The reports folder contains model evaluation outputs generated during the machine learning workflow. These reports provide insights into model performance, feature importance, and predictive capabilities, helping stakeholders understand both the effectiveness and business implications of the solution. 1. Feature Importance Report Feature importance analysis was performed to identify the variables that contributed most to customer churn predictions, providing valuable to business insights. 2. ROC Curve Report ROC-AUC analysis was used to compare multiple machine learning models and identify the model with the strongest predictive performance. 3. Confusion Matrix Report A confusion matrix was generated to evaluate classification outcomes and understand the strengths and limitations of the predictive model.