diff --git a/README.md b/README.md
index 7683649..e77b4ac 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@ Costa Rica
 [![GitHub](https://img.shields.io/badge/--181717?logo=github&logoColor=ffffff)](https://github.com/)
 [brown9804](https://github.com/brown9804)
 
-Last updated: 2025-04-29
+Last updated: 2025-05-06
 
 ------------------------------------------
 
@@ -15,7 +15,7 @@ Last updated: 2025-04-29
 - Terraform [Demonstration: Deploying Azure Resources for a Data Platform (Microsoft Fabric)](./infrastructure/msFabric/)
 - Terraform [Demonstration: Deploying Azure Resources for an ML Platform](./infrastructure/azMachineLearning/)
 - [Demostration: How to integrate AI in Microsoft Fabric](./msFabric-AI_integration/)
-- [Demostration: Creating a Machine Learning Model](./azML-modelcreation/)
+- [Demostration: Creating a Machine Learning Model](./azML-modelcreation/) - in progress
 
 > Azure Machine Learning (PaaS) is a cloud-based platform from Microsoft designed to help `data scientists and machine learning engineers build, train, deploy, and manage machine learning models at scale`. It supports the `entire machine learning lifecycle, from data preparation and experimentation to deployment and monitoring.` It provides powerful tools for `both code-first and low-code users`, including Jupyter notebooks, drag-and-drop interfaces, and automated machine learning (AutoML). `Azure ML integrates seamlessly with other Azure services and supports popular frameworks like TensorFlow, PyTorch, and Scikit-learn.`
 
@@ -284,9 +284,6 @@ Read more about [Endpoints for inference in production](https://learn.microsoft.
 </details>
 
 
-
-
-
 <div align="center">
   <h3 style="color: #4CAF50;">Total Visitors</h3>
   <img src="https://profile-counter.glitch.me/brown9804/count.svg" alt="Visitor Count" style="border: 2px solid #4CAF50; border-radius: 5px; padding: 5px;"/>
diff --git a/azML-modelcreation/README.md b/azML-modelcreation/README.md
index f111e74..8404179 100644
--- a/azML-modelcreation/README.md
+++ b/azML-modelcreation/README.md
@@ -5,7 +5,7 @@ Costa Rica
 [![GitHub](https://img.shields.io/badge/--181717?logo=github&logoColor=ffffff)](https://github.com/)
 [brown9804](https://github.com/brown9804)
 
-Last updated: 2025-04-29
+Last updated: 2025-05-06
 
 ------------------------------------------
 
@@ -13,11 +13,26 @@ Last updated: 2025-04-29
 <details>
 <summary><b>List of References </b> (Click to expand)</summary>
 
+- [AutoML Regression](https://learn.microsoft.com/en-us/azure/machine-learning/component-reference-v2/regression?view=azureml-api-2)
+- [Evaluate automated machine learning experiment results](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml?view=azureml-api-2)
+- [Evaluate Model component](https://learn.microsoft.com/en-us/azure/machine-learning/component-reference/evaluate-model?view=azureml-api-2)
+
 </details>
 
 <details>
 <summary><b>Table of Content </b> (Click to expand)</summary>
 
+- [Step 1: Set Up Your Azure ML Workspace](#step-1-set-up-your-azure-ml-workspace)
+- [Step 2: Create a Compute Instance](#step-2-create-a-compute-instance)
+- [Step 3: Prepare Your Data](#step-3-prepare-your-data)
+- [Step 4: Create a New Notebook or Script](#step-4-create-a-new-notebook-or-script)
+- [Step 5: Load and Explore the Data](#step-5-load-and-explore-the-data)
+- [Step 6: Train Your Model](#step-6-train-your-model)
+- [Step 7: Evaluate the Model](#step-7-evaluate-the-model)
+- [Step 8: Register the Model](#step-8-register-the-model)
+- [Step 9: Deploy the Model](#step-9-deploy-the-model)
+- [Step 10: Test the Endpoint](#step-10-test-the-endpoint)
+
 </details>
 
 ## Step 1: Set Up Your Azure ML Workspace
@@ -69,10 +84,11 @@ https://github.com/user-attachments/assets/c199156f-96cf-4ed0-a8b5-c88db3e7a552
 
 https://github.com/user-attachments/assets/f8cbd32c-94fc-43d3-a7a8-00f63cdc543d
 
+## Step 4: Create a New Notebook or Script
 
-### **4. Create a New Notebook or Script**
 - Use the compute instance to open a **Jupyter notebook** or create a Python script.
 - Import necessary libraries:
+
   ```python
   import pandas as pd
   from sklearn.model_selection import train_test_split
@@ -80,75 +96,227 @@ https://github.com/user-attachments/assets/f8cbd32c-94fc-43d3-a7a8-00f63cdc543d
   from sklearn.metrics import accuracy_score
   ```
 
----
+  https://github.com/user-attachments/assets/16650584-11cb-48fb-928d-c032e519c14b
+
+## Step 5: Load and Explore the Data
+
+> Load the dataset and perform basic EDA (exploratory data analysis):
 
-### **5. Load and Explore the Data**
-- Load the dataset and perform basic EDA (exploratory data analysis):
   ```python
-  data = pd.read_csv('your_dataset.csv')
-  print(data.head())
+  import mltable
+  from azure.ai.ml import MLClient
+  from azure.identity import DefaultAzureCredential
+  
+  ml_client = MLClient.from_config(credential=DefaultAzureCredential())
+  data_asset = ml_client.data.get("employee_data", version="1")
+  
+  tbl = mltable.load(f'azureml:/{data_asset.id}')
+  
+  df = tbl.to_pandas_dataframe()
+  df
   ```
 
----
+  https://github.com/user-attachments/assets/5fa65d95-8502-4ab7-ba0d-dfda66378cc2
 
-### **6. Train Your Model**
-- Split the data and train a model:
-  ```python
-  X = data.drop('target', axis=1)
-  y = data['target']
-  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
+## Step 6: Train Your Model
+
+> Split the data and train a model:
 
-  model = RandomForestClassifier()
+  ```python
+  # Step 1: Preprocessing
+  from sklearn.preprocessing import LabelEncoder, StandardScaler
+  
+  # Encode categorical columns
+  label_encoder = LabelEncoder()
+  df['Department'] = label_encoder.fit_transform(df['Department'])
+  
+  # Drop non-informative or high-cardinality columns
+  if 'Name' in df.columns:
+      df = df.drop(columns=['Name'])  # 'Name' is likely not predictive
+  
+  # Optional: Check for missing values
+  if df.isnull().sum().any():
+      df = df.dropna()  # or use df.fillna(method='ffill') for imputation
+  
+  # Step 2: Define Features and Target
+  X = df.drop('Salary', axis=1)  # Features: Age and Department
+  y = df['Salary']               # Target: Salary
+  
+  # Optional: Feature Scaling (especially useful for models sensitive to scale)
+  scaler = StandardScaler()
+  X_scaled = scaler.fit_transform(X)
+  
+  # Step 3: Split the Data
+  from sklearn.model_selection import train_test_split
+  
+  X_train, X_test, y_train, y_test = train_test_split(
+      X_scaled, y, test_size=0.2, random_state=42
+  )
+  
+  # Step 4: Train a Regression Model
+  from sklearn.ensemble import RandomForestRegressor
+  
+  model = RandomForestRegressor(
+      n_estimators=100,
+      max_depth=None,
+      random_state=42,
+      n_jobs=-1  # Use all available cores
+  )
   model.fit(X_train, y_train)
   ```
 
----
+  https://github.com/user-attachments/assets/2176c795-5fda-4746-93c7-8b137b526a09
+
+## Step 7: Evaluate the Model
+
+> Check performance:
 
-### **7. Evaluate the Model**
-- Check performance:
   ```python
+  # Step 5: Make Predictions
   predictions = model.predict(X_test)
-  print("Accuracy:", accuracy_score(y_test, predictions))
+  
+  # Step 6: Evaluate the Model
+  from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+  import numpy as np
+  
+  mae = mean_absolute_error(y_test, predictions)
+  mse = mean_squared_error(y_test, predictions)
+  rmse = np.sqrt(mse)
+  r2 = r2_score(y_test, predictions)
+  
+  print("Model Evaluation Metrics")
+  print(f"Mean Absolute Error (MAE): {mae:.2f}")
+  print(f"Mean Squared Error (MSE): {mse:.2f}")
+  print(f"Root Mean Squared Error (RMSE): {rmse:.2f}")
+  print(f"R² Score: {r2:.2f}")
   ```
 
----
+  <img width="550" alt="image" src="https://github.com/user-attachments/assets/6aa19680-cadb-4fe4-a419-a626942e15f9" />
+
+> Distribution of prediction errors:
+
+```python
+import matplotlib.pyplot as plt
+
+# Plot 1: Distribution of prediction errors
+errors = y_test - predictions
+plt.figure(figsize=(10, 6))
+plt.hist(errors, bins=30, color='skyblue', edgecolor='black')
+plt.title('Distribution of Prediction Errors')
+plt.xlabel('Prediction Error')
+plt.ylabel('Frequency')
+plt.grid(True)
+plt.show()
+
+# Plot 2: Predicted vs Actual values
+plt.figure(figsize=(10, 6))
+plt.scatter(y_test, predictions, alpha=0.3, color='darkorange')
+plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)
+plt.title('Predicted vs Actual Salary')
+plt.xlabel('Actual Salary')
+plt.ylabel('Predicted Salary')
+plt.grid(True)
+plt.show()
+```
+
+<img width="550" alt="image" src="https://github.com/user-attachments/assets/d8ec1f2c-eb97-4106-9cee-809849d02796">
+
+## Step 8: Register the Model
+
+> Save and register the model in Azure ML:
 
-### **8. Register the Model**
-- Save and register the model in Azure ML:
   ```python
   import joblib
   joblib.dump(model, 'model.pkl')
-
+  
   from azureml.core import Workspace, Model
   ws = Workspace.from_config()
-  Model.register(workspace=ws, model_path="model.pkl", model_name="my_model")
+  Model.register(workspace=ws, model_path="model.pkl", model_name="my_model_RegressionModel")
   ```
 
----
+https://github.com/user-attachments/assets/a82ff03e-437c-41bc-85fa-8b9903384a5b
+
+
+> [!TIP]
+> Click [here](./src/0_ml-model-creation.ipynb) to read the script used.
+
+## Step 9: Deploy the Model
+
+> Create the Scoring Script:
+
+```python
+import joblib
+import numpy as np
+from azureml.core.model import Model
+
+def init():
+    global model
+    model_path = Model.get_model_path("my_model_RegressionModel")
+    model = joblib.load(model_path)
+
+def run(data):
+    try:
+        input_data = np.array(data["data"])
+        result = model.predict(input_data)
+        return result.tolist()
+    except Exception as e:
+        return str(e)
+```
+
+https://github.com/user-attachments/assets/cdc64857-3bde-4ec9-957d-5399d9447813
+
+> Create the Environment File (env.yml):
+
+https://github.com/user-attachments/assets/8e7c37a2-e32b-4630-8516-f95926c374c0
+
+> Create a new notebook:
+
+https://github.com/user-attachments/assets/1b3e5602-dc64-4c39-be72-ed1cbd74361e
+
+> Create an **inference configuration** and deploy to a web service:
 
-### **9. Deploy the Model**
-- Create an **inference configuration** and deploy to a web service:
   ```python
+  from azureml.core import Workspace
   from azureml.core.environment import Environment
-  from azureml.core.model import InferenceConfig
+  from azureml.core.model import InferenceConfig, Model
   from azureml.core.webservice import AciWebservice
-
-  env = Environment.from_conda_specification(name="myenv", file_path="env.yml")
+  
+  # Load the workspace
+  ws = Workspace.from_config()
+  
+  # Get the registered model
+  registered_model = Model(ws, name="my_model_RegressionModel")
+  
+  # Create environment from requirements.txt (no conda)
+  env = Environment.from_pip_requirements(
+      name="regression-env",
+      file_path="requirements.txt"  # Make sure this file exists in your working directory
+  )
+  
+  # Define inference configuration
   inference_config = InferenceConfig(entry_script="score.py", environment=env)
-
+  
+  # Define deployment configuration
   deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1)
-  service = Model.deploy(workspace=ws,
-                         name="my-service",
-                         models=[model],
-                         inference_config=inference_config,
-                         deployment_config=deployment_config)
+  
+  # Deploy the model
+  service = Model.deploy(
+      workspace=ws,
+      name="regression-model-service",
+      models=[registered_model],
+      inference_config=inference_config,
+      deployment_config=deployment_config
+  )
+  
   service.wait_for_deployment(show_output=True)
+  print(f"Scoring URI: {service.scoring_uri}")
   ```
 
----
 
-### **10. Test the Endpoint**
-- Once deployed, you can send HTTP requests to the endpoint to get predictions.
+
+## Step 10: Test the Endpoint
+
+> Once deployed, you can send HTTP requests to the endpoint to get predictions.
 
 
 
diff --git a/azML-modelcreation/src/0_ml-model-creation.ipynb b/azML-modelcreation/src/0_ml-model-creation.ipynb
new file mode 100644
index 0000000..50bf398
--- /dev/null
+++ b/azML-modelcreation/src/0_ml-model-creation.ipynb
@@ -0,0 +1,836 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Demo: Machine Learning RandomForestRegressor - Model Creation "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "gather": {
+          "logged": 1745944415132
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "from sklearn.ensemble import RandomForestClassifier\n",
+        "from sklearn.metrics import accuracy_score"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "gather": {
+          "logged": 1745944596997
+        }
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Found the config file in: /config.json\n",
+            "Overriding of current TracerProvider is not allowed\n",
+            "Overriding of current LoggerProvider is not allowed\n",
+            "Overriding of current MeterProvider is not allowed\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n",
+            "Overriding of current TracerProvider is not allowed\n",
+            "Overriding of current LoggerProvider is not allowed\n",
+            "Overriding of current MeterProvider is not allowed\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n",
+            "Attempting to instrument while already instrumented\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>Name</th>\n",
+              "      <th>Age</th>\n",
+              "      <th>Department</th>\n",
+              "      <th>Salary</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>Quincy</td>\n",
+              "      <td>58</td>\n",
+              "      <td>Legal</td>\n",
+              "      <td>64769</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>Xander</td>\n",
+              "      <td>46</td>\n",
+              "      <td>Operations</td>\n",
+              "      <td>79604</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>Frank</td>\n",
+              "      <td>44</td>\n",
+              "      <td>R&amp;D</td>\n",
+              "      <td>88645</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>Hannah</td>\n",
+              "      <td>25</td>\n",
+              "      <td>HR</td>\n",
+              "      <td>61634</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>Leo</td>\n",
+              "      <td>42</td>\n",
+              "      <td>IT</td>\n",
+              "      <td>69256</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49995</th>\n",
+              "      <td>Yara</td>\n",
+              "      <td>31</td>\n",
+              "      <td>Legal</td>\n",
+              "      <td>114507</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49996</th>\n",
+              "      <td>Grace</td>\n",
+              "      <td>61</td>\n",
+              "      <td>Sales</td>\n",
+              "      <td>71374</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49997</th>\n",
+              "      <td>Xander</td>\n",
+              "      <td>36</td>\n",
+              "      <td>Sales</td>\n",
+              "      <td>73774</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49998</th>\n",
+              "      <td>Rita</td>\n",
+              "      <td>50</td>\n",
+              "      <td>IT</td>\n",
+              "      <td>74944</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>49999</th>\n",
+              "      <td>Leo</td>\n",
+              "      <td>48</td>\n",
+              "      <td>Marketing</td>\n",
+              "      <td>104217</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>50000 rows × 4 columns</p>\n",
+              "</div>"
+            ],
+            "text/plain": [
+              "         Name  Age  Department  Salary\n",
+              "0      Quincy   58       Legal   64769\n",
+              "1      Xander   46  Operations   79604\n",
+              "2       Frank   44         R&D   88645\n",
+              "3      Hannah   25          HR   61634\n",
+              "4         Leo   42          IT   69256\n",
+              "...       ...  ...         ...     ...\n",
+              "49995    Yara   31       Legal  114507\n",
+              "49996   Grace   61       Sales   71374\n",
+              "49997  Xander   36       Sales   73774\n",
+              "49998    Rita   50          IT   74944\n",
+              "49999     Leo   48   Marketing  104217\n",
+              "\n",
+              "[50000 rows x 4 columns]"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import mltable\n",
+        "from azure.ai.ml import MLClient\n",
+        "from azure.identity import DefaultAzureCredential\n",
+        "\n",
+        "ml_client = MLClient.from_config(credential=DefaultAzureCredential())\n",
+        "data_asset = ml_client.data.get(\"employee_data\", version=\"1\")\n",
+        "\n",
+        "tbl = mltable.load(f'azureml:/{data_asset.id}')\n",
+        "\n",
+        "df = tbl.to_pandas_dataframe()\n",
+        "df"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "gather": {
+          "logged": 1745945249386
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "<style>#sk-container-id-1 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-1 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-1 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-1 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestRegressor</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre></div> </div></div></div></div>"
+            ],
+            "text/plain": [
+              "RandomForestRegressor(n_jobs=-1, random_state=42)"
+            ]
+          },
+          "execution_count": 13,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# Step 1: Preprocessing\n",
+        "from sklearn.preprocessing import LabelEncoder, StandardScaler\n",
+        "\n",
+        "# Encode categorical columns\n",
+        "label_encoder = LabelEncoder()\n",
+        "df['Department'] = label_encoder.fit_transform(df['Department'])\n",
+        "\n",
+        "# Drop non-informative or high-cardinality columns\n",
+        "if 'Name' in df.columns:\n",
+        "    df = df.drop(columns=['Name'])  # 'Name' is likely not predictive\n",
+        "\n",
+        "# Optional: Check for missing values\n",
+        "if df.isnull().sum().any():\n",
+        "    df = df.dropna()  # or use df.fillna(method='ffill') for imputation\n",
+        "\n",
+        "# Step 2: Define Features and Target\n",
+        "X = df.drop('Salary', axis=1)  # Features: Age and Department\n",
+        "y = df['Salary']               # Target: Salary\n",
+        "\n",
+        "# Optional: Feature Scaling (especially useful for models sensitive to scale)\n",
+        "scaler = StandardScaler()\n",
+        "X_scaled = scaler.fit_transform(X)\n",
+        "\n",
+        "# Step 3: Split the Data\n",
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X_scaled, y, test_size=0.2, random_state=42\n",
+        ")\n",
+        "\n",
+        "# Step 4: Train a Regression Model\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "\n",
+        "model = RandomForestRegressor(\n",
+        "    n_estimators=100,\n",
+        "    max_depth=None,\n",
+        "    random_state=42,\n",
+        "    n_jobs=-1  # Use all available cores\n",
+        ")\n",
+        "model.fit(X_train, y_train)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "gather": {
+          "logged": 1745945706307
+        }
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Model Evaluation Metrics\n",
+            "Mean Absolute Error (MAE): 19930.05\n",
+            "Mean Squared Error (MSE): 534509305.15\n",
+            "Root Mean Squared Error (RMSE): 23119.46\n",
+            "R² Score: -0.01\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Step 5: Make Predictions\n",
+        "predictions = model.predict(X_test)\n",
+        "\n",
+        "# Step 6: Evaluate the Model\n",
+        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
+        "import numpy as np\n",
+        "\n",
+        "mae = mean_absolute_error(y_test, predictions)\n",
+        "mse = mean_squared_error(y_test, predictions)\n",
+        "rmse = np.sqrt(mse)\n",
+        "r2 = r2_score(y_test, predictions)\n",
+        "\n",
+        "print(\"Model Evaluation Metrics\")\n",
+        "print(f\"Mean Absolute Error (MAE): {mae:.2f}\")\n",
+        "print(f\"Mean Squared Error (MSE): {mse:.2f}\")\n",
+        "print(f\"Root Mean Squared Error (RMSE): {rmse:.2f}\")\n",
+        "print(f\"R² Score: {r2:.2f}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "gather": {
+          "logged": 1745945717512
+        }
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1000x600 with 1 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "# Plot 1: Distribution of prediction errors\n",
+        "errors = y_test - predictions\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.hist(errors, bins=30, color='skyblue', edgecolor='black')\n",
+        "plt.title('Distribution of Prediction Errors')\n",
+        "plt.xlabel('Prediction Error')\n",
+        "plt.ylabel('Frequency')\n",
+        "plt.grid(True)\n",
+        "plt.show()\n",
+        "\n",
+        "# Plot 2: Predicted vs Actual values\n",
+        "plt.figure(figsize=(10, 6))\n",
+        "plt.scatter(y_test, predictions, alpha=0.3, color='darkorange')\n",
+        "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
+        "plt.title('Predicted vs Actual Salary')\n",
+        "plt.xlabel('Actual Salary')\n",
+        "plt.ylabel('Predicted Salary')\n",
+        "plt.grid(True)\n",
+        "plt.show()\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "gather": {
+          "logged": 1745946275635
+        }
+      },
+      "outputs": [],
+      "source": [
+        "import joblib\n",
+        "joblib.dump(model, 'model.pkl')\n",
+        "\n",
+        "from azureml.core import Workspace, Model\n",
+        "ws = Workspace.from_config()\n",
+        "Model.register(workspace=ws, model_path=\"model.pkl\", model_name=\"my_model_RegressionModel\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "kernel_info": {
+      "name": "python310-sdkv2"
+    },
+    "kernelspec": {
+      "display_name": "Python 3.10 - SDK v2",
+      "language": "python",
+      "name": "python310-sdkv2"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.16"
+    },
+    "microsoft": {
+      "host": {
+        "AzureML": {
+          "notebookHasBeenCompleted": true
+        }
+      },
+      "ms_spell_check": {
+        "ms_spell_check_language": "en"
+      }
+    },
+    "nteract": {
+      "version": "nteract-front-end@1.0.0"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 2
+}