CSEIT172155 | Received: 22 Feb 2017 | Accepted: 28 Feb 2017 | January-February-2017 [(2)1: 225-230 ] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2017 IJSRCSEIT | Volume 2 | Issue 1 | ISSN : 2456-3307 225 An Optimal Churn Prediction Model using Support Vector Machine with Adaboost A. Saran Kumar, Dr. D. Chandrakala Kumaraguru College of Technology, Coimbatore, Tamil Nadu, India ABSTRACT Customer churn is a common measure of lost customers. By minimizing churn, a company can maximize its profits. Companies have recognized that existing customers are most valuable assets. Customer retention is important for a good marketing and a customer relationship management strategy. In this paper, a detailed scheme is worked out to convert raw customer data into meaningful and useful data that suits modelling buying behaviour and in turn to convert this meaningful data into knowledge for which predictive data mining techniques are adopted. In this work, a boosted version of SVM which is a combination of SVM with Adaboost is used for increasing the accuracy of generated rules. Boosted versions have high accuracy and performance than non-boosted versions. The aim of churn prediction model is to detect the customers with high tendency to leave the firm and also increase the revenue for the firm. Keywords : Churn, Adaboost, SVM, Classification and Prediction. I. INTRODUCTION Data classification is the process of sorting and categorizing data into various types or any other distinct class. Data classification enables the separation and classification of data according to data set requirements for various business objectives [1]. It is mainly a data management process. Classification in data mining consists of predicting a particular outcome based on the given input [2]. In order to predict the outcome, the algorithm processes a training set containing a set of attributes and the respective outcome, usually called goal or prediction attribute [3]. The algorithm tries to find relationships between the attributes that would make it possible to predict the outcome. Next the algorithm is given a data set not seen before, which is called as prediction set, that contains the same set of attributes, except for the prediction attribute – not yet known. The algorithm analyses the input and produces a prediction. The prediction accuracy defines how “good” the algorithm is [4]. Classification models predict categorical class labels; and prediction models predict continuous valued functions [5]. For example, we can build a model to classify bank loan applications as either safe or risky, or a prediction model to predict the expenditures in dollars of potential customers on computer equipment given their income and occupation. Data mining tools predict patterns and behaviors, allowing businesses to effect knowledge-driven decisions [6]. The automated, prospective analyses offered by data mining tools move beyond the analysis of past events provided by retrospective tools typical of decision support system. Customer churn is a marketing-related term which means customers defect to another supplier or purchase less [7]. As existing customers are an important source of business profits, being able to identify customers who show signs that they are about to leave can create more income for businesses. This is especially more important for online customers, as the phenomenon of customer churn appears to be very rapid and difficult to grasp. If companies do not take measures to hold customers before their status deteriorates, the customers may never come back, resulting in wasted investment and loss of income. A timely retention strategy can keep customers, and it is the best way to retain customers [8]. II. METHODS AND MATERIAL A. Related Work In [9] authors study, a framework with ensemble techniques is presented for customer churn prediction