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>

Related Content you might be interested in

Current Title:

Small book image
R Data Analytics Projects [Video]
Dipanjan Sarkar | Raghav Bali
May, 2018 | 4 hours 15 minutes
No titles found
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
Content Locked
Section 3
Modeling Using Logistic Regression
Logistic regression is a type of regression model where the dependent or class variable is not continuous but categorical, just as in our case, credit rating is the dependent variable with two classes. For our model, since we are dealing with a binary classification problem, we will be dealing with binomial logistic regression. - Train the initial model with all the independent variables - Perform predictions on the test data and evaluate the results - Build another model and test model performance on test data
Previous Section
End of Section 3
Next Section