diff --git a/cbs_with_qrf.ipynb b/cbs_with_qrf.ipynb
index 079b5bf27a5a5beea5d91f22f81e0dcd45c4b0a0..a6d415e04162a0c6f58f2e14bac4d5fbfe2fbd17 100644
--- a/cbs_with_qrf.ipynb
+++ b/cbs_with_qrf.ipynb
@@ -2685,6 +2685,16 @@
     "plt.tight_layout()\n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eba4149d",
+   "metadata": {},
+   "source": [
+    "# Conclusion\n",
+    "\n",
+    "In this tutorial you've learned how machine learning methods can be used to perform CBS extrapolation of total energies. Specifically, we used the quantile random forest algorithm to perform inferences and the get prediction intervals. We analyzed the method's effective with respect to other models in the literature and also looked at how the model is built from the features and how it performs across the wide r"
+   ]
   }
  ],
  "metadata": {