diff --git a/examples/clustering/sklearn_clustering_with_aeon_distances.ipynb b/examples/clustering/sklearn_clustering_with_aeon_distances.ipynb
index 412ec83d5e..256aab95bb 100644
--- a/examples/clustering/sklearn_clustering_with_aeon_distances.ipynb
+++ b/examples/clustering/sklearn_clustering_with_aeon_distances.ipynb
@@ -125,7 +125,6 @@
     "# Visualize the clustering results\n",
     "plt.figure(figsize=(10, 6))\n",
     "for label in np.unique(labels):\n",
-<<<<<<< HEAD
     "    cluster_data = X[labels == label]  # Ensure correct slicing\n",
     "    plt.plot(np.mean(cluster_data, axis=0), label=f\"Cluster {label}\", linewidth=2)\n",
     "\n",
@@ -134,15 +133,13 @@
     "plt.ylabel(\"Mean Value\")\n",
     "plt.legend(loc=\"upper right\", fontsize=\"small\", ncol=2)\n",
     "plt.grid(True)\n",
-    "plt.show()\n"
-=======
+    "plt.show()\n",
     "    plt.plot(\n",
     "        np.mean(X[labels == label], axis=0), label=f\"Cluster {label}\"\n",
     "    )  # Fix indexing\n",
     "plt.title(\"Hierarchical Clustering with DTW Distance\")\n",
     "plt.legend()\n",
     "plt.show()"
->>>>>>> fb45d2921c7f2c8ca58b871275140d14e02c6956
    ]
   },
   {