Predicting Consumer Behaviour via Hadoop

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Predicting Consumer
Behaviour via Hadoop

Session Objectives
In this session you will understand
ᗍ Big Data and Hadoop
ᗍ HDFS
ᗍ MapReduce with examples and Scenarios
ᗍ Predictive Analytics and its process
ᗍ Three Pillars of Predictive Analytics
ᗍ Applications of Predictive Analytics

Big Data and its Challenges
Big data is the term for a collection of data sets so
large and complex that it becomes difficult to
process using on-hand database management tools
or traditional data processing applications
Systems / Enterprises generate huge amount of data
from Terabytes to and even Petabytes of information
It’s very difficult to manage such huge data……

Who Generates Big Data?
Have you ever wondered how Google, Facebook or LinkedIn manages to store and utilize the huge data?
Today, it is becoming a problem for all of us to manage such BIG DATA….

Hadoop and its Characteristics
Apache Hadoop is a framework that allows the distributed processing of large data sets across clusters of
commodity computers using a simple programming model
It is an Open-source Data Management technology with scale-out storage and distributed processing
Hadoop
Characteristics
Flexible
Reliable
Economical
Scalable

Hadoop Ecosystem
Flume Sqoop
Import Or Export
Unstructured or
Semi-Structured data Structured Data
Apache Oozie (Workflow)
HDFS
(Hadoop Distributed File System)
Pig Latin
Data Analysis
Hive
DW System
MapReduce Framework HBase
Other
YARN
Frameworks (MPI,
GIRAPH)
YARN
Cluster Resource Management

Data(Sources, Types,
Forms)
Capture Predict
• Data Mining
• Text Mining
• Statistical Analytics
Act
Act on the model
Predictive Analysis

Why Predictive Analytics?
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

Monitor Progress
Implement
Results
Draw Conclusions
Run Analysis
Check the data fits
the tool
Draw Hypothesis
Implement
Results
Extract data
needed
Predictive Analytics – Process

Three Pillars of Predictive Analytics
Predictive Operational Analytics
ᗍ Plan
ᗍ Manage
ᗍ Maximize
Predictive Threat and Fraud
Analytics
ᗍ Monitor
ᗍ Detect
ᗍ Control
Predictive Customer Analytics
ᗍ Acquire
ᗍ Grow
ᗍ Retain

Most Common Predictive Modelling Tasks
ᗍ Classification
ᗍ Clustering
ᗍ Association
ᗍ Detection
ᗍ Estimation and Time Series
ᗍ Link Analysis
ᗍ Web and Text Mining

Applications of Predictive Analytics
ᗍ Analytical customer relationship management (CRM)
ᗍ Clinical decision support systems
ᗍ Customer retention
ᗍ Direct marketing
ᗍ Fraud detection
ᗍ Risk management
ᗍ Underwriting

What is Predictive Analytics all about?
Predictive analytics is really about solving problems with data
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

Map Reduce – Scenario
Let us consider a real life scenario to understand the importance of “Map Reduce” in Hadoop
Suppose, you are the handling
a project which has x tasks and
takes 100 hours for one
resource to complete
1 x 100 = 100 hours
100/10(resources) = 10 hours

Similarly,
= 100 hours 100/10 = 10 hours
Map Reduce – Scenario

More Scenarios on Map-Reduce
Problem Statement:
Find maximum stock market levels recorded in a span of 5 years
Problem Statement:
De-identify personal identifier information

Traditional Solution
matchesSplit Data
Very
Big
Data
All
matches
grep
grep
grep
cat
grep
:
matches
matches
matches
Split Data
Split Data
Split Data

MapReduce Solution
Very
Big
Input
Split Data
All
matches
:
Split Data
Split Data
Split Data
M
A
P
R
E
D
U
C
E
MapReduce Framework

MapReduce Advantages
Two biggest advantages:
ᗍ Takes processing to the data
ᗍ Allows processing data in parallel
a b
c
Map Task
HDFS Block
Data Center
Rack
Node

MapReduce Flow
1. Input data is present in data nodes
2. Map tasks = Input Splits
3. Mappers produce intermediate data
4. Data exchanged among nodes in “shuffling”
5. All data of same key goes to same reducer
6. Reducer output stored at output location
Node 1
INPUT DATA
Map
Node 2
Map
Node 1
Reduce
Node 1
Reduce

Job Trends – Hadoop

Course Topics
Module 1
Introduction to Big
Data and Hadoop
Module 2
HDFS Internals, Hadoop
Configurations and Data
Loading
Module 3
Introduction to Map
Reduce
Module 4
Advanced Map Reduce
Concepts
Module 5
Introduction to Pig
Module 6
Advanced Pig and
Introduction to Hive
Module 7
Advanced Hive Concepts
Module 8
Extending Hive and HBase
Introduction
Module 9
Advanced HBase and
Oozie Introduction
Module 10
Project Set-up Discussion

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Image References
Images Credits: Google, Facebook and LinkedIn LOGO and Snapshots
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https://accounts.it.et.byu.edu/
http://www.clipartsfree.net/tag/server.html
http://www.gopixpic.com/16/time-clock-icon-png-download
http://blog.smartbear.com/requirements/how-to-interview-users-to-find-out-what-they-really-want/
http://www.lincs.fr/research/areas/big-data/
http://www.counsellingpages.co.uk/
http://langfordsconsultancy.com/langfords-training-support-package/
http://cbsepathshala.blogspot.in/2012/05/physics-class-x-chapter-electricity.html
http://mmatycoon.com/tycoontimes/tycoontimesstory.php?SID=1010

Predicting Consumer Behaviour via Hadoop

Predicting Consumer Behaviour via Hadoop

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