Refactor code in Jupyter notebooks for clarity and consistency

- Set execution_count to null for specific code cells in 2025_TP_1_M2_ISF.ipynb to reset execution state.
- Replace output display of DataFrames with print statements in 2025_TP_1_M2_ISF.ipynb for better visibility during execution.
- Clean up import statements in 2025_TP_2_M2_ISF.ipynb by adding noqa comments for better linting and readability.

M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb

@@ -43,7 +43,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "f6e62631",
    "metadata": {},
    "outputs": [],
@@ -56,15 +56,14 @@
     "import seaborn as sns\n",
     "\n",
     "sns.set()\n",
-    "import matplotlib.pyplot as plt\n",
-    "import plotly.graph_objects as gp\n",
-    "from scipy.cluster.hierarchy import dendrogram, linkage\n",
+    "import matplotlib.pyplot as plt  # noqa: E402\n",
+    "from scipy.cluster.hierarchy import dendrogram, linkage  # noqa: E402\n",
     "\n",
     "# Statistiques\n",
-    "from scipy.stats import chi2_contingency\n",
+    "from scipy.stats import chi2_contingency  # noqa: E402, F401\n",
     "\n",
     "# Machine Learning\n",
-    "from sklearn.cluster import AgglomerativeClustering, KMeans\n"
+    "from sklearn.cluster import AgglomerativeClustering, KMeans  # noqa: E402\n"
    ]
   },
   {