diff --git a/credit-card-fraud-detection/DBSCAN_clustering.ipynb b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
new file mode 100644
index 0000000..036a647
--- /dev/null
+++ b/credit-card-fraud-detection/DBSCAN_clustering.ipynb
@@ -0,0 +1,1344 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "background_save": true,
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "6vPIHEvB5ggH",
+        "outputId": "f01b194e-2221-4083-e020-a667c175cdfc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "     <input type=\"file\" id=\"files-c64ffd57-8637-4fe1-8773-a8c3c2dad2ba\" name=\"files[]\" multiple disabled\n",
+              "        style=\"border:none\" />\n",
+              "     <output id=\"result-c64ffd57-8637-4fe1-8773-a8c3c2dad2ba\">\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> "
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Saving creditcard.csv to creditcard.csv\n",
+            "✅ Credit Card Dataset Loaded Successfully!\n",
+            "\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            },
+            "text/html": [
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+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
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+              "      <th>V28</th>\n",
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+              "      <td>0.133558</td>\n",
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+              "      <td>2.69</td>\n",
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+              "      <td>1.0</td>\n",
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+              "      <td>0.061458</td>\n",
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+              "      <td>0.141267</td>\n",
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+              "      <td>0.219422</td>\n",
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+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4f0efab-1522-46af-b817-ec0d9f8c48b1')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a4f0efab-1522-46af-b817-ec0d9f8c48b1 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-a4f0efab-1522-46af-b817-ec0d9f8c48b1');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-3436c023-72f8-4173-988b-d424f965a968\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3436c023-72f8-4173-988b-d424f965a968')\"\n",
+              "                title=\"Suggest charts\"\n",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-3436c023-72f8-4173-988b-d424f965a968 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
+              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
+              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
+              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
+              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
+              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
+              "\n",
+              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
+              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
+              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
+              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
+              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
+              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
+              "\n",
+              "        V26       V27       V28  Amount  Class  \n",
+              "0 -0.189115  0.133558 -0.021053  149.62      0  \n",
+              "1  0.125895 -0.008983  0.014724    2.69      0  \n",
+              "2 -0.139097 -0.055353 -0.059752  378.66      0  \n",
+              "3 -0.221929  0.062723  0.061458  123.50      0  \n",
+              "4  0.502292  0.219422  0.215153   69.99      0  \n",
+              "\n",
+              "[5 rows x 31 columns]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "📋 Columns in dataset:\n",
+            "['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount', 'Class']\n"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe"
+            },
+            "text/html": [
+              "\n",
+              "  <div id=\"df-11a404c1-8452-43f4-9c08-43e28f6335e6\" class=\"colab-df-container\">\n",
+              "    <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>Time</th>\n",
+              "      <th>V1</th>\n",
+              "      <th>V2</th>\n",
+              "      <th>V3</th>\n",
+              "      <th>V4</th>\n",
+              "      <th>V5</th>\n",
+              "      <th>V6</th>\n",
+              "      <th>V7</th>\n",
+              "      <th>V8</th>\n",
+              "      <th>V9</th>\n",
+              "      <th>...</th>\n",
+              "      <th>V21</th>\n",
+              "      <th>V22</th>\n",
+              "      <th>V23</th>\n",
+              "      <th>V24</th>\n",
+              "      <th>V25</th>\n",
+              "      <th>V26</th>\n",
+              "      <th>V27</th>\n",
+              "      <th>V28</th>\n",
+              "      <th>Amount</th>\n",
+              "      <th>Cluster</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>-1.359807</td>\n",
+              "      <td>-0.072781</td>\n",
+              "      <td>2.536347</td>\n",
+              "      <td>1.378155</td>\n",
+              "      <td>-0.338321</td>\n",
+              "      <td>0.462388</td>\n",
+              "      <td>0.239599</td>\n",
+              "      <td>0.098698</td>\n",
+              "      <td>0.363787</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.018307</td>\n",
+              "      <td>0.277838</td>\n",
+              "      <td>-0.110474</td>\n",
+              "      <td>0.066928</td>\n",
+              "      <td>0.128539</td>\n",
+              "      <td>-0.189115</td>\n",
+              "      <td>0.133558</td>\n",
+              "      <td>-0.021053</td>\n",
+              "      <td>149.62</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.0</td>\n",
+              "      <td>1.191857</td>\n",
+              "      <td>0.266151</td>\n",
+              "      <td>0.166480</td>\n",
+              "      <td>0.448154</td>\n",
+              "      <td>0.060018</td>\n",
+              "      <td>-0.082361</td>\n",
+              "      <td>-0.078803</td>\n",
+              "      <td>0.085102</td>\n",
+              "      <td>-0.255425</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.225775</td>\n",
+              "      <td>-0.638672</td>\n",
+              "      <td>0.101288</td>\n",
+              "      <td>-0.339846</td>\n",
+              "      <td>0.167170</td>\n",
+              "      <td>0.125895</td>\n",
+              "      <td>-0.008983</td>\n",
+              "      <td>0.014724</td>\n",
+              "      <td>2.69</td>\n",
+              "      <td>0</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-1.358354</td>\n",
+              "      <td>-1.340163</td>\n",
+              "      <td>1.773209</td>\n",
+              "      <td>0.379780</td>\n",
+              "      <td>-0.503198</td>\n",
+              "      <td>1.800499</td>\n",
+              "      <td>0.791461</td>\n",
+              "      <td>0.247676</td>\n",
+              "      <td>-1.514654</td>\n",
+              "      <td>...</td>\n",
+              "      <td>0.247998</td>\n",
+              "      <td>0.771679</td>\n",
+              "      <td>0.909412</td>\n",
+              "      <td>-0.689281</td>\n",
+              "      <td>-0.327642</td>\n",
+              "      <td>-0.139097</td>\n",
+              "      <td>-0.055353</td>\n",
+              "      <td>-0.059752</td>\n",
+              "      <td>378.66</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>1.0</td>\n",
+              "      <td>-0.966272</td>\n",
+              "      <td>-0.185226</td>\n",
+              "      <td>1.792993</td>\n",
+              "      <td>-0.863291</td>\n",
+              "      <td>-0.010309</td>\n",
+              "      <td>1.247203</td>\n",
+              "      <td>0.237609</td>\n",
+              "      <td>0.377436</td>\n",
+              "      <td>-1.387024</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.108300</td>\n",
+              "      <td>0.005274</td>\n",
+              "      <td>-0.190321</td>\n",
+              "      <td>-1.175575</td>\n",
+              "      <td>0.647376</td>\n",
+              "      <td>-0.221929</td>\n",
+              "      <td>0.062723</td>\n",
+              "      <td>0.061458</td>\n",
+              "      <td>123.50</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>-1.158233</td>\n",
+              "      <td>0.877737</td>\n",
+              "      <td>1.548718</td>\n",
+              "      <td>0.403034</td>\n",
+              "      <td>-0.407193</td>\n",
+              "      <td>0.095921</td>\n",
+              "      <td>0.592941</td>\n",
+              "      <td>-0.270533</td>\n",
+              "      <td>0.817739</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.009431</td>\n",
+              "      <td>0.798278</td>\n",
+              "      <td>-0.137458</td>\n",
+              "      <td>0.141267</td>\n",
+              "      <td>-0.206010</td>\n",
+              "      <td>0.502292</td>\n",
+              "      <td>0.219422</td>\n",
+              "      <td>0.215153</td>\n",
+              "      <td>69.99</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>2.0</td>\n",
+              "      <td>-0.425966</td>\n",
+              "      <td>0.960523</td>\n",
+              "      <td>1.141109</td>\n",
+              "      <td>-0.168252</td>\n",
+              "      <td>0.420987</td>\n",
+              "      <td>-0.029728</td>\n",
+              "      <td>0.476201</td>\n",
+              "      <td>0.260314</td>\n",
+              "      <td>-0.568671</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.208254</td>\n",
+              "      <td>-0.559825</td>\n",
+              "      <td>-0.026398</td>\n",
+              "      <td>-0.371427</td>\n",
+              "      <td>-0.232794</td>\n",
+              "      <td>0.105915</td>\n",
+              "      <td>0.253844</td>\n",
+              "      <td>0.081080</td>\n",
+              "      <td>3.67</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>4.0</td>\n",
+              "      <td>1.229658</td>\n",
+              "      <td>0.141004</td>\n",
+              "      <td>0.045371</td>\n",
+              "      <td>1.202613</td>\n",
+              "      <td>0.191881</td>\n",
+              "      <td>0.272708</td>\n",
+              "      <td>-0.005159</td>\n",
+              "      <td>0.081213</td>\n",
+              "      <td>0.464960</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.167716</td>\n",
+              "      <td>-0.270710</td>\n",
+              "      <td>-0.154104</td>\n",
+              "      <td>-0.780055</td>\n",
+              "      <td>0.750137</td>\n",
+              "      <td>-0.257237</td>\n",
+              "      <td>0.034507</td>\n",
+              "      <td>0.005168</td>\n",
+              "      <td>4.99</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>7.0</td>\n",
+              "      <td>-0.644269</td>\n",
+              "      <td>1.417964</td>\n",
+              "      <td>1.074380</td>\n",
+              "      <td>-0.492199</td>\n",
+              "      <td>0.948934</td>\n",
+              "      <td>0.428118</td>\n",
+              "      <td>1.120631</td>\n",
+              "      <td>-3.807864</td>\n",
+              "      <td>0.615375</td>\n",
+              "      <td>...</td>\n",
+              "      <td>1.943465</td>\n",
+              "      <td>-1.015455</td>\n",
+              "      <td>0.057504</td>\n",
+              "      <td>-0.649709</td>\n",
+              "      <td>-0.415267</td>\n",
+              "      <td>-0.051634</td>\n",
+              "      <td>-1.206921</td>\n",
+              "      <td>-1.085339</td>\n",
+              "      <td>40.80</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>7.0</td>\n",
+              "      <td>-0.894286</td>\n",
+              "      <td>0.286157</td>\n",
+              "      <td>-0.113192</td>\n",
+              "      <td>-0.271526</td>\n",
+              "      <td>2.669599</td>\n",
+              "      <td>3.721818</td>\n",
+              "      <td>0.370145</td>\n",
+              "      <td>0.851084</td>\n",
+              "      <td>-0.392048</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.073425</td>\n",
+              "      <td>-0.268092</td>\n",
+              "      <td>-0.204233</td>\n",
+              "      <td>1.011592</td>\n",
+              "      <td>0.373205</td>\n",
+              "      <td>-0.384157</td>\n",
+              "      <td>0.011747</td>\n",
+              "      <td>0.142404</td>\n",
+              "      <td>93.20</td>\n",
+              "      <td>3</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>9.0</td>\n",
+              "      <td>-0.338262</td>\n",
+              "      <td>1.119593</td>\n",
+              "      <td>1.044367</td>\n",
+              "      <td>-0.222187</td>\n",
+              "      <td>0.499361</td>\n",
+              "      <td>-0.246761</td>\n",
+              "      <td>0.651583</td>\n",
+              "      <td>0.069539</td>\n",
+              "      <td>-0.736727</td>\n",
+              "      <td>...</td>\n",
+              "      <td>-0.246914</td>\n",
+              "      <td>-0.633753</td>\n",
+              "      <td>-0.120794</td>\n",
+              "      <td>-0.385050</td>\n",
+              "      <td>-0.069733</td>\n",
+              "      <td>0.094199</td>\n",
+              "      <td>0.246219</td>\n",
+              "      <td>0.083076</td>\n",
+              "      <td>3.68</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>10 rows × 31 columns</p>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-11a404c1-8452-43f4-9c08-43e28f6335e6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-11a404c1-8452-43f4-9c08-43e28f6335e6 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-11a404c1-8452-43f4-9c08-43e28f6335e6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-4a209259-4d4b-48e5-bea1-c0f3822087f6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a209259-4d4b-48e5-bea1-c0f3822087f6')\"\n",
+              "                title=\"Suggest charts\"\n",
+              "                style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "      <script>\n",
+              "        async function quickchart(key) {\n",
+              "          const quickchartButtonEl =\n",
+              "            document.querySelector('#' + key + ' button');\n",
+              "          quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "          quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "          try {\n",
+              "            const charts = await google.colab.kernel.invokeFunction(\n",
+              "                'suggestCharts', [key], {});\n",
+              "          } catch (error) {\n",
+              "            console.error('Error during call to suggestCharts:', error);\n",
+              "          }\n",
+              "          quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "          quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "        }\n",
+              "        (() => {\n",
+              "          let quickchartButtonEl =\n",
+              "            document.querySelector('#df-4a209259-4d4b-48e5-bea1-c0f3822087f6 button');\n",
+              "          quickchartButtonEl.style.display =\n",
+              "            google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "        })();\n",
+              "      </script>\n",
+              "    </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ],
+            "text/plain": [
+              "   Time        V1        V2        V3        V4        V5        V6        V7  \\\n",
+              "0   0.0 -1.359807 -0.072781  2.536347  1.378155 -0.338321  0.462388  0.239599   \n",
+              "1   0.0  1.191857  0.266151  0.166480  0.448154  0.060018 -0.082361 -0.078803   \n",
+              "2   1.0 -1.358354 -1.340163  1.773209  0.379780 -0.503198  1.800499  0.791461   \n",
+              "3   1.0 -0.966272 -0.185226  1.792993 -0.863291 -0.010309  1.247203  0.237609   \n",
+              "4   2.0 -1.158233  0.877737  1.548718  0.403034 -0.407193  0.095921  0.592941   \n",
+              "5   2.0 -0.425966  0.960523  1.141109 -0.168252  0.420987 -0.029728  0.476201   \n",
+              "6   4.0  1.229658  0.141004  0.045371  1.202613  0.191881  0.272708 -0.005159   \n",
+              "7   7.0 -0.644269  1.417964  1.074380 -0.492199  0.948934  0.428118  1.120631   \n",
+              "8   7.0 -0.894286  0.286157 -0.113192 -0.271526  2.669599  3.721818  0.370145   \n",
+              "9   9.0 -0.338262  1.119593  1.044367 -0.222187  0.499361 -0.246761  0.651583   \n",
+              "\n",
+              "         V8        V9  ...       V21       V22       V23       V24       V25  \\\n",
+              "0  0.098698  0.363787  ... -0.018307  0.277838 -0.110474  0.066928  0.128539   \n",
+              "1  0.085102 -0.255425  ... -0.225775 -0.638672  0.101288 -0.339846  0.167170   \n",
+              "2  0.247676 -1.514654  ...  0.247998  0.771679  0.909412 -0.689281 -0.327642   \n",
+              "3  0.377436 -1.387024  ... -0.108300  0.005274 -0.190321 -1.175575  0.647376   \n",
+              "4 -0.270533  0.817739  ... -0.009431  0.798278 -0.137458  0.141267 -0.206010   \n",
+              "5  0.260314 -0.568671  ... -0.208254 -0.559825 -0.026398 -0.371427 -0.232794   \n",
+              "6  0.081213  0.464960  ... -0.167716 -0.270710 -0.154104 -0.780055  0.750137   \n",
+              "7 -3.807864  0.615375  ...  1.943465 -1.015455  0.057504 -0.649709 -0.415267   \n",
+              "8  0.851084 -0.392048  ... -0.073425 -0.268092 -0.204233  1.011592  0.373205   \n",
+              "9  0.069539 -0.736727  ... -0.246914 -0.633753 -0.120794 -0.385050 -0.069733   \n",
+              "\n",
+              "        V26       V27       V28  Amount  Cluster  \n",
+              "0 -0.189115  0.133558 -0.021053  149.62       -1  \n",
+              "1  0.125895 -0.008983  0.014724    2.69        0  \n",
+              "2 -0.139097 -0.055353 -0.059752  378.66       -1  \n",
+              "3 -0.221929  0.062723  0.061458  123.50       -1  \n",
+              "4  0.502292  0.219422  0.215153   69.99       -1  \n",
+              "5  0.105915  0.253844  0.081080    3.67        1  \n",
+              "6 -0.257237  0.034507  0.005168    4.99        2  \n",
+              "7 -0.051634 -1.206921 -1.085339   40.80       -1  \n",
+              "8 -0.384157  0.011747  0.142404   93.20        3  \n",
+              "9  0.094199  0.246219  0.083076    3.68        1  \n",
+              "\n",
+              "[10 rows x 31 columns]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "📊 Cluster Distribution:\n",
+            "Cluster\n",
+            "-1      98184\n",
+            " 9      19238\n",
+            " 565    11903\n",
+            " 1      11429\n",
+            " 562    10724\n",
+            "        ...  \n",
+            " 335        2\n",
+            " 534        2\n",
+            " 989        2\n",
+            " 168        2\n",
+            " 540        1\n",
+            "Name: count, Length: 1261, dtype: int64\n",
+            "\n",
+            "🧩 Silhouette Score (excluding noise): -0.0396\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128179 (\\N{CREDIT CARD}) missing from font(s) DejaVu Sans.\n",
+            "  fig.canvas.print_figure(bytes_io, **kw)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 800x600 with 2 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# =====================================\n",
+        "# 💳 DBSCAN Clustering on Credit Card Fraud Dataset\n",
+        "# =====================================\n",
+        "\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.cluster import DBSCAN\n",
+        "from sklearn.decomposition import PCA\n",
+        "from sklearn.metrics import silhouette_score\n",
+        "from google.colab import files\n",
+        "import io\n",
+        "from IPython.display import display\n",
+        "\n",
+        "# Step 1️⃣: Upload CSV file\n",
+        "uploaded = files.upload()\n",
+        "csv_filename = list(uploaded.keys())[0]\n",
+        "\n",
+        "# Step 2️⃣: Load dataset\n",
+        "df = pd.read_csv(io.BytesIO(uploaded[csv_filename]))\n",
+        "print(\"✅ Credit Card Dataset Loaded Successfully!\\n\")\n",
+        "display(df.head())\n",
+        "\n",
+        "# Step 3️⃣: Clean column names\n",
+        "df.columns = df.columns.str.strip()\n",
+        "print(\"\\n📋 Columns in dataset:\")\n",
+        "print(df.columns.tolist())\n",
+        "\n",
+        "# Step 4️⃣: Separate target (for reference only, NOT used in clustering)\n",
+        "if 'Class' in df.columns:\n",
+        "    y_true = df['Class']   # 1 = Fraud, 0 = Normal\n",
+        "    df = df.drop(columns=['Class'])\n",
+        "\n",
+        "# Step 5️⃣: Select numerical features\n",
+        "df_clean = df.select_dtypes(include=[np.number])\n",
+        "\n",
+        "# Handle missing or infinite values\n",
+        "df_clean = df_clean.replace([np.inf, -np.inf], np.nan).dropna()\n",
+        "\n",
+        "# Step 6️⃣: Standardize data\n",
+        "scaler = StandardScaler()\n",
+        "X_scaled = scaler.fit_transform(df_clean)\n",
+        "\n",
+        "# Step 7️⃣: Apply DBSCAN\n",
+        "dbscan = DBSCAN(\n",
+        "    eps=1.5,        # Tunable parameter\n",
+        "    min_samples=5\n",
+        ")\n",
+        "clusters = dbscan.fit_predict(X_scaled)\n",
+        "\n",
+        "# Step 8️⃣: Add cluster labels\n",
+        "df_clean['Cluster'] = clusters\n",
+        "display(df_clean.head(10))\n",
+        "\n",
+        "# Step 9️⃣: Cluster distribution\n",
+        "print(\"\\n📊 Cluster Distribution:\")\n",
+        "print(df_clean['Cluster'].value_counts())\n",
+        "\n",
+        "# Step 🔟: Silhouette Score (excluding noise points)\n",
+        "mask = clusters != -1\n",
+        "unique_clusters = len(set(clusters)) - (1 if -1 in clusters else 0)\n",
+        "\n",
+        "if unique_clusters > 1:\n",
+        "    sil_score = silhouette_score(X_scaled[mask], clusters[mask])\n",
+        "    print(f\"\\n🧩 Silhouette Score (excluding noise): {sil_score:.4f}\")\n",
+        "else:\n",
+        "    print(\"\\n⚠️ Not enough clusters for silhouette evaluation.\")\n",
+        "\n",
+        "# Step 1️⃣1️⃣: PCA Visualization (Sampling for performance)\n",
+        "pca = PCA(n_components=2, random_state=42)\n",
+        "X_pca = pca.fit_transform(X_scaled)\n",
+        "\n",
+        "# Create DataFrame for plotting\n",
+        "plot_df = pd.DataFrame({\n",
+        "    'PC1': X_pca[:, 0],\n",
+        "    'PC2': X_pca[:, 1],\n",
+        "    'Cluster': clusters\n",
+        "})\n",
+        "\n",
+        "# Sample data to avoid rendering issues\n",
+        "plot_df_sample = plot_df.sample(n=10000, random_state=42)\n",
+        "\n",
+        "plt.figure(figsize=(8, 6))\n",
+        "plt.scatter(\n",
+        "    plot_df_sample['PC1'],\n",
+        "    plot_df_sample['PC2'],\n",
+        "    c=plot_df_sample['Cluster'],\n",
+        "    cmap='tab10',\n",
+        "    alpha=0.6\n",
+        ")\n",
+        "\n",
+        "plt.title(\"💳 DBSCAN Clustering on Credit Card Transactions (PCA Sample)\")\n",
+        "plt.xlabel(\"Principal Component 1\")\n",
+        "plt.ylabel(\"Principal Component 2\")\n",
+        "plt.colorbar(label=\"Cluster\")\n",
+        "plt.show()\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/credit-card-fraud-detection/README.md b/credit-card-fraud-detection/README.md
new file mode 100644
index 0000000..bb64674
--- /dev/null
+++ b/credit-card-fraud-detection/README.md
@@ -0,0 +1,31 @@
+# 💳 Credit Card Fraud Detection using DBSCAN Clustering
+
+An unsupervised machine learning approach to detect fraudulent credit 
+card transactions using DBSCAN clustering algorithm.
+
+## 🎯 Overview
+Implements an unsupervised anomaly detection system for identifying 
+potentially fraudulent credit card transactions without labeled data.
+
+## 🚀 Installation
+```bash
+pip install pandas numpy matplotlib scikit-learn
+```
+
+## 💻 Usage
+Open `DBSCAN_clustering.ipynb` in Jupyter Notebook or Google Colab 
+and run all cells sequentially.
+
+## 📊 Dataset
+Works with credit card transaction datasets. Recommended:
+[Kaggle Credit Card Fraud Detection Dataset](https://www.kaggle.com/mlg-ulb/creditcardfraud)
+
+## 🔬 Methodology
+1. Data Preprocessing — handles missing values and standardizes features
+2. DBSCAN Clustering — density-based anomaly detection
+3. Anomaly Detection — points labeled -1 are potential fraud
+4. PCA Visualization — 2D projection of clusters
+
+## 📚 References
+- Ester, M., et al. (1996). "A density-based algorithm for discovering clusters"
+- Scikit-learn DBSCAN Documentation
\ No newline at end of file
diff --git a/credit-card-fraud-detection/requirements.txt b/credit-card-fraud-detection/requirements.txt
new file mode 100644
index 0000000..c596dd4
--- /dev/null
+++ b/credit-card-fraud-detection/requirements.txt
@@ -0,0 +1,4 @@
+pandas
+numpy
+matplotlib
+scikit-learn