Book Image

R Data Analytics Projects [Video]

By : Dipanjan Sarkar, Raghav Bali
Book Image

R Data Analytics Projects [Video]

By: Dipanjan Sarkar, Raghav Bali

Overview of this book

<p>With powerful features and packages, R empowers users to build sophisticated machine learning systems to solve real-world data problems.</p> <p>This video course takes you on a data-driven journey that starts with the very basics of R and machine learning. You will then work on three different projects to apply the concepts of machine learning. Each project will help you to understand, explore, visualize, and derive domain- and algorithm-based insights.</p> <p>By the end of this course, you will have learned to apply the concepts of machine learning to data-related problems and solve them with help of R.</p> <p>All the code and supporting files for this course are available on Github at<br /><a style="color: #fa8d11;" href="https://github.com/PacktPublishing/R-Data-Analytics-Projects" target="blank">https://github.com/PacktPublishing/R-Data-Analytics-Projects</a></p> <h1>Style and Approach</h1> <p>The course is an enticing journey that starts from the very basics and gradually picks up the pace as it unfolds. Each topic is explained with the help of a project that solves a real-world problem hands-on, thus giving you a deep insight into the world of machine learning.</p>

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R Data Analytics Projects [Video]
Dipanjan Sarkar | Raghav Bali
May, 2018 | 4 hours 15 minutes
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Table of Contents (8 chapters)
Free Chapter
1
Getting Started with R and Machine Learning
The Course Overview
Delving into the Basics of R
Data Structures in R
Lists and Data Frames
Working with Functions
Controlling Code Flow
Advanced Constructs
Next Steps with R
Machine Learning Basics
2
Let's Help Machines Learn
Algorithms in Machine Learning
Supervised Learning Algorithms
Unsupervised Learning Algorithms
3
Predicting Customer Shopping Trends with Market Basket Analysis
Market Basket Analysis
Evaluating a Product Contingency Matrix
Frequent Itemset Generation
Association Rule Mining
4
Building a Product Recommendation System
Understanding Recommendation Systems
Building a Recommender Engine
Production Ready Recommender Engines
5
Credit Risk Detection and Prediction – Descriptive Analytics
Understanding Credit Risk
Data Preprocessing
Data Analysis and Transformation
Analyzing the Dataset
6
Credit Risk Detection and Prediction – Predictive Analytics
Data Preprocessing
Feature Selection
Modeling Using Logistic Regression
Modeling Using Support Vector Machines
Modeling Using Decision Trees
Modeling Using Random Forests
Modeling Using Neural Networks
7
Social Media Analysis – Analyzing Twitter Data
Getting Started with Twitter APIs
Twitter Data Mining
Hierarchical Clustering and Topic Modeling
8
Sentiment Analysis of Twitter Data
Understanding Sentiment Analysis
Sentiment Analysis Upon Tweets – Polarity Analysis
Sentiment Analysis Upon Tweets –Classification-Based Algorithms
Chapter 6
Credit Risk Detection and Prediction – Predictive Analytics
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Section 4
Modeling Using Support Vector Machines
Support vector machines belong to the family of supervised machine learning algorithms used for both classification and regression. The samples on the margins are typically called the support vectors. The middle of the margin which separates the two classes is called the optimal separating hyperplane. - Build the SVM model using the training data and the RBF kernel on all the training set features - Use testing data on model to make predictions and evaluate the results - Build a new SVM model based on the top five features
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