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Will the Customer Survive or Not in the Organization?: A Perspective of Churn Prediction Using Supervised Learning

Will the Customer Survive or Not in the Organization?: A Perspective of Churn Prediction Using Supervised Learning

Neelamadhab Padhy, Sanskruti Panda, Jigyashu Suraj
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 20
ISSN: 1942-3926|EISSN: 1942-3934|EISBN13: 9781683180975|DOI: 10.4018/IJOSSP.300753
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MLA

Padhy, Neelamadhab, et al. "Will the Customer Survive or Not in the Organization?: A Perspective of Churn Prediction Using Supervised Learning." IJOSSP vol.13, no.1 2022: pp.1-20. http://doi.org/10.4018/IJOSSP.300753

APA

Padhy, N., Panda, S., & Suraj, J. (2022). Will the Customer Survive or Not in the Organization?: A Perspective of Churn Prediction Using Supervised Learning. International Journal of Open Source Software and Processes (IJOSSP), 13(1), 1-20. http://doi.org/10.4018/IJOSSP.300753

Chicago

Padhy, Neelamadhab, Sanskruti Panda, and Jigyashu Suraj. "Will the Customer Survive or Not in the Organization?: A Perspective of Churn Prediction Using Supervised Learning," International Journal of Open Source Software and Processes (IJOSSP) 13, no.1: 1-20. http://doi.org/10.4018/IJOSSP.300753

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Abstract

Context: The technology of machine learning and data science is gradually evolving and improving. In this process, we feel the importance of data science to solve a problem. Objective: In this article our main objective is to predict the customer churn, i.e. whether the customer will leave the telecom service or they will continue with the service. In this paper, we have also followed some statistical measures like we have computed the mean, standard deviation, min, max, 25%, 50%, 75% values of the data. Mean is the average value of the data values. The standard deviation is a measure of the amount of variation or dispersion of a set of values. Conclusion: We have done an extensive data pre-processing and built Machine Learning models, and found out that among all the models Logistic regression gives the best performance i.e 81.5%., and hence we chose that as our final model to indicates the churn prediction

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