diff --git a/Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb b/Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb
index 3428cccc5dac802031be446f1b9a4a246a82223b..9b7ed7490c573d66db8770cb8a37415360fae533 100644
--- a/Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb	
+++ b/Machine Learning/DS_mRNA_limma_dataset_svm_F.ipynb	
@@ -1657,61 +1657,6 @@
     "print('Specificity : ', specificity1)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "id": "8d6a7110",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "#######CONFUSION MATRIX ###########\n",
-    "y_train_pred_svm = model_svm.predict(X_train)\n",
-    "confusion_matrix_train = metrics.confusion_matrix(y_train, y_train_pred_svm)\n",
-    "cm_display = metrics.ConfusionMatrixDisplay(confusion_matrix = confusion_matrix_train)\n",
-    "cm_display.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "id": "81d0fac2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy :  0.9863013698630136\n",
-      "Sensitivity :  0.990990990990991\n",
-      "Specificity :  0.9814814814814815\n"
-     ]
-    }
-   ],
-   "source": [
-    "total1=sum(sum(confusion_matrix_train))\n",
-    "#####from confusion matrix calculate accuracy\n",
-    "accuracy1=(confusion_matrix_train[0,0]+confusion_matrix_train[1,1])/total1\n",
-    "print ('Accuracy : ', accuracy1)\n",
-    "\n",
-    "sensitivity1 = confusion_matrix_train[0,0]/(confusion_matrix_train[0,0]+confusion_matrix_train[0,1])\n",
-    "print('Sensitivity : ', sensitivity1 )\n",
-    "\n",
-    "specificity1 = confusion_matrix_train[1,1]/(confusion_matrix_train[1,0]+confusion_matrix_train[1,1])\n",
-    "print('Specificity : ', specificity1)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 227,