diff --git a/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb b/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb
index 75612b0..982ff55 100644
--- a/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb	
+++ b/M2/Machine Learning/TP_1/2025_TP_1_M2_ISF.ipynb	
@@ -83899,7 +83899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": null,
    "id": "1b7e6ff4",
    "metadata": {},
    "outputs": [
@@ -84752,7 +84752,7 @@
     "#Calcul de la fréquence\n",
     "plot_data[\"FREQ\"] = plot_data[\"NB\"] / plot_data[\"EXPO\"]\n",
     "plot_data[\"FREQ\"] = plot_data[\"FREQ\"].fillna(0)\n",
-    "plot_data\n",
+    "print(plot_data)\n",
     "\n",
     "#Représentation graphique\n",
     "fig = px.scatter(plot_data, x=\"GENRE\", y=\"FREQ\", title=\"Sinistralité selon le genre\")\n",
@@ -84793,7 +84793,7 @@
     "#Calcul de la fréquence\n",
     "plot_data[\"FREQ\"] = plot_data[\"NB\"] / plot_data[\"EXPO\"]\n",
     "plot_data[\"FREQ\"] = plot_data[\"FREQ\"].fillna(0)\n",
-    "plot_data\n",
+    "print(plot_data)\n",
     "\n",
     "#Représentation graphique\n",
     "fig = px.scatter(plot_data, x=\"ZONE_RISQUE\", y=\"FREQ\", title=\"Sinistralité selon la zone géographique\")\n",
@@ -84810,7 +84810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": null,
    "id": "2185245a",
    "metadata": {},
    "outputs": [
@@ -85664,7 +85664,7 @@
     "#Calcul de la fréquence\n",
     "plot_data[\"FREQ\"] = plot_data[\"NB\"] / plot_data[\"EXPO\"]\n",
     "plot_data[\"FREQ\"] = plot_data[\"FREQ\"].fillna(0)\n",
-    "plot_data\n",
+    "print(plot_data)\n",
     "\n",
     "#Représentation graphique\n",
     "fig = px.scatter(plot_data, x=\"ENERGIE\", y=\"FREQ\", title=\"Sinistralité selon le carburant\")\n",
@@ -85681,7 +85681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": null,
    "id": "2e2486e5",
    "metadata": {},
    "outputs": [
@@ -86539,7 +86539,7 @@
     "#Calcul du CM\n",
     "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
     "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
-    "plot_data\n",
+    "print(plot_data)\n",
     "\n",
     "#Représentation graphique\n",
     "fig = px.scatter(plot_data, x=\"VALEUR_DU_BIEN\", y=\"CM\", title=\"Coût moyen selon le prix\")\n",
@@ -86556,7 +86556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": null,
    "id": "63b97150",
    "metadata": {},
    "outputs": [
@@ -87413,7 +87413,7 @@
     "#Calcul du CM\n",
     "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
     "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
-    "plot_data\n",
+    "print(plot_data)\n",
     "\n",
     "#Représentation graphique\n",
     "fig = px.line(plot_data, x=\"ANNEE_CONSTRUCTION\", y=\"CM\", title=\"Coût moyen selon l'ancienneté du bien\")\n",
@@ -87430,7 +87430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": null,
    "id": "d1506c6c",
    "metadata": {},
    "outputs": [
@@ -88284,7 +88284,7 @@
     "#Calcul du CM\n",
     "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
     "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
-    "plot_data\n",
+    "print(plot_data)\n",
     "\n",
     "#Représentation graphique\n",
     "fig = px.scatter(plot_data, x=\"AGE_ASSURE_PRINCIPAL\", y=\"CM\", title=\"Coût moyen selon l'âge de l'assuré\")\n",
@@ -88301,7 +88301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": null,
    "id": "9f19cc0d",
    "metadata": {},
    "outputs": [
@@ -89155,7 +89155,7 @@
     "#Calcul du CM\n",
     "plot_data[\"CM\"] = plot_data[\"CHARGE\"] / plot_data[\"NB\"]\n",
     "plot_data[\"CM\"] = plot_data[\"CM\"].fillna(0)\n",
-    "plot_data\n",
+    "print(plot_data)\n",
     "\n",
     "#Représentation graphique\n",
     "fig = px.scatter(plot_data, x=\"GENRE\", y=\"CM\", title=\"Coût moyen selon l'âge de l'assuré\")\n",
diff --git a/M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb b/M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb
index 659992e..6da44da 100644
--- a/M2/Machine Learning/TP_2/2025_TP_2_M2_ISF.ipynb	
+++ b/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"
    ]
   },
   {