Webinar : Introduction to R Programming and Machine Learning

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Business Analytics with R
Introduction to R Programming and Machine
Learning
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Objectives
 What is R
 Domains and companies in which R is used
 Characteristics of R
 Get an overview of Machine Learning
 Understand the difference between Supervised and Unsupervised Learning
 Learn Clustering and K-means Clustering
 Implement K-means clustering in R
 Google Trends for R
At the end of this session, you will be able to

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Business Analytics
Why Business Analytics is getting popular these days ?
Cost of storing data Cost of processing data

Business Analytics
“Study of business data using statistical techniques and programming for creating decision support
and insights for achieving business goals”.
Business analytics is used to evaluate organization-wide operations, and can be implemented in any
department from sales to product development to customer service.
Business analytics solutions typically use statistical and quantitative analysis and fact-based data to
measure past performance to guide an organization's business planning.
Definition
Who creates it? How?
Who uses it? How?

Who Uses R : Domains
 Telecom
 Pharmaceuticals
 Financial Services
 Life Sciences
 Education, etc

Who Uses R : Companies
Consumer Financial Protection Bureau
The Consumer Financial Protection Bureau uses R for data analysis
Mozilla
Mozilla, the foundation responsible for the Firefox web browser, uses R to visualize Web
activity
Bank of America
Bank of America uses R for reporting
Foursquare
R is part of technology stack behind Foursquare’s famed recommendation engine
ANZ Bank
ANZ, the fourth largest bank in Australia, using R for credit risk analysis
Google
Google uses R to predict Economic Activity

Who Uses R : Companies
Corporate Clients of R
http://www.revolutionanalytics.com/aboutus/our-customers.php

R : Characteristics
 R is open source and free.
 R has lots of packages and multiple ways of doing the same thing.
 By default stores memory in RAM.
 R has the most advanced graphics. You need much better programming skills.
 R has GUI to help make learning easier.
 Customization needs command line.
 R can connect to many database and data types.
“The great beauty of R is that you can modify it to do all sorts of things,” said Hal Varian, chief economist at
Google. “And you have a lot of pre-packaged stuff that’s already available, so you’re standing on the shoulders of
giants.”

What is R : Data Analysis Software
 Data Scientists, Statisticians, Analysts, Quants, and
others who need to make sense of data use R for
statistical analysis, data visualization, and
predictive modelling.
 Rexer Analytics’s Annual Data Miner Survey is the
largest survey of data mining, data science, and
analytics professionals in the industry.
 It has concluded that R's popularity has increased
substantially in recent years.
R is Data Analysis Software

What is R : Programming Language
 You do data analysis in R by writing scripts and functions
in the R programming language.
 R has also quickly found the following because
statisticians, engineers and scientists without computer
programming skills find it easy to use.
Do not get intimated by the
term ‘Programming Language’,
the concepts from the very
basic will be taught during the
course.
R is Programming Language

What is R : Environment for Statistical Analysis
 R language consists of functions for almost every
data manipulation, statistical model, or chart that a
data analyst could ever need.
 For statisticians, however, R is particularly useful
because it contains a number of built-in mechanisms
for organizing data, running calculations on the
information and creating graphical representations of
data sets.
R is Environment for Statistical Analysis

Basics Of R - Command Line
Basics of R - Command Line

Machine Learning Categories
Types of Learning
Supervised
Learning
Unsupervised
Learning
Inferring a function
from labelled
training data.
Trying to find hidden
structure in
unlabelled data.

Machine Learning Categories
What category do the applications below fall into?
Supervised Learning Supervised Learning
Unsupervised Learning Unsupervised Learning

Common Machine Learning Algorithms
Types of Learning
Supervised Learning
Unsupervised Learning
Algorithms
 Naïve Bayes
 Support Vector Machines
 Random Forests
 Decision Trees
Algorithms
 K-means
 Fuzzy Clustering
 Hierarchical Clustering

Clustering

Clustering: Scenarios
The following scenarios implement Clustering:
 A telephone company needs to establish its network by putting its towers in a particular region it has acquired.
The location of putting these towers can be found by clustering algorithm so that all its users receive optimum
signal strength.
 The Miami DEA wants to make its law enforcement more stringent and hence have decided to make their patrol
vans stationed across the area so that the areas of high crime rates are in vicinity to the patrol vans.
 A Hospital Care chain wants to open a series of Emergency-Care wards, keeping in mind the factor of maximum
accident prone areas in a region.

Some More Use-Cases of Clustering
 Organizing data into clusters shows internal structure of the data
Ex. Clusty and clustering genes
 Sometimes the partitioning is the goal
Ex. Market segmentation
 Prepare for other AI techniques
Ex. Summarize news (cluster and then find centroid)
 Discovery in data
Ex. Underlying rules, reoccurring patterns, topics, etc.

What is Clustering?
Organizing data into clusters such that there is:
 High intra-cluster similarity
 Low inter-cluster similarity
 Informally, finding natural groupings among objects
http://en.wikipedia.org/wiki/Cluster_analysis

K-Means Clustering

K-Means Clustering
The process by which objects are classified into
a number of groups so that they are as much
dissimilar as possible from one group to another
group, but as much similar as possible within
each group.
The objects in group 1 should be as similar as
possible.
But there should be much difference between an
object in group 1 and group 2.
The attributes of the objects are allowed to
determine which objects should be grouped
together.
Total population
Group 1
Group 2 Group 3
Group 4

K-Means: Pizza Hut Clustering Example

Let us suppose the following points are the delivery locations for Pizza.

Lets locate three cluster centres randomly
C1
C3
C2

Find the distance of the points as shown.
C1
C3
C2

Assign the points to the nearest cluster centres based on the distance between each centre and the points.
C1
C2
C3

Re-assign the cluster centres and locate nearest points.
C1
C2
C3

Re-assign the cluster centres and locate nearest points, calculate the distance.
C1
C2
C3

Form the three clusters.
C1
C2
C3

ObjectiveFunctionValue
i.e.,Distortion
Elbow method
The value of k should be such that even if we increase the value of k from here on, the distortion remains constant. This
is the ideal value of k, for the clusters created.
The Elbow Curve

Now let us consider the another scenario of clustering :
The data from “Google page rank”.
Notice, that the data given here are sentences and not vectors.
Can we apply K-means clustering to it?
We will take a deep dive into TF-IDF in module 3 of the course.
Let’s look at the Another Scenario
For analyzing this type of data we use “TF-IDF algorithm” which converts these attributes to vectors.

“R has really become the second
language for people coming out of
grad school now, and there’s an
amazing amount of code being
written for it,” said
Max Kuhn,
Associate Director of Nonclinical
Statistics at Pfizer.
Comparing R and others
“You can look on the SAS
message boards and see there
is a proportional downturn in
traffic.”
Google Trends in R

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R: Demand Supply Gap

 Module 1
» Introduction to Business Analytics
 Module 2
» Introduction to R Programming
 Module 3
» Data Manipulation in R
 Module 4
» Data Import Techniques in R
 Module 5
» Exploratory Data Analysis
 Module 6
» Data Visualization in R
Course Topics
 Module 7
» Data mining: Clustering Techniques
 Module 8
» Data Mining: Association rule mining and
Sentiment analysis
 Module 9
» Linear and Logistic Regression
 Module 10
» Annova and Predictive Analysis
 Module 11
» Data Mining: Decision Trees and Random forest
 Module 12
» Final Project Business Analytics with R class –
Census Data

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24/7 Post Class Support
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Webinar : Introduction to R Programming and Machine Learning

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