BD4091 Predictive Modelling Syllabus:
BD4091 Predictive Modelling Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To understand the terms and terminologies of predictive modeling.
To study the various predictive models, their merits, demerits and application.
To get exposure to various analytical tools available for predictive modeling.
To learn the predictive modeling markup language.
To get familiar with the technologies in predictive modeling.
UNIT I INTRODUCTION TO PREDICTIVE MODELING
Core ideas in data mining – Supervised and unsupervised learning – Classification vs. Prediction – Steps in data mining- SEMMA Approach – Sampling -Pre-processing – Data cleaning – Data Partitioning – Building a model – Statistical models – Statistical models for predictive analytics.
UNIT II PREDICTIVE MODELING BASICS
Data splitting – Balancing- Over fitting –Oversampling –Multiple Regression Artificial neural networks (MLP) – Variable importance- Profit/loss/prior probabilities – Model specification – Model selection – Multivariate Analysis.
UNIT III PREDICTIVE MODELS
Association Rules-Clustering Models –Decision Trees- Ruleset Models- KNearest Neighbors – Naive Bayes – Neural Network Model – Regression Models – Regression Trees – Classification & Regression Trees (CART) – Logistic Regression – Multiple Linear Regression Scorecards – Support Vector Machines – Time Series Models – Comparison between models – Lift chart Assessment of a single model.
UNIT IV PREDICTIVE MODELING MARKUP LANGUAGE
Introduction to PMML – PMML Converter – PMML Structure – Data Manipulation in PMML – PMML Modeling Techniques – Multiple Model Support – Model Verification.
UNIT V TECHNOLOGIES AND CASE STUDIES
Weka – RapidMiner – IBM SPSS Statistics- IBM SPSS Modeler – SAS Enterprise Miner – Apache Mahout – R Programming Language.-Real time case study with modeling and analysis.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon completion of the course, the student should be able to:
CO1: Design and analyze appropriate predictive models.
CO2: Define the predictive models using PMML.
CO3: Apply statistical tools for analysis.
CO4: Use various analytical tools available for predictive modeling.
CO5: Apply predictive modeling markup language in data manipulation .
REFERENCES:
1. Kattamuri S. Sarma, “Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications”, 3rd Edition, SAS Publishing, 2017.
2. Alex Guazzelli, Wen-Ching Lin, Tridivesh Jena, James Taylor, “PMML in Action Unleashing the Power of Open Standards for Data Mining and Predictive Analytics”, 2nd Edition, Create Space Independent Publishing Platform,2012.
3. Ian H. Witten, Eibe Frank , “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann Series in Data Management Systems, Morgan Kaufmann, 3rd Edition, 2011.
4. Eric Siegel , “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”, 2nd Edition, Wiley, 2016.
5. Conrad Carlberg, “Predictive Analytics: Microsoft Excel”, 1st Edition, Que Publishing, 2012.
6. Jeremy Howard, Margit Zwemer, Mike Loukides, “Designing Great Data Products- Inside the Drivetrain train Approach, a Four-Step Process for Building Data Products – Ebook”, 1st Edition, O’Reilly Media, March 2012.
WEB REFERENCES:
1. https://nptel.ac.in/courses/108108111/
2. https://www.coursera.org/learn/predictive-modeling-analytics
ONLINE RESOURCES:
1. https://bookdown.org/egarpor/PM-UC3M/
2. https://cics.nd.edu/research/applications/materials/