Refactor code for improved readability and consistency across multiple Jupyter notebooks

- Added missing commas in various print statements and function calls for better syntax.
- Reformatted code to enhance clarity, including breaking long lines and aligning parameters.
- Updated function signatures to use float type for sigma parameters instead of int for better precision.
- Cleaned up comments and documentation strings for clarity and consistency.
- Ensured consistent formatting in plotting functions and data handling.

M1/Statistical Learning/TP2_bis_OPTIONAL.ipynb

@@ -37,6 +37,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
+    "\n",
     "from sklearn import datasets\n",
     "\n",
     "iris = datasets.load_iris(as_frame=True)"
@@ -402,7 +403,10 @@
     "from sklearn.neighbors import KNeighborsClassifier\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X, y, test_size=0.33, random_state=42\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.33,\n",
+    "    random_state=42,\n",
     ")\n",
     "knn_clf = KNeighborsClassifier(n_neighbors=5)\n",
     "knn_clf.fit(X_train, y_train)\n",
@@ -583,7 +587,11 @@
    ],
    "source": [
     "X_train_strat, X_test_strat, y_train_strat, y_test_strat = train_test_split(\n",
-    "    X, y, test_size=0.33, random_state=42, stratify=y\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.33,\n",
+    "    random_state=42,\n",
+    "    stratify=y,\n",
     ")\n",
     "\n",
     "y_test_strat.value_counts()"