--- a
+++ b/scripts/model/testing.py
@@ -0,0 +1,47 @@
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, confusion_matrix, classification_report
+import warnings
+import pandas as pd
+import joblib
+import os
+
+warnings.filterwarnings('ignore')
+
+# Load trained model & holdout test set
+try:
+    model = joblib.load(open(os.path.join(os.path.dirname(__file__), '../../models/gradient_boosting.joblib'), 'rb'))
+    data = pd.read_csv(open(os.path.join(os.path.dirname(__file__), '../../data/input/test.csv'), 'r'))
+except FileNotFoundError as err:
+    print(f'Ann error occoured: {err}')
+
+# Predict holdout test set
+def _predict(data):
+    return model.predict(data.drop('LUNG_CANCER', axis='columns'))
+
+# Get the score metrices
+def score_metrics(data, metrics):
+    return metrics(data['LUNG_CANCER'], _predict(data))
+
+# Get the class matrix
+def matrix(data, matrix):
+    classes = matrix(data['LUNG_CANCER'], _predict(data))
+
+    return f'TP: {classes[1][1]} - TN: {classes[0][0]} - FP: {classes[0][1]} - FN: {classes[1][0]}'
+
+# Get the auc (TPR/FPR)
+def auc(data):
+    probs = model.predict_proba(data.drop('LUNG_CANCER', axis='columns'))[:, 1] 
+    
+    return roc_auc_score(data['LUNG_CANCER'], probs)
+
+# Get the class reports
+def report(data, class_metric):
+    return class_metric(data['LUNG_CANCER'], _predict(data))
+
+if __name__ == '__main__':
+   print(f'Accuracy: {score_metrics(data, accuracy_score)}')
+   print(f'Precision: {score_metrics(data, precision_score)}')
+   print(f'Recall: {score_metrics(data, recall_score)}')
+   print(f'Measure: {score_metrics(data, f1_score)}')
+   print(f'\n{matrix(data, confusion_matrix)}')
+   print(f'\nAUC: {auc(data)}')
+   print(f'\n{report(data, classification_report)}')
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