Learning Outcome

By completing this project, you will learn how to:

• Load and work with real-world customer datasets using pandas for preprocessing and analysis
• Handle missing values, encode categorical variables, and engineer meaningful features
• Perform exploratory data analysis (EDA) to understand churn distribution, tenure, and payment patterns
• Build and train a Logistic Regression model to predict customer churn
• Evaluate model performance using accuracy, precision, recall, F1 score, and confusion matrix
• Interpret model outputs and feature importance to understand key factors driving churn
• Deploy a machine learning model using Gradio to create an interactive web interface

This project demonstrates practical skills in data preprocessing, predictive modeling, evaluation, and lightweight deployment for real-world business applications.