diff --git a/model_data/simulation_system/training.py b/model_data/simulation_system/training.py
index 358abb41..04ef3493 100644
--- a/model_data/simulation_system/training.py
+++ b/model_data/simulation_system/training.py
@@ -20,6 +20,8 @@ from core.Settings import (
     SUBSAMPLE_FACTOR,
     MODEL_HYPERPARAMETERS
 )
+import seaborn as sns
+import matplotlib.pyplot as plt
 
 TIMESTAMP = datetime.now().strftime(format="%Y-%m-%d_%H-%M-%S")     
 
@@ -121,6 +123,29 @@ def training(
         metrics_location = output_base / METRICS_FOLDER
         )
     
+    logger.info("--- Generate metric outputs using predictions ---")
+    # TODO: can have a model.metric_outputs method
+    # FOr not just do it here
+    residual_df = pd.DataFrame(list(zip(test_df[target_column], model.predictions)), columns=['true', 'pred'])
+    
+    # image formatting
+    # TODO: move to settings file , AXIS_FONT, TITLE_FONT
+    axis_fs = 18 #fontsize
+    title_fs = 22 #fontsize
+    sns.set(style="whitegrid")
+    ax = sns.scatterplot(x="true", y="pred",data=residual_df)
+    ax.set_aspect('equal')
+    ax.set_xlabel(f'True {target_column}',fontsize = axis_fs) 
+    ax.set_ylabel(f'Predicted {target_column}', fontsize = axis_fs)#ylabel
+    ax.set_title('Residuals', fontsize = title_fs)
+
+    # Square aspect ratio
+    ax.plot([-100, 100], [-100, 100], 'black', linewidth=1)
+
+    plt.tight_layout()
+    RESIDUAL_FILE = "residuals.png"
+    plt.savefig(output_base / METRICS_FOLDER / RESIDUAL_FILE, dpi=120) 
+    
     # TODO: introduce a seperate script for model optimisation, and from there, optimise for deployment
     # Imagining for now that the model trained here is the best model amongst all models built
 
@@ -139,6 +164,7 @@ def training(
         logger.info("Registry file found - Loading into Dataframe")
         registry_df = pd.read_csv(registry_path, index_col=None)
     else:
+        # TODO: Moved columns into settings: MODEL_DETAILS and Metrics class columns 
         registry_df = pd.DataFrame(columns=['model_type', 'model_name', 'model_location', 'mean_absolute_error', 'root_mean_squared_error', 'mean_squared_error', 'r2', 'pearsonr', 'median_absolute_error', 'mape', 'best_model'])
 
     model_details_df = pd.DataFrame(