Customers Churn Prediction and Attribute Selection in Telecom Industry Using Kernelized Extreme Learning Machine and Bat Algorithms

With the fast development of digital systems and concomitant information technologies, there is certainly an incipient spirit in the extensive overall economy to put together digital Customer Relationship Management (CRM) systems. This slanting is further more palpable in the telecommunications industry, in which businesses turn out to be increasingly digitalized. Customer churn prediction is a foremost aspect of a contemporary telecom CRM system. Churn prediction model leads the customer relationship management to retain the customers who will be possible to give up. Currently scenario, a lot of outfit and monitored classifiers and data mining techniques are employed to model the churn prediction in telecom. Within this paper, Kernelized Extreme Learning Machine (KELM) algorithm is proposed to categorize customer churn patterns in telecom industry. The primary strategy of proposed work is organized the data from telecommunication mobile customers dataset. The data preparation is conducted by using pre-processing with Expectation Maximization (EM) clustering algorithm. After that, customer churn behavior is examined by using Naive Bayes Classifier (NBC) in accordance with the four conditions like customer dissatisfaction (H1), switching costs (H2), service usage (H3) and customer status (H4). The attributes originate from call details and customer profiles which is enhanced the precision of customer churn prediction in the telecom industry. The attributes are measured using BAT algorithm and KELM algorithm used for churn prediction. The experimental results prove that proposed model is better than AdaBoost and Hybrid Support Vector Machine (HSVM) models in terms of the performance of ROC, sensitivity, specificity, accuracy and processing time.