Customer churn, in general terms, refers to the rate at which customers or subscribers cease their relationship with a company or service over a particular period of time. It's a crucial metric for businesses across various industries, indicating the percentage of customers who have stopped using a company's products or services within a given time frame.

Churn can occur for various reasons, including dissatisfaction with the product or service, better offerings from competitors, changes in personal circumstances, or lack of engagement. Understanding and managing churn is vital for businesses as it directly impacts revenue, profitability, and long-term sustainability.

Customer churn, particularly in the context of a telecom company, refers to the phenomenon where subscribers or customers terminate their relationship with the company by discontinuing the use of its services. This could involve canceling a phone plan, switching to another provider, or entirely ceasing to use telecommunication services altogether.

This project focuses on developing predictive models for a telecom company to address customer churn prediction and segmentation based on revenue and usage patterns.

Utilizes historical customer data from a telecom dataset.

Predicts which customers are likely to churn.

Employs machine learning algorithms and evaluation metrics to assess model performance.

Uses customer data related to revenue, usage, and subscription plans.

Groups customers into segments based on similarities in revenue and usage patterns.

Assigns labels (e.g., high, medium, low profitability) to each segment for targeted marketing strategies.