<<< Assessing Customer Churn Using Machine Learning

Abstract:

The Indian telecommunications sector is undergoing rapid transformation, with heightened competition among providers such as Airtel, Reliance Jio, Vodafone, and BSNL. In this dynamic environment, customer churn—the discontinuation of a subscriber’s services—poses a significant challenge to business sustainability. Accurately predicting churn and understanding the factors that influence customer retention are critical for telecom companies seeking to improve service quality, enhance customer satisfaction, and maintain market share.

This study leverages two comprehensive datasets, telecom_demographics.csv and telecom_usage.csv, to analyze the interplay between customer demographics and service usage patterns in churn prediction. The demographic dataset includes attributes such as age, gender, state, city, salary, and registration history, while the usage dataset captures behavioral metrics including calls made, SMS sent, and data consumption. The target variable, churn, indicates whether a customer has discontinued services.

By applying machine learning techniques to these datasets, this project aims to develop a predictive model that not only identifies customers at high risk of churn but also highlights the demographic and behavioral drivers influencing their decisions. The outcomes can support telecom providers in designing targeted retention strategies, optimizing customer engagement, and sustaining long-term business growth in a highly competitive market.

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