diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
index 95bed174..01790853 100644
--- a/2-Regression/4-Logistic/README.md
+++ b/2-Regression/4-Logistic/README.md
@@ -200,6 +200,8 @@ You can visualize variables side-by-side with Seaborn plots.
 
     ![A swarm of visualized data](images/swarm_2.png)
 
+**Watch Out**: the code above might generate a warning, since seaborn fails to represent such amount of datapoints into a swam plot. A possible solution is decreasing the size of the marker, by using the 'size' parameter. However, be aware that this affects the readability of the plot.
+
 
 > **🧮 Show Me The Math**
 >
diff --git a/2-Regression/4-Logistic/solution/notebook.ipynb b/2-Regression/4-Logistic/solution/notebook.ipynb
index 53eec2d1..0fdceae9 100644
--- a/2-Regression/4-Logistic/solution/notebook.ipynb
+++ b/2-Regression/4-Logistic/solution/notebook.ipynb
@@ -1011,39 +1011,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 63.4% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
-      "  warnings.warn(msg, UserWarning)\n",
-      "/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 21.8% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
-      "  warnings.warn(msg, UserWarning)\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "<Axes: xlabel='Color', ylabel='ord__Item Size'>"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 79.2% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
-      "  warnings.warn(msg, UserWarning)\n",
-      "/home/vscode/.local/lib/python3.11/site-packages/seaborn/categorical.py:3544: UserWarning: 35.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot.\n",
-      "  warnings.warn(msg, UserWarning)\n"
-     ]
-    },
     {
      "data": {
       "image/png": "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",
@@ -1056,6 +1046,10 @@
     }
    ],
    "source": [
+    "# Suppressing warning message claiming that a portion of points cannot be placed into the plot due to the high number of data points\n",
+    "import warnings\n",
+    "warnings.filterwarnings(action='ignore', category=UserWarning, module='seaborn')\n",
+    "\n",
     "palette = {\n",
     "    0: 'orange',\n",
     "    1: 'wheat'\n",
@@ -1063,6 +1057,15 @@
     "sns.swarmplot(x=\"Color\", y=\"ord__Item Size\", hue=\"Color\", data=encoded_pumpkins, palette=palette)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Watch out**: Ignoring warnings is NOT a best practice and should be avoid, whenever possible. Warnings often contain useful messages that let us improve our code and solve an issue.\n",
+    "The reason why we are ignoring this specific warning is to guarantee the readability of the plot. Plotting all the data points with a reduced marker size, while keeping consistency with the palette color, generates an unclear visualization."
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",