--- a
+++ b/util.py
@@ -0,0 +1,34 @@
+import matplotlib.pyplot as plt
+from sklearn.metrics import roc_curve, auc
+
+
+def results(y_pred, y_test):
+    """
+    :param y_pred: the predicted y-values
+    :param y_test: the actual y-values
+    :return: the number of correct predictions, incorrect predictions, and the percent correct
+    """
+    num_right = 0
+    num_wrong = 0
+    for i in range(len(y_pred)):
+        if y_pred[i] == y_test[i]:
+            num_right += 1
+        else:
+            num_wrong += 1
+    return num_right/(num_right + num_wrong)
+
+
+def roc_results(y_pred, y_test, model_type):
+    fpr, tpr, thresholds = roc_curve(y_test, y_pred, pos_label=1)
+    plt.figure(figsize=(8, 6))
+    lw = 2
+    plt.plot(fpr, tpr,
+             lw=lw, label=f'{model_type} (AUC = {round(auc(fpr, tpr), 3)})')
+    plt.plot([0, 1], [0, 1], lw=lw, linestyle='--')
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
+    plt.title('ROC Curve')
+    plt.legend(loc="lower right")
+    plt.show()