Customer churn, the rate at which customers leave a service provider for a competitor, is a significant challenge for telecom companies. High churn rates lead to substantial revenue losses, increased customer acquisition costs, and reduced market share. This project aims to develop a predictive model that accurately identifies customers likely to churn, enabling telecom companies to implement proactive retention strategies, improve customer satisfaction, and enhance long-term loyalty.

The objective of this project is to develop a machine learning model that predicts customer churn and provides actionable insights to help telecom companies prioritize retention efforts and mitigate churn.

The dataset used in this project contains approximately 100,000 records and 100 variables, including customer demographics, usage patterns, service plans, billing information, and more. The target variable is churn, indicating whether a customer has left the service provider.

The model has been deployed using three different frameworks:

• Streamlit: Streamlit App

  

  

  

  

• Flask: Flask App

  

  

  

• Django: Django App