This project focuses on a telecom company’s problem: predicting when customers will leave (churn) and understanding their behavior. Using techniques like classification, regression, and clustering, we analyze customer data to find insights. The aim is to understand customer traits and create actions based on data to improve business results.
The project aims to solve three key business problems for the telecom company:
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Churn Prediction: Identifying customers likely to churn allows the company to implement targeted retention strategies.
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Billing Estimation: Predicting MonthlyCharges helps in forecasting future revenue.
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Customer Segmentation: Grouping customers by behavior enables the creation of tailored marketing plans and offers for different customer segments.
By leveraging these predictive models, the telecom company can gain deeper insights into customer behavior, work towards reducing churn, and make data-informed decisions for sustainable revenue planning.