FULLY SOLVED BOOK LASY 5 YEARS PAPERS SOLVED PLUS GUESS

Predictive Business Analytics


UNIT-I
Introduction to Predictive Analytics: overview, business intelligence, predictive analytics in relation to business
intelligence, statistics, data mining; Big data, importance in decision making; Setting up problem-CRISP-DM,
business understanding, Defining data, target variable and measures of success for predictive modelling;
Methodology of predictive modelling.
UNIT-II
Prediction Methods: Linear Regression- best subset selection, forward selection, backward selection, step-wise
regression, Cp mallows and adjusted R-square criteria; k-Nearest Neighbours (k-NN); Regression TreesCART,CHAID; Neural Nets- architecture of neural nets, neurons, input layer, hidden layers, output layer.
UNIT-III
Classification Methods: the naïve rule, Naïve-Bayes classifier, K-Nearest neighbours, Classification Trees, Neural
Nets, Logistic Regression.
UNIT-IV
Non-supervised Learning: Association Rules- support and confidence, the apriori algorithm, the selection of strong
rules; Cluster Analysis- hierarchical methods, optimization and the k-means algorithm, similarity measures, other
distance measures. Ensemble Methods: Nelson and Granger-Ramanathan methods for continuous targets,
Majority voting for categorical targets, Bagging, Boosting.