In this video, we will look at two ways to subset data. - Index by position and name - Retrieve all data - Get mpg and car_name for all cars