A Python-based data analysis project focused on identifying customer churn patterns and understanding the factors associated with customer attrition.

Key areas covered: • Data cleaning and preprocessing using Pandas • Exploratory Data Analysis (EDA) • Churn distribution and customer segmentation • Analysis of customer behavior and key patterns • Data visualization using Matplotlib and Seaborn • Identification of factors associated with higher churn risk • Business-focused insights from customer data

The project demonstrates how Python and data analysis techniques can be used to explore customer behavior and generate actionable insights for retention-focused decision-making.