Classification, Catboost, Fastapi, Docker, Streamlit.
cat_model.predict(X_test) # Calculate evaluation metrics accuracy, recall, roc_auc, precision = [round(metric(y_test, y_pred), 4) for metric in [accuracy_score, recall_score, roc_auc_score, precision_score]] # Create a DataFrame to store results model_names = ['CatBoost_Model'] result =