Efficient Extraction of Actionable Data using Decision Tree

Abstract

Extracting Actionable Knowledge from decision tree is aimed towards providing a novel algorithm which can predict churn in telecommunication industry which helps in maximizing the expected net profit. The approach we take, integrates a mathematical model which predicts the profitable customers using the transaction data and a novel data mining algorithm which would predict whether a profitable customer is going to churn and taking necessary actions directly on top of data mining results in post processing step where, there by suggesting the special promotional offers and discounts only to profitable customers to maximize the expected net profit.
DOI: 10.17762/ijritcc2321-8169.150772