diff --git a/movies.py b/movies.py
index 3212de3eadc7d5f42fcc00e29de6f508bcfc61d2..858d463ed448c8613ee888db080787aef4b54bf0 100644
--- a/movies.py
+++ b/movies.py
@@ -1,7 +1,11 @@
 
+"""
+Movie recommender system as a showcase project
+"""
+
 import collections
 import pandas as pd
-import tensorflow as tf
+# import tensorflow as tf
 
 def load_movielense():
     """Load an return movies and ratings as dataframes"""
@@ -24,6 +28,3 @@ def collect_user_context(ratings, min_rating=2.1):
             user_movies[user_id].append(movie_id)
 
     return user_movies
-
-
-
diff --git a/movies_test.py b/movies_test.py
index a37ae83eb831cc7b31416311b246ae6b07eb562a..07d02807f948dcceecb8ecf0538e7e5838f33e72 100644
--- a/movies_test.py
+++ b/movies_test.py
@@ -1,6 +1,10 @@
 
-import pandas as pd
+"""
+Test cases for the movie recommender system
+"""
+
 import unittest
+import pandas as pd
 import movies
 
 class LoadTests(unittest.TestCase):
@@ -9,20 +13,19 @@ class LoadTests(unittest.TestCase):
     @staticmethod
     def toy_ratings():
         """Return a toy dataframe with ratings"""
-        return pd.DataFrame(data={
+        return pd.DataFrame({
             "userId": [1, 1, 2, 2, 1],
             "movieId": [1, 2, 1, 3, 4],
             "rating": [3, 1, 4, 3, 5],
             "timestamp": [1, 2, 7, 5, 4],
         },
         columns=["userId", "movieId", "rating", "timestamp"])
-    
+
     def test_collect_user_context(self):
         """Check that ratings are correctly aggregated"""
         rating = self.toy_ratings()
         user_movies = movies.collect_user_context(rating)
-        
-        self.assertEqual(user_movies[1], [1, 4]) 
-        self.assertEqual(user_movies[2], [3, 1]) 
-        self.assertEqual(set(user_movies.keys()), {1, 2})
 
+        self.assertEqual(user_movies[1], [1, 4])
+        self.assertEqual(user_movies[2], [3, 1])
+        self.assertEqual(set(user_movies.keys()), {1, 2})