diff --git a/code/notebooks/stft.ipynb b/code/notebooks/stft.ipynb
index 4510922..9ea2f18 100644
--- a/code/notebooks/stft.ipynb
+++ b/code/notebooks/stft.ipynb
@@ -217,9 +217,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
     "from scipy.signal import hann\n",
     "import multiprocessing"
    ]
@@ -244,16 +241,6 @@
     "Fs = 1024"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define the base directory where DAMAGE_X folders are located\n",
-    "damage_base_path = 'D:/thesis/data/converted/raw'"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -304,11 +291,11 @@
     "# Import custom in-house functions\n",
     "from src.process_stft import process_damage_case\n",
     "\n",
-    "num_damage_cases = 7  # DAMAGE_0 to DAMAGE_6\n",
+    "num_damage_cases = 6  # DAMAGE_0 to DAMAGE_6\n",
     "\n",
     "with multiprocessing.Pool() as pool:\n",
     "    # Process each DAMAGE_X case in parallel\n",
-    "    pool.map(process_damage_case, range(num_damage_cases), Fs, window_size, hop_size, output_dirs)"
+    "    pool.map(process_damage_case, range(num_damage_cases + 1))"
    ]
   },
   {
@@ -665,24 +652,33 @@
     "    # \"Decision Tree\": DecisionTreeClassifier(),\n",
     "    # \"KNN\": KNeighborsClassifier(),\n",
     "    # \"LDA\": LinearDiscriminantAnalysis(),\n",
-    "    # \"SVM\": make_pipeline(\n",
-    "    #     StandardScaler(),\n",
-    "    #     SVC(kernel='rbf', probability=True)\n",
-    "    # ),\n",
-    "    # \"SVM with StandardScaler and PCA\": make_pipeline(\n",
-    "    #                                     StandardScaler(),\n",
-    "    #                                     PCA(n_components=10),\n",
-    "    #                                     SVC(kernel='rbf')\n",
-    "    #                                     ),\n",
+    "    \"SVM\": make_pipeline(\n",
+    "        StandardScaler(),\n",
+    "        SVC(kernel='rbf')\n",
+    "    ),\n",
+    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "                                        StandardScaler(),\n",
+    "                                        PCA(n_components=10),\n",
+    "                                        SVC(kernel='rbf')\n",
+    "                                        ),\n",
     "\n",
     "    # \"XGBoost\": XGBClassifier()\n",
-    "    \"MLPClassifier\": make_pipeline(\n",
-    "        StandardScaler(),\n",
-    "        MLPClassifier(hidden_layer_sizes=(1, 10), max_iter=500, random_state=42)\n",
-    "    )\n",
+    "    # \"MLPClassifier\": make_pipeline(\n",
+    "    #     StandardScaler(),\n",
+    "    #     MLPClassifier(hidden_layer_sizes=(1, 10), max_iter=500, random_state=42)\n",
+    "    # )\n",
     "}"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -712,9 +708,15 @@
     "    # \"Decision Tree\": DecisionTreeClassifier(),\n",
     "    # \"KNN\": KNeighborsClassifier(),\n",
     "    # \"LDA\": LinearDiscriminantAnalysis(),\n",
-    "    \"SVM\": SVC(),\n",
-    "    # \"SVM with StandardScaler and PCA\": make_pipeline(\n",
-    "    # StandardScaler(),\n",
+    "    \"SVM\": make_pipeline(\n",
+    "        StandardScaler(),\n",
+    "        SVC(kernel='rbf')\n",
+    "    ),\n",
+    "    \"SVM with StandardScaler and PCA\": make_pipeline(\n",
+    "                                        StandardScaler(),\n",
+    "                                        PCA(n_components=10),\n",
+    "                                        SVC(kernel='rbf')\n",
+    "                                        ),\n",
     "    # PCA(n_components=10),\n",
     "    # SVC(kernel='rbf')\n",
     "    # ),\n",
@@ -730,8 +732,8 @@
    "source": [
     "results_sensor2 = []\n",
     "for name, model in models_sensor2.items():\n",
-    "    res = train_and_evaluate_model(model, name, \"sensor2\", x_train2, y_train2, x_test2, y_test2, \n",
-    "                                   export='D:/thesis/models/sensor2')\n",
+    "    res = train_and_evaluate_model(model, name, \"Sensor B\", x_train2, y_train2, x_test2, y_test2, \n",
+    "                                   export='D:/thesis/models/Sensor B')\n",
     "    results_sensor2.append(res)\n",
     "    print(f\"{name} on sensor2: Accuracy = {res['accuracy']:.2f}%\")\n",
     "\n",