Modeling Data Mining Applications for Prediction of Prepaid Churn in Telecommunication Services

U radu je definirana unaprijeđena metodologija za proces modeliranja aplikacija zasnovanih na metodama dubinske analiza podataka (Data Mining), koja predstavlja logički okvir (framework) za razvoj različitih Data Mining aplikacija. Predložena metodologija je primjenjena na primjeru Data Mining aplikacije za detekciju churna Prepaid korisnika telekomunikacijskih usluga. Glavno težište ovog rada je na definiranju uspješnog modela za predviđanje potencijalnih Prepaid churn-era. Najvažniji dio izgradnje modela je definiranje onog skupa ulaznih varijabli koje su u toj mjeri snažne da model predviđanja bude točan i pouzdan. Kroz navedeni primjer je kreirano i uspoređeno više modela zasnovanih na različitim Data Mining metodama i algoritmima (neuronske mreže, stabla odlučivanja, logistička regresija). Za primjere modeliranja korišten je analitički alat WEKA.This paper defines an advanced methodology for modeling applications based on Data Mining methods that represents a logical framework for development of Data Mining applications. Methodology suggested here for Data Mining modeling process has been applied and tested through Data Mining applications for predicting Prepaid users churn in the telecom industry. The main emphasis of this paper is defining of a successful model for prediction of potential Prepaid churners, in which the most important part is to identify the very set of input variables that are high enough to make the prediction model precise and reliable. Several models have been created and compared on the basis of different Data Mining methods and algorithms (neural networks, decision trees, logistic regression). For the modeling examples we used WEKA analysis tool