Customer Churn Prediction System Using Machine Learning

Developed a machine learning application to analyze customer churn in the telecommunications industry using the Telco Customer Churn dataset

The project includes data cleaning, handling missing values, removing duplicate records, encoding categorical features, and feature scaling. It implements four classification algorithms: Logistic Regression, Random Forest, Decision Tree, and Support Vector Machine (SVM)

A graphical user interface built with CustomTkinter allows users to select a model, train it, and view its performance through accuracy scores and classification reports

Technologies: Python, Pandas, NumPy, Scikit-learn, CustomTkinter, and Tkinter