CUSTOMER CHURN PREDICTION IN TELECOM USING AI-ML
Author(s):
T Prithvi Charan, Tanush T, Vinyas S R Gowda, Keerthi Mohan
Keywords:
Abstract
The telecom industry generates enormous amounts of data every day from a large number of clients. The phenomenon known as "churn" refers to customers switching companies within a predetermined amount of time. Getting new clients is more expensive than maintaining your current clientele. Managers and analysts in the telecom industry need to understand why customers terminate their subscriptions and what patterns of behavior they consistently see. This technology identifies the customers who are most likely to leave the telecom industry by compiling the factors that affect their decisions and applying classification algorithms to that data. In order to give customers retention strategies and plans, this system's primary goal is to compare various machine learning algorithms that may be used to create customer churn prediction models and identify the reasons behind churn. This system uses machine learning techniques like KNN and decision tree classifiers, along with classification algorithms like Random Forest (RF) to analyze customer turnover data. It provides a successful business model that can precisely forecast customer attrition and assist management in taking appropriate action during the attrition period to avoid attrition and lost revenue. In order to give customers retention strategies and plans, this system's primary goal is to compare various machine learning algorithms that may be used to create customer churn prediction models and identify the reasons behind churn.
Article Details
Unique Paper ID: 162117

Publication Volume & Issue: Volume 10, Issue 8

Page(s): 25 - 29
Article Preview & Download


Share This Article

Join our RMS



Conference Alert

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

Latest Publication

Call For Paper

Volume 11 Issue 1

Last Date for paper submitting for Latest Issue is 25 June 2024

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies

Important Links

Browse Rsearch papers