Telecom-Customer-Churn-prediction and Telecom-Churn-Prediction
Both projects are independent implementations of the same machine learning approach (classification algorithms applied to telecom customer churn datasets), making them competitors that solve the identical problem through similar methodologies rather than complementary or dependent tools.
About Telecom-Customer-Churn-prediction
Pradnya1208/Telecom-Customer-Churn-prediction
Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.
This project helps telecom companies predict which customers are likely to switch providers so they can proactively offer retention incentives. By analyzing customer data such as services used, contract details, payment methods, and demographics, it identifies 'high-risk' clients. This allows customer retention teams to focus their efforts on those most likely to churn.
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work