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    Churn prediction with ensemble classifiers for telecom sectors
    (İstanbul Ticaret Üniversitesi, 2021) Mohamed, Faiza Hassan; Kasapbaşı, Mustafa Cem
    Churn Prediction has been implemented in the researches and published works using different advanced mechanisims including Machine Learning, Data Mining, and Hybrid mechanism. These mechanisms support big companies and small businesses to classify and predict churning customers to be able retaining them to stay with their company using their services. Also, helps top managers and decision makers to take reliable decisions and Customer Relation Management CRM department too. In this study, a telecom sector churn dataset named Orange is used for customer churn prediction. Ensemble classifiers are used AdaBoostM1, PCA, Gain Ratio, Info Gain, Bagging in combination with J4.8, Naïve Bayes, Logistic Regression, Random Forest, KNN, LMT (Logistic model Tree). Highest accuracy of %94 is obtained by combination of bagging and J4.8. The results are compared with other studies as well and this study performed as good as the surveyed literature and surpassed in same cases.