diff --git a/advanced_tutorials/fraud_cheque_detection/1_feature_pipeline.ipynb b/advanced_tutorials/fraud_cheque_detection/1_feature_pipeline.ipynb
index a412a4a6..6ad2ae39 100644
--- a/advanced_tutorials/fraud_cheque_detection/1_feature_pipeline.ipynb
+++ b/advanced_tutorials/fraud_cheque_detection/1_feature_pipeline.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "93202236",
+   "id": "610d4489",
    "metadata": {},
    "source": [
     "## <span style='color:#ff5f27'> 📝 Imports"
@@ -11,24 +11,44 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "712610c8",
+   "id": "c0bcaa61",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
+      "\u001b[0m"
+     ]
+    }
+   ],
    "source": [
-    "# !pip install -r requirements.txt -q"
+    "!pip install -r requirements.txt -q"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "0edd1da0",
+   "id": "c5760f50",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-05-07 12:50:06,474 INFO: generated new fontManager\n"
+     ]
+    }
+   ],
    "source": [
+    "import config\n",
     "import pandas as pd\n",
+    "\n",
     "from functions.utils import (\n",
     "    load_image,\n",
     "    show_image,\n",
+    "    download_and_extract_zip,\n",
     ")\n",
     "from functions.donut import (\n",
     "    load_cheque_parser,\n",
@@ -43,7 +63,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "be633a1e",
+   "id": "c25e5d83",
    "metadata": {},
    "source": [
     "## <span style='color:#ff5f27'>🗄️ Data Loading"
@@ -51,8 +71,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "id": "67366ca2",
+   "execution_count": null,
+   "id": "65065778",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "download_and_extract_zip(config.DOWNLOAD_URL)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e977adb3",
    "metadata": {},
    "outputs": [
     {
@@ -168,7 +198,7 @@
        "4              2912         26.png      axis          Clarice Blanc      1  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -188,8 +218,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "id": "c1f83970",
+   "execution_count": 3,
+   "id": "88c9bf6c",
    "metadata": {},
    "outputs": [
     {
@@ -209,7 +239,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,8 +250,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "id": "19fa12ff",
+   "execution_count": 4,
+   "id": "a08ffae4",
    "metadata": {},
    "outputs": [
     {
@@ -233,7 +263,7 @@
        "Name: count, dtype: int64"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -244,7 +274,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "cd0a4565",
+   "id": "f5822567",
    "metadata": {},
    "source": [
     "## <span style='color:#ff5f27'> 👨🏻‍🎨 Data Visualization"
@@ -252,8 +282,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "id": "17a65a59",
+   "execution_count": 5,
+   "id": "7bdbd2f6",
    "metadata": {},
    "outputs": [
     {
@@ -275,8 +305,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "id": "cfad7f71",
+   "execution_count": 6,
+   "id": "9207bb77",
    "metadata": {},
    "outputs": [
     {
@@ -336,7 +366,7 @@
        "0              3755        120.png      axis  Edmee Pelletier      1  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,8 +377,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "127e74de",
+   "execution_count": 7,
+   "id": "367d510f",
    "metadata": {},
    "outputs": [
     {
@@ -464,7 +494,7 @@
        "504                           68.png      axis  Colette Monjeau      0  "
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -475,8 +505,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "aa6510e8",
+   "execution_count": 8,
+   "id": "38e49887",
    "metadata": {},
    "outputs": [
     {
@@ -497,8 +527,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "8545d92e",
+   "execution_count": 9,
+   "id": "9359ff42",
    "metadata": {},
    "outputs": [
     {
@@ -520,7 +550,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "c2992e1e",
+   "id": "08848b90",
    "metadata": {},
    "outputs": [
     {
@@ -541,7 +571,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "750c5861",
+   "id": "def821e2",
    "metadata": {},
    "source": [
     "## <span style='color:#ff5f27'>👩🏻‍🔬 Feature Engineering </span>\n"
@@ -549,7 +579,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b8f72b96",
+   "id": "f63c2ba5",
    "metadata": {},
    "source": [
     "### <span style='color:#ff5f27'>⛳️ Spell Check </span>\n"
@@ -558,7 +588,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "8852eaa8",
+   "id": "0c92a0c0",
    "metadata": {},
    "outputs": [
     {
@@ -579,7 +609,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "id": "a0758b6e",
+   "id": "4f32a09d",
    "metadata": {},
    "outputs": [
     {
@@ -600,7 +630,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "8c888d66",
+   "id": "aa6e373f",
    "metadata": {},
    "outputs": [
     {
@@ -621,7 +651,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "aec3c28e",
+   "id": "64a54945",
    "metadata": {},
    "outputs": [
     {
@@ -642,7 +672,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "6a914a54",
+   "id": "9dc26883",
    "metadata": {},
    "outputs": [
     {
@@ -663,7 +693,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "id": "75d4f920",
+   "id": "6c1699fc",
    "metadata": {},
    "outputs": [
     {
@@ -779,7 +809,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "id": "49b32414",
+   "id": "4362adec",
    "metadata": {},
    "outputs": [
     {
@@ -891,7 +921,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b26f754a",
+   "id": "7798f0c5",
    "metadata": {},
    "source": [
     "### <span style='color:#ff5f27'>⛳️ Amount in Letter and Number Match </span>\n"
@@ -900,7 +930,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "id": "061e85f3",
+   "id": "253c16d8",
    "metadata": {},
    "outputs": [
     {
@@ -921,7 +951,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "id": "c457f090",
+   "id": "17e7799d",
    "metadata": {},
    "outputs": [
     {
@@ -942,7 +972,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "id": "3a7d873e",
+   "id": "a268a403",
    "metadata": {},
    "outputs": [
     {
@@ -963,7 +993,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "id": "a8c3fce2",
+   "id": "771196e4",
    "metadata": {},
    "outputs": [
     {
@@ -984,7 +1014,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "id": "fd965acb",
+   "id": "151b4791",
    "metadata": {},
    "outputs": [
     {
@@ -1110,7 +1140,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "id": "2e25d5a1",
+   "id": "d7352554",
    "metadata": {},
    "outputs": [
     {
@@ -1231,7 +1261,7 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "id": "b5dab7e4",
+   "id": "fca40774",
    "metadata": {},
    "outputs": [
     {
@@ -1254,7 +1284,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "f1930485",
+   "id": "1037b491",
    "metadata": {},
    "source": [
     "## <span style=\"color:#ff5f27;\"> 🔮 Connecting to Hopsworks Feature Store </span>"
@@ -1263,7 +1293,7 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "id": "d6386c67",
+   "id": "57b46c7b",
    "metadata": {},
    "outputs": [
     {
@@ -1287,7 +1317,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6df04ec",
+   "id": "c46a9f7b",
    "metadata": {},
    "source": [
     "## <span style=\"color:#ff5f27;\"> 🪄 Feature Group Creation </span>"
@@ -1296,7 +1326,7 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "id": "c1cb8ad0",
+   "id": "84f64635",
    "metadata": {},
    "outputs": [
     {
@@ -1347,7 +1377,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "998001fe",
+   "id": "d70a5d38",
    "metadata": {},
    "source": [
     "---"
diff --git a/advanced_tutorials/fraud_cheque_detection/config.py b/advanced_tutorials/fraud_cheque_detection/config.py
index 6c57dcdb..d13f6e1a 100644
--- a/advanced_tutorials/fraud_cheque_detection/config.py
+++ b/advanced_tutorials/fraud_cheque_detection/config.py
@@ -1,6 +1,9 @@
 ### Dataset Configuration
 DATASET_NAME = "shivi/cheques_sample_data"
 
+### Cheque Data URL
+DOWNLOAD_URL = "https://repo.hops.works/dev/jdowling/cheque-fraud.zip"
+
 ### Donut Configuration
 DONUT_BASE_REPO = "naver-clova-ix/donut-base" #"nielsr/donut-base"
 DONUT_FT_REPO = "shivi/donut-cheque-parser"
diff --git a/advanced_tutorials/fraud_cheque_detection/functions/utils.py b/advanced_tutorials/fraud_cheque_detection/functions/utils.py
index 9f00f123..90b2f9f1 100644
--- a/advanced_tutorials/fraud_cheque_detection/functions/utils.py
+++ b/advanced_tutorials/fraud_cheque_detection/functions/utils.py
@@ -1,6 +1,10 @@
 import matplotlib.pyplot as plt
 from PIL import Image
 import numpy as np
+import requests
+import zipfile
+import os
+
 
 def load_image(image_name, folder_name='data/images/'):
     """
@@ -17,6 +21,7 @@ def load_image(image_name, folder_name='data/images/'):
     image = Image.open(folder_name + image_name).convert('RGB')
     return image
 
+
 def show_image(image):
     """
     Displays an image using matplotlib with specific figure sizing and no axis.
@@ -37,3 +42,38 @@ def show_image(image):
     plt.axis('off')
     # Show the plot
     plt.show()
+        
+
+def download_and_extract_zip(url: str, extract_to: str = '.') -> None:
+    """
+    Download a .zip file from a given URL and extract it into the specified directory.
+    
+    This function handles the downloading and extraction of a zip file from a specified URL.
+    It saves the zip file to the local directory, extracts it to a target directory, and handles
+    basic HTTP status checks to ensure the file is downloaded correctly.
+
+    Args:
+    url (str): The URL of the .zip file to download.
+    extract_to (str): Directory to extract the files into, defaults to the current directory.
+    
+    Raises:
+    Exception: Raises an exception if the file could not be downloaded (non-200 status code).
+    """
+    # Get the filename from the URL by splitting it and getting the last segment
+    filename = url.split('/')[-1]
+
+    # Attempt to download the file with HTTP GET request
+    response = requests.get(url, stream=True)
+    if response.status_code == 200:
+        # Write the response content to a file in binary write mode
+        with open(filename, 'wb') as f:
+            f.write(response.content)
+        print(f"Downloaded {filename}")
+
+        # Extract the .zip file using the ZipFile class
+        with zipfile.ZipFile(filename, 'r') as zip_ref:
+            zip_ref.extractall(extract_to)
+        print(f"Extracted {filename} to {extract_to}")
+    else:
+        # If the download fails, raise an exception with the status code
+        raise Exception(f"❌ Failed to download the file. Status code: {response.status_code}")