21AI743 Predictive Analytics
Course Learning Objectives
CLO 1. Comprehend the fundamental principles of analytics for business
CLO 2. Explore various techniques for predictive modelling
CLO 3. Analyse the data transformation of different predictors
CLO 4. Examine how predictive analytics can be used in decision making
CLO 5. Apply predictive models to generate predictions for new data
SYLLABUS COPY
MODULE - 1
Introduction to Predictive analytics – Business analytics: types, applications, Analytical Techniques, Tools
Predictive Modelling: Propensity Models, Cluster Models,Applications.
MODULE - 2
Modelling Techniques: Statistical Modelling, Machine Learning, Empirical Bayes Method,Point Estimation.
MODULE - 3
Data Pre-processing: Data Transformations for Individual Predictors, Data Transformation for Multiple
Predictors, Dealing with Missing Values, Removing Predictors,Adding Predictors, Binning Predictors.
Over-Fitting and Model Tuning.
MODULE - 4
Regression Models: Measuring Performance in Regression Models – Linear Regression and Its Cousins –
Non-Linear Regression Models – Regression Trees and Rule-Based Models Case Study: Compressive Strength
of Concrete Mixtures.
MODULE - 5
Classification Models: Measuring Performance in Classification Models – Discriminant Analysis and Other
Linear Classification Models – Non-Linear Classification Models – Classification Trees and Rule-Based Models
– Model Evaluation Techniques.
Course outcome
At the end of the course the student will be able to:
CO 1. Understand the importance of predictive analytics, able to prepare and process data for the models
CO 2. Apply the statistical techniques for predictive models
CO 3. Comprehend the transformation of data in the predictors.
CO 4. Apply regression and classification models for decision making and evaluate the performance
CO 5. Apply and build the time series forecasting models in a variety of business contexts
Suggested Learning Resources
Text Books
1. Jeffrey S. Strickland, Predictive Analytics using R,2014
2. Max Kuhn and Kjell Johnson, Applied Predictive Modeling, 1st edition Springer, 2013.
Reference
1. Dean Abbott, Applied Predictive Analytics: Principles and Techniques for the Professional Data
Analyst, 1st Edition Wiley, 2014