diff --git a/.github/workflows/quick-checks.yml b/.github/workflows/quick-checks.yml
new file mode 100644
index 0000000..c00aff9
--- /dev/null
+++ b/.github/workflows/quick-checks.yml
@@ -0,0 +1,51 @@
+---
+name: Quick Checks
+env:
+  KHIOPS_PYTHON_REVISION: 171-implement-multi-table-helper-functions
+on:
+  pull_request:
+  workflow_dispatch:
+concurrency:
+  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout sources
+        uses: actions/checkout@v4
+      - name: Setup Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: '3.11'
+      - name: Install khiops-python dev dependencies
+        run: |
+          pip install pre-commit
+      - name: Run pre-commit checks
+        uses: pre-commit/action@v3.0.1
+
+  execute-nb:
+    runs-on: ubuntu-22.04
+    container:
+      image: ghcr.io/khiopsml/khiops-python/khiopspydev-ubuntu22.04:10.2.3-b.5.0
+    steps:
+      - name: Checkout sources
+        uses: actions/checkout@v4
+      - name: Install pre-requisites
+        run: |
+          pip install nbconvert nbformat jupyter
+      - name: Install khiops-python
+        run: |
+          git clone https://github.com/khiopsml/khiops-python
+          cd khiops-python
+          git switch $KHIOPS_PYTHON_REVISION
+          pip install .
+          kh-status
+          kh-download-datasets
+      - name: Execute the convert hook
+        run: |
+          mkdir output_nb
+          export KHIOPS_PROC_NUMBER=1
+          python khiops-python/doc/convert_tutorials.py --execute-notebooks ./ output_nb
+          ls -ltr output_nb
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 183bef9..a467bea 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,9 +3,7 @@ repos:
     rev: 22.10.0
     hooks:
       - id: black
-        language_version: python3.9
       - id: black-jupyter
-        language_version: python3.9
   - repo: local
     hooks:
       - id: my-nb-clean
diff --git a/Core Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb b/Core Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb
index 938cee3..8d73c85 100644
--- a/Core Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb	
+++ b/Core Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb	
@@ -7,7 +7,9 @@
     "# Core Basics 1: Train, Evaluate and Deploy a Classifier\n",
     "In this lesson we will learn how to train, evaluate and deploy classifiers with Khiops.\n",
     "\n",
-    "We start by importing Khiops, some helper functions and saving the location of the Khiops `Samples` directory into a variable"
+    "Make sure you have installed [Khiops](https://khiops.org/setup/) and [Khiops Visualization](https://khiops.org/setup/visualization/).\n",
+    "\n",
+    "We start by importing Khiops and defining some helper functions:"
    ]
   },
   {
@@ -16,13 +18,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
-    "\n",
+    "import os\n",
+    "import platform\n",
+    "import subprocess\n",
     "from khiops import core as kh\n",
-    "from helper_functions import explorer_open, peek\n",
     "\n",
-    "samples_dir = kh.get_samples_dir()\n",
-    "print(f\"Khiops samples directory located at {samples_dir}\")"
+    "# Define helper functions\n",
+    "def peek(file_path, n=10):\n",
+    "    \"\"\"Shows the first n lines of a file\"\"\"\n",
+    "    with open(file_path, encoding=\"utf8\", errors=\"replace\") as file:\n",
+    "        for line in file.readlines()[:n]:\n",
+    "            print(line, end=\"\")\n",
+    "    print(\"\")\n",
+    "\n",
+    "\n",
+    "def os_open(path):\n",
+    "    \"\"\"Opens a file or directory with its default application\"\"\"\n",
+    "    if platform.system() == \"Windows\":\n",
+    "        os.startfile(path)\n",
+    "    elif platform.system() == \"Darwin\":\n",
+    "        subprocess.call([\"open\", path])\n",
+    "    else:\n",
+    "        subprocess.call([\"xdg-open\", path])\n",
+    "\n",
+    "\n",
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -46,18 +67,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "iris_kdic = path.join(samples_dir, \"Iris\", \"Iris.kdic\")\n",
-    "iris_data_file = path.join(samples_dir, \"Iris\", \"Iris.txt\")\n",
+    "iris_kdic = os.path.join(kh.get_samples_dir(), \"Iris\", \"Iris.kdic\")\n",
+    "iris_data_file = os.path.join(kh.get_samples_dir(), \"Iris\", \"Iris.txt\")\n",
     "\n",
-    "print(\"\")\n",
-    "print(f\"Iris dictionary file location: {iris_kdic}\")\n",
-    "print(\"\")\n",
+    "print(f\"Iris dictionary file: {iris_kdic}\")\n",
     "peek(iris_kdic)\n",
-    "\n",
-    "print(\"\")\n",
-    "print(\"\")\n",
-    "print(f\"Iris data location: {iris_data_file}\")\n",
-    "print(\"\")\n",
+    "print(f\"Iris data file: {iris_data_file}\\n\")\n",
     "peek(iris_data_file)"
    ]
   },
@@ -74,7 +89,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "iris_results_dir = path.join(\"exercises\", \"Iris\")\n",
+    "iris_results_dir = os.path.join(\"exercises\", \"Iris\")\n",
     "print(f\"Iris results directory: {iris_results_dir}\")"
    ]
   },
@@ -101,15 +116,15 @@
     "    results_dir=iris_results_dir,\n",
     "    max_trees=0,  # by default Khiops constructs 10 decision tree variables\n",
     ")\n",
-    "print(f\"Iris report file located at: {iris_report}\")\n",
-    "print(f\"Iris modeling dictionary file located at: {iris_model_kdic}\")"
+    "print(f\"Iris report file: {iris_report}\")\n",
+    "print(f\"Iris modeling dictionary: {iris_model_kdic}\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "You can verify that the result files were created in `iris_results_dir`. In the next sections, we'll use the file at `iris_report` to assess the models' performances and the file at `iris_model_kdic` to deploy it."
+    "You can verify that the result files were created in `iris_results_dir`. In the next sections, we'll use the file at `iris_report` to assess the models' performances and the file at `iris_model_kdic` to deploy it. Now we can see the report with the Khiops Visualization app:"
    ]
   },
   {
@@ -118,8 +133,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# To take a look at the directory where the resulting files are stored\n",
-    "# explorer_open(iris_results_dir)"
+    "# To visualize uncomment the line below\n",
+    "# os_open(iris_report)"
    ]
   },
   {
@@ -139,8 +154,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adult_kdic = path.join(samples_dir, \"Adult\", \"Adult.kdic\")\n",
-    "adult_data_file = path.join(samples_dir, \"Adult\", \"Adult.txt\")"
+    "adult_kdic = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.kdic\")\n",
+    "adult_data_file = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")"
    ]
   },
   {
@@ -158,15 +173,9 @@
    },
    "outputs": [],
    "source": [
-    "print(\"\")\n",
-    "print(f\"Adult dictionary file location: {adult_kdic}\")\n",
-    "print(\"\")\n",
+    "print(f\"Adult dictionary file: {adult_kdic}\")\n",
     "peek(adult_kdic)\n",
-    "\n",
-    "print(\"\")\n",
-    "print(\"\")\n",
-    "print(f\"Adult data location: {adult_data_file}\")\n",
-    "print(\"\")\n",
+    "print(f\"Adult data file: {adult_data_file}\\n\")\n",
     "peek(adult_data_file)"
    ]
   },
@@ -183,8 +192,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adult_results_dir = path.join(\"exercises\", \"Adult\")\n",
-    "print(f\"Adult exercise results directory: {adult_results_dir}\")"
+    "adult_results_dir = os.path.join(\"exercises\", \"Adult\")\n",
+    "print(f\"Adult results directory: {adult_results_dir}\")"
    ]
   },
   {
@@ -211,8 +220,27 @@
     "    results_dir=adult_results_dir,\n",
     "    max_trees=0,\n",
     ")\n",
-    "print(f\"Adult report file located at: {adult_report}\")\n",
-    "print(f\"Adult modeling dictionary file located at: {adult_model_kdic}\")"
+    "print(f\"Adult report file: {adult_report}\")\n",
+    "print(f\"Adult modeling dictionary file: {adult_model_kdic}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Inspect the results with the Khiops Visualization app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "is_khiops_tutorial_solution": true
+   },
+   "outputs": [],
+   "source": [
+    "# To visualize uncomment the line below\n",
+    "# os_open(adult_report)"
    ]
   },
   {
@@ -388,7 +416,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "iris_deployment_file = path.join(iris_results_dir, \"iris_deployment.txt\")\n",
+    "iris_deployment_file = os.path.join(iris_results_dir, \"iris_deployment.txt\")\n",
     "kh.deploy_model(\n",
     "    iris_model_kdic,\n",
     "    dictionary_name=\"SNB_Iris\",\n",
@@ -416,7 +444,7 @@
    },
    "outputs": [],
    "source": [
-    "adult_deployment_file = path.join(adult_results_dir, \"adult_deployment.txt\")\n",
+    "adult_deployment_file = os.path.join(adult_results_dir, \"adult_deployment.txt\")\n",
     "kh.deploy_model(\n",
     "    adult_model_kdic,\n",
     "    dictionary_name=\"SNB_Adult\",\n",
@@ -447,5 +475,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/Core Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb b/Core Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb
index 052e644..739c731 100644
--- a/Core Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb	
+++ b/Core Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb	
@@ -8,7 +8,9 @@
     "\n",
     "In this notebook we learn how to train a classifier with a multi-table data composed of two tables (a root table and a secondary table). It is highly recommended to see the _Core Basics 1_ lesson if you are not familiar with Khiops.\n",
     "\n",
-    "We start by importing Khiops and some helper functions:"
+    "Make sure you have installed [Khiops](https://khiops.org/setup/) and [Khiops Visualization](https://khiops.org/setup/visualization/).\n",
+    "\n",
+    "We start by importing Khiops, checking its installation and defining some helper functions:"
    ]
   },
   {
@@ -17,10 +19,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
-    "\n",
+    "import os\n",
+    "import platform\n",
+    "import subprocess\n",
     "from khiops import core as kh\n",
-    "from helper_functions import explorer_open, peek"
+    "\n",
+    "# Define helper functions\n",
+    "def peek(file_path, n=10):\n",
+    "    \"\"\"Shows the first n lines of a file\"\"\"\n",
+    "    with open(file_path, encoding=\"utf8\", errors=\"replace\") as file:\n",
+    "        for line in file.readlines()[:n]:\n",
+    "            print(line, end=\"\")\n",
+    "    print(\"\")\n",
+    "\n",
+    "\n",
+    "def os_open(path):\n",
+    "    \"\"\"Opens a file or directory with its default application\"\"\"\n",
+    "    if platform.system() == \"Windows\":\n",
+    "        os.startfile(path)\n",
+    "    elif platform.system() == \"Darwin\":\n",
+    "        subprocess.call([\"open\", path])\n",
+    "    else:\n",
+    "        subprocess.call([\"xdg-open\", path])\n",
+    "\n",
+    "\n",
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -75,10 +99,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sarcasm_kdic = path.join(\"data\", \"HeadlineSarcasm\", \"HeadlineSarcasm.kdic\")\n",
+    "sarcasm_kdic = os.path.join(\"data\", \"HeadlineSarcasm\", \"HeadlineSarcasm.kdic\")\n",
     "\n",
-    "print(\"\")\n",
-    "print(f\"HeadlineSarcasm dictionary file location: {sarcasm_kdic}\")\n",
+    "print(f\"HeadlineSarcasm dictionary file: {sarcasm_kdic}\")\n",
     "print(\"\")\n",
     "peek(sarcasm_kdic, n=15)"
    ]
@@ -101,15 +124,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sarcasm_headlines_file = path.join(\"data\", \"HeadlineSarcasm\", \"Headlines.txt\")\n",
-    "sarcasm_words_file = path.join(\"data\", \"HeadlineSarcasm\", \"HeadlineWords.txt\")\n",
+    "sarcasm_headlines_file = os.path.join(\"data\", \"HeadlineSarcasm\", \"Headlines.txt\")\n",
+    "sarcasm_words_file = os.path.join(\"data\", \"HeadlineSarcasm\", \"HeadlineWords.txt\")\n",
     "\n",
-    "print(\"\")\n",
-    "print(f\"HeadlineSarcasm main table file location: {sarcasm_headlines_file}\")\n",
+    "print(f\"HeadlineSarcasm main table file: {sarcasm_headlines_file}\")\n",
     "print(\"\")\n",
     "peek(sarcasm_headlines_file, n=3)\n",
     "\n",
-    "print(\"\")\n",
     "print(f\"HeadlineSarcasm secondary table file location: {sarcasm_words_file}\")\n",
     "print(\"\")\n",
     "peek(sarcasm_words_file, n=15)"
@@ -144,7 +165,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sarcasm_results_dir = path.join(\"exercises\", \"HeadlineSarcasm\")\n",
+    "sarcasm_results_dir = os.path.join(\"exercises\", \"HeadlineSarcasm\")\n",
     "\n",
     "sarcasm_report, sarcasm_model_kdic = kh.train_predictor(\n",
     "    sarcasm_kdic,\n",
@@ -173,7 +194,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# explorer_open(sarcasm_report)"
+    "# To visualize uncomment the line below\n",
+    "# os_open(sarcasm_report)"
    ]
   },
   {
@@ -193,7 +215,8 @@
     "```\n",
     "+---------------+\n",
     "|Accidents      |\n",
-    "+---------------+n|AccidentId*    |\n",
+    "+---------------+\n",
+    "|AccidentId*    |\n",
     "|Gravity        |\n",
     "|Date           |\n",
     "|Hour           | +---------------+\n",
@@ -218,11 +241,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "accidents_kdic = path.join(kh.get_samples_dir(), \"AccidentsSummary\", \"Accidents.kdic\")\n",
-    "accidents_data_file = path.join(\n",
+    "accidents_kdic = os.path.join(\n",
+    "    kh.get_samples_dir(), \"AccidentsSummary\", \"Accidents.kdic\"\n",
+    ")\n",
+    "accidents_data_file = os.path.join(\n",
     "    kh.get_samples_dir(), \"AccidentsSummary\", \"Accidents.txt\"\n",
     ")\n",
-    "vehicles_data_file = path.join(kh.get_samples_dir(), \"AccidentsSummary\", \"Vehicles.txt\")"
+    "vehicles_data_file = os.path.join(\n",
+    "    kh.get_samples_dir(), \"AccidentsSummary\", \"Vehicles.txt\"\n",
+    ")"
    ]
   },
   {
@@ -241,18 +268,15 @@
    },
    "outputs": [],
    "source": [
-    "print(\"\")\n",
-    "print(f\"Accidents dictionary file location: {accidents_kdic}\")\n",
+    "print(f\"Accidents dictionary file: {accidents_kdic}\")\n",
     "print(\"\")\n",
     "peek(accidents_kdic, n=40)\n",
     "\n",
-    "print(\"\")\n",
-    "print(f\"Accidents data table location: {accidents_data_file}\")\n",
+    "print(f\"Accidents (main) data table: {accidents_data_file}\")\n",
     "print(\"\")\n",
     "peek(accidents_data_file)\n",
     "\n",
-    "print(\"\")\n",
-    "print(f\"Vehicles main data table location: {vehicles_data_file}\")\n",
+    "print(f\"Vehicles data table: {vehicles_data_file}\")\n",
     "print(\"\")\n",
     "peek(vehicles_data_file)"
    ]
@@ -270,7 +294,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "accidents_results_dir = path.join(\"exercises\", \"AccidentSummary\")\n",
+    "accidents_results_dir = os.path.join(\"exercises\", \"AccidentSummary\")\n",
     "print(f\"AccidentsSummary exercise results directory: {accidents_results_dir}\")"
    ]
   },
@@ -278,7 +302,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Train a classifier for the `AccidentsEn` database with 1000 variables\n",
+    "#### Train a classifier for the `Accidents` database with 1000 variables\n",
     "Save the resulting file locations into the variables `accidents_report` and `accidents_model_kdic` and print them.\n",
     "\n",
     "Do not forget:\n",
@@ -305,8 +329,8 @@
     "    max_constructed_variables=1000,\n",
     "    max_trees=0,\n",
     ")\n",
-    "print(f\"AccidentsSummary report file located at: {accidents_report}\")\n",
-    "print(f\"AccidentsSummary modeling dictionary file located at: {accidents_model_kdic}\")"
+    "print(f\"AccidentsSummary report file: {accidents_report}\")\n",
+    "print(f\"AccidentsSummary modeling dictionary: {accidents_model_kdic}\")"
    ]
   },
   {
@@ -320,10 +344,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "is_khiops_tutorial_solution": true
+   },
    "outputs": [],
    "source": [
-    "# explorer_open(accidents_report)"
+    "# To visualize uncomment the line below\n",
+    "# os_open(accidents_report)"
    ]
   }
  ],
@@ -348,4 +375,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/Core Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb b/Core Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb
index c7d40c3..7066da6 100644
--- a/Core Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb	
+++ b/Core Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb	
@@ -8,7 +8,9 @@
     "\n",
     "In this notebook, we learn how to train a classifier with a more complex multi-table data where a secondary table is itself a parent table of another table (ie. snowflake schema). It is highly recommended to see the _Basics 1_ and _Basics 2_  lessons if you are not familiar with Khiops.\n",
     "\n",
-    "We start by importing `khiops` and some helper functions:"
+    "Make sure you have installed [Khiops](https://khiops.org/setup/) and [Khiops Visualization](https://khiops.org/setup/visualization/).\n",
+    "\n",
+    "We start by importing Khiops, checking its installation and defining some helper functions:"
    ]
   },
   {
@@ -17,10 +19,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
-    "\n",
+    "import os\n",
+    "import platform\n",
+    "import subprocess\n",
     "from khiops import core as kh\n",
-    "from helper_functions import explorer_open, peek"
+    "\n",
+    "# Define helper functions\n",
+    "def peek(file_path, n=10):\n",
+    "    \"\"\"Shows the first n lines of a file\"\"\"\n",
+    "    with open(file_path, encoding=\"utf8\", errors=\"replace\") as file:\n",
+    "        for line in file.readlines()[:n]:\n",
+    "            print(line, end=\"\")\n",
+    "    print(\"\")\n",
+    "\n",
+    "\n",
+    "def os_open(path):\n",
+    "    \"\"\"Opens a file or directory with its default application\"\"\"\n",
+    "    if platform.system() == \"Windows\":\n",
+    "        os.startfile(path)\n",
+    "    elif platform.system() == \"Darwin\":\n",
+    "        subprocess.call([\"open\", path])\n",
+    "    else:\n",
+    "        subprocess.call([\"xdg-open\", path])\n",
+    "\n",
+    "\n",
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -52,13 +76,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "accidents_dataset_dir = path.join(kh.get_samples_dir(), \"Accidents\")\n",
-    "accidents_kdic = path.join(accidents_dataset_dir, \"Accidents.kdic\")\n",
+    "accidents_dataset_dir = os.path.join(kh.get_samples_dir(), \"Accidents\")\n",
+    "accidents_kdic = os.path.join(accidents_dataset_dir, \"Accidents.kdic\")\n",
     "\n",
-    "print(\"\")\n",
     "print(f\"Accidents dictionary file location: {accidents_kdic}\")\n",
     "print(\"\")\n",
-    "peek(accidents_kdic, n=40)"
+    "peek(accidents_kdic, n=45)"
    ]
   },
   {
@@ -79,27 +102,23 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\"\")\n",
-    "accidents_data_file = path.join(accidents_dataset_dir, \"Accidents.txt\")\n",
-    "print(f\"Accidents data table location: {accidents_data_file}\")\n",
+    "accidents_data_file = os.path.join(accidents_dataset_dir, \"Accidents.txt\")\n",
+    "print(f\"Accidents data table: {accidents_data_file}\")\n",
     "print(\"\")\n",
     "peek(accidents_data_file)\n",
     "\n",
-    "print(\"\")\n",
-    "vehicles_data_file = path.join(accidents_dataset_dir, \"Vehicles.txt\")\n",
-    "print(f\"Vehicles data table location: {vehicles_data_file}\")\n",
+    "vehicles_data_file = os.path.join(accidents_dataset_dir, \"Vehicles.txt\")\n",
+    "print(f\"Vehicles data table: {vehicles_data_file}\")\n",
     "print(\"\")\n",
     "peek(vehicles_data_file)\n",
     "\n",
-    "print(\"\")\n",
-    "places_data_file = path.join(accidents_dataset_dir, \"Places.txt\")\n",
-    "print(f\"Places data table location: {places_data_file}\")\n",
+    "places_data_file = os.path.join(accidents_dataset_dir, \"Places.txt\")\n",
+    "print(f\"Places data table: {places_data_file}\")\n",
     "print(\"\")\n",
     "peek(places_data_file)\n",
     "\n",
-    "print(\"\")\n",
-    "users_data_file = path.join(accidents_dataset_dir, \"Users.txt\")\n",
-    "print(f\"Users data table location: {users_data_file}\")\n",
+    "users_data_file = os.path.join(accidents_dataset_dir, \"Users.txt\")\n",
+    "print(f\"Users data table: {users_data_file}\")\n",
     "print(\"\")\n",
     "peek(users_data_file)"
    ]
@@ -131,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "accidents_results_dir = path.join(\"exercises\", \"Accidents\")\n",
+    "accidents_results_dir = os.path.join(\"exercises\", \"Accidents\")\n",
     "accidents_report, accidents_model_kdic = kh.train_predictor(\n",
     "    accidents_kdic,\n",
     "    dictionary_name=\"Accident\",\n",
@@ -146,8 +165,8 @@
     "    max_constructed_variables=1000,\n",
     "    max_trees=0,\n",
     ")\n",
-    "print(f\"Accidents report file located at: {accidents_report}\")\n",
-    "print(f\"Accidents modeling dictionary file located at: {accidents_model_kdic}\")"
+    "print(f\"Accidents report file: {accidents_report}\")\n",
+    "print(f\"Accidents modeling dictionary file: {accidents_model_kdic}\")"
    ]
   },
   {
@@ -164,7 +183,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# explorer_open(accidents_report)"
+    "# To visualize uncomment the line below\n",
+    "# os_open(accidents_report)"
    ]
   }
  ],
@@ -189,4 +209,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/Core Basics 4 - Train a Coclustering.ipynb b/Core Basics 4 - Train a Coclustering.ipynb
index ccf13ec..7c18627 100644
--- a/Core Basics 4 - Train a Coclustering.ipynb	
+++ b/Core Basics 4 - Train a Coclustering.ipynb	
@@ -8,7 +8,9 @@
     "# Core Basics 4: Train a Coclustering\n",
     "The steps to train a coclustering model with Khiops are very similar to what we have already seen in the basic classifier tutorials.\n",
     "\n",
-    "We now execute the tutorial setup:"
+    "Make sure you have installed [Khiops](https://khiops.org/setup/) and [Khiops CoVisualization](https://khiops.org/setup/visualization/).\n",
+    "\n",
+    "We start by importing Khiops, checking its installation and defining some helper functions:"
    ]
   },
   {
@@ -17,9 +19,32 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
+    "import os\n",
+    "import platform\n",
+    "import subprocess\n",
     "from khiops import core as kh\n",
-    "from helper_functions import explorer_open, peek"
+    "\n",
+    "# Define helper functions\n",
+    "def peek(file_path, n=10):\n",
+    "    \"\"\"Shows the first n lines of a file\"\"\"\n",
+    "    with open(file_path, encoding=\"utf8\", errors=\"replace\") as file:\n",
+    "        for line in file.readlines()[:n]:\n",
+    "            print(line, end=\"\")\n",
+    "    print(\"\")\n",
+    "\n",
+    "\n",
+    "def os_open(path):\n",
+    "    \"\"\"Opens a file or directory with its default application\"\"\"\n",
+    "    if platform.system() == \"Windows\":\n",
+    "        os.startfile(path)\n",
+    "    elif platform.system() == \"Darwin\":\n",
+    "        subprocess.call([\"open\", path])\n",
+    "    else:\n",
+    "        subprocess.call([\"xdg-open\", path])\n",
+    "\n",
+    "\n",
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -35,19 +60,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries_kdic = path.join(\n",
+    "countries_kdic = os.path.join(\n",
     "    \"data\", \"CountriesByOrganization\", \"CountriesByOrganization.kdic\"\n",
     ")\n",
-    "countries_data_file = path.join(\n",
+    "countries_data_file = os.path.join(\n",
     "    \"data\", \"CountriesByOrganization\", \"CountriesByOrganization.csv\"\n",
     ")\n",
     "\n",
-    "print(\"\")\n",
     "print(f\"CountriesByOrganization dictionary file location: {countries_kdic}\")\n",
     "print(\"\")\n",
-    "peek(countries_kdic, n=15)\n",
+    "peek(countries_kdic)\n",
     "\n",
-    "print(\"\")\n",
     "print(f\"CountriesByOrganization data table file location: {countries_data_file}\")\n",
     "print(\"\")\n",
     "peek(countries_data_file)"
@@ -66,7 +89,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries_results_dir = path.join(\"exercises\", \"CountriesByOrganization\")\n",
+    "countries_results_dir = os.path.join(\"exercises\", \"CountriesByOrganization\")\n",
     "\n",
     "countries_cc_report = kh.train_coclustering(\n",
     "    countries_kdic,\n",
@@ -91,7 +114,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# explorer_open(path.dirname(countries_cc_report))"
+    "# To visualize uncomment the line below\n",
+    "# os_open(countries_cc_report)"
    ]
   },
   {
@@ -107,7 +131,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "country_clusters_file = path.join(\n",
+    "country_clusters_file = os.path.join(\n",
     "    \"exercises\", \"CountriesByOrganization\", \"CountryClusters.txt\"\n",
     ")\n",
     "kh.extract_clusters(\n",
@@ -139,9 +163,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "tokyo_kdic = path.join(\"data\", \"Tokyo2021\", \"Athletes.kdic\")\n",
-    "tokyo_data_file = path.join(\"data\", \"Tokyo2021\", \"Athletes.csv\")\n",
-    "tokyo_results_dir = path.join(\"exercises\", \"Tokyo2021\")"
+    "tokyo_kdic = os.path.join(\"data\", \"Tokyo2021\", \"Athletes.kdic\")\n",
+    "tokyo_data_file = os.path.join(\"data\", \"Tokyo2021\", \"Athletes.csv\")\n",
+    "tokyo_results_dir = os.path.join(\"exercises\", \"Tokyo2021\")"
    ]
   },
   {
@@ -157,13 +181,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "print(\"\")\n",
-    "print(f\"Tokyo2021 dictionary file location: {tokyo_kdic}\")\n",
+    "print(f\"Tokyo2021 dictionary file: {tokyo_kdic}\")\n",
     "print(\"\")\n",
     "peek(tokyo_kdic, n=15)\n",
     "\n",
-    "print(\"\")\n",
-    "print(f\"Tokyo data table file location: {tokyo_data_file}\")\n",
+    "print(f\"Tokyo data table file: {tokyo_data_file}\")\n",
     "print(\"\")\n",
     "peek(tokyo_data_file)"
    ]
@@ -207,7 +229,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# explorer_open(path.dirname(tokyo_coclustering_report))"
+    "# To visualize uncomment the line below\n",
+    "# os_open(tokyo_cc_report)"
    ]
   },
   {
@@ -225,7 +248,9 @@
    },
    "outputs": [],
    "source": [
-    "tokyo_country_clusters_file = path.join(\"exercises\", \"Tokyo2021\", \"CountryClusters.txt\")\n",
+    "tokyo_country_clusters_file = os.path.join(\n",
+    "    \"exercises\", \"Tokyo2021\", \"CountryClusters.txt\"\n",
+    ")\n",
     "\n",
     "kh.extract_clusters(\n",
     "    tokyo_cc_report,\n",
@@ -250,7 +275,7 @@
    },
    "outputs": [],
    "source": [
-    "tokyo_discipline_clusters_file = path.join(\n",
+    "tokyo_discipline_clusters_file = os.path.join(\n",
     "    \"exercises\", \"Tokyo2021\", \"CountryClusters.txt\"\n",
     ")\n",
     "\n",
@@ -259,7 +284,7 @@
     "    cluster_variable=\"Discipline\",\n",
     "    clusters_file_path=tokyo_discipline_clusters_file,\n",
     ")\n",
-    "peek(tokyo_discipline_clusters_file, n=200, l=100)"
+    "peek(tokyo_discipline_clusters_file, n=200)"
    ]
   }
  ],
@@ -283,5 +308,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/Sklearn Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb b/Sklearn Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb
index 9dd200b..61f107c 100644
--- a/Sklearn Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb	
+++ b/Sklearn Basics 1 - Train, Evaluate and Deploy a Classifier.ipynb	
@@ -7,7 +7,7 @@
     "# Sklearn Basics 1: Train, Evaluate and Deploy a Classifier\n",
     "In this lesson, we will learn how to train, evaluate and deploy a classifier with Khiops sklearn.\n",
     "\n",
-    "We start by importing Khiops sklearn classifier `KhiopsClassifier` and saving the location of the Khiops `Samples` directory into a variable:"
+    "We start by importing the sklearn estimator `KhiopsClassifier`:"
    ]
   },
   {
@@ -16,14 +16,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
+    "import os\n",
     "import pandas as pd\n",
-    "\n",
     "from khiops import core as kh\n",
     "from khiops.sklearn import KhiopsClassifier\n",
     "\n",
-    "samples_dir = kh.get_samples_dir()\n",
-    "print(f\"Khiops samples directory located at {samples_dir}\")"
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -45,10 +44,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "iris_data_file = path.join(samples_dir, \"Iris\", \"Iris.txt\")\n",
-    "print(\"\")\n",
-    "print(f\"Iris data: 10 first records\")\n",
+    "iris_data_file = os.path.join(kh.get_samples_dir(), \"Iris\", \"Iris.txt\")\n",
     "iris_df = pd.read_csv(iris_data_file, sep=\"\\t\")\n",
+    "\n",
+    "print(f\"Iris data: 10 first records\")\n",
     "iris_df.head()"
    ]
   },
@@ -135,10 +134,10 @@
    },
    "outputs": [],
    "source": [
-    "adult_data_file = path.join(samples_dir, \"Adult\", \"Adult.txt\")\n",
-    "print(\"\")\n",
-    "print(f\"Adult data: 10 first records\")\n",
+    "adult_data_file = os.path.join(kh.get_samples_dir(), \"Adult\", \"Adult.txt\")\n",
     "adult_df = pd.read_csv(adult_data_file, sep=\"\\t\")\n",
+    "\n",
+    "print(f\"Adult data: 10 first records\")\n",
     "adult_df.head()"
    ]
   },
@@ -160,9 +159,9 @@
    "source": [
     "X_adult_train = adult_df.drop([\"class\"], axis=1)\n",
     "y_adult_train = adult_df[\"class\"]\n",
+    "\n",
     "print(\"Adult dataset feature matrix (first 10 rows):\")\n",
     "display(X_adult_train.head(10))\n",
-    "print(\"\")\n",
     "print(\"Adult dataset target vector (first 10 values):\")\n",
     "display(y_adult_train.head(10))"
    ]
@@ -441,5 +440,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/Sklearn Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb b/Sklearn Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb
index d61bf26..b48355d 100644
--- a/Sklearn Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb	
+++ b/Sklearn Basics 2 - Train a Classifier on a Star Multi-Table Dataset.ipynb	
@@ -9,7 +9,7 @@
     "In this notebook, we will learn how to train a classifier with a multi-table data composed of two tables (a root table and a secondary table). It is highly recommended to see the _Sklearn Basics 1_ lesson if you are not familiar with Khiops' sklearn estimators.\n",
     "\n",
     "\n",
-    "We start by importing Khiops sklearn classifier `KhiopsClassifier` and saving the location of the Khiops `Samples` directory into a variable:"
+    "We start by importing the sklearn estimator `KhiopsClassifier`:"
    ]
   },
   {
@@ -18,11 +18,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
+    "import os\n",
     "import pandas as pd\n",
-    "\n",
     "from khiops import core as kh\n",
-    "from khiops.sklearn import KhiopsClassifier"
+    "from khiops.sklearn import KhiopsClassifier\n",
+    "from khiops.utils.helpers import train_test_split_dataset\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -79,13 +83,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sarcasm_dataset_dir = path.join(\"data\", \"HeadlineSarcasm\")\n",
-    "headlines_file = path.join(sarcasm_dataset_dir, \"Headlines.txt\")\n",
+    "sarcasm_dataset_dir = os.path.join(\"data\", \"HeadlineSarcasm\")\n",
+    "headlines_file = os.path.join(sarcasm_dataset_dir, \"Headlines.txt\")\n",
     "headlines_df = pd.read_csv(headlines_file, sep=\"\\t\")\n",
     "print(\"Headlines table (first 10 rows)\")\n",
     "display(headlines_df.head(10))\n",
     "\n",
-    "headlines_words_file = path.join(sarcasm_dataset_dir, \"HeadlineWords.txt\")\n",
+    "headlines_words_file = os.path.join(sarcasm_dataset_dir, \"HeadlineWords.txt\")\n",
     "headlines_words_df = pd.read_csv(headlines_words_file, sep=\"\\t\")\n",
     "print(\"HeadlineWords table (first 10 rows)\")\n",
     "display(headlines_words_df.head(10))"
@@ -104,8 +108,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "headlines_train_df = headlines_df.drop(\"IsSarcasm\", axis=1)\n",
-    "y_sarcasm_train = headlines_df[\"IsSarcasm\"]"
+    "headlines_main_df = headlines_df.drop(\"IsSarcasm\", axis=1)\n",
+    "y_sarcasm = headlines_df[\"IsSarcasm\"]"
    ]
   },
   {
@@ -136,15 +140,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_sarcasm_train = {\n",
+    "X_sarcasm = {\n",
     "    \"main_table\": \"headlines\",\n",
     "    \"tables\": {\n",
-    "        \"headlines\": (headlines_train_df, \"HeadlineId\"),\n",
+    "        \"headlines\": (headlines_main_df, \"HeadlineId\"),\n",
     "        \"headline_words\": (headlines_words_df, \"HeadlineId\"),\n",
     "    },\n",
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To separate this dataset into train and test, we user the `khiops-python` helper function `train_test_split_dataset`. This function allows to separate ``dict`` dataset specifications:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    X_sarcasm_train,\n",
+    "    X_sarcasm_test,\n",
+    "    y_sarcasm_train,\n",
+    "    y_sarcasm_test,\n",
+    ") = train_test_split_dataset(X_sarcasm, y_sarcasm)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -194,7 +219,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we use our sarcasm classifier to obtain predictions on the training data. We normally do that on new test data, and again a multi-table dataset specification would have been needed."
+    "Now, we use our sarcasm classifier to obtain predictions and probabilities on the test data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_sarcasm_test_predicted = khc_sarcasm.predict(X_sarcasm_test)\n",
+    "probas_sarcasm_test = khc_sarcasm.predict_proba(X_sarcasm_test)\n",
+    "\n",
+    "print(\"HeadlineSarcasm test predictions (first 10 values):\")\n",
+    "display(y_sarcasm_test_predicted[:10])\n",
+    "print(\"HeadlineSarcasm test prediction probabilities (first 10 values):\")\n",
+    "display(probas_sarcasm_test[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally we may estimate the accuracy and AUC for the test data:"
    ]
   },
   {
@@ -203,9 +250,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sarcasm_predictions = khc_sarcasm.predict(X_sarcasm_train)\n",
-    "print(\"HeadlineSarcasm train predictions (first 10 values):\")\n",
-    "display(sarcasm_predictions[:10])"
+    "sarcasm_test_accuracy = metrics.accuracy_score(y_sarcasm_test, y_sarcasm_test_predicted)\n",
+    "sarcasm_test_auc = metrics.roc_auc_score(y_sarcasm_test, probas_sarcasm_test[:, 1])\n",
+    "\n",
+    "print(f\"Sarcasm test accuracy: {sarcasm_test_accuracy}\")\n",
+    "print(f\"Sarcasm test auc     : {sarcasm_test_auc}\")"
    ]
   },
   {
@@ -244,16 +293,16 @@
    },
    "outputs": [],
    "source": [
-    "accidents_dataset_dir = path.join(kh.get_samples_dir(), \"AccidentsSummary\")\n",
+    "accidents_dataset_dir = os.path.join(kh.get_samples_dir(), \"AccidentsSummary\")\n",
     "\n",
-    "accidents_file = path.join(accidents_dataset_dir, \"Accidents.txt\")\n",
-    "accidents_df = pd.read_csv(accidents_file, sep=\"\\t\", encoding=\"ISO-8859-1\")\n",
+    "accidents_file = os.path.join(accidents_dataset_dir, \"Accidents.txt\")\n",
+    "accidents_df = pd.read_csv(accidents_file, sep=\"\\t\", encoding=\"latin1\")\n",
     "print(f\"Accidents dataframe (first 10 rows):\")\n",
     "display(accidents_df.head(10))\n",
     "print()\n",
     "\n",
-    "vehicles_file = path.join(accidents_dataset_dir, \"Vehicles.txt\")\n",
-    "vehicles_df = pd.read_csv(vehicles_file, sep=\"\\t\", encoding=\"ISO-8859-1\")\n",
+    "vehicles_file = os.path.join(accidents_dataset_dir, \"Vehicles.txt\")\n",
+    "vehicles_df = pd.read_csv(vehicles_file, sep=\"\\t\", encoding=\"latin1\")\n",
     "print(f\"Vehicles dataframe (first 10 rows):\")\n",
     "display(vehicles_df.head(10))"
    ]
@@ -276,7 +325,7 @@
    "outputs": [],
    "source": [
     "accidents_main_df = accidents_df.drop(\"Gravity\", axis=1)\n",
-    "y_accidents_train = accidents_df[\"Gravity\"]"
+    "y_accidents = accidents_df[\"Gravity\"]"
    ]
   },
   {
@@ -296,7 +345,7 @@
    },
    "outputs": [],
    "source": [
-    "X_accidents_train = {\n",
+    "X_accidents = {\n",
     "    \"main_table\": \"accidents\",\n",
     "    \"tables\": {\n",
     "        \"accidents\": (accidents_main_df, \"AccidentId\"),\n",
@@ -305,6 +354,29 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Split the dataset into train and test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "is_khiops_tutorial_solution": true
+   },
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    X_accidents_train,\n",
+    "    X_accidents_test,\n",
+    "    y_accidents_train,\n",
+    "    y_accidents_test,\n",
+    ") = train_test_split_dataset(X_accidents, y_accidents)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -331,13 +403,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Print the accuracy and auc of the model\n"
+    "#### Print the train accuracy and auc of the model\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "is_khiops_tutorial_solution": true
+   },
    "outputs": [],
    "source": [
     "accidents_train_performance = (\n",
@@ -351,9 +425,32 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Deploy the classifier to obtain predictions on the training data\n",
+    "#### Deploy the classifier to obtain predictions and its probabilites on the test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "is_khiops_tutorial_solution": true
+   },
+   "outputs": [],
+   "source": [
+    "y_accidents_test_predicted = khc_accidents.predict(X_accidents_test)\n",
+    "probas_accidents_test = khc_accidents.predict_proba(X_accidents_test)\n",
     "\n",
-    "*Note that usually one deploys the model on new test data. We deploy on the train dataset to keep the tutorial simple*.\n"
+    "print(\"Accidents test predictions (first 10 values):\")\n",
+    "display(y_accidents_test_predicted[:10])\n",
+    "print(\"Accidentns test prediction probabilities (first 10 values):\")\n",
+    "display(probas_accidents_test[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Obtain the accuracy and AUC on the test dataset\n",
+    "\n"
    ]
   },
   {
@@ -364,7 +461,15 @@
    },
    "outputs": [],
    "source": [
-    "khc_accidents.predict(X_accidents_train)"
+    "accidents_test_accuracy = metrics.accuracy_score(\n",
+    "    y_accidents_test, y_accidents_test_predicted\n",
+    ")\n",
+    "accidents_test_auc = metrics.roc_auc_score(\n",
+    "    y_accidents_test, probas_accidents_test[:, 1]\n",
+    ")\n",
+    "\n",
+    "print(f\"Accidents test accuracy: {accidents_test_accuracy}\")\n",
+    "print(f\"Accidents test auc     : {accidents_test_auc}\")"
    ]
   }
  ],
@@ -389,4 +494,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/Sklearn Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb b/Sklearn Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb
index 06bd3fa..8b3f816 100644
--- a/Sklearn Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb	
+++ b/Sklearn Basics 3 - Train a Classifier on a Snowflake Multi-Table Dataset.ipynb	
@@ -8,7 +8,7 @@
     "\n",
     "In this notebook, we will learn how to train a classifier with a more complex multi-table data where a secondary table is itself a parent tables of another table (ie. snowflake schema). It is highly recommended to see the _Sklearn Basics 1_ and _Sklearn Basics 2_ lessons if you are not familiar with Khiops' sklearn estimators.\n",
     "\n",
-    "We start by importing Khiops sklearn classifier `KhiopsClassifier` and saving the location of the Khiops `Samples` directory into a variable:"
+    "We start by importing the sklearn estimator `KhiopsClassifier`:"
    ]
   },
   {
@@ -17,11 +17,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
+    "import os\n",
     "import pandas as pd\n",
-    "\n",
     "from khiops import core as kh\n",
-    "from khiops.sklearn import KhiopsClassifier"
+    "from khiops.sklearn import KhiopsClassifier\n",
+    "from khiops.utils.helpers import train_test_split_dataset\n",
+    "from sklearn import metrics\n",
+    "\n",
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -58,49 +62,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "accidents_dataset_dir = path.join(kh.get_samples_dir(), \"Accidents\")\n",
+    "accidents_dataset_dir = os.path.join(kh.get_samples_dir(), \"Accidents\")\n",
     "\n",
-    "accidents_file = path.join(\n",
-    "    path.join(kh.get_samples_dir(), \"AccidentsSummary\"), \"Accidents.txt\"\n",
-    ")\n",
+    "accidents_file = os.path.join(kh.get_samples_dir(), \"AccidentsSummary\", \"Accidents.txt\")\n",
     "accidents_df = pd.read_csv(accidents_file, sep=\"\\t\", encoding=\"latin1\")\n",
     "print(f\"Accident dataframe (first 10 rows):\")\n",
     "display(accidents_df.head(10))\n",
     "print()\n",
     "\n",
-    "vehicles_file = path.join(accidents_dataset_dir, \"Vehicles.txt\")\n",
+    "vehicles_file = os.path.join(accidents_dataset_dir, \"Vehicles.txt\")\n",
     "vehicles_df = pd.read_csv(vehicles_file, sep=\"\\t\", encoding=\"latin1\")\n",
     "print(f\"Vehicle dataframe (first 10 rows):\")\n",
     "display(vehicles_df.head(10))\n",
     "\n",
     "# We drop the \"Gravity\" column as it was used to create the target\n",
-    "users_file = path.join(accidents_dataset_dir, \"Users.txt\")\n",
+    "users_file = os.path.join(accidents_dataset_dir, \"Users.txt\")\n",
     "users_df = pd.read_csv(users_file, sep=\"\\t\", encoding=\"latin1\").drop(\"Gravity\", axis=1)\n",
     "print(f\"User dataframe (first 10 rows):\")\n",
     "display(users_df.head(10))\n",
     "print()\n",
     "\n",
-    "places_file = path.join(accidents_dataset_dir, \"Places.txt\")\n",
+    "places_file = os.path.join(accidents_dataset_dir, \"Places.txt\")\n",
     "places_df = pd.read_csv(places_file, sep=\"\\t\", encoding=\"latin1\")\n",
     "print(f\"Places dataframe (first 10 rows):\")\n",
-    "display(places_df.head(10))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Create the main feature matrix and the target vector for `Accidents`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "accidents_main_df = accidents_df.drop(\"Gravity\", axis=1)\n",
-    "y_accidents_train = accidents_df[\"Gravity\"]"
+    "display(places_df.head(10))"
    ]
   },
   {
@@ -112,8 +97,7 @@
     "Note the main table `Accidents` and the secondary table `Places` have one key `AccidentId`.\n",
     "Tables `Vehicles` (the other secondary table) and `Users` (the tertiary table) have two keys: `AccidentId` and `VehicleId`.\n",
     "\n",
-    "To describe relations between tables, the field `relations` must be added to the dictionary of table specifications. This field\n",
-    "contains a list of tuples describing the relations between tables. The first two values (`str`) of each tuple correspond to names of both the parent and the child table involved in the relation. A third value (`bool`) can be optionally set as `True` to indicate that the relation is `1:1`. For example, if the tuple `(table1, table2, True)` is contained in this field, it means that:\n",
+    "To describe relations between tables, we add the  `relations` field must to the dataset spec. This field contains a list of tuples describing the relations between tables. The first two values (`str`) of each tuple correspond to names of both the parent and the child table involved in the relation. A third value (`bool`) can be optionally set as `True` to indicate that the relation is `1:1`. For example, if the tuple `(table1, table2, True)` is contained in this field, it means that:\n",
     "\n",
     "  - `table1` and `table2` are in a `1:1` relationship\n",
     "  - The key of `table1` is contained in that of `table2` (ie. keys are hierarchical)\n",
@@ -127,22 +111,43 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X_accidents_train = {\n",
+    "X_accidents = {\n",
     "    \"main_table\": \"Accidents\",\n",
     "    \"tables\": {\n",
-    "        \"Accidents\": (accidents_main_df, \"AccidentId\"),\n",
+    "        \"Accidents\": (accidents_df.drop(\"Gravity\", axis=1), \"AccidentId\"),\n",
     "        \"Vehicles\": (vehicles_df, [\"AccidentId\", \"VehicleId\"]),\n",
     "        \"Users\": (users_df, [\"AccidentId\", \"VehicleId\"]),\n",
     "        \"Places\": (places_df, [\"AccidentId\"]),\n",
-    "\n",
     "    },\n",
     "    \"relations\": [\n",
     "        (\"Accidents\", \"Vehicles\"),\n",
     "        (\"Vehicles\", \"Users\"),\n",
     "        (\"Accidents\", \"Places\", True),\n",
-    "\n",
     "    ],\n",
-    "}"
+    "}\n",
+    "y_accidents = accidents_df[\"Gravity\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Split the dataset into train and test\n",
+    "We use the helper function `train_test_split_dataset` with the `X` dataset spec to obtain one spec for train and another for test."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    X_accidents_train,\n",
+    "    X_accidents_test,\n",
+    "    y_accidents_train,\n",
+    "    y_accidents_test,\n",
+    ") = train_test_split_dataset(X_accidents, y_accidents, test_size=0.3)"
    ]
   },
   {
@@ -169,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Print the accuracy and auc of the model\n"
+    "#### Print the train accuracy and train auc of the model"
    ]
   },
   {
@@ -189,20 +194,29 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Deploy the classifier to obtain predictions on the training data\n",
-    "\n",
-    "Note that usually one deploys the model on new test data. We deploy on the train dataset to keep the tutorial simple*.\n"
+    "#### Deploy the classifier to obtain predictions and probabilities on the test data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "is_khiops_tutorial_solution": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "khc_accidents.predict(X_accidents_train)"
+    "y_accidents_test_predicted = khc_accidents.predict(X_accidents_test)\n",
+    "probas_accidents_test = khc_accidents.predict_proba(X_accidents_test)\n",
+    "\n",
+    "print(\"Accidents test predictions (first 10 values):\")\n",
+    "display(y_accidents_test_predicted[:10])\n",
+    "print(\"Accidentns test prediction probabilities (first 10 values):\")\n",
+    "display(probas_accidents_test[:10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Estimate the accuracy and AUC metrics on the test data"
    ]
   },
   {
@@ -210,13 +224,23 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "accidents_test_accuracy = metrics.accuracy_score(\n",
+    "    y_accidents_test, y_accidents_test_predicted\n",
+    ")\n",
+    "accidents_test_auc = metrics.roc_auc_score(\n",
+    "    y_accidents_test, probas_accidents_test[:, 1]\n",
+    ")\n",
+    "\n",
+    "print(f\"Accidents test accuracy: {accidents_test_accuracy}\")\n",
+    "print(f\"Accidents test auc     : {accidents_test_auc}\")"
+   ]
   }
  ],
  "metadata": {
   "celltoolbar": "Edit Metadata",
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python 3",
    "language": "python",
    "name": "python3"
   },
@@ -229,10 +253,9 @@
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.5"
+   "pygments_lexer": "ipython3"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}
\ No newline at end of file
diff --git a/Sklearn Basics 4 - Train a Coclustering.ipynb b/Sklearn Basics 4 - Train a Coclustering.ipynb
index b6d77e3..f0f3ba2 100644
--- a/Sklearn Basics 4 - Train a Coclustering.ipynb	
+++ b/Sklearn Basics 4 - Train a Coclustering.ipynb	
@@ -7,7 +7,7 @@
     "# Sklearn Basics 4: Train a Coclustering\n",
     "The steps to train a coclustering model with Khiops are very similar to what we have already seen in the basic classifier tutorials.\n",
     "\n",
-    "We start by importing `KhiopsCoclustering` estimators and some helper functions:"
+    "We start by importing the sklearn estimator `KhiopsCoclustering` and defining a helper function:"
    ]
   },
   {
@@ -16,12 +16,26 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from os import path\n",
-    "import numpy as np\n",
+    "import os\n",
+    "import platform\n",
+    "import subprocess\n",
     "import pandas as pd\n",
-    "from helper_functions import explorer_open\n",
+    "from khiops import core as kh\n",
+    "from khiops.sklearn import KhiopsCoclustering\n",
     "\n",
-    "from khiops.sklearn import KhiopsCoclustering"
+    "\n",
+    "def os_open(path):\n",
+    "    \"\"\"Opens a file or directory with its default application\"\"\"\n",
+    "    if platform.system() == \"Windows\":\n",
+    "        os.startfile(path)\n",
+    "    elif platform.system() == \"Darwin\":\n",
+    "        subprocess.call([\"open\", path])\n",
+    "    else:\n",
+    "        subprocess.call([\"xdg-open\", path])\n",
+    "\n",
+    "\n",
+    "# If there are any issues you may Khiops status with the following command\n",
+    "# kh.get_runner().print_status()"
    ]
   },
   {
@@ -44,7 +58,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries_data_file = path.join(\n",
+    "countries_data_file = os.path.join(\n",
     "    \"data\", \"CountriesByOrganization\", \"CountriesByOrganization.csv\"\n",
     ")\n",
     "X_countries = pd.read_csv(countries_data_file, sep=\";\")\n",
@@ -124,7 +138,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "countries_report = path.join(\"exercises\", \"countries.khcj\")\n",
+    "countries_report = os.path.join(\"exercises\", \"countries.khcj\")\n",
     "khcc_countries.export_report_file(countries_report)\n",
     "# explorer_open(countries_report)"
    ]
@@ -163,11 +177,13 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "is_khiops_tutorial_solution": true
+   },
    "outputs": [],
    "source": [
-    "tokyo_data_file = path.join(\"data\", \"Tokyo2021\", \"Athletes.csv\")\n",
-    "X_tokyo = pd.read_csv(tokyo_data_file, encoding=\"ISO-8859-1\")\n",
+    "tokyo_data_file = os.path.join(\"data\", \"Tokyo2021\", \"Athletes.csv\")\n",
+    "X_tokyo = pd.read_csv(tokyo_data_file, encoding=\"latin1\")\n",
     "print(\"Tokyo2021 dataset (first 10 rows):\")\n",
     "display(X_tokyo.head(10))"
    ]
@@ -186,7 +202,9 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "is_khiops_tutorial_solution": true
+   },
    "outputs": [],
    "source": [
     "khcc_tokyo = KhiopsCoclustering()\n",
@@ -213,7 +231,7 @@
     ").clusters\n",
     "tokyo_leaf_clusters = [cluster for cluster in tokyo_clusters if cluster.is_leaf]\n",
     "print(f\"Number of leaf clusters: {len(tokyo_leaf_clusters)}:\")\n",
-    "for index, cluster in enumerate(tokyo_leaf_clusters, start=1):\n",
+    "for index, cluster in enumerate(tokyo_leaf_clusters):\n",
     "    print(f\"cluster {index:02d}: {cluster.name}\")"
    ]
   },
@@ -232,8 +250,8 @@
    },
    "outputs": [],
    "source": [
-    "print(f\"Members of the cluster {tokyo_leaf_clusters[4].name}:\")\n",
-    "for value_obj in tokyo_leaf_clusters[4].leaf_part.values:\n",
+    "print(f\"Members of the cluster {tokyo_leaf_clusters[29].name}:\")\n",
+    "for value_obj in tokyo_leaf_clusters[29].leaf_part.values:\n",
     "    print(value_obj.value)"
    ]
   },
@@ -252,9 +270,11 @@
    },
    "outputs": [],
    "source": [
-    "tokyo_report = path.join(\"exercises\", \"tokyo.khcj\")\n",
+    "tokyo_report = os.path.join(\"exercises\", \"tokyo.khcj\")\n",
     "khcc_tokyo.export_report_file(tokyo_report)\n",
-    "# explorer_open(tokyo_report)"
+    "\n",
+    "# To visualize uncomment the line below\n",
+    "# os_open(tokyo_report)"
    ]
   },
   {
@@ -298,5 +318,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
-}
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/create-coursework.py b/create-coursework.py
index 85b4a95..a6934f4 100644
--- a/create-coursework.py
+++ b/create-coursework.py
@@ -14,9 +14,6 @@ def main():
     os.mkdir(coursework_dir_path)
 
     print("Creating resources ...")
-    helper_functions_path = os.path.join(".", "./helper_functions.py")
-    shutil.copy(helper_functions_path, coursework_dir_path)
-
     data_dir_path = os.path.join(".", "data")
     coursework_data_dir_path = os.path.join(coursework_dir_path, "data")
     shutil.copytree(data_dir_path, coursework_data_dir_path)
diff --git a/helper_functions.py b/helper_functions.py
deleted file mode 100644
index c0c6c07..0000000
--- a/helper_functions.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import platform
-import os
-import subprocess
-
-
-def peek(filePath, n=10, l=80):
-    """Shows the first n lines of a file with a maximum of l columns"""
-    with open(filePath, encoding="utf8", errors="replace") as file:
-        for i, line in enumerate(file):
-            if i > n:
-                print("...")
-                break
-            print(line.rstrip()[0:l], end="")
-            if len(line) > l:
-                print(" ...", end="")
-            print("")
-
-
-def explorer_open(file_path):
-    if platform.system() == "Windows":
-        os.startfile(file_path)
-    else:
-        if platform.system() == "Darwin":
-            subprocess.call("open", file_path)
-        else:
-            subprocess.call("xdg-open", file_path)