diff --git a/template.py b/template.py
index e69de29..ec070a4 100644
--- a/template.py
+++ b/template.py
@@ -0,0 +1,38 @@
+import argparse
+import sklearn
+import sklearn.datasets
+import sklearn.pipeline
+import sklearn.model_selection
+import sklearn.linear_model
+
+parser = argparse.ArgumentParser(description='Playground for sklearn models')
+parser.add_argument('--seed', type=int, default=42, help='random seed')
+parser.add_argument('--test_size', type=float, default=0.2, help='float indicating the ratio of the test examples to the whole dataset')
+
+def fetch_dataset(args):
+    return sklearn.datasets.fetch_california_housing()
+
+def create_model(args):
+    model = sklearn.pipeline.Pipeline([
+        ('linear_regression', sklearn.linear_model.SGDClassifier(verbose=1, random_state=args.seed))
+    ])
+
+    return model
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    dataset = sklearn.datasets.load_breast_cancer()
+    train_data, test_data, train_target, test_target = sklearn.model_selection.train_test_split(dataset.data, dataset.target, test_size=args.test_size, random_state=args.seed)
+
+    # create the model
+    # this is your main playground
+    model = create_model(args)
+
+    # fit (train) the model on the training data
+    model.fit(train_data, train_target)
+
+    # predict on the test set
+    prediction = model.predict(test_data)
+    accuracy = sklearn.metrics.accuracy_score(test_target, prediction)
+    print(accuracy)
+