Refactor code formatting and improve readability in Jupyter notebooks for TP_4 and TP_5

- Adjusted indentation and line breaks for better clarity in function definitions and import statements.
- Standardized string quotes for consistency across the codebase.
- Enhanced readability of DataFrame creation and manipulation by breaking long lines into multiple lines.
- Cleaned up print statements and comments for improved understanding.
- Ensured consistent use of whitespace around operators and after commas.

M1/Statistical Learning/TP2_KNN.ipynb

@@ -1412,7 +1412,10 @@
     "f, axarr = plt.subplots(2, 3, sharex=\"col\", sharey=\"row\", figsize=(15, 12))\n",
     "\n",
     "for idx, clf, tt in zip(\n",
-    "    product([0, 1, 2], [0, 1, 2]), KNNs, [f\"KNN (k={k})\" for k in nb_neighbors], strict=False\n",
+    "    product([0, 1, 2], [0, 1, 2]),\n",
+    "    KNNs,\n",
+    "    [f\"KNN (k={k})\" for k in nb_neighbors],\n",
+    "    strict=False,\n",
     "):\n",
     "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
     "    Z = Z.reshape(xx.shape)\n",