Final project for my MSc Applied AI Module: Churn Reduction using Random-Forest and XGBoost Classification Models

For this project, I developed classification models to predict customer churn in a mock mobile network dataset. Using Random-Forest and XGBoost algorithms, I built and fine-tuned models to achieve high accuracy in predicting which customers were likely to leave the service. This project enhanced my skills in feature engineering, model evaluation, and hyperparameter tuning.