diff --git a/docs/tutorials/autogluon-assistant-quick-start.ipynb b/docs/tutorials/autogluon-assistant-quick-start.ipynb
index 75add8e..66a6282 100644
--- a/docs/tutorials/autogluon-assistant-quick-start.ipynb
+++ b/docs/tutorials/autogluon-assistant-quick-start.ipynb
@@ -160,6 +160,23 @@
     "train_data.head()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cbe4200e",
+   "metadata": {
+    "code_font_size": "6pt"
+   },
+   "source": [
+    "```\n",
+    "  PassengerId HomePlanet CryoSleep  Destination   Age    VIP  RoomService  FoodCourt  ShoppingMall  Spa  VRDeck                Name  Transported Deck  Cabin_num Side\n",
+    "0     5647_01     Europa      True  TRAPPIST-1e  35.0  False          0.0        NaN           0.0  0.0     NaN  Dyonevi Matoltuble         True    C      178.0    P\n",
+    "1     4061_02       Mars      True  TRAPPIST-1e   0.0  False          0.0        0.0           0.0  0.0     0.0          Graw Kashe         True    F      766.0    S\n",
+    "2     0691_03       Mars      True  TRAPPIST-1e  23.0  False          0.0        0.0           0.0  0.0     0.0          Moss Potte         True    E       49.0    S\n",
+    "3     1094_01      Earth     False  TRAPPIST-1e  60.0    NaN        437.0        2.0           0.0  0.0   365.0   Carona Webstenson        False    F      224.0    P\n",
+    "4     6394_01      Earth      True  TRAPPIST-1e  41.0  False          0.0        0.0           0.0  0.0     0.0   Pattie Lambleyoun         True    G     1037.0    S\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -171,6 +188,19 @@
     "    print(f.read())"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "bd190e32",
+   "metadata": {
+    "code_font_size": "6pt"
+   },
+   "source": [
+    "```\n",
+    "You are solving this data science tasks of binary classification: \n",
+    "The dataset presented here (the spaceship dataset) comprises a lot of features, including both numerical and categorical features. Some of the features are missing, with nan value. We have splitted the dataset into three parts of train, valid and test. Your task is to predict the Transported item, which is a binary label with True and False. The evaluation metric is the classification accuracy.\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "ec8a61ef4291bc39",
@@ -197,7 +227,9 @@
   {
    "cell_type": "markdown",
    "id": "8adda2cc",
-   "metadata": {},
+   "metadata": {
+    "code_font_size": "6pt"
+   },
    "source": [
     "```\n",
     "INFO:root:Starting AutoGluon-Assistant\n",
@@ -390,7 +422,9 @@
   {
    "cell_type": "markdown",
    "id": "52a7d48a",
-   "metadata": {},
+   "metadata": {
+    "code_font_size": "6pt"
+   },
    "source": [
     "```\n",
     "First few predictions:\n",