diff --git a/scripts/test_model.py b/scripts/test_model.py
new file mode 100644
index 0000000..7aaeaba
--- /dev/null
+++ b/scripts/test_model.py
@@ -0,0 +1,63 @@
+import json
+import click
+from collections import Counter
+from sklearn.metrics import classification_report
+from sklearn.externals import joblib
+from sklearn.metrics import f1_score, recall_score, precision_score
+import os
+import sys
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from pkspace.utils.loaders import PKSpaceLoader, PKLotLoader # noqa
+
+
+@click.command()
+@click.option('--loader', '-l', type=click.Choice(['PKLot', 'PKSpace']),
+              default='PKSpace', help='Loader used to load dataset')
+@click.argument('dataset_dir',
+                type=click.Path(exists=True, file_okay=False, dir_okay=True,
+                                resolve_path=True))
+@click.argument('model_file',
+                type=click.Path(exists=True, file_okay=True, dir_okay=False,
+                                resolve_path=True))
+@click.option("--machine_friendly", '-f', is_flag=True,
+              help='prints serialized dictionary of results')
+def test_model(loader, dataset_dir, model_file, machine_friendly):
+    if loader == 'PKSpace':
+        loader = PKSpaceLoader()
+    elif loader == 'PKLot':
+        loader = PKLotLoader()
+
+    model = joblib.load(model_file)
+    spaces, ground_answers = loader.load(dataset_dir)
+    model_answers = model.predict(spaces)
+    if machine_friendly:
+        answer = {'avg': {}, 0: {}, 1: {}}
+        metrics = [precision_score, recall_score, f1_score]
+        classes_counter = Counter(ground_answers)
+
+        for i in [0, 1]:
+            for func in metrics:
+                score = func(ground_answers, model_answers, pos_label=i)
+                answer[i][func.__name__] = score
+            class_support = classes_counter[i]
+
+            # summing total support
+            answer[i]['support'] = class_support
+            old_sum_support = answer['avg'].get('support', 0)
+            answer['avg']['support'] = old_sum_support + class_support
+
+        # calculating weighted average for all functions
+        for column in [x.__name__ for x in metrics]:
+            col_sum = 0
+            for ans_class in [0, 1]:
+                row = answer[ans_class]
+                col_sum += row[column] * row['support']
+            answer['avg'][column] = col_sum / answer['avg']['support']
+        print(json.dumps(answer))
+
+    else:
+        print(classification_report(ground_answers, model_answers))
+
+
+if __name__ == '__main__':
+    test_model()