diff --git a/boruta/test/test_boruta.py b/boruta/test/test_boruta.py
new file mode 100644
index 0000000..27d42e6
--- /dev/null
+++ b/boruta/test/test_boruta.py
@@ -0,0 +1,67 @@
+import numpy as np
+import pandas as pd
+import pytest
+from sklearn.ensemble import RandomForestClassifier
+
+from boruta import BorutaPy
+
+
+@pytest.mark.parametrize("tree_n,expected", [(10, 44), (100, 141)])
+def test_get_tree_num(tree_n, expected):
+    rfc = RandomForestClassifier(max_depth=10)
+    bt = BorutaPy(rfc)
+    assert bt._get_tree_num(tree_n) == expected
+
+
+@pytest.fixture(scope="module")
+def Xy():
+    np.random.seed(42)
+    y = np.random.binomial(1, 0.5, 1000)
+    X = np.zeros((1000, 10))
+
+    z = (y - np.random.binomial(1, 0.1, 1000) +
+         np.random.binomial(1, 0.1, 1000))
+    z[z == -1] = 0
+    z[z == 2] = 1
+
+    # 5 relevant features
+    X[:, 0] = z
+    X[:, 1] = (y * np.abs(np.random.normal(0, 1, 1000))
+               + np.random.normal(0, 0.1, 1000))
+    X[:, 2] = y + np.random.normal(0, 1, 1000)
+    X[:, 3] = y**2 + np.random.normal(0, 1, 1000)
+    X[:, 4] = np.sqrt(y) + np.random.binomial(2, 0.1, 1000)
+
+    # 5 irrelevant features
+    X[:, 5] = np.random.normal(0, 1, 1000)
+    X[:, 6] = np.random.poisson(1, 1000)
+    X[:, 7] = np.random.binomial(1, 0.3, 1000)
+    X[:, 8] = np.random.normal(0, 1, 1000)
+    X[:, 9] = np.random.poisson(1, 1000)
+
+    return X, y
+
+
+def test_if_boruta_extracts_relevant_features(Xy):
+    X, y = Xy
+    rfc = RandomForestClassifier()
+    bt = BorutaPy(rfc)
+    bt.fit(X, y)
+    assert list(range(5)) == list(np.where(bt.support_)[0])
+
+
+def test_if_it_works_with_dataframe_input(Xy):
+    X, y = Xy
+    X_df, y_df = pd.DataFrame(X), pd.Series(y)
+    bt = BorutaPy(RandomForestClassifier())
+    bt.fit(X_df, y_df)
+    assert list(range(5)) == list(np.where(bt.support_)[0])
+
+
+def test_dataframe_is_returned(Xy):
+    X, y = Xy
+    X_df, y_df = pd.DataFrame(X), pd.Series(y)
+    rfc = RandomForestClassifier()
+    bt = BorutaPy(rfc)
+    bt.fit(X_df, y_df)
+    assert isinstance(bt.transform(X_df, return_df=True), pd.DataFrame)
diff --git a/boruta/test/unit_tests.py b/boruta/test/unit_tests.py
deleted file mode 100644
index 5d5ce9f..0000000
--- a/boruta/test/unit_tests.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import unittest
-from boruta import BorutaPy
-import pandas as pd
-from sklearn.ensemble import RandomForestClassifier
-import numpy as np
-
-
-class BorutaTestCases(unittest.TestCase):
-
-    def test_get_tree_num(self):
-        rfc = RandomForestClassifier(max_depth=10)
-        bt = BorutaPy(rfc)
-        self.assertEqual(bt._get_tree_num(10), 44, "Tree Est. Math Fail")
-        self.assertEqual(bt._get_tree_num(100), 141, "Tree Est. Math Fail")
-
-    def test_if_boruta_extracts_relevant_features(self):
-        np.random.seed(42)
-        y = np.random.binomial(1, 0.5, 1000)
-        X = np.zeros((1000, 10))
-
-        z = y - np.random.binomial(1, 0.1, 1000) + np.random.binomial(1, 0.1, 1000)
-        z[z == -1] = 0
-        z[z == 2] = 1
-
-        # 5 relevant features
-        X[:, 0] = z
-        X[:, 1] = y * np.abs(np.random.normal(0, 1, 1000)) + np.random.normal(0, 0.1, 1000)
-        X[:, 2] = y + np.random.normal(0, 1, 1000)
-        X[:, 3] = y ** 2 + np.random.normal(0, 1, 1000)
-        X[:, 4] = np.sqrt(y) + np.random.binomial(2, 0.1, 1000)
-
-        # 5 irrelevant features
-        X[:, 5] = np.random.normal(0, 1, 1000)
-        X[:, 6] = np.random.poisson(1, 1000)
-        X[:, 7] = np.random.binomial(1, 0.3, 1000)
-        X[:, 8] = np.random.normal(0, 1, 1000)
-        X[:, 9] = np.random.poisson(1, 1000)
-
-        rfc = RandomForestClassifier()
-        bt = BorutaPy(rfc)
-        bt.fit(X, y)
-
-        # make sure that only all the relevant features are returned
-        self.assertListEqual(list(range(5)), list(np.where(bt.support_)[0]))
-
-        # test if this works as expected for dataframe input
-        X_df, y_df = pd.DataFrame(X), pd.Series(y)
-        bt.fit(X_df, y_df)
-        self.assertListEqual(list(range(5)), list(np.where(bt.support_)[0]))
-
-        # check it dataframe is returned when return_df=True
-        self.assertIsInstance(bt.transform(X_df, return_df=True), pd.DataFrame)
-
-if __name__ == '__main__':
-    unittest.main()
-
-
diff --git a/test_requirements.txt b/test_requirements.txt
new file mode 100644
index 0000000..eedc6fd
--- /dev/null
+++ b/test_requirements.txt
@@ -0,0 +1,7 @@
+-r requirements.txt
+pytest>=5.4.1
+
+# repo maintenance tooling
+black>=21.5b1
+flake8>=3.9.2
+isort>=5.8.0
\ No newline at end of file