Telecom-Customer-Churn-prediction and Telecom-Customer-Churn-Prediction-using-Machine-Learning
These are ecosystem siblings—both are standalone machine learning projects addressing the same telecom churn prediction problem using similar classification approaches, likely built on common libraries (scikit-learn, pandas) rather than one depending on or complementing the other.
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-Customer-Churn-Prediction-using-Machine-Learning
mdzaheerjk/Telecom-Customer-Churn-Prediction-using-Machine-Learning
This project focuses on developing a machine learning system to predict customer churn in the telecommunications industry. It covers the entire data science lifecycle, from exploratory data analysis to model deployment, enabling proactive intervention and customer retention
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