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_KNN.ipynb

@@ -238,7 +238,10 @@
     "from sklearn.model_selection import train_test_split\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",
     ")"
    ]
   },
@@ -702,10 +705,10 @@
     "predictions2 = [knn_class_2(X_train, y_train, data, 3) for data in X_test]\n",
     "\n",
     "print(\n",
-    "    f\"The accuracy rate of our classifier is {np.sum(predictions == y_test) / len(predictions) * 100}%\"\n",
+    "    f\"The accuracy rate of our classifier is {np.sum(predictions == y_test) / len(predictions) * 100}%\",\n",
     ")\n",
     "print(\n",
-    "    f\"The accuracy rate of our classifier is {np.sum(predictions2 == y_test) / len(predictions2) * 100}%\"\n",
+    "    f\"The accuracy rate of our classifier is {np.sum(predictions2 == y_test) / len(predictions2) * 100}%\",\n",
     ")"
    ]
   },
@@ -1278,6 +1281,7 @@
     "\n",
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
+    "\n",
     "from sklearn.neighbors import KNeighborsClassifier"
    ]
   },
@@ -2094,7 +2098,10 @@
    "outputs": [],
    "source": [
     "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",
     ")"
    ]
   },