Telecom-Churn-Prediction and telecom-customer-churn-prediction
These two projects are competitors, as both aim to provide a complete solution for telecom customer churn prediction using data analytics and machine learning.
About Telecom-Churn-Prediction
ChaitanyaC22/Telecom-Churn-Prediction
In this project, data analytics is used to analyze customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn, and identify the main indicators of churn. The project focuses on a four-month window, wherein the first two months are the ‘good’ phase, the third month is the ‘action’ phase, while the fourth month is the ‘churn’ phase. The business objective is to predict the churn in the last i.e. fourth month using the data from the first three months.
This project helps telecom companies predict which high-value prepaid customers are likely to switch providers. By analyzing past customer usage and recharge data, it identifies individuals at high risk of churn, providing insights that allow for targeted retention efforts. It's designed for business analysts and customer retention teams in the telecom sector to proactively address customer attrition.
About telecom-customer-churn-prediction
virajbhutada/telecom-customer-churn-prediction
Predict and prevent customer churn in the telecom industry with our advanced analytics and Machine Learning project. Uncover key factors driving churn and gain valuable insights into customer behavior with interactive Power BI visualizations. Empower your decision-making process with data-driven strategies and improve customer retention.
This project helps telecom companies predict which customers are likely to switch to another provider. It takes customer usage patterns, behavior, and other service-related data as input and provides a list of at-risk customers along with insights into why they might leave. This is ideal for customer retention managers, marketing strategists, and business analysts in the telecom sector.
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