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Customer churn is a major problem and one of the most important concerns forlarge companies. Due to the direct effect on the revenues of the companies,especially in the telecom field, companies are seeking to develop means topredict potential customer to churn. Therefore, finding factors that increasecustomer churn is important to take necessary actions to reduce this churn. Themain contribution of our work is to develop a churn prediction model whichassists telecom operators to predict customers who are most likely subject tochurn. The model developed in this work uses machine learning techniques on bigdata platform and builds a new way of features' engineering and selection. Inorder to measure the performance of the model, the Area Under Curve (AUC)standard measure is adopted, and the AUC value obtained is 93.3%. Another maincontribution is to use customer social network in the prediction model byextracting Social Network Analysis (SNA) features. The use of SNA enhanced theperformance of the model from 84 to 93.3% against AUC standard. The model wasprepared and tested through Spark environment by working on a large datasetcreated by transforming big raw data provided by SyriaTel telecom company. Thedataset contained all customers' information over 9 months, and was used totrain, test, and evaluate the system at SyriaTel. The model experimented fouralgorithms: Decision Tree, Random Forest, Gradient Boosted Machine Tree "GBM"and Extreme Gradient Boosting "XGBOOST". However, the best results wereobtained by applying XGBOOST algorithm. This algorithm was used forclassification in this churn predictive model.

Customer churn prediction in telecom using machine learning and social network analysis in big data platform