diff --git a/utils_ml.py b/utils_ml.py
index b276600..c622f86 100644
--- a/utils_ml.py
+++ b/utils_ml.py
@@ -161,7 +161,7 @@ def preprocess(type_of_model,  type_of_activity):
 
 def draw_confusion_matrix(y_test, y_pred, model):
     conf_matrix = confusion_matrix(y_test, y_pred)
-    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'])
+    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', xticklabels=['Class 0', 'Class 1'], yticklabels=['Class 0', 'Class 1'], annot_kws={"size": 14})
     plt.xlabel('Predicted')
     plt.ylabel('Actual')
     plt.title('Confusion Matrix')
@@ -180,10 +180,10 @@ def draw_roc_curve(X_test, y_pred_prob, model):
     plt.plot(fpr, tpr, label="ROC curve(area = %0.3f)" % roc_auc)
     plt.plot([0, 1], [0, 1], color="red",label="Random Baseline", linestyle="--")
     plt.grid(color='gray', linestyle='--', linewidth=0.5)
-    plt.xlabel("False Positive Rate")
-    plt.ylabel("True Positive Rate")
+    plt.xlabel("False Positive Rate", fontsize=14)
+    plt.ylabel("True Positive Rate", fontsize=14)
     plt.title("ROC Curve", size=18)
-    plt.legend(loc="lower right")
+    plt.legend(loc="lower right", fontsize=14)
     plt.show()
     save_file_s3("Roc_curve_", type_of_activity, type_of_model, model)