diff --git a/notebooks/06_sklearn/06_machine_learning_sckit_learn.ipynb b/notebooks/06_sklearn/06_machine_learning_sckit_learn.ipynb
index ac42d02..6f30538 100644
--- a/notebooks/06_sklearn/06_machine_learning_sckit_learn.ipynb
+++ b/notebooks/06_sklearn/06_machine_learning_sckit_learn.ipynb
@@ -491,6 +491,27 @@
     "iris_df"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "y visualizamos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import seaborn as sns; \n",
+    "sns.set(style=\"ticks\", color_codes=True)\n",
+    "sns.pairplot(iris_df, hue=\"species\")\n",
+    "\n",
+    "# Nota--> ayuda sobre alguna función...\n",
+    "#? sns.pairplot"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -533,23 +554,6 @@
     "Hay muchas más tareas de preprocesamiento que se pueden hacer en scikit-learn. **Consulta el paquete sklearn.preprocessing**.\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "import seaborn as sns; \n",
-    "sns.set(style=\"ticks\", color_codes=True)\n",
-    "iris = sns.load_dataset(\"iris\")\n",
-    "sns.pairplot(iris, hue=\"species\")\n",
-    "\n",
-    "# Nota--> ayuda sobre alguna función...\n",
-    "#? sns.pairplot"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1325,7 +1329,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.6"
   }
  },
  "nbformat": 4,