Misclassified Reduced Instance and Stochastic Gradient Descent with Logistic Regression Model for Customer Churn Prediction
Isabella Amali1, R. Arunkumar2, R. Madhan Mohan3

1Isabella Amali*, Research Scholar, Department of Computer and Information science, Annamalai University.
2Dr. R. Arunkumar, Assistant Professor, Department of Computer Science and Engineering, Annamalai University.
3Dr. R. Madhan Mohan, Assistant Professor, Department of Computer Science and Engineering, Annamalai University.
Manuscript received on January 11, 2020. | Revised Manuscript received on January 22, 2020. | Manuscript published on February 10, 2020. | PP: 23752382 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1784029420/2020©BEIESP | DOI: 10.35940/ijitee.D1784.029420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Customer Churn Prediction (CCP) is a difficult problem found to be helpful to make decisions due to the rapid growth in the number of telecom providers. At present, deep learning models are familiar because of the significant improvement in different areas. In this paper, a deep learning based CCP is introduced by the use of Stochastic Gradient Boosting (SGD) with Logistic regression (LR) classifier model. By the integration of SGD and LR, effective classification can be accomplished. To further improve the classifier efficiency, misclassified instances are removed from the dataset. Then, the processed data is again provided as input to the classification model. The presented SGD-LR model is validated on a benchmark dataset and the results are examiner with respect to different measures. The experimental outcome pointed out the projected model is superior to available CCP models on the identical dataset. 
Keywords: CCP, Classifier, Machine learning, Deep learning
Scope of the Article: Machine learning