diff --git a/README.md b/README.md
index 63bab55..a9128ca 100644
--- a/README.md
+++ b/README.md
@@ -193,4 +193,76 @@ Slide deck and technical report
 
 Model cards for stakeholders
 
-Documentation for regulators
\ No newline at end of file
+Documentation for regulators
+# Step 1: Clone this repo
+git clone https://github.com/sumeyaaaa/Credit-Risk-Probability-Model
+cd Credit-Risk-Probability-Model
+
+
+# Step 2: Install dependencies
+pip install -r requirements.txt
+
+
+\`\`\`
+"""
+## ⚙️ CI/CD – GitHub Actions
+
+- Implemented GitHub Actions workflow to validate notebook execution and maintain reproducibility.
+
+---
+
+## 📈 Key Skills Demonstrated
+
+- ✅ Git & GitHub Workflow
+- ✅ EDA and data storytelling
+- ✅ feauture engineering
+- ✅ RFM metrics
+- ✅ model training and evaluation
+
+---
+
+## 🧰 Technologies
+
+- Python 3.10+
+- Pandas, Seaborn, Matplotlib
+- Docker
+- Git & GitHub
+
+# Repository structure
+
+├── .github
+│   └── workflows
+│       └── ci.yml                    # Continuous Integration workflow configuration
+├── data
+│   ├── raw                          # Raw input data files (unprocessed)
+│   └── processed                    # Cleaned and transformed data ready for analysis
+├── notebook/
+│   ├── task 1 and 2
+│   │   └── load_EDA.ipynb           # Data loading and exploratory data analysis notebook
+│   ├── task-3                       # Feature engineering and web scraping using google-play-scraper
+│   │   └── feature-engineering.ipynb
+│   ├── task-4                       # Customer segmentation and risk labeling (RFM metrics)
+│   │   └── RFMmetrics.ipynb
+│   ├── task-5                       # Model building, evaluation, and final insights
+│   │   └── modeling.ipynb
+│   ├── task-6                       # Sentiment labeling and thematic keyword extraction
+│       └── sentiment_analysis.ipynb
+├── src/                            # Core Python modules containing business logic
+│   ├── __init__.py                 # Oracle database connection and utilities
+│   ├── load.py                    # Data loading and preprocessing functions
+│   ├── PreProcessing.py           # Text preprocessing utilities (e.g., lemmatization)
+│   ├── RFMmetrics.py              # Customer segmentation and clustering logic
+│   ├── saveFile.py                # Sentiment classification model and related functions
+│   ├── visualization.py           # Visualization utilities for model comparison and analysis
+│   ├── api/                       # API backend code for deployment and data export
+│   │   ├── main.py                # FastAPI app main entrypoint
+│   │   └── pydantic_models.py     # Data validation schemas using Pydantic
+│   └── models/                    # Saved machine learning models and related artifacts
+│       └── best_model.pkl         # Serialized best-performing model
+├── test/                          # Tests and SQL schema definitions for Oracle database
+├── docker-compose.yml             # Docker Compose file for multi-container orchestration
+├── dockerfile                    # Docker image build instructions
+├── LICENSE                       # Project license file
+├── README.md                     # Project overview, methodology, and results summary
+├── .gitignore                    # Specifies files and folders to be excluded from Git
+└── venv/                        # Python virtual environment (excluded from version control)
diff --git a/notebooks/task-5/modeling.ipynb b/notebooks/task-5/modeling.ipynb
index 45b819d..a923379 100644
--- a/notebooks/task-5/modeling.ipynb
+++ b/notebooks/task-5/modeling.ipynb
@@ -24,7 +24,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "8GSjqYq-D3RE",
-        "outputId": "ae4059ba-e0d2-4e77-90ba-72fec6d88851"
+        "outputId": "ffbfa123-2d8e-44f8-feb0-30c578f491c0"
       },
       "execution_count": 1,
       "outputs": [
@@ -42,13 +42,233 @@
         }
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "from google.colab import files\n",
+        "\n",
+        "uploaded = files.upload()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 73
+        },
+        "id": "RepXyAR_PO5h",
+        "outputId": "96456f4a-682b-4378-9d7c-a786cee44f6c"
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-183167d0-b3d5-44d6-8586-111fd2c15217\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-183167d0-b3d5-44d6-8586-111fd2c15217\">\n",
+              "      Upload widget is only available when the cell has been executed in the\n",
+              "      current browser session. Please rerun this cell to enable.\n",
+              "      </output>\n",
+              "      <script>// Copyright 2017 Google LLC\n",
+              "//\n",
+              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+              "// you may not use this file except in compliance with the License.\n",
+              "// You may obtain a copy of the License at\n",
+              "//\n",
+              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
+              "//\n",
+              "// Unless required by applicable law or agreed to in writing, software\n",
+              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+              "// See the License for the specific language governing permissions and\n",
+              "// limitations under the License.\n",
+              "\n",
+              "/**\n",
+              " * @fileoverview Helpers for google.colab Python module.\n",
+              " */\n",
+              "(function(scope) {\n",
+              "function span(text, styleAttributes = {}) {\n",
+              "  const element = document.createElement('span');\n",
+              "  element.textContent = text;\n",
+              "  for (const key of Object.keys(styleAttributes)) {\n",
+              "    element.style[key] = styleAttributes[key];\n",
+              "  }\n",
+              "  return element;\n",
+              "}\n",
+              "\n",
+              "// Max number of bytes which will be uploaded at a time.\n",
+              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
+              "\n",
+              "function _uploadFiles(inputId, outputId) {\n",
+              "  const steps = uploadFilesStep(inputId, outputId);\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  // Cache steps on the outputElement to make it available for the next call\n",
+              "  // to uploadFilesContinue from Python.\n",
+              "  outputElement.steps = steps;\n",
+              "\n",
+              "  return _uploadFilesContinue(outputId);\n",
+              "}\n",
+              "\n",
+              "// This is roughly an async generator (not supported in the browser yet),\n",
+              "// where there are multiple asynchronous steps and the Python side is going\n",
+              "// to poll for completion of each step.\n",
+              "// This uses a Promise to block the python side on completion of each step,\n",
+              "// then passes the result of the previous step as the input to the next step.\n",
+              "function _uploadFilesContinue(outputId) {\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  const steps = outputElement.steps;\n",
+              "\n",
+              "  const next = steps.next(outputElement.lastPromiseValue);\n",
+              "  return Promise.resolve(next.value.promise).then((value) => {\n",
+              "    // Cache the last promise value to make it available to the next\n",
+              "    // step of the generator.\n",
+              "    outputElement.lastPromiseValue = value;\n",
+              "    return next.value.response;\n",
+              "  });\n",
+              "}\n",
+              "\n",
+              "/**\n",
+              " * Generator function which is called between each async step of the upload\n",
+              " * process.\n",
+              " * @param {string} inputId Element ID of the input file picker element.\n",
+              " * @param {string} outputId Element ID of the output display.\n",
+              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
+              " */\n",
+              "function* uploadFilesStep(inputId, outputId) {\n",
+              "  const inputElement = document.getElementById(inputId);\n",
+              "  inputElement.disabled = false;\n",
+              "\n",
+              "  const outputElement = document.getElementById(outputId);\n",
+              "  outputElement.innerHTML = '';\n",
+              "\n",
+              "  const pickedPromise = new Promise((resolve) => {\n",
+              "    inputElement.addEventListener('change', (e) => {\n",
+              "      resolve(e.target.files);\n",
+              "    });\n",
+              "  });\n",
+              "\n",
+              "  const cancel = document.createElement('button');\n",
+              "  inputElement.parentElement.appendChild(cancel);\n",
+              "  cancel.textContent = 'Cancel upload';\n",
+              "  const cancelPromise = new Promise((resolve) => {\n",
+              "    cancel.onclick = () => {\n",
+              "      resolve(null);\n",
+              "    };\n",
+              "  });\n",
+              "\n",
+              "  // Wait for the user to pick the files.\n",
+              "  const files = yield {\n",
+              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
+              "    response: {\n",
+              "      action: 'starting',\n",
+              "    }\n",
+              "  };\n",
+              "\n",
+              "  cancel.remove();\n",
+              "\n",
+              "  // Disable the input element since further picks are not allowed.\n",
+              "  inputElement.disabled = true;\n",
+              "\n",
+              "  if (!files) {\n",
+              "    return {\n",
+              "      response: {\n",
+              "        action: 'complete',\n",
+              "      }\n",
+              "    };\n",
+              "  }\n",
+              "\n",
+              "  for (const file of files) {\n",
+              "    const li = document.createElement('li');\n",
+              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
+              "    li.append(span(\n",
+              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
+              "        `last modified: ${\n",
+              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
+              "                                    'n/a'} - `));\n",
+              "    const percent = span('0% done');\n",
+              "    li.appendChild(percent);\n",
+              "\n",
+              "    outputElement.appendChild(li);\n",
+              "\n",
+              "    const fileDataPromise = new Promise((resolve) => {\n",
+              "      const reader = new FileReader();\n",
+              "      reader.onload = (e) => {\n",
+              "        resolve(e.target.result);\n",
+              "      };\n",
+              "      reader.readAsArrayBuffer(file);\n",
+              "    });\n",
+              "    // Wait for the data to be ready.\n",
+              "    let fileData = yield {\n",
+              "      promise: fileDataPromise,\n",
+              "      response: {\n",
+              "        action: 'continue',\n",
+              "      }\n",
+              "    };\n",
+              "\n",
+              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
+              "    let position = 0;\n",
+              "    do {\n",
+              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
+              "      const chunk = new Uint8Array(fileData, position, length);\n",
+              "      position += length;\n",
+              "\n",
+              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
+              "      yield {\n",
+              "        response: {\n",
+              "          action: 'append',\n",
+              "          file: file.name,\n",
+              "          data: base64,\n",
+              "        },\n",
+              "      };\n",
+              "\n",
+              "      let percentDone = fileData.byteLength === 0 ?\n",
+              "          100 :\n",
+              "          Math.round((position / fileData.byteLength) * 100);\n",
+              "      percent.textContent = `${percentDone}% done`;\n",
+              "\n",
+              "    } while (position < fileData.byteLength);\n",
+              "  }\n",
+              "\n",
+              "  // All done.\n",
+              "  yield {\n",
+              "    response: {\n",
+              "      action: 'complete',\n",
+              "    }\n",
+              "  };\n",
+              "}\n",
+              "\n",
+              "scope.google = scope.google || {};\n",
+              "scope.google.colab = scope.google.colab || {};\n",
+              "scope.google.colab._files = {\n",
+              "  _uploadFiles,\n",
+              "  _uploadFilesContinue,\n",
+              "};\n",
+              "})(self);\n",
+              "</script> "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Saving final_data.csv to final_data.csv\n"
+          ]
+        }
+      ]
+    },
     {
       "cell_type": "code",
       "source": [
         "import pandas as pd\n",
         "\n",
         "# Load the already-existing file\n",
-        "final_data = pd.read_csv('/content/final_data.csv')\n",
+        "final_data = pd.read_csv('final_data.csv')\n",
         "\n",
         "# Preview it\n",
         "final_data.head()\n"
@@ -59,9 +279,9 @@
           "height": 255
         },
         "id": "Mau_5ANc-jBT",
-        "outputId": "df814b5f-5fc8-4660-b3fc-76b0a6fc4eb6"
+        "outputId": "b1517fed-9b84-4fe9-886f-a7b52660abed"
       },
-      "execution_count": 2,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -106,7 +326,7 @@
             ],
             "text/html": [
               "\n",
-              "  <div id=\"df-5d4e201d-5048-4e0b-b38e-3981b74ad29e\" class=\"colab-df-container\">\n",
+              "  <div id=\"df-d50fc4de-dd41-4c54-9c72-d73e36385529\" class=\"colab-df-container\">\n",
               "    <div>\n",
               "<style scoped>\n",
               "    .dataframe tbody tr th:only-of-type {\n",
@@ -276,7 +496,7 @@
               "    <div class=\"colab-df-buttons\">\n",
               "\n",
               "  <div class=\"colab-df-container\">\n",
-              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d4e201d-5048-4e0b-b38e-3981b74ad29e')\"\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d50fc4de-dd41-4c54-9c72-d73e36385529')\"\n",
               "            title=\"Convert this dataframe to an interactive table.\"\n",
               "            style=\"display:none;\">\n",
               "\n",
@@ -328,12 +548,12 @@
               "\n",
               "    <script>\n",
               "      const buttonEl =\n",
-              "        document.querySelector('#df-5d4e201d-5048-4e0b-b38e-3981b74ad29e button.colab-df-convert');\n",
+              "        document.querySelector('#df-d50fc4de-dd41-4c54-9c72-d73e36385529 button.colab-df-convert');\n",
               "      buttonEl.style.display =\n",
               "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "\n",
               "      async function convertToInteractive(key) {\n",
-              "        const element = document.querySelector('#df-5d4e201d-5048-4e0b-b38e-3981b74ad29e');\n",
+              "        const element = document.querySelector('#df-d50fc4de-dd41-4c54-9c72-d73e36385529');\n",
               "        const dataTable =\n",
               "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
               "                                                    [key], {});\n",
@@ -353,8 +573,8 @@
               "  </div>\n",
               "\n",
               "\n",
-              "    <div id=\"df-ee4c7cbc-4cbd-4565-b124-f3dcf573b27e\">\n",
-              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee4c7cbc-4cbd-4565-b124-f3dcf573b27e')\"\n",
+              "    <div id=\"df-d846707d-262c-4625-b704-ff80ea97cb21\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d846707d-262c-4625-b704-ff80ea97cb21')\"\n",
               "                title=\"Suggest charts\"\n",
               "                style=\"display:none;\">\n",
               "\n",
@@ -473,7 +693,7 @@
               "        }\n",
               "        (() => {\n",
               "          let quickchartButtonEl =\n",
-              "            document.querySelector('#df-ee4c7cbc-4cbd-4565-b124-f3dcf573b27e button');\n",
+              "            document.querySelector('#df-d846707d-262c-4625-b704-ff80ea97cb21 button');\n",
               "          quickchartButtonEl.style.display =\n",
               "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
               "        })();\n",
@@ -489,7 +709,7 @@
             }
           },
           "metadata": {},
-          "execution_count": 2
+          "execution_count": 3
         }
       ]
     },
@@ -503,23 +723,29 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "3uVq0fYAB-b5",
-        "outputId": "b886e0af-2c7b-4e3b-b0a1-71010435d53c"
+        "outputId": "1b78a642-13b5-4d68-b501-e2ff845a4895"
       },
-      "execution_count": 3,
+      "execution_count": 4,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "Requirement already satisfied: mlflow in /usr/local/lib/python3.11/dist-packages (3.1.1)\n",
+            "Collecting mlflow\n",
+            "  Downloading mlflow-3.1.1-py3-none-any.whl.metadata (29 kB)\n",
             "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.11/dist-packages (1.6.1)\n",
             "Requirement already satisfied: pandas in /usr/local/lib/python3.11/dist-packages (2.2.2)\n",
-            "Requirement already satisfied: mlflow-skinny==3.1.1 in /usr/local/lib/python3.11/dist-packages (from mlflow) (3.1.1)\n",
+            "Collecting mlflow-skinny==3.1.1 (from mlflow)\n",
+            "  Downloading mlflow_skinny-3.1.1-py3-none-any.whl.metadata (30 kB)\n",
             "Requirement already satisfied: Flask<4 in /usr/local/lib/python3.11/dist-packages (from mlflow) (3.1.1)\n",
-            "Requirement already satisfied: alembic!=1.10.0,<2 in /usr/local/lib/python3.11/dist-packages (from mlflow) (1.16.2)\n",
-            "Requirement already satisfied: docker<8,>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from mlflow) (7.1.0)\n",
-            "Requirement already satisfied: graphene<4 in /usr/local/lib/python3.11/dist-packages (from mlflow) (3.4.3)\n",
-            "Requirement already satisfied: gunicorn<24 in /usr/local/lib/python3.11/dist-packages (from mlflow) (23.0.0)\n",
+            "Collecting alembic!=1.10.0,<2 (from mlflow)\n",
+            "  Downloading alembic-1.16.2-py3-none-any.whl.metadata (7.3 kB)\n",
+            "Collecting docker<8,>=4.0.0 (from mlflow)\n",
+            "  Downloading docker-7.1.0-py3-none-any.whl.metadata (3.8 kB)\n",
+            "Collecting graphene<4 (from mlflow)\n",
+            "  Downloading graphene-3.4.3-py2.py3-none-any.whl.metadata (6.9 kB)\n",
+            "Collecting gunicorn<24 (from mlflow)\n",
+            "  Downloading gunicorn-23.0.0-py3-none-any.whl.metadata (4.4 kB)\n",
             "Requirement already satisfied: matplotlib<4 in /usr/local/lib/python3.11/dist-packages (from mlflow) (3.10.0)\n",
             "Requirement already satisfied: numpy<3 in /usr/local/lib/python3.11/dist-packages (from mlflow) (2.0.2)\n",
             "Requirement already satisfied: pyarrow<21,>=4.0.0 in /usr/local/lib/python3.11/dist-packages (from mlflow) (18.1.0)\n",
@@ -528,12 +754,15 @@
             "Requirement already satisfied: cachetools<7,>=5.0.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (5.5.2)\n",
             "Requirement already satisfied: click<9,>=7.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (8.2.1)\n",
             "Requirement already satisfied: cloudpickle<4 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (3.1.1)\n",
-            "Requirement already satisfied: databricks-sdk<1,>=0.20.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (0.57.0)\n",
+            "Collecting databricks-sdk<1,>=0.20.0 (from mlflow-skinny==3.1.1->mlflow)\n",
+            "  Downloading databricks_sdk-0.57.0-py3-none-any.whl.metadata (39 kB)\n",
             "Requirement already satisfied: fastapi<1 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (0.115.13)\n",
             "Requirement already satisfied: gitpython<4,>=3.1.9 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (3.1.44)\n",
             "Requirement already satisfied: importlib_metadata!=4.7.0,<9,>=3.7.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (8.7.0)\n",
-            "Requirement already satisfied: opentelemetry-api<3,>=1.9.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (1.34.1)\n",
-            "Requirement already satisfied: opentelemetry-sdk<3,>=1.9.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (1.34.1)\n",
+            "Collecting opentelemetry-api<3,>=1.9.0 (from mlflow-skinny==3.1.1->mlflow)\n",
+            "  Downloading opentelemetry_api-1.34.1-py3-none-any.whl.metadata (1.5 kB)\n",
+            "Collecting opentelemetry-sdk<3,>=1.9.0 (from mlflow-skinny==3.1.1->mlflow)\n",
+            "  Downloading opentelemetry_sdk-1.34.1-py3-none-any.whl.metadata (1.6 kB)\n",
             "Requirement already satisfied: packaging<26 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (24.2)\n",
             "Requirement already satisfied: protobuf<7,>=3.12.0 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (5.29.5)\n",
             "Requirement already satisfied: pydantic<3,>=1.10.8 in /usr/local/lib/python3.11/dist-packages (from mlflow-skinny==3.1.1->mlflow) (2.11.7)\n",
@@ -554,8 +783,10 @@
             "Requirement already satisfied: jinja2>=3.1.2 in /usr/local/lib/python3.11/dist-packages (from Flask<4->mlflow) (3.1.6)\n",
             "Requirement already satisfied: markupsafe>=2.1.1 in /usr/local/lib/python3.11/dist-packages (from Flask<4->mlflow) (3.0.2)\n",
             "Requirement already satisfied: werkzeug>=3.1.0 in /usr/local/lib/python3.11/dist-packages (from Flask<4->mlflow) (3.1.3)\n",
-            "Requirement already satisfied: graphql-core<3.3,>=3.1 in /usr/local/lib/python3.11/dist-packages (from graphene<4->mlflow) (3.2.6)\n",
-            "Requirement already satisfied: graphql-relay<3.3,>=3.1 in /usr/local/lib/python3.11/dist-packages (from graphene<4->mlflow) (3.2.0)\n",
+            "Collecting graphql-core<3.3,>=3.1 (from graphene<4->mlflow)\n",
+            "  Downloading graphql_core-3.2.6-py3-none-any.whl.metadata (11 kB)\n",
+            "Collecting graphql-relay<3.3,>=3.1 (from graphene<4->mlflow)\n",
+            "  Downloading graphql_relay-3.2.0-py3-none-any.whl.metadata (12 kB)\n",
             "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.11/dist-packages (from matplotlib<4->mlflow) (1.3.2)\n",
             "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.11/dist-packages (from matplotlib<4->mlflow) (0.12.1)\n",
             "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.11/dist-packages (from matplotlib<4->mlflow) (4.58.4)\n",
@@ -568,7 +799,8 @@
             "Requirement already satisfied: starlette<0.47.0,>=0.40.0 in /usr/local/lib/python3.11/dist-packages (from fastapi<1->mlflow-skinny==3.1.1->mlflow) (0.46.2)\n",
             "Requirement already satisfied: gitdb<5,>=4.0.1 in /usr/local/lib/python3.11/dist-packages (from gitpython<4,>=3.1.9->mlflow-skinny==3.1.1->mlflow) (4.0.12)\n",
             "Requirement already satisfied: zipp>=3.20 in /usr/local/lib/python3.11/dist-packages (from importlib_metadata!=4.7.0,<9,>=3.7.0->mlflow-skinny==3.1.1->mlflow) (3.23.0)\n",
-            "Requirement already satisfied: opentelemetry-semantic-conventions==0.55b1 in /usr/local/lib/python3.11/dist-packages (from opentelemetry-sdk<3,>=1.9.0->mlflow-skinny==3.1.1->mlflow) (0.55b1)\n",
+            "Collecting opentelemetry-semantic-conventions==0.55b1 (from opentelemetry-sdk<3,>=1.9.0->mlflow-skinny==3.1.1->mlflow)\n",
+            "  Downloading opentelemetry_semantic_conventions-0.55b1-py3-none-any.whl.metadata (2.5 kB)\n",
             "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=1.10.8->mlflow-skinny==3.1.1->mlflow) (0.7.0)\n",
             "Requirement already satisfied: pydantic-core==2.33.2 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=1.10.8->mlflow-skinny==3.1.1->mlflow) (2.33.2)\n",
             "Requirement already satisfied: typing-inspection>=0.4.0 in /usr/local/lib/python3.11/dist-packages (from pydantic<3,>=1.10.8->mlflow-skinny==3.1.1->mlflow) (0.4.1)\n",
@@ -581,7 +813,32 @@
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             "Requirement already satisfied: anyio<5,>=3.6.2 in /usr/local/lib/python3.11/dist-packages (from starlette<0.47.0,>=0.40.0->fastapi<1->mlflow-skinny==3.1.1->mlflow) (4.9.0)\n",
             "Requirement already satisfied: sniffio>=1.1 in /usr/local/lib/python3.11/dist-packages (from anyio<5,>=3.6.2->starlette<0.47.0,>=0.40.0->fastapi<1->mlflow-skinny==3.1.1->mlflow) (1.3.1)\n",
-            "Requirement already satisfied: pyasn1<0.7.0,>=0.6.1 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth~=2.0->databricks-sdk<1,>=0.20.0->mlflow-skinny==3.1.1->mlflow) (0.6.1)\n"
+            "Requirement already satisfied: pyasn1<0.7.0,>=0.6.1 in /usr/local/lib/python3.11/dist-packages (from pyasn1-modules>=0.2.1->google-auth~=2.0->databricks-sdk<1,>=0.20.0->mlflow-skinny==3.1.1->mlflow) (0.6.1)\n",
+            "Downloading mlflow-3.1.1-py3-none-any.whl (24.7 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m24.7/24.7 MB\u001b[0m \u001b[31m36.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading mlflow_skinny-3.1.1-py3-none-any.whl (1.9 MB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m59.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading alembic-1.16.2-py3-none-any.whl (242 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m242.7/242.7 kB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading docker-7.1.0-py3-none-any.whl (147 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m147.8/147.8 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading graphene-3.4.3-py2.py3-none-any.whl (114 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.9/114.9 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading gunicorn-23.0.0-py3-none-any.whl (85 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.0/85.0 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading databricks_sdk-0.57.0-py3-none-any.whl (733 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m733.8/733.8 kB\u001b[0m \u001b[31m33.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading graphql_core-3.2.6-py3-none-any.whl (203 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m203.4/203.4 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading graphql_relay-3.2.0-py3-none-any.whl (16 kB)\n",
+            "Downloading opentelemetry_api-1.34.1-py3-none-any.whl (65 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.8/65.8 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading opentelemetry_sdk-1.34.1-py3-none-any.whl (118 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m118.5/118.5 kB\u001b[0m \u001b[31m8.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hDownloading opentelemetry_semantic_conventions-0.55b1-py3-none-any.whl (196 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.2/196.2 kB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: gunicorn, graphql-core, opentelemetry-api, graphql-relay, docker, alembic, opentelemetry-semantic-conventions, graphene, databricks-sdk, opentelemetry-sdk, mlflow-skinny, mlflow\n",
+            "Successfully installed alembic-1.16.2 databricks-sdk-0.57.0 docker-7.1.0 graphene-3.4.3 graphql-core-3.2.6 graphql-relay-3.2.0 gunicorn-23.0.0 mlflow-3.1.1 mlflow-skinny-3.1.1 opentelemetry-api-1.34.1 opentelemetry-sdk-1.34.1 opentelemetry-semantic-conventions-0.55b1\n"
           ]
         }
       ]
@@ -609,31 +866,66 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "QTE3VACyCCMt",
-        "outputId": "99a7871d-ad0e-425b-e961-7ad810e7eff8"
+        "outputId": "74589259-62c8-426a-d1d4-3e66ce959241"
       },
-      "execution_count": 4,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "2025/06/29 10:06:15 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
-            "2025/06/29 10:06:15 INFO mlflow.store.db.utils: Updating database tables\n",
+            "2025/06/29 17:22:59 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
+            "2025/06/29 17:22:59 INFO mlflow.store.db.utils: Updating database tables\n",
             "INFO  [alembic.runtime.migration] Context impl SQLiteImpl.\n",
             "INFO  [alembic.runtime.migration] Will assume non-transactional DDL.\n",
+            "INFO  [alembic.runtime.migration] Running upgrade  -> 451aebb31d03, add metric step\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 451aebb31d03 -> 90e64c465722, migrate user column to tags\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 90e64c465722 -> 181f10493468, allow nulls for metric values\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 181f10493468 -> df50e92ffc5e, Add Experiment Tags Table\n",
+            "INFO  [alembic.runtime.migration] Running upgrade df50e92ffc5e -> 7ac759974ad8, Update run tags with larger limit\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 7ac759974ad8 -> 89d4b8295536, create latest metrics table\n",
+            "INFO  [89d4b8295536_create_latest_metrics_table_py] Migration complete!\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 89d4b8295536 -> 2b4d017a5e9b, add model registry tables to db\n",
+            "INFO  [2b4d017a5e9b_add_model_registry_tables_to_db_py] Adding registered_models and model_versions tables to database.\n",
+            "INFO  [2b4d017a5e9b_add_model_registry_tables_to_db_py] Migration complete!\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 2b4d017a5e9b -> cfd24bdc0731, Update run status constraint with killed\n",
+            "INFO  [alembic.runtime.migration] Running upgrade cfd24bdc0731 -> 0a8213491aaa, drop_duplicate_killed_constraint\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 0a8213491aaa -> 728d730b5ebd, add registered model tags table\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 728d730b5ebd -> 27a6a02d2cf1, add model version tags table\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 27a6a02d2cf1 -> 84291f40a231, add run_link to model_version\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 84291f40a231 -> a8c4a736bde6, allow nulls for run_id\n",
+            "INFO  [alembic.runtime.migration] Running upgrade a8c4a736bde6 -> 39d1c3be5f05, add_is_nan_constraint_for_metrics_tables_if_necessary\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 39d1c3be5f05 -> c48cb773bb87, reset_default_value_for_is_nan_in_metrics_table_for_mysql\n",
+            "INFO  [alembic.runtime.migration] Running upgrade c48cb773bb87 -> bd07f7e963c5, create index on run_uuid\n",
+            "INFO  [alembic.runtime.migration] Running upgrade bd07f7e963c5 -> 0c779009ac13, add deleted_time field to runs table\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 0c779009ac13 -> cc1f77228345, change param value length to 500\n",
+            "INFO  [alembic.runtime.migration] Running upgrade cc1f77228345 -> 97727af70f4d, Add creation_time and last_update_time to experiments table\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 97727af70f4d -> 3500859a5d39, Add Model Aliases table\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 3500859a5d39 -> 7f2a7d5fae7d, add datasets inputs input_tags tables\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 7f2a7d5fae7d -> 2d6e25af4d3e, increase max param val length from 500 to 8000\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 2d6e25af4d3e -> acf3f17fdcc7, add storage location field to model versions\n",
+            "INFO  [alembic.runtime.migration] Running upgrade acf3f17fdcc7 -> 867495a8f9d4, add trace tables\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 867495a8f9d4 -> 5b0e9adcef9c, add cascade deletion to trace tables foreign keys\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 5b0e9adcef9c -> 4465047574b1, increase max dataset schema size\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 4465047574b1 -> f5a4f2784254, increase run tag value limit to 8000\n",
+            "INFO  [alembic.runtime.migration] Running upgrade f5a4f2784254 -> 0584bdc529eb, add cascading deletion to datasets from experiments\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 0584bdc529eb -> 400f98739977, add logged model tables\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 400f98739977 -> 6953534de441, add step to inputs table\n",
+            "INFO  [alembic.runtime.migration] Running upgrade 6953534de441 -> bda7b8c39065, increase_model_version_tag_value_limit\n",
             "INFO  [alembic.runtime.migration] Context impl SQLiteImpl.\n",
-            "INFO  [alembic.runtime.migration] Will assume non-transactional DDL.\n"
+            "INFO  [alembic.runtime.migration] Will assume non-transactional DDL.\n",
+            "2025/06/29 17:23:02 INFO mlflow.tracking.fluent: Experiment with name 'CreditRisk_RFM_Experiment' does not exist. Creating a new experiment.\n"
           ]
         },
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<Experiment: artifact_location='/content/mlruns/1', creation_time=1751190154767, experiment_id='1', last_update_time=1751190154767, lifecycle_stage='active', name='CreditRisk_RFM_Experiment', tags={}>"
+              "<Experiment: artifact_location='/content/mlruns/1', creation_time=1751217782274, experiment_id='1', last_update_time=1751217782274, lifecycle_stage='active', name='CreditRisk_RFM_Experiment', tags={}>"
             ]
           },
           "metadata": {},
-          "execution_count": 4
+          "execution_count": 5
         }
       ]
     },
@@ -657,9 +949,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "CT5Ut_oM-bXz",
-        "outputId": "9db9c658-f79d-4310-9061-7d2dd6ab82d4"
+        "outputId": "daea4e55-a627-43b0-c0d0-ccc9c50b9333"
       },
-      "execution_count": 5,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "stream",
@@ -713,7 +1005,7 @@
       "metadata": {
         "id": "wGTSdkFk-t5P"
       },
-      "execution_count": 6,
+      "execution_count": 7,
       "outputs": []
     },
     {
@@ -730,7 +1022,7 @@
       "metadata": {
         "id": "SPHilNQ1-wqq"
       },
-      "execution_count": 7,
+      "execution_count": 8,
       "outputs": []
     },
     {
@@ -747,7 +1039,7 @@
       "metadata": {
         "id": "lmdLys2i-ytG"
       },
-      "execution_count": 8,
+      "execution_count": 9,
       "outputs": []
     },
     {
@@ -772,9 +1064,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "T7Cl8DQi-2Q7",
-        "outputId": "b42a3fde-5223-4dbe-9916-41f4d34f8485"
+        "outputId": "0d33585a-6756-4189-94b8-a9298fdb417b"
       },
-      "execution_count": 9,
+      "execution_count": 10,
       "outputs": [
         {
           "output_type": "stream",
@@ -835,16 +1127,47 @@
       "metadata": {
         "id": "rz3skP7ACsxb"
       },
-      "execution_count": 10,
+      "execution_count": 11,
       "outputs": []
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Model Setup and Evaluation – Explanation\n",
+        "This section of the project prepares for training and comparing multiple machine learning models. It includes three key components:\n",
+        "\n",
+        "1. Model Initialization\n",
+        "A group of classification models is defined, including Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting. These models are selected because they provide a mix of simplicity, interpretability, and performance across different types of data. Each model is instantiated and stored for use in training and evaluation.\n",
+        "\n",
+        "2. Hyperparameter Configuration\n",
+        "For each model, a set of hyperparameters is defined for tuning. These parameters control how the models learn from data (e.g., regularization strength, tree depth, number of estimators). This setup enables hyperparameter tuning via grid search, helping to identify the best-performing model configuration.\n",
+        "\n",
+        "3. Evaluation Metrics Function\n",
+        "A custom evaluation function is created to assess the performance of each model. It calculates and returns key classification metrics:\n",
+        "\n",
+        "Accuracy – overall correctness\n",
+        "\n",
+        "Precision – correctness of positive predictions\n",
+        "\n",
+        "Recall – ability to detect actual positives\n",
+        "\n",
+        "F1 Score – balance between precision and recall\n",
+        "\n",
+        "ROC AUC – model’s ability to distinguish between classes\n",
+        "\n",
+        "This function ensures that each model is judged fairly and consistently using multiple performance indicators, especially valuable in imbalanced datasets where accuracy alone may be misleading."
+      ],
+      "metadata": {
+        "id": "eo9sNOs0tgt8"
+      }
+    },
     {
       "cell_type": "code",
       "source": [],
       "metadata": {
         "id": "AW_payPVCtly"
       },
-      "execution_count": 10,
+      "execution_count": null,
       "outputs": []
     },
     {
@@ -892,9 +1215,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "Y8tgIA4xCyKp",
-        "outputId": "66591cce-6562-4e12-8e39-74ac12f96c28"
+        "outputId": "83d911de-7dac-4cd3-f959-698e3a42dd6d"
       },
-      "execution_count": 11,
+      "execution_count": 12,
       "outputs": [
         {
           "output_type": "stream",
@@ -908,8 +1231,8 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "2025/06/29 10:06:53 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n",
-            "\u001b[31m2025/06/29 10:07:00 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
+            "2025/06/29 17:23:44 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n",
+            "\u001b[31m2025/06/29 17:23:50 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
           ]
         },
         {
@@ -923,7 +1246,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "2025/06/29 10:07:02 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
+            "2025/06/29 17:23:52 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
           ]
         },
         {
@@ -937,7 +1260,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "\u001b[31m2025/06/29 10:07:06 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
+            "\u001b[31m2025/06/29 17:23:57 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
           ]
         },
         {
@@ -951,21 +1274,21 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "2025/06/29 10:07:32 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
+            "2025/06/29 17:24:24 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
           ]
         },
         {
           "output_type": "stream",
           "name": "stdout",
           "text": [
-            "RandomForest metrics: {'accuracy': 0.9997386304234187, 'precision': 0.9998137455764574, 'recall': 0.9998758226747796, 'f1_score': 0.9998447831620775, 'roc_auc': np.float64(0.999999856276244)}\n"
+            "RandomForest metrics: {'accuracy': 0.9998431782540512, 'precision': 0.9999379074821484, 'recall': 0.9998758226747796, 'f1_score': 0.9999068641147434, 'roc_auc': np.float64(0.9999999589360696)}\n"
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         },
         {
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "\u001b[31m2025/06/29 10:07:36 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
+            "\u001b[31m2025/06/29 17:24:27 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
           ]
         },
         {
@@ -979,7 +1302,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "2025/06/29 10:08:24 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
+            "2025/06/29 17:25:13 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n"
           ]
         },
         {
@@ -993,7 +1316,7 @@
           "output_type": "stream",
           "name": "stderr",
           "text": [
-            "\u001b[31m2025/06/29 10:08:28 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
+            "\u001b[31m2025/06/29 17:25:17 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
           ]
         },
         {
@@ -1006,6 +1329,64 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Model Training, Evaluation, and Experiment Tracking\n",
+        "This code block performs the full model training and evaluation loop across multiple classifiers. It systematically tests and tracks model performance using the following steps:\n",
+        "\n",
+        "🔧 1. Model Selection and Hyperparameter Tuning\n",
+        "For each model (LogisticRegression, DecisionTree, RandomForest, GradientBoosting):\n",
+        "\n",
+        "Uses GridSearchCV with 5-fold cross-validation to tune hyperparameters defined in param_grids.\n",
+        "\n",
+        "The F1 score is used as the optimization metric during cross-validation.\n",
+        "\n",
+        "🧠 2. Model Training and Prediction\n",
+        "Trains each model on X_train using the best parameters from GridSearch.\n",
+        "\n",
+        "Makes predictions (y_pred) and prediction probabilities (y_proba) on X_test.\n",
+        "\n",
+        "📊 3. Evaluation Metrics\n",
+        "For each model, the following metrics are calculated using a custom evaluate() function:\n",
+        "\n",
+        "Accuracy\n",
+        "\n",
+        "Precision\n",
+        "\n",
+        "Recall\n",
+        "\n",
+        "F1 Score\n",
+        "\n",
+        "ROC AUC\n",
+        "\n",
+        "These metrics provide a well-rounded assessment of classification performance, especially in imbalanced datasets where F1 and AUC are more informative.\n",
+        "\n",
+        "📦 4. Result Logging and Model Tracking with MLflow\n",
+        "Each model’s:\n",
+        "\n",
+        "Best parameters (log_params)\n",
+        "\n",
+        "Evaluation metrics (log_metrics)\n",
+        "\n",
+        "Trained model (log_model)\n",
+        "\n",
+        "are logged to MLflow, enabling experiment tracking and reproducibility.\n",
+        "\n",
+        "🥇 5. Best Model Selection\n",
+        "The model with the highest F1 Score is tracked as the best.\n",
+        "\n",
+        "best_model, best_model_name, and best_f1 hold the top-performing model details for final reporting.\n",
+        "\n",
+        "🏁 Final Output\n",
+        "After all models are trained and evaluated:\n",
+        "\n",
+        "The name of the best-performing model and its F1 score are printed."
+      ],
+      "metadata": {
+        "id": "0lLpw81nsyAs"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1050,9 +1431,9 @@
           "height": 567
         },
         "id": "eeoaV4scGqt-",
-        "outputId": "98dfc198-33e1-4f1b-ec29-2fc6bbf7ff11"
+        "outputId": "24dbb037-7ecf-4360-beb1-a814824757ae"
       },
-      "execution_count": 12,
+      "execution_count": 13,
       "outputs": [
         {
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@@ -1062,9 +1443,9 @@
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Model Evaluation Metrics Comparison (Interactive)\",\"x\":0.5},\"barmode\":\"group\",\"width\":950,\"height\":550,\"bargap\":0.25},                        {\"responsive\": true}                    ).then(function(){\n",
               "                            \n",
-              "var gd = document.getElementById('2ddd71f0-ddb1-4cb8-b6a9-ad37c8508367');\n",
+              "var gd = document.getElementById('af6e613d-cdd2-4fec-970d-89090b6a4524');\n",
               "var x = new MutationObserver(function (mutations, observer) {{\n",
               "        var display = window.getComputedStyle(gd).display;\n",
               "        if (!display || display === 'none') {{\n",
@@ -1095,6 +1476,107 @@
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "source": [
+        " Interpretation of Model Evaluation Chart\n",
+        "Overall High Performance:\n",
+        "\n",
+        "Nearly all models exhibit metrics at or extremely close to 1.00 across accuracy, precision, recall, F1-score, and ROC-AUC.\n",
+        "\n",
+        "The first set of bars shows scores just under perfect (accuracy: 0.98, precision: 0.99, recall: 0.99, F1-score: 0.99, ROC-AUC: 1.00).\n",
+        "\n",
+        "All other models demonstrate perfect scores of 1.00 across all metrics.\n",
+        "\n",
+        "⚠️ Potential Concerns\n",
+        "Overfitting:\n",
+        "Perfect or near-perfect scores on evaluation metrics often indicate the model may be overfitting—learning noise and specific quirks of the training data rather than generalizable patterns. This can reduce real-world predictive performance.\n",
+        "\n",
+        "Data Leakage:\n",
+        "Such flawless results can also arise if information from the target variable unintentionally leaks into the training features, leading the model to “cheat” by using future or target-related data.\n",
+        "\n",
+        "Trivial Problem:\n",
+        "Although less likely in credit risk tasks, the classification problem might be inherently simple or perfectly separable, making it easy for models to achieve near-perfect discrimination.\n",
+        "\n",
+        "Evaluation Errors:\n",
+        "There might be mistakes in the evaluation process, such as testing on training data or incorrect metric calculations, artificially inflating performance."
+      ],
+      "metadata": {
+        "id": "ucj7ZqKtu96a"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Interactive Visualization of Model Evaluation Metrics\n",
+        "This code creates an interactive grouped bar chart using Plotly Express to visually compare the performance of different machine learning models across multiple evaluation metrics.\n",
+        "\n",
+        "🔄 Steps Explained\n",
+        "Convert Results to DataFrame\n",
+        "\n",
+        "python\n",
+        "Copy\n",
+        "Edit\n",
+        "results_df = pd.DataFrame(results)\n",
+        "Converts the results list of dictionaries (each containing model name and its evaluation metrics) into a Pandas DataFrame.\n",
+        "\n",
+        "Reshape Data for Plotting\n",
+        "\n",
+        "python\n",
+        "Copy\n",
+        "Edit\n",
+        "melted = results_df.melt(id_vars='model', var_name='metric', value_name='score')\n",
+        "Transforms the DataFrame into a long format suitable for grouped bar plotting, where:\n",
+        "\n",
+        "model is the category (x-axis),\n",
+        "\n",
+        "metric is the grouping (colors),\n",
+        "\n",
+        "score is the value (height of bars).\n",
+        "\n",
+        "Create Interactive Grouped Bar Chart\n",
+        "\n",
+        "python\n",
+        "Copy\n",
+        "Edit\n",
+        "fig = px.bar(\n",
+        "    melted,\n",
+        "    x='model',\n",
+        "    y='score',\n",
+        "    color='metric',\n",
+        "    barmode='group',\n",
+        "    text_auto='.2f',\n",
+        "    ...\n",
+        ")\n",
+        "Creates a bar chart where bars for each metric are grouped by model.\n",
+        "\n",
+        "Each bar displays the score value with two decimal precision.\n",
+        "\n",
+        "Customize Appearance\n",
+        "\n",
+        "python\n",
+        "Copy\n",
+        "Edit\n",
+        "fig.update_layout(...)\n",
+        "Sets plot width, height, legend title, axis labels, spacing between bars, centering the title, and applies a clean white theme.\n",
+        "\n",
+        "Display the Chart\n",
+        "\n",
+        "python\n",
+        "Copy\n",
+        "Edit\n",
+        "fig.show()\n",
+        "✅ Why This is Useful\n",
+        "Compare models side-by-side across metrics like accuracy, precision, recall, F1 score, and ROC AUC.\n",
+        "\n",
+        "Interactive: You can hover, zoom, and isolate metrics or models in real-time.\n",
+        "\n",
+        "Reproducible: Integrates seamlessly with Jupyter/Colab environments."
+      ],
+      "metadata": {
+        "id": "Yk_cwcvHs82h"
+      }
+    },
     {
       "cell_type": "code",
       "source": [
@@ -1113,9 +1595,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "y-xZu2dsHNx4",
-        "outputId": "4c23bcef-5908-4dc0-ee7c-ca0e388c60b6"
+        "outputId": "4acc476b-77b9-46d1-972d-12766c0f426a"
       },
-      "execution_count": 13,
+      "execution_count": 14,
       "outputs": [
         {
           "output_type": "stream",
@@ -1137,9 +1619,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "2fN32_44C1XA",
-        "outputId": "442a539d-d26e-4bfb-c3a4-2bc438fecfa9"
+        "outputId": "79be40ec-e52b-4b91-84e1-4211d345ddfb"
       },
-      "execution_count": 14,
+      "execution_count": 15,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -1149,7 +1631,7 @@
             ]
           },
           "metadata": {},
-          "execution_count": 14
+          "execution_count": 15
         }
       ]
     },
@@ -1168,9 +1650,9 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "H5t1GS0RC4Tf",
-        "outputId": "22db0433-dced-4a9a-9387-99c44f7f6031"
+        "outputId": "736cd298-46a3-4c93-ef15-aefd72141620"
       },
-      "execution_count": 15,
+      "execution_count": 16,
       "outputs": [
         {
           "output_type": "stream",
@@ -1181,13 +1663,199 @@
         }
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Download the MLflow tracking database and artifacts\n",
+        "!zip -r mlruns.zip mlruns/\n",
+        "from google.colab import files\n",
+        "files.download(\"mlruns.zip\")\n",
+        "files.download(\"mlruns.db\")\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 578
+        },
+        "id": "HwtdfzcuJeoy",
+        "outputId": "859945e4-a06f-408b-afdb-26a681a541cb"
+      },
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  adding: mlruns/ (stored 0%)\n",
+            "  adding: mlruns/1/ (stored 0%)\n",
+            "  adding: mlruns/1/models/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-c45ed651b5cf472fb48596468a9d25bf/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-c45ed651b5cf472fb48596468a9d25bf/artifacts/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-c45ed651b5cf472fb48596468a9d25bf/artifacts/requirements.txt (deflated 21%)\n",
+            "  adding: mlruns/1/models/m-c45ed651b5cf472fb48596468a9d25bf/artifacts/MLmodel (deflated 48%)\n",
+            "  adding: mlruns/1/models/m-c45ed651b5cf472fb48596468a9d25bf/artifacts/python_env.yaml (deflated 16%)\n",
+            "  adding: mlruns/1/models/m-c45ed651b5cf472fb48596468a9d25bf/artifacts/conda.yaml (deflated 34%)\n",
+            "  adding: mlruns/1/models/m-c45ed651b5cf472fb48596468a9d25bf/artifacts/model.pkl (deflated 73%)\n",
+            "  adding: mlruns/1/models/m-542126bcbd434ae5a941498d5cb27200/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-542126bcbd434ae5a941498d5cb27200/artifacts/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-542126bcbd434ae5a941498d5cb27200/artifacts/requirements.txt (deflated 21%)\n",
+            "  adding: mlruns/1/models/m-542126bcbd434ae5a941498d5cb27200/artifacts/MLmodel (deflated 48%)\n",
+            "  adding: mlruns/1/models/m-542126bcbd434ae5a941498d5cb27200/artifacts/python_env.yaml (deflated 16%)\n",
+            "  adding: mlruns/1/models/m-542126bcbd434ae5a941498d5cb27200/artifacts/conda.yaml (deflated 34%)\n",
+            "  adding: mlruns/1/models/m-542126bcbd434ae5a941498d5cb27200/artifacts/model.pkl (deflated 58%)\n",
+            "  adding: mlruns/1/models/m-8605c51f44c54fa0b2ce00b7b09ed8ae/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-8605c51f44c54fa0b2ce00b7b09ed8ae/artifacts/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-8605c51f44c54fa0b2ce00b7b09ed8ae/artifacts/requirements.txt (deflated 21%)\n",
+            "  adding: mlruns/1/models/m-8605c51f44c54fa0b2ce00b7b09ed8ae/artifacts/MLmodel (deflated 48%)\n",
+            "  adding: mlruns/1/models/m-8605c51f44c54fa0b2ce00b7b09ed8ae/artifacts/python_env.yaml (deflated 16%)\n",
+            "  adding: mlruns/1/models/m-8605c51f44c54fa0b2ce00b7b09ed8ae/artifacts/conda.yaml (deflated 34%)\n",
+            "  adding: mlruns/1/models/m-8605c51f44c54fa0b2ce00b7b09ed8ae/artifacts/model.pkl (deflated 39%)\n",
+            "  adding: mlruns/1/models/m-4262b7e157694b42a19a6c25a2723780/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-4262b7e157694b42a19a6c25a2723780/artifacts/ (stored 0%)\n",
+            "  adding: mlruns/1/models/m-4262b7e157694b42a19a6c25a2723780/artifacts/requirements.txt (deflated 21%)\n",
+            "  adding: mlruns/1/models/m-4262b7e157694b42a19a6c25a2723780/artifacts/MLmodel (deflated 48%)\n",
+            "  adding: mlruns/1/models/m-4262b7e157694b42a19a6c25a2723780/artifacts/python_env.yaml (deflated 16%)\n",
+            "  adding: mlruns/1/models/m-4262b7e157694b42a19a6c25a2723780/artifacts/conda.yaml (deflated 34%)\n",
+            "  adding: mlruns/1/models/m-4262b7e157694b42a19a6c25a2723780/artifacts/model.pkl (deflated 76%)\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_e36866fe-5cb3-4d52-83f5-78f565fa7abd\", \"mlruns.zip\", 517348)"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "\n",
+              "    async function download(id, filename, size) {\n",
+              "      if (!google.colab.kernel.accessAllowed) {\n",
+              "        return;\n",
+              "      }\n",
+              "      const div = document.createElement('div');\n",
+              "      const label = document.createElement('label');\n",
+              "      label.textContent = `Downloading \"${filename}\": `;\n",
+              "      div.appendChild(label);\n",
+              "      const progress = document.createElement('progress');\n",
+              "      progress.max = size;\n",
+              "      div.appendChild(progress);\n",
+              "      document.body.appendChild(div);\n",
+              "\n",
+              "      const buffers = [];\n",
+              "      let downloaded = 0;\n",
+              "\n",
+              "      const channel = await google.colab.kernel.comms.open(id);\n",
+              "      // Send a message to notify the kernel that we're ready.\n",
+              "      channel.send({})\n",
+              "\n",
+              "      for await (const message of channel.messages) {\n",
+              "        // Send a message to notify the kernel that we're ready.\n",
+              "        channel.send({})\n",
+              "        if (message.buffers) {\n",
+              "          for (const buffer of message.buffers) {\n",
+              "            buffers.push(buffer);\n",
+              "            downloaded += buffer.byteLength;\n",
+              "            progress.value = downloaded;\n",
+              "          }\n",
+              "        }\n",
+              "      }\n",
+              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
+              "      const a = document.createElement('a');\n",
+              "      a.href = window.URL.createObjectURL(blob);\n",
+              "      a.download = filename;\n",
+              "      div.appendChild(a);\n",
+              "      a.click();\n",
+              "      div.remove();\n",
+              "    }\n",
+              "  "
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ],
+            "application/javascript": [
+              "download(\"download_3e99766f-3b7d-4ee1-8468-abdaa0e70b1e\", \"mlruns.db\", 266240)"
+            ]
+          },
+          "metadata": {}
+        }
+      ]
+    },
     {
       "cell_type": "code",
       "source": [],
       "metadata": {
         "id": "iHQR-YrlCPub"
       },
-      "execution_count": 15,
+      "execution_count": null,
       "outputs": []
     }
   ]
diff --git a/test/test_api.py b/test/test_api.py
deleted file mode 100644
index a4a15a7..0000000
--- a/test/test_api.py
+++ /dev/null
@@ -1,18 +0,0 @@
-from fastapi.testclient import TestClient
-from src.api.main import app
-
-client = TestClient(app)
-
-def test_predict():
-    payload = {
-        "Recency": 10.0,
-        "Frequency": 5,
-        "Monetary": 1000.0,
-        "Transaction_Hour": 14
-        # add other features here
-    }
-    response = client.post("/predict", json=payload)
-    assert response.status_code == 200
-    data = response.json()
-    assert "risk_probability" in data
-    assert 0.0 <= data["risk_probability"] <= 1.0