Customer Churn Prediction and Upselling using MRF (Modified Random Forest) technique
Swetha P1, Dayananda R B2
1Swetha P, Asst. Professor, Department of CSE, Rajarajeswari College of Engineering Bangalore.
2Dayananda R B*, Professor, Dept. of CSE, K.S Institute of Technology, Bangalore,
Manuscript received on December 16, 2019. | Revised Manuscript received on December 25, 2019. | Manuscript published on January 10, 2020. | PP: 475-481 | Volume-9 Issue-3, January 2020. | Retrieval Number: C8392019320/2020©BEIESP | DOI: 10.35940/ijitee.C8392.019320
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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 has become one of the eminent topic in the telecom industry, it has gained a lot of attention in the research industry due to fierce competition from the various, and hence companies have focused on the larger size of the data for churning and upselling prediction. The model of customer churn prediction detects and identify the customer who are willing to terminate the subscription, customer churn prediction and upselling can be done through the data mining process. Hence, In this paper we have introduce a model Named MRF(Modified Random Forest), this model helps in enhancing the accuracy and also helps in ignoring the regression issue. Our methodology has been performed on the provided orange Datasets. For the evaluation of our algorithm comparative analysis between the existing and proposed methodology is done considering the two scenario i.e. churn and upselling. Later our model is compared with the various existing churn prediction model, the result of the analysis indicates that our model outperforms the existing method including the standard random forest in terms of AUC and classification accuracy.
Keywords: Churn, Prediction, Upselling, MRF, Customer Churn.
Scope of the Article: Forest Genomics and Informatics