diff --git a/code/notebooks/stft.ipynb b/code/notebooks/stft.ipynb
index 5deb481..ceb9176 100644
--- a/code/notebooks/stft.ipynb
+++ b/code/notebooks/stft.ipynb
@@ -803,6 +803,13 @@
     "Following code is load the exported model for specific sensor side that was trained on Dataset A and then used to predict the class labels for the unseen Dataset B."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sensor A"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -828,84 +835,11 @@
     "print(classification_report(y, y_pred_svm_1))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "svm_model = load('D:/thesis/models/sensor2/SVM.joblib')\n",
-    "# svm_model = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')\n",
-    "y_pred_svm_2 = svm_model.predict(X2b)\n",
-    "\n",
-    "# 5. Evaluate\n",
-    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_2))\n",
-    "print(classification_report(y, y_pred_svm_2))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Model sensor 1 to predict sensor 2 data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import accuracy_score, classification_report\n",
-    "# 4. Validate on Dataset B\n",
-    "from joblib import load\n",
-    "svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
-    "y_pred_svm = svm_model.predict(X2b)\n",
-    "\n",
-    "# 5. Evaluate\n",
-    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
-    "print(classification_report(y, y_pred_svm))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.metrics import accuracy_score, classification_report\n",
-    "# 4. Validate on Dataset B\n",
-    "y_pred = rf_model2.predict(X2b)\n",
-    "\n",
-    "# 5. Evaluate\n",
-    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred))\n",
-    "print(classification_report(y, y_pred))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_predict = svm_model2.predict(X2b.iloc[[5312],:])\n",
-    "print(y_predict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y[5312]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Confusion Matrix"
+    "#### Confusion Matrix Sensor A"
    ]
   },
   {
@@ -930,6 +864,35 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Sensor B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "svm_model = load('D:/thesis/models/sensor2/SVM.joblib')\n",
+    "# svm_model = load('D:/thesis/models/sensor2/SVM with StandardScaler and PCA.joblib')\n",
+    "y_pred_svm_2 = svm_model.predict(X2b)\n",
+    "\n",
+    "# 5. Evaluate\n",
+    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm_2))\n",
+    "print(classification_report(y, y_pred_svm_2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Confusion Matrix Sensor B"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -951,6 +914,30 @@
     "plt.title(\"Confusion Matrix of Sensor B Test on Dataset B\")\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Model sensor 1 to predict sensor 2 data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import accuracy_score, classification_report\n",
+    "# 4. Validate on Dataset B\n",
+    "from joblib import load\n",
+    "svm_model = load('D:/thesis/models/sensor1/SVM.joblib')\n",
+    "y_pred_svm = svm_model.predict(X2b)\n",
+    "\n",
+    "# 5. Evaluate\n",
+    "print(\"Accuracy on Dataset B:\", accuracy_score(y, y_pred_svm))\n",
+    "print(classification_report(y, y_pred_svm))"
+   ]
   }
  ],
  "metadata": {