minor improvements in wording and import statement order

notebooks/linear_models_ex_02.ipynb

@@ -22,7 +22,7 @@
     "disease. This would depend on the patient's Body Mass Index which is defined\n",
     "as `weight / height ** 2`.\n",
     "\n",
-    "We load the dataset penguins dataset. We first use a set of 3 numerical\n",
+    "We load the penguins dataset. We first use a set of 3 numerical\n",
     "features to predict the target, i.e. the body mass of the penguin."
    ]
   },

notebooks/linear_models_sol_02.ipynb

@@ -22,7 +22,7 @@
     "disease. This would depend on the patient's Body Mass Index which is defined\n",
     "as `weight / height ** 2`.\n",
     "\n",
-    "We load the dataset penguins dataset. We first use a set of 3 numerical\n",
+    "We load the penguins dataset. We first use a set of 3 numerical\n",
     "features to predict the target, i.e. the body mass of the penguin."
    ]
   },

notebooks/linear_regression_non_linear_link.ipynb

@@ -119,6 +119,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from sklearn.metrics import mean_squared_error\n",
+    "\n",
+    "\n",
     "def fit_score_plot_regression(model, title=None):\n",
     "    model.fit(data, target)\n",
     "    target_predicted = model.predict(data)\n",
@@ -147,7 +150,6 @@
    "outputs": [],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
-    "from sklearn.metrics import mean_squared_error\n",
     "\n",
     "linear_regression = LinearRegression()\n",
     "linear_regression"

python_scripts/linear_models_ex_02.py

@@ -30,7 +30,7 @@
 # disease. This would depend on the patient's Body Mass Index which is defined
 # as `weight / height ** 2`.
 #
-# We load the dataset penguins dataset. We first use a set of 3 numerical
+# We load the penguins dataset. We first use a set of 3 numerical
 # features to predict the target, i.e. the body mass of the penguin.
 
 # %% [markdown]

python_scripts/linear_models_sol_02.py

@@ -24,7 +24,7 @@
 # disease. This would depend on the patient's Body Mass Index which is defined
 # as `weight / height ** 2`.
 #
-# We load the dataset penguins dataset. We first use a set of 3 numerical
+# We load the penguins dataset. We first use a set of 3 numerical
 # features to predict the target, i.e. the body mass of the penguin.
 
 # %% [markdown]

python_scripts/linear_regression_non_linear_link.py

@@ -76,6 +76,9 @@
 
 
 # %%
+from sklearn.metrics import mean_squared_error
+
+
 def fit_score_plot_regression(model, title=None):
     model.fit(data, target)
     target_predicted = model.predict(data)
@@ -95,7 +98,6 @@ def fit_score_plot_regression(model, title=None):
 
 # %%
 from sklearn.linear_model import LinearRegression
-from sklearn.metrics import mean_squared_error
 
 linear_regression = LinearRegression()
 linear_regression