Customer churn analysis and prediction in the telecom sector is an issue nowadays because it’s very important for telecommunication industries to analyze behavior’s of various customers to predict which customers are about to leave the subscription from telecom companies. So machine learning techniques and algorithms play an important role for companies in today’s commercial conditions because gaining a new customer’s cost is more than retaining the existing ones. This project focuses on various machine learning techniques for predicting customer churn through which we can build the classification models such as Logistic Regression, Random Forest and lazy learning and also compare the performance of these models.

• Customer Churn
• Logistic Regression
• Random Forest