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/TP5_Naive_Bayes.ipynb

@@ -1246,14 +1246,15 @@
     "# 2. Displaying the vectors :\n",
     "\n",
     "print(\n",
-    "    \"2. The vectors corresponding to the sms are :  \\n\", X.toarray()\n",
+    "    \"2. The vectors corresponding to the sms are :  \\n\",\n",
+    "    X.toarray(),\n",
     ")  # X.toarray because\n",
     "# X is a \"sparse\" matrix.\n",
     "\n",
     "# 3. For a new data x_0=\"iphone gratuit\",\n",
     "# you must also transform x_0 into a numerical vector before predicting.\n",
     "\n",
-    "vec_x_0 = vec.transform([\"iphone gratuit\"]).toarray()  #\n",
+    "vec_x_0 = vec.transform([\"iphone gratuit\"]).toarray()\n",
     "print(\"3. The numerical vector corresponding to  (x_0=iphone gratuit) is \\n\", vec_x_0)"
    ]
   },
@@ -1410,7 +1411,10 @@
     "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=0.30, random_state=50\n",
+    "    X,\n",
+    "    y,\n",
+    "    test_size=0.30,\n",
+    "    random_state=50,\n",
     ")\n",
     "\n",
     "print(\"size of the training set: \", X_train.shape[0])\n",
@@ -1986,7 +1990,7 @@
     "        \"Iphone 15 is now free\",\n",
     "        \"I want coffee\",\n",
     "        \"I want to buy a new iphone\",\n",
-    "    ]\n",
+    "    ],\n",
     ")\n",
     "\n",
     "pred_my_sms = sms_bayes.predict(my_sms)\n",
@@ -2055,7 +2059,10 @@
     "X_copy = (X.copy() >= 127).astype(int)\n",
     "\n",
     "X_train, X_test, y_train, y_test = train_test_split(\n",
-    "    X_copy, y, test_size=0.25, random_state=42\n",
+    "    X_copy,\n",
+    "    y,\n",
+    "    test_size=0.25,\n",
+    "    random_state=42,\n",
     ")\n",
     "\n",
     "ber_bayes = BernoulliNB()\n",