Minor updates to notebook

projects/customer_segments/customer_segments.ipynb

@@ -144,7 +144,7 @@
     "\n",
     "In the code block below, you will need to implement the following:\n",
     " - Assign `new_data` a copy of the data by removing a feature of your choice using the `DataFrame.drop` function.\n",
-    " - Use `sklearn.cross_validation.train_test_split` to split the dataset into training and testing sets.\n",
+    " - Use `sklearn.model_selection.train_test_split` to split the dataset into training and testing sets.\n",
     "   - Use the removed feature as your target label. Set a `test_size` of `0.25` and set a `random_state`.\n",
     " - Import a decision tree regressor, set a `random_state`, and fit the learner to the training data.\n",
     " - Report the prediction score of the testing set using the regressor's `score` function."
@@ -209,7 +209,7 @@
    "outputs": [],
    "source": [
     "# Produce a scatter matrix for each pair of features in the data\n",
-    "pd.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
+    "pd.plotting.scatter_matrix(data, alpha = 0.3, figsize = (14,8), diagonal = 'kde');"
    ]
   },
   {