Customer churn is a major challenge for telecom companies. Retaining existing customers is more cost-effective than acquiring new ones. In this project, I built a Machine Learning model to predict whether a telecom customer will churn or not based on their service usage, contract details, and billing information. The goal is to help companies identify customers who are likely to leave and take preventive actions.

This project focuses on Exploratory Data Analysis (EDA) of the Sample Superstore dataset to identify profitability patterns, loss-making products, and regional performance. The goal is to support data-driven business decisions using visualization and statistical insights.