Implementation of Machine Learning Algorithms for Customer Churn Prediction
محورهای موضوعی : Machine learning
Manal Loukili
1
,
Fayçal Messaoudi
2
,
Raouya El Youbi
3
1 - National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
2 - National School of Business and Management, Sidi Mohamed Ben Abdellah University, Fez, Morocco
3 - National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez, Morocco
کلید واژه: Machine Learning, Churn Prediction, Consumer Behavior, Bagging SVM, k-NN, Random Forest,
چکیده مقاله :
Churn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, we propose the following process which consists of six stages. The first phase consists of data pre-processing, followed by feature analysis. In the third phase, the selection of features. Then the data was divided into two parts: the training set and the test set. In the prediction process, the most popular predictive models were adopted, namely random forest, k-nearest neighbor, and support vector machine. In addition, we used cross-validation on the training set for hyperparameter tuning and to avoid model overfitting. Then, the results obtained on the test set were evaluated using the confusion matrix and the AUC curve. Finally, we found that the models used gave high accuracy values (over 79%). The highest AUC score, 84%, is achieved by the SVM and bagging classifiers as an ensemble method which surpasses them.
Churn prediction is one of the most critical issues in the telecommunications industry. The possibilities of predicting churn have increased considerably due to the remarkable progress made in the field of machine learning and artificial intelligence. In this context, we propose the following process which consists of six stages. The first phase consists of data pre-processing, followed by feature analysis. In the third phase, the selection of features. Then the data was divided into two parts: the training set and the test set. In the prediction process, the most popular predictive models were adopted, namely random forest, k-nearest neighbor, and support vector machine. In addition, we used cross-validation on the training set for hyperparameter tuning and to avoid model overfitting. Then, the results obtained on the test set were evaluated using the confusion matrix and the AUC curve. Finally, we found that the models used gave high accuracy values (over 79%). The highest AUC score, 84%, is achieved by the SVM and bagging classifiers as an ensemble method which surpasses them.
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