Customer Churn Prediction

Developed a classification model to predict customer churn based on customer behavior and usage data. Performed data cleaning, encoding categorical variables, and feature scaling before training models such as Logistic Regression, Decision Trees, and Random Forest.

Analyzed model performance using accuracy, precision, recall, and confusion matrix. This project highlights the ability to solve real-world business problems using machine learning and make data-driven decisions to improve customer retention.