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  Published Paper Details:

  Paper Title

Dynamic Customer Churn Prediction Using Machine Learning Algorithm

  Authors

  Asif Khan,  Anas Khan

  Keywords

Regression, Decision Trees, K-Nearest Neighbors and Support Vector Machine algorithms

  Abstract


clients are the most vital belongings in any enterprise considering the fact that they're considered as the principle profit supply. organizations are operating difficult to live to tell the tale in nowadays's competitive marketplace relying on a couple of techniques. 3 fundamental strategies have been proposed to generate more sales: (1) acquiring new customers, (2) upselling the present customers, and (3) increasing the retention period of customers. however, assessment of these techniques has proven that keeping an current purchaser fees a good deal decrease than obtaining a brand new one, further to being taken into consideration a good deal less difficult than the upselling method. to use the 1/3 approach, groups must decrease the capacity of customer's churn. customer churn is a time period that refers back to the lack of a purchaser or consumer--that is, when a purchaser ceases to have interaction with a enterprise or commercial enterprise. further, the churn price is the rate at which clients or customers are leaving a enterprise inside a particular period of time. consumer churn is one of the maximum crucial worries for massive agencies. due to the direct impact on the revenues of the organizations, mainly within the telecom area, companies are in search of to expand approach to are expecting ability customers that would churn. therefore, locating elements that growth purchaser churn is important to take important actions to reduce this churn.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT24A4673

  Paper ID - 257444

  Page Number(s) - o511-o521

  Pubished in - Volume 12 | Issue 4 | April 2024

  DOI (Digital Object Identifier) -    http://doi.one/10.1729/Journal.39103

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Asif Khan,  Anas Khan,   "Dynamic Customer Churn Prediction Using Machine Learning Algorithm", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.12, Issue 4, pp.o511-o521, April 2024, Available at :http://www.ijcrt.org/papers/IJCRT24A4673.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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