predictive analytics via r programming

Slide ‹#›© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.com

Predictive Analyticsvia R Programming


Session Objectives

In this session you would understand

ᗍ Applications of R through use-cases and examplesᗍ Predictive Analytics and its processᗍ Three Pillars of Predictive Analyticsᗍ Common Predictive Modelling Tasksᗍ Applications of Predictive Analyticsᗍ Job Trends for R


Introduction


Ever Wondered?

How the weather forecasts are made?


Use Case: Customer Predictive Analysis

Prediction about the new customers

Predicting the outcome of new customers remains challenge, a lot of effort has gone in building similar kind of unsupervised technique like building pattern recognition and then predicting what a customer is going to like?

Problem statement:


Use Case: Customer Predictive Analysis

In R using Decision trees and supervised methods we can predict the outcome of customers for the problem defined

Based on customers spending habit and past records, we could predict behaviour of new customers

Say, our goal is to predict people who will stay more than a week. Decision trees could reveal things like ( if recently married if in their late 30s from Europe ) couples tend to stay more than a week

The same decision tree could also tell: (if recently married in recent 20s), couple tend to stay only for few days. The obvious revelation is that the latter group stays lot less

We can examine why that happens and what can be done to improvise customer retention

Solution:


R is an Open Source “Programming” Language

ᗍ R is a language and environment for statistical computing and graphics

ᗍ It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers and colleagues

ᗍ R helps people perform a wide variety of computing tasks by giving them access to various commands


ᗍ The R programming language is a free and open source software which is quickly gaining popularity due to its data handling capacity

ᗍ R has many nice GUI’s. The most famous GUI is R-studio

ᗍ R comes with supportive help and good inbuilt graphic commands

ᗍ It has inbuilt data sets from real data

About R

Slide ‹#›© 2015 BlueCamphor Technologies (P) Ltd. www.skillspeed.comSource: http://www.r-project.org/about.html

Applications of R

R applications span the universe from theoretical computational statistics and the data sciences such as

ᗍ Astronomyᗍ Healthcareᗍ Financeᗍ Web applicationsᗍ Data science in HRᗍ Marketing Analysisᗍ Environmental studiesᗍ And much more


Who Uses R? – Companies


Predictive Analytics

Data(Sources, Types, Forms)

Capture Predict

• Data Mining• Text Mining• Statistical Analytics

Act

Act on the model


Predictive Analytics is the technology that learns from experience(data) to predict the future behaviour of individuals in order to drive better decisions

Predictive Analytics helps to connect data to effective action by drawing reliable conclusions about current conditions and future events

Enables businesses to use predictive models to exploit patterns found in historical data to identify potential risks and opportunities before they occur

What is Predictive Analytics all about?

Predictive analytics is really about solving problems with data


ᗍ Predictive analytics automatically synthesizes big data, mathematical sciences, business rules, and machine learning to make predictions and then suggests decision options to take advantage of a future opportunity

ᗍ The purpose of predictive analytics is to tell you what will happen in the future

ᗍ Predictive Analytics is branch of the Data Mining process

ᗍ An example of using predictive analytics is optimizing customer relationship management systems

Why Predictive Analytics?


Predictive Analytics – Process

Monitor Progress

Implement Results

Draw Conclusions

Run Analysis

Check the data fits the tool

Draw Hypothesis

Implement Results

Extract data needed


Three Pillars of Predictive Analytics

Predictive Operational Analyticsᗍ Plan ᗍ Manageᗍ Maximize

Predictive Threat and Fraud Analyticsᗍ Monitor ᗍ Detect ᗍ Control

Predictive Customer Analyticsᗍ Acquire ᗍ Grow ᗍ Retain


ᗍ Classificationᗍ Clusteringᗍ Associationᗍ Detectionᗍ Estimation and Time Seriesᗍ Link Analysisᗍ Web and Text Mining

Most Common Predictive Modelling Tasks


ᗍ Analytical customer relationship management (CRM)ᗍ Clinical decision support systemsᗍ Customer retentionᗍ Direct marketingᗍ Fraud detectionᗍ Risk managementᗍ Underwriting

Applications of Predictive Analytics


Job Trends – R


Course Topics

Module 1Introduction to Business

Analytics and R

Module 2Data Types and Functions

Module 4Data Import Techniques

Module 5Exploratory Data

Analysis

Module 6Data Visualization

Module 7Data Mining

Concepts in R - I

Module 8Data Mining

Concepts in R - II

Module 9Statistical

Techniques in R – I

Module 10Statistical

Techniques in R – II

Module 11Data Mining Concepts in

R

Module 12Project Discussion

Module 3Data Manipulation in R


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predictive analytics via r programming

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