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Title: :  Dynamic Churn Prediction System using Machine Learning Algorithms
PaperId: :  18727
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 8 Issue 6 2022
DUI:    16.0415/IJARIIE-18727
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Prof. S. N. BhadanePune Vidyarthi Griha's College of Engineering, Nashik, MH, India
Gauri RandhirPune Vidyarthi Griha's College of Engineering, Nashik, MH, India
Mamta BoradePune Vidyarthi Griha's College of Engineering, Nashik, MH, India
Sahil BhatiaPune Vidyarthi Griha's College of Engineering, Nashik, MH, India
Gaurav MorePune Vidyarthi Griha's College of Engineering

Abstract

Computer Engineering
Customer churn prediction, Churn in telecom, Machine learning, Feature selection, Classification.
Customer churn occurs when customers or subscribers stop doing business with a company or service. Customers in the telecom business have the option of selecting from several telecom operator and actively switching from one to the next. In this extremely competitive sector, the telecoms industry has an annual turnover rate of 15-30 percent. Individualized customer retention is difficult since most businesses have a huge number of customers and cannot afford to devote a significant amount of time to each of them. The greater revenue would be outweighed by the costs. However, if a company can predict which customers are likely to depart ahead of time, it can target customer retention efforts solely on these "high-risk" consumers. The goal is to broaden its coverage area and re-establish consumer loyalty. The client is at the heart of success in this market. Customer churn is an important indicator since retaining existing customers is substantially less expensive than acquiring new customers. To discover early warning indications of probable churn, one must first establish a comprehensive perspective of the consumers and their interactions across several channels. As a result, by managing churn, these companies may be able to not only maintain their market position but also grow and thrive. The greater the number of consumers in their network, the lower the cost of initiation and the greater the profit. As a result, lowering client attrition and creating an effective retention plan is the company's primary priority for success.

Citations

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IJARIIE Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More. "Dynamic Churn Prediction System using Machine Learning Algorithms" International Journal Of Advance Research And Innovative Ideas In Education Volume 8 Issue 6 2022 Page 1144-1147
MLA Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More. "Dynamic Churn Prediction System using Machine Learning Algorithms." International Journal Of Advance Research And Innovative Ideas In Education 8.6(2022) : 1144-1147.
APA Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, & Gaurav More. (2022). Dynamic Churn Prediction System using Machine Learning Algorithms. International Journal Of Advance Research And Innovative Ideas In Education, 8(6), 1144-1147.
Chicago Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More. "Dynamic Churn Prediction System using Machine Learning Algorithms." International Journal Of Advance Research And Innovative Ideas In Education 8, no. 6 (2022) : 1144-1147.
Oxford Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More. 'Dynamic Churn Prediction System using Machine Learning Algorithms', International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 6, 2022, p. 1144-1147. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Dynamic_Churn_Prediction_System_using_Machine_Learning_Algorithms_ijariie18727.pdf (Accessed : ).
Harvard Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More. (2022) 'Dynamic Churn Prediction System using Machine Learning Algorithms', International Journal Of Advance Research And Innovative Ideas In Education, 8(6), pp. 1144-1147IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Dynamic_Churn_Prediction_System_using_Machine_Learning_Algorithms_ijariie18727.pdf (Accessed : )
IEEE Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More, "Dynamic Churn Prediction System using Machine Learning Algorithms," International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 6, pp. 1144-1147, Nov-Dec 2022. [Online]. Available: https://ijariie.com/AdminUploadPdf/Dynamic_Churn_Prediction_System_using_Machine_Learning_Algorithms_ijariie18727.pdf [Accessed : ].
Turabian Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More. "Dynamic Churn Prediction System using Machine Learning Algorithms." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 8 number 6 ().
Vancouver Prof. S. N. Bhadane, Gauri Randhir, Mamta Borade, Sahil Bhatia, and Gaurav More. Dynamic Churn Prediction System using Machine Learning Algorithms. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2022 [Cited : ]; 8(6) : 1144-1147. Available from: https://ijariie.com/AdminUploadPdf/Dynamic_Churn_Prediction_System_using_Machine_Learning_Algorithms_ijariie18727.pdf
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