Predictive Modeling

GANPAT UNIVERSITY

FACULTY OF ENGINEERING AND  TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (BDA)

Semester

VI

Version

1.0.0.0

Effective from Academic Year

2021-22

Effective for the batch Admitted in

June 2019

Subject  code

2CSE608

Subject Name

PREDICTIVE MODELING

Teaching scheme

Examination scheme (Marks)

(Per week)

Lecture(DT)

Practical(Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit

3

0

2

0

5

Theory

40

60

100

Hours

3

0

4

0

7

Practical

60

40

100

Pre-requisites:

Data Structures, Mathematics , Probability and Statistics

Objectives of the Course:

Upon Completion of the course, the students will be able to

• Apply predictive modelling using various techniques using SPSS Modeler
• Design and analyze appropriate predictive models
• Build statistical models for analysis
• Demonstrate data mining process life cycle

Practicals are defined based on the following topics:

Unit

Content

Hrs.

1

Introduction to Predictive Analytics

Introduction to Predictive Analytics and its use cases. CRISP – DM methodology and the skills required for successfully implementing Predictive Analytics / Machine Learning Use Cases

4

2

Introduction to IBM SPSS Modeler

SPSS Modeler interface, and the terminologies such as streams,nodes, palettes

4

3

Collecting, Understanding and Analysis of Data

Data Understanding stage: Collecting Initial Data and Describing Data, exploring the data and assessing the quality of data.

6

4

Integrate Data

Integrating datasets which are typically stored in different tables / databases.

3

5

Identifying Relationships and Modeling

Modeling techniques and algorithms in Predictive Analytics

4

6

Using Functions in IBM SPSS Modeler

Inbuilt functions in IBM SPSS Modeler.

4

7

Field Transformations: Derive, Binning, Reclassify

Three nodes to cleanse and enrich data: Derive, Binning, Reclassify

4

8

Additional Field Transformations: Filler, Transform

additional nodes for Data Preparation: Filler and Transform

8

9

Sequence Data, Sampling, Balancing and Partitioning Data

concept of Sequence Data, and how it can be handled, Sampling, Balancing and Partitioning Data

8

Practical content

Practicals will be based on various algorithms implementation using SPSS

Text Books:

1.

Kattamuri S. Sarma, “Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications”, SAS Publishing.

Reference Books:

1

Alex Guazzelli, Wen-Ching Lin, Tridivesh Jena, James Taylor, “PMML in Action Unleashing the Power of Open Standards for Data Mining and Predictive Analytics, Create Space Independent Publishing Platform.

2

Ian H. Witten, EibeFrank , “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann.

3

Eric Siegel , “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”, Wiley.

4

Conrad Carlberg, “Predictive Analytics: Microsoft Excel”, Que Publishing.

Course Outcomes:

COs

Description

CO1

Apply predictive modelling using various techniques using SPSS Modeler

CO2

Design and analyze appropriate predictive models

CO3

Build statistical models for analysis

CO4

Demonstrate data mining process life cycle

Mapping of CO and PO: