Telecommunication Services Churn Prediction - Deep Learning Approach

Churn is a phenomenon that concerns the majority of companies, especially in the telecommunication industry. This paper describes experiment on data provided by the telecommunications company - Orange, for predicting churn. The preprocessing phase of the experiment included removal of missing values and redundant data, Lasso and manual feature engineering. Convolutional Neural Network was applied as classifier on preprocessed one-dimensional dataset with accuracy of 98.85%. Proposed model can be applicable in telecommunication systems for detection.

[1]  Ramakanta Mohanty,et al.  Application of Computational Intelligence to Predict Churn and Non-Churn of Customers in Indian Telecommunication , 2015, 2015 International Conference on Computational Intelligence and Communication Networks (CICN).

[2]  Vladimir S. Crnojevic,et al.  Predicting the churn of telecommunication service users using open source data mining tools , 2011, 2011 10th International Conference on Telecommunication in Modern Satellite Cable and Broadcasting Services (TELSIKS).

[3]  Rinkle Rani,et al.  Churn prediction in telecommunication using machine learning , 2017, 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS).

[4]  Hossam Faris,et al.  Predicting Customer Churn in Telecom Industry using Multilayer Preceptron Neural Networks : Modeling and Analysis , 2014 .

[5]  Chun Gui,et al.  Analysis of imbalanced data set problem: The case of churn prediction for telecommunication , 2017, Artif. Intell. Res..

[6]  Mokhairi Makhtar,et al.  A Multi-Layer Perceptron Approach for Customer Churn Prediction , 2015, International Conference on Multimedia and Ubiquitous Engineering.

[7]  Ionut Brandusoiu,et al.  CHURN PREDICTION IN THE TELECOMMUNICATIONS SECTOR USING NEURAL NETWORKS , 2016 .

[8]  Artit Wangperawong,et al.  Churn analysis using deep convolutional neural networks and autoencoders , 2016, ArXiv.

[9]  Konstantinos I. Diamantaras,et al.  A comparison of machine learning techniques for customer churn prediction , 2015, Simul. Model. Pract. Theory.

[10]  F. Keynia,et al.  Designing of customer and employee churn prediction model based on data mining method and neural predictor , 2017, 2017 2nd International Conference on Computer and Communication Systems (ICCCS).