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 7e51dd0..c81c357 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	
@@ -3598,7 +3598,14 @@
    "id": "d9342ad6",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "X = data_model_preprocessed\n",
+    "Y = data_model[\"CM\"]\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, Y, test_size=0.2, random_state=42\n",
+    ")"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -3618,7 +3625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": null,
    "id": "6d58dbc2",
    "metadata": {},
    "outputs": [],
@@ -3636,9 +3643,9 @@
     "RMSE_scores = []\n",
     "\n",
     "# Hyperparamètres à tester\n",
-    "n_estimators_values = [] #Complétez ici par les paramètres à tester\n",
-    "max_depth_values = [] #Complétez ici par les paramètres à tester\n",
-    "min_samples_split_values = [] #Complétez ici par les paramètres à tester\n",
+    "n_estimators_values = []\n",
+    "max_depth_values = []\n",
+    "min_samples_split_values = []\n",
     "\n",
     "# Liste pour sauveagrder les meilleurs résultats\n",
     "best_score = np.inf\n",
@@ -3651,17 +3658,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 87,
    "id": "47da5172",
    "metadata": {},
    "outputs": [],
    "source": [
-    "#Complétez ici avec votre code"
+    "#Complétez ici avec votre code\n",
+    "for n_estimators in n_estimators_values:\n",
+    "    for max_depth in max_depth_values:\n",
+    "        for min_samples_split in min_samples_split_values:\n",
+    "            rf_regressor = RandomForestRegressor(\n",
+    "                n_estimators=n_estimators,\n",
+    "                max_depth=max_depth,\n",
+    "                min_samples_split=min_samples_split,\n",
+    "                random_state=42\n",
+    "            )\n",
+    "            MAE_scores = []\n",
+    "            MSE_scores = []\n",
+    "            RMSE_scores = []\n",
+    "\n",
+    "            for train_index, val_index in kf.split(X_train):\n",
+    "                X_train_fold, X_val_fold = X_train.iloc[train_index], X_train.iloc[val_index]\n",
+    "                y_train_fold, y_val_fold = y_train.iloc[train_index], y_train.iloc[val_index]\n",
+    "\n",
+    "                rf_regressor.fit(X_train_fold, y_train_fold)\n",
+    "                y_pred_fold = rf_regressor.predict(X_val_fold)\n",
+    "\n",
+    "                mae = metrics.mean_absolute_error(y_val_fold, y_pred_fold)\n",
+    "                mse = metrics.mean_squared_error(y_val_fold, y_pred_fold)\n",
+    "                rmse = metrics.root_mean_squared_error(y_val_fold, y_pred_fold)\n",
+    "\n",
+    "                MAE_scores.append(mae)\n",
+    "                MSE_scores.append(mse)\n",
+    "                RMSE_scores.append(rmse)\n",
+    "\n",
+    "            avg_mae = np.mean(MAE_scores)\n",
+    "            avg_mse = np.mean(MSE_scores)\n",
+    "            avg_rmse = np.mean(RMSE_scores)\n",
+    "\n",
+    "            if avg_rmse < best_score:\n",
+    "                best_score = avg_rmse\n",
+    "                best_params = {\n",
+    "                    'n_estimators': n_estimators,\n",
+    "                    'max_depth': max_depth,\n",
+    "                    'min_samples_split': min_samples_split\n",
+    "                }\n",
+    "                MAE_best_score = MAE_scores\n",
+    "                MSE_best_score = MSE_scores\n",
+    "                RMSE_best_score = RMSE_scores"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 88,
    "id": "d4936c46",
    "metadata": {},
    "outputs": [