diff --git a/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb b/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb
index 5869f44..d594d08 100644
--- a/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb	
+++ b/M2/Machine Learning/TP_3/2025_TP_3_M2_ISF.ipynb	
@@ -46,7 +46,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 157,
    "id": "97d58527",
    "metadata": {},
    "outputs": [],
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 158,
    "id": "c9597b48",
    "metadata": {},
    "outputs": [],
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 159,
    "id": "8051b5f4",
    "metadata": {},
    "outputs": [],
@@ -164,7 +164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 160,
    "id": "c427a4b8",
    "metadata": {},
    "outputs": [
@@ -174,7 +174,7 @@
        "(824, 14)"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -201,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 161,
    "id": "c8fd3ee1",
    "metadata": {},
    "outputs": [
@@ -285,7 +285,7 @@
          "type": "float"
         }
        ],
-       "ref": "8d8166c3-6828-4361-92de-ebce2dadb512",
+       "ref": "e80a8f38-8160-41fb-bbfa-ae1f7b39de11",
        "rows": [
         [
          "count",
@@ -761,7 +761,7 @@
        "max               NaN  83421.850000  "
       ]
      },
-     "execution_count": 94,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -796,7 +796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 162,
    "id": "1b156435",
    "metadata": {},
    "outputs": [
@@ -806,7 +806,7 @@
        "(824, 13)"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -818,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 163,
    "id": "0ef0fcc0",
    "metadata": {},
    "outputs": [],
@@ -854,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 164,
    "id": "e130aae5",
    "metadata": {},
    "outputs": [],
@@ -864,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 165,
    "id": "c39e2ad0",
    "metadata": {},
    "outputs": [
@@ -1812,7 +1812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 166,
    "id": "a16215ab",
    "metadata": {},
    "outputs": [],
@@ -1822,7 +1822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 167,
    "id": "532ca6c4",
    "metadata": {},
    "outputs": [
@@ -2765,7 +2765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 168,
    "id": "b8530717",
    "metadata": {},
    "outputs": [],
@@ -2773,7 +2773,7 @@
     "encoder = preproc.OneHotEncoder()\n",
     "encoder.fit(vars_categorielles)\n",
     "vars_categorielles_enc = encoder.transform(vars_categorielles)\n",
-    "vars_categorielles_enc = pd.DataFrame(vars_categorielles_enc.toarray(), columns=encoder.get_feature_names_out(vars_categorielles.columns)) # type: ignore"
+    "vars_categorielles_enc = pd.DataFrame(vars_categorielles_enc.toarray(), columns=encoder.get_feature_names_out(vars_categorielles.columns))"
    ]
   },
   {
@@ -2786,7 +2786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 169,
    "id": "4ff3847d",
    "metadata": {},
    "outputs": [],
@@ -2797,3153 +2797,6 @@
     "vars_numeriques_scaled = pd.DataFrame(vars_numeriques_scaled, columns=vars_numeriques.columns)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "id": "128d4a36",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(824, 46)\n"
-     ]
-    },
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-       "..         ...                   ...                ...                 ...   \n",
-       "819  -0.914052              0.406998           0.894262           -2.597324   \n",
-       "820  -0.253948              0.406998           1.565789            0.249100   \n",
-       "821   0.406156             -1.766941          -1.533567            0.565370   \n",
-       "822  -0.253948             -1.766941          -1.275287           -1.648516   \n",
-       "823   1.066260              0.406998           0.067767            0.565370   \n",
-       "\n",
-       "     CONTRAT_ANCIENNETE_(-1,0]  CONTRAT_ANCIENNETE_(0,1]  \\\n",
-       "0                          0.0                       1.0   \n",
-       "1                          1.0                       0.0   \n",
-       "2                          1.0                       0.0   \n",
-       "3                          1.0                       0.0   \n",
-       "4                          0.0                       0.0   \n",
-       "..                         ...                       ...   \n",
-       "819                        0.0                       0.0   \n",
-       "820                        0.0                       1.0   \n",
-       "821                        0.0                       0.0   \n",
-       "822                        0.0                       1.0   \n",
-       "823                        0.0                       0.0   \n",
-       "\n",
-       "     CONTRAT_ANCIENNETE_(1,2]  CONTRAT_ANCIENNETE_(2,5]  \\\n",
-       "0                         0.0                       0.0   \n",
-       "1                         0.0                       0.0   \n",
-       "2                         0.0                       0.0   \n",
-       "3                         0.0                       0.0   \n",
-       "4                         1.0                       0.0   \n",
-       "..                        ...                       ...   \n",
-       "819                       1.0                       0.0   \n",
-       "820                       0.0                       0.0   \n",
-       "821                       1.0                       0.0   \n",
-       "822                       0.0                       0.0   \n",
-       "823                       0.0                       1.0   \n",
-       "\n",
-       "     CONTRAT_ANCIENNETE_(5,10]  FREQUENCE_PAIEMENT_COTISATION_ANNUEL  ...  \\\n",
-       "0                          0.0                                   0.0  ...   \n",
-       "1                          0.0                                   0.0  ...   \n",
-       "2                          0.0                                   0.0  ...   \n",
-       "3                          0.0                                   0.0  ...   \n",
-       "4                          0.0                                   0.0  ...   \n",
-       "..                         ...                                   ...  ...   \n",
-       "819                        0.0                                   0.0  ...   \n",
-       "820                        0.0                                   0.0  ...   \n",
-       "821                        0.0                                   0.0  ...   \n",
-       "822                        0.0                                   0.0  ...   \n",
-       "823                        0.0                                   0.0  ...   \n",
-       "\n",
-       "     ENERGIE_ESSENCE  EQUIPEMENT_SECURITE_FAUX  EQUIPEMENT_SECURITE_VRAI  \\\n",
-       "0                1.0                       0.0                       1.0   \n",
-       "1                0.0                       1.0                       0.0   \n",
-       "2                1.0                       0.0                       1.0   \n",
-       "3                0.0                       1.0                       0.0   \n",
-       "4                1.0                       1.0                       0.0   \n",
-       "..               ...                       ...                       ...   \n",
-       "819              0.0                       0.0                       1.0   \n",
-       "820              1.0                       1.0                       0.0   \n",
-       "821              0.0                       0.0                       1.0   \n",
-       "822              0.0                       1.0                       0.0   \n",
-       "823              0.0                       1.0                       0.0   \n",
-       "\n",
-       "     VALEUR_DU_BIEN_[0;10000[  VALEUR_DU_BIEN_[10000;15000[  \\\n",
-       "0                         0.0                           0.0   \n",
-       "1                         0.0                           0.0   \n",
-       "2                         1.0                           0.0   \n",
-       "3                         0.0                           0.0   \n",
-       "4                         0.0                           0.0   \n",
-       "..                        ...                           ...   \n",
-       "819                       0.0                           0.0   \n",
-       "820                       0.0                           1.0   \n",
-       "821                       0.0                           0.0   \n",
-       "822                       0.0                           1.0   \n",
-       "823                       0.0                           0.0   \n",
-       "\n",
-       "     VALEUR_DU_BIEN_[15000;20000[  VALEUR_DU_BIEN_[20000;25000[  \\\n",
-       "0                             1.0                           0.0   \n",
-       "1                             0.0                           0.0   \n",
-       "2                             0.0                           0.0   \n",
-       "3                             1.0                           0.0   \n",
-       "4                             0.0                           0.0   \n",
-       "..                            ...                           ...   \n",
-       "819                           0.0                           1.0   \n",
-       "820                           0.0                           0.0   \n",
-       "821                           1.0                           0.0   \n",
-       "822                           0.0                           0.0   \n",
-       "823                           1.0                           0.0   \n",
-       "\n",
-       "     VALEUR_DU_BIEN_[25000;35000[  VALEUR_DU_BIEN_[35000;99999[        CM  \n",
-       "0                             0.0                           0.0  1072.980  \n",
-       "1                             0.0                           1.0  3750.000  \n",
-       "2                             0.0                           0.0  1838.490  \n",
-       "3                             0.0                           0.0  4892.740  \n",
-       "4                             1.0                           0.0   166.730  \n",
-       "..                            ...                           ...       ...  \n",
-       "819                           0.0                           0.0  1216.755  \n",
-       "820                           0.0                           0.0  2071.560  \n",
-       "821                           0.0                           0.0  5077.640  \n",
-       "822                           0.0                           0.0  5228.550  \n",
-       "823                           0.0                           0.0  5880.340  \n",
-       "\n",
-       "[824 rows x 46 columns]"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Concatenate the transformed variables\n",
-    "data_model_preprocessed = pd.concat([vars_numeriques_scaled, vars_categorielles_enc], axis=1) # type: ignore\n",
-    "\n",
-    "# Add the CM column (target variable) to get 824x46 shape\n",
-    "data_model_preprocessed['CM'] = data_model['CM'].values\n",
-    "\n",
-    "print(data_model_preprocessed.shape)\n",
-    "data_model_preprocessed"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "62d49546",
@@ -5962,12 +2815,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 170,
    "id": "6a1c7907",
    "metadata": {},
    "outputs": [],
    "source": [
-    "train, test = train_test_split(data_model_preprocessed, test_size=0.2, random_state=42)"
+    "X = data_model_preprocessed = vars_numeriques_scaled.merge(vars_categorielles_enc, left_index=True, right_index=True) # type: ignore\n",
+    "Y = data_model[\"CM\"]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
    ]
   },
   {
@@ -5988,14 +2844,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 171,
    "id": "053e013c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
-       "<style>#sk-container-id-2 {\n",
+       "<style>#sk-container-id-4 {\n",
        "  /* Definition of color scheme common for light and dark mode */\n",
        "  --sklearn-color-text: #000;\n",
        "  --sklearn-color-text-muted: #666;\n",
@@ -6026,15 +2882,15 @@
        "  }\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 {\n",
+       "#sk-container-id-4 {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 pre {\n",
+       "#sk-container-id-4 pre {\n",
        "  padding: 0;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "#sk-container-id-4 input.sk-hidden--visually {\n",
        "  border: 0;\n",
        "  clip: rect(1px 1px 1px 1px);\n",
        "  clip: rect(1px, 1px, 1px, 1px);\n",
@@ -6046,7 +2902,7 @@
        "  width: 1px;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
        "  border: 1px dashed var(--sklearn-color-line);\n",
        "  margin: 0 0.4em 0.5em 0.4em;\n",
        "  box-sizing: border-box;\n",
@@ -6054,7 +2910,7 @@
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-container {\n",
+       "#sk-container-id-4 div.sk-container {\n",
        "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
        "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
        "     so we also need the `!important` here to be able to override the\n",
@@ -6064,7 +2920,7 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
        "  display: none;\n",
        "}\n",
        "\n",
@@ -6080,14 +2936,14 @@
        "\n",
        "/* Parallel-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item::after {\n",
        "  content: \"\";\n",
        "  width: 100%;\n",
        "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
        "  flex-grow: 1;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
+       "#sk-container-id-4 div.sk-parallel {\n",
        "  display: flex;\n",
        "  align-items: stretch;\n",
        "  justify-content: center;\n",
@@ -6095,28 +2951,28 @@
        "  position: relative;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "#sk-container-id-4 div.sk-parallel-item {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
        "  align-self: flex-end;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
        "  align-self: flex-start;\n",
        "  width: 50%;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
        "  width: 0;\n",
        "}\n",
        "\n",
        "/* Serial-specific style estimator block */\n",
        "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
+       "#sk-container-id-4 div.sk-serial {\n",
        "  display: flex;\n",
        "  flex-direction: column;\n",
        "  align-items: center;\n",
@@ -6134,14 +2990,14 @@
        "\n",
        "/* Pipeline and ColumnTransformer style (default) */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
+       "#sk-container-id-4 div.sk-toggleable {\n",
        "  /* Default theme specific background. It is overwritten whether we have a\n",
        "  specific estimator or a Pipeline/ColumnTransformer */\n",
        "  background-color: var(--sklearn-color-background);\n",
        "}\n",
        "\n",
        "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "#sk-container-id-4 label.sk-toggleable__label {\n",
        "  cursor: pointer;\n",
        "  display: flex;\n",
        "  width: 100%;\n",
@@ -6154,13 +3010,13 @@
        "  gap: 0.5em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
+       "#sk-container-id-4 label.sk-toggleable__label .caption {\n",
        "  font-size: 0.6rem;\n",
        "  font-weight: lighter;\n",
        "  color: var(--sklearn-color-text-muted);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
        "  /* Arrow on the left of the label */\n",
        "  content: \"▸\";\n",
        "  float: left;\n",
@@ -6168,13 +3024,13 @@
        "  color: var(--sklearn-color-icon);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
        "  color: var(--sklearn-color-text);\n",
        "}\n",
        "\n",
        "/* Toggleable content - dropdown */\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "#sk-container-id-4 div.sk-toggleable__content {\n",
        "  max-height: 0;\n",
        "  max-width: 0;\n",
        "  overflow: hidden;\n",
@@ -6183,12 +3039,12 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
        "  margin: 0.2em;\n",
        "  border-radius: 0.25em;\n",
        "  color: var(--sklearn-color-text);\n",
@@ -6196,79 +3052,79 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
        "  /* Expand drop-down */\n",
        "  max-height: 200px;\n",
        "  max-width: 100%;\n",
        "  overflow: auto;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
        "  content: \"▾\";\n",
        "}\n",
        "\n",
        "/* Pipeline/ColumnTransformer-specific style */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator-specific style */\n",
        "\n",
        "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  /* The background is the default theme color */\n",
        "  color: var(--sklearn-color-text-on-default-background);\n",
        "}\n",
        "\n",
        "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
        "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
        "  color: var(--sklearn-color-text);\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
        "\n",
        "/* Estimator label */\n",
        "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
+       "#sk-container-id-4 div.sk-label label {\n",
        "  font-family: monospace;\n",
        "  font-weight: bold;\n",
        "  display: inline-block;\n",
        "  line-height: 1.2em;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
+       "#sk-container-id-4 div.sk-label-container {\n",
        "  text-align: center;\n",
        "}\n",
        "\n",
        "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
+       "#sk-container-id-4 div.sk-estimator {\n",
        "  font-family: monospace;\n",
        "  border: 1px dotted var(--sklearn-color-border-box);\n",
        "  border-radius: 0.25em;\n",
@@ -6278,18 +3134,18 @@
        "  background-color: var(--sklearn-color-unfitted-level-0);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-0);\n",
        "}\n",
        "\n",
        "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "#sk-container-id-4 div.sk-estimator:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-2);\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-2);\n",
        "}\n",
@@ -6377,7 +3233,7 @@
        "\n",
        "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "#sk-container-id-4 a.estimator_doc_link {\n",
        "  float: right;\n",
        "  font-size: 1rem;\n",
        "  line-height: 1em;\n",
@@ -6392,38 +3248,38 @@
        "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
        "  /* fitted */\n",
        "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
        "  color: var(--sklearn-color-fitted-level-1);\n",
        "}\n",
        "\n",
        "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
        "  /* unfitted */\n",
        "  background-color: var(--sklearn-color-unfitted-level-3);\n",
        "  color: var(--sklearn-color-background);\n",
        "  text-decoration: none;\n",
        "}\n",
        "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
        "  /* fitted */\n",
        "  background-color: var(--sklearn-color-fitted-level-3);\n",
        "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(max_depth=5, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor(max_depth=5, random_state=42)</pre></div> </div></div></div></div>"
+       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeRegressor.html\">?<span>Documentation for DecisionTreeRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeRegressor()</pre></div> </div></div></div></div>"
       ],
       "text/plain": [
-       "DecisionTreeRegressor(max_depth=5, random_state=42)"
+       "DecisionTreeRegressor()"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tree = DecisionTreeRegressor(max_depth=5, random_state=42)\n",
-    "tree.fit(train.drop(\"CM\", axis=1), train[\"CM\"])"
+    "tree = DecisionTreeRegressor()\n",
+    "tree.fit(X_train, y_train)"
    ]
   },
   {
@@ -6436,7 +3292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 172,
    "id": "c4ca2cf9",
    "metadata": {},
    "outputs": [
@@ -6444,24 +3300,53 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "MAE: 3683.84\n",
-      "MSE: 55216550.75\n",
-      "RMSE: 7430.78\n"
+      "MAE: 0.00\n",
+      "MSE: 0.00\n",
+      "RMSE: 0.00\n"
      ]
     }
    ],
    "source": [
-    "y_pred = tree.predict(test.drop(\"CM\", axis=1))\n",
+    "# Prédictions sur l'ensemble d'entraînement\n",
+    "y_pred_train = tree.predict(X_train)\n",
     "\n",
-    "mae = metrics.mean_absolute_error(test[\"CM\"], y_pred)\n",
-    "mse = metrics.mean_squared_error(test[\"CM\"], y_pred)\n",
-    "rmse = metrics.root_mean_squared_error(test[\"CM\"], y_pred)\n",
+    "mae = metrics.mean_absolute_error(y_train, y_pred_train)\n",
+    "mse = metrics.mean_squared_error(y_train, y_pred_train)\n",
+    "rmse = metrics.root_mean_squared_error(y_train, y_pred_train)\n",
     "\n",
     "print(f\"MAE: {mae:.2f}\")\n",
     "print(f\"MSE: {mse:.2f}\")\n",
     "print(f\"RMSE: {rmse:.2f}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "id": "4b739d5b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAE: 5950.05\n",
+      "MSE: 160067768.70\n",
+      "RMSE: 12651.79\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred_test = tree.predict(X_test)\n",
+    "\n",
+    "mae = metrics.mean_absolute_error(y_test, y_pred_test)\n",
+    "mse = metrics.mean_squared_error(y_test, y_pred_test)\n",
+    "rmse = metrics.root_mean_squared_error(y_test, y_pred_test)\n",
+    "\n",
+    "print(f\"MAE: {mae:.2f}\")\n",
+    "print(f\"MSE: {mse:.2f}\")\n",
+    "print(f\"RMSE: {rmse:.2f}\")\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "fb2fe98c",
@@ -6532,7 +3417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 174,
    "id": "b515460e",
    "metadata": {},
    "outputs": [],
@@ -6555,7 +3440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 175,
    "id": "eebb394f",
    "metadata": {},
    "outputs": [],
@@ -6565,7 +3450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 176,
    "id": "b067126c",
    "metadata": {},
    "outputs": [],
@@ -6579,7 +3464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 177,
    "id": "6597152c",
    "metadata": {},
    "outputs": [],
@@ -6591,7 +3476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 178,
    "id": "63ff1c9d",
    "metadata": {},
    "outputs": [],
@@ -6651,7 +3536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 179,
    "id": "6d58dbc2",
    "metadata": {},
    "outputs": [],
@@ -6684,7 +3569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 180,
    "id": "47da5172",
    "metadata": {},
    "outputs": [],
@@ -6694,7 +3579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 181,
    "id": "d4936c46",
    "metadata": {},
    "outputs": [
@@ -6715,7 +3600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 182,
    "id": "3215c463",
    "metadata": {},
    "outputs": [],
@@ -6729,7 +3614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 183,
    "id": "bb9a5c9b",
    "metadata": {},
    "outputs": [],
@@ -6741,7 +3626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 184,
    "id": "0f0768ad",
    "metadata": {},
    "outputs": [],