From 0f88387c874f832019ee8385daf3aa7088c30755 Mon Sep 17 00:00:00 2001
From: khyati-1711 <khyatimankar1@gmail.com>
Date: Tue, 14 Jul 2026 00:54:02 +0530
Subject: [PATCH] Add Isolation Forest notebook

---
 final_anomaly.ipynb | 1597 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1597 insertions(+)
 create mode 100644 final_anomaly.ipynb

diff --git a/final_anomaly.ipynb b/final_anomaly.ipynb
new file mode 100644
index 0000000..9a93878
--- /dev/null
+++ b/final_anomaly.ipynb
@@ -0,0 +1,1597 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 136,
+      "metadata": {
+        "id": "7Fjr3drbC8oH"
+      },
+      "outputs": [],
+      "source": [
+        "# Imports\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "from sklearn.ensemble import IsolationForest\n",
+        "from scipy.io import loadmat\n",
+        "from sklearn.preprocessing import StandardScaler\n",
+        "from sklearn.metrics import ( classification_report,confusion_matrix,precision_score,recall_score,f1_score,roc_auc_score)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.datasets import fetch_openml\n",
+        "\n",
+        "dataset = fetch_openml(\n",
+        "    name=\"mammography\",\n",
+        "    version=1,\n",
+        "    as_frame=True\n",
+        ")\n",
+        "\n",
+        "X = dataset.data\n",
+        "y = dataset.target\n",
+        "\n",
+        "print(X.shape)\n",
+        "print(y.value_counts())"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mzWYRV0ePRQL",
+        "outputId": "42070091-d18d-41e6-b4f0-3d939364b80f"
+      },
+      "execution_count": 137,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "(11183, 6)\n",
+            "class\n",
+            "-1    10923\n",
+            "1       260\n",
+            "Name: count, dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(type(dataset))"
+      ],
+      "metadata": {
+        "id": "UHygciCWQ77r",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "578849a0-18cb-44e5-a6aa-27add68c7e29"
+      },
+      "execution_count": 138,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'sklearn.utils._bunch.Bunch'>\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = X.copy()\n",
+        "df[\"Class\"] = y"
+      ],
+      "metadata": {
+        "id": "3MEjDaQSPODe"
+      },
+      "execution_count": 139,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Dataset Shape:\", df.shape)"
+      ],
+      "metadata": {
+        "id": "0xbDlcPiQZ8k",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "0c48e5dc-59e3-46a7-fdad-0b7867d30cdf"
+      },
+      "execution_count": 140,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Dataset Shape: (11183, 7)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.head()"
+      ],
+      "metadata": {
+        "id": "bp-gwn_rQ1vR",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "outputId": "d47f6f95-d987-4671-b55a-51fc53e6e438"
+      },
+      "execution_count": 141,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "      attr1     attr2     attr3     attr4     attr5     attr6 Class\n",
+              "0  0.230020  5.072578 -0.276061  0.832444 -0.377866  0.480322    -1\n",
+              "1  0.155491 -0.169390  0.670652 -0.859553 -0.377866 -0.945723    -1\n",
+              "2 -0.784415 -0.443654  5.674705 -0.859553 -0.377866 -0.945723    -1\n",
+              "3  0.546088  0.131415 -0.456387 -0.859553 -0.377866 -0.945723    -1\n",
+              "4 -0.102987 -0.394994 -0.140816  0.979703 -0.377866  1.013566    -1"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a50593a3-b729-4d61-bc60-45b933c3a15c\" 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>attr1</th>\n",
+              "      <th>attr2</th>\n",
+              "      <th>attr3</th>\n",
+              "      <th>attr4</th>\n",
+              "      <th>attr5</th>\n",
+              "      <th>attr6</th>\n",
+              "      <th>Class</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>0.230020</td>\n",
+              "      <td>5.072578</td>\n",
+              "      <td>-0.276061</td>\n",
+              "      <td>0.832444</td>\n",
+              "      <td>-0.377866</td>\n",
+              "      <td>0.480322</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>0.155491</td>\n",
+              "      <td>-0.169390</td>\n",
+              "      <td>0.670652</td>\n",
+              "      <td>-0.859553</td>\n",
+              "      <td>-0.377866</td>\n",
+              "      <td>-0.945723</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>-0.784415</td>\n",
+              "      <td>-0.443654</td>\n",
+              "      <td>5.674705</td>\n",
+              "      <td>-0.859553</td>\n",
+              "      <td>-0.377866</td>\n",
+              "      <td>-0.945723</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>0.546088</td>\n",
+              "      <td>0.131415</td>\n",
+              "      <td>-0.456387</td>\n",
+              "      <td>-0.859553</td>\n",
+              "      <td>-0.377866</td>\n",
+              "      <td>-0.945723</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>-0.102987</td>\n",
+              "      <td>-0.394994</td>\n",
+              "      <td>-0.140816</td>\n",
+              "      <td>0.979703</td>\n",
+              "      <td>-0.377866</td>\n",
+              "      <td>1.013566</td>\n",
+              "      <td>-1</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a50593a3-b729-4d61-bc60-45b933c3a15c')\"\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-a50593a3-b729-4d61-bc60-45b933c3a15c 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-a50593a3-b729-4d61-bc60-45b933c3a15c');\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>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "variable_name": "df",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11183,\n  \"fields\": [\n    {\n      \"column\": \"attr1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000000020730389,\n        \"min\": -0.78441482,\n        \"max\": 31.508443,\n        \"num_unique_values\": 5435,\n        \"samples\": [\n          0.31183157,\n          -0.062165963,\n          0.58995836\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000000002552323,\n        \"min\": -0.47019533,\n        \"max\": 5.0858491,\n        \"num_unique_values\": 883,\n        \"samples\": [\n          -0.15611958,\n          4.1038094,\n          3.2412069\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0000000027226326,\n        \"min\": -0.59163147,\n        \"max\": 29.477769,\n        \"num_unique_values\": 160,\n        \"samples\": [\n          3.4657089,\n          2.2935883,\n          18.207379\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.000000001945281,\n        \"min\": -0.85955255,\n        \"max\": 9.591164,\n        \"num_unique_values\": 2800,\n        \"samples\": [\n          1.1826579,\n          0.76662081,\n          0.9970024\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999990706335,\n        \"min\": -0.37786573,\n        \"max\": 23.617122,\n        \"num_unique_values\": 1739,\n        \"samples\": [\n          5.1251739,\n          0.81024454,\n          0.91323495\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.9999999983376999,\n        \"min\": -0.94572324,\n        \"max\": 1.9490273,\n        \"num_unique_values\": 550,\n        \"samples\": [\n          0.96481214,\n          0.91910555,\n          0.035444847\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Class\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"1\",\n          \"-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 141
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.info()"
+      ],
+      "metadata": {
+        "id": "khWugZcDRGlA",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f307892d-2bdc-4c3e-8d3e-3a43ce7f3bdd"
+      },
+      "execution_count": 142,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "<class 'pandas.core.frame.DataFrame'>\n",
+            "RangeIndex: 11183 entries, 0 to 11182\n",
+            "Data columns (total 7 columns):\n",
+            " #   Column  Non-Null Count  Dtype   \n",
+            "---  ------  --------------  -----   \n",
+            " 0   attr1   11183 non-null  float64 \n",
+            " 1   attr2   11183 non-null  float64 \n",
+            " 2   attr3   11183 non-null  float64 \n",
+            " 3   attr4   11183 non-null  float64 \n",
+            " 4   attr5   11183 non-null  float64 \n",
+            " 5   attr6   11183 non-null  float64 \n",
+            " 6   Class   11183 non-null  category\n",
+            "dtypes: category(1), float64(6)\n",
+            "memory usage: 535.4 KB\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df.describe()"
+      ],
+      "metadata": {
+        "id": "rUfv5_n5Rf9x",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "outputId": "d5596672-0bde-4791-abc4-2f39ee0d0dd0"
+      },
+      "execution_count": 143,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              attr1         attr2         attr3         attr4         attr5  \\\n",
+              "count  1.118300e+04  1.118300e+04  1.118300e+04  1.118300e+04  1.118300e+04   \n",
+              "mean   1.096536e-10  1.297595e-09  5.698113e-10 -2.435706e-09 -1.120680e-09   \n",
+              "std    1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00  1.000000e+00   \n",
+              "min   -7.844148e-01 -4.701953e-01 -5.916315e-01 -8.595525e-01 -3.778657e-01   \n",
+              "25%   -7.844148e-01 -4.701953e-01 -5.916315e-01 -8.595525e-01 -3.778657e-01   \n",
+              "50%   -1.085769e-01 -3.949941e-01 -2.309790e-01 -8.595525e-01 -3.778657e-01   \n",
+              "75%    3.139489e-01 -7.649473e-02  2.198366e-01  8.202077e-01 -3.778657e-01   \n",
+              "max    3.150844e+01  5.085849e+00  2.947777e+01  9.591164e+00  2.361712e+01   \n",
+              "\n",
+              "              attr6  \n",
+              "count  1.118300e+04  \n",
+              "mean   1.459483e-09  \n",
+              "std    1.000000e+00  \n",
+              "min   -9.457232e-01  \n",
+              "25%   -9.457232e-01  \n",
+              "50%   -9.457232e-01  \n",
+              "75%    1.016613e+00  \n",
+              "max    1.949027e+00  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c683f7a5-94d5-40a2-97ae-acdea304a209\" 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>attr1</th>\n",
+              "      <th>attr2</th>\n",
+              "      <th>attr3</th>\n",
+              "      <th>attr4</th>\n",
+              "      <th>attr5</th>\n",
+              "      <th>attr6</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>1.118300e+04</td>\n",
+              "      <td>1.118300e+04</td>\n",
+              "      <td>1.118300e+04</td>\n",
+              "      <td>1.118300e+04</td>\n",
+              "      <td>1.118300e+04</td>\n",
+              "      <td>1.118300e+04</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>1.096536e-10</td>\n",
+              "      <td>1.297595e-09</td>\n",
+              "      <td>5.698113e-10</td>\n",
+              "      <td>-2.435706e-09</td>\n",
+              "      <td>-1.120680e-09</td>\n",
+              "      <td>1.459483e-09</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>1.000000e+00</td>\n",
+              "      <td>1.000000e+00</td>\n",
+              "      <td>1.000000e+00</td>\n",
+              "      <td>1.000000e+00</td>\n",
+              "      <td>1.000000e+00</td>\n",
+              "      <td>1.000000e+00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>-7.844148e-01</td>\n",
+              "      <td>-4.701953e-01</td>\n",
+              "      <td>-5.916315e-01</td>\n",
+              "      <td>-8.595525e-01</td>\n",
+              "      <td>-3.778657e-01</td>\n",
+              "      <td>-9.457232e-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>-7.844148e-01</td>\n",
+              "      <td>-4.701953e-01</td>\n",
+              "      <td>-5.916315e-01</td>\n",
+              "      <td>-8.595525e-01</td>\n",
+              "      <td>-3.778657e-01</td>\n",
+              "      <td>-9.457232e-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>-1.085769e-01</td>\n",
+              "      <td>-3.949941e-01</td>\n",
+              "      <td>-2.309790e-01</td>\n",
+              "      <td>-8.595525e-01</td>\n",
+              "      <td>-3.778657e-01</td>\n",
+              "      <td>-9.457232e-01</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>3.139489e-01</td>\n",
+              "      <td>-7.649473e-02</td>\n",
+              "      <td>2.198366e-01</td>\n",
+              "      <td>8.202077e-01</td>\n",
+              "      <td>-3.778657e-01</td>\n",
+              "      <td>1.016613e+00</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>3.150844e+01</td>\n",
+              "      <td>5.085849e+00</td>\n",
+              "      <td>2.947777e+01</td>\n",
+              "      <td>9.591164e+00</td>\n",
+              "      <td>2.361712e+01</td>\n",
+              "      <td>1.949027e+00</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c683f7a5-94d5-40a2-97ae-acdea304a209')\"\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-c683f7a5-94d5-40a2-97ae-acdea304a209 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-c683f7a5-94d5-40a2-97ae-acdea304a209');\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>\n",
+              "  </div>\n"
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "dataframe",
+              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"attr1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3952.2299881378726,\n        \"min\": -0.78441482,\n        \"max\": 11183.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11183.0,\n          1.0965358226874777e-10,\n          0.313948855\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3953.5519358026763,\n        \"min\": -0.47019533,\n        \"max\": 11183.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11183.0,\n          1.2975945437818801e-09,\n          -0.076494735\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3952.322056394103,\n        \"min\": -0.59163147,\n        \"max\": 11183.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11183.0,\n          5.698113034102937e-10,\n          0.2198366\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3953.342978546111,\n        \"min\": -0.85955255,\n        \"max\": 11183.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11183.0,\n          -2.4357059873352577e-09,\n          9.591164\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3952.6292846711403,\n        \"min\": -0.37786573,\n        \"max\": 11183.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          -1.1206795800555205e-09,\n          23.617122,\n          0.9999999990706335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"attr6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3953.73072119164,\n        \"min\": -0.94572324,\n        \"max\": 11183.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          11183.0,\n          1.4594831799932164e-09,\n          1.9490273\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
+            }
+          },
+          "metadata": {},
+          "execution_count": 143
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.dtypes)"
+      ],
+      "metadata": {
+        "id": "UOr9Tt37RnZ4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "7f210634-976d-4b99-e5c0-e2bc84fafd81"
+      },
+      "execution_count": 144,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "attr1     float64\n",
+            "attr2     float64\n",
+            "attr3     float64\n",
+            "attr4     float64\n",
+            "attr5     float64\n",
+            "attr6     float64\n",
+            "Class    category\n",
+            "dtype: object\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df.isnull().sum())     #data exploration\n",
+        "df = df.dropna()     #drop missing value"
+      ],
+      "metadata": {
+        "id": "MWBBJax6RtdT",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "5d65fc74-841b-48b7-c1f1-84e6c539f667"
+      },
+      "execution_count": 145,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "attr1    0\n",
+            "attr2    0\n",
+            "attr3    0\n",
+            "attr4    0\n",
+            "attr5    0\n",
+            "attr6    0\n",
+            "Class    0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "ZpGKUy24USbK"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"Duplicate Rows:\", df.duplicated().sum())\n",
+        "df = df.drop_duplicates()"
+      ],
+      "metadata": {
+        "id": "gg8zT_GKSPSL",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f5033821-16de-46c2-d69f-3729a8ded24f"
+      },
+      "execution_count": 146,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Duplicate Rows: 3334\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(\"After:\", len(df))\n"
+      ],
+      "metadata": {
+        "id": "RbeBbCU7Ucny",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "f20ba32e-1c7f-47ee-9922-dd3aa3fbffa8"
+      },
+      "execution_count": 147,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "After: 7849\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(df[\"Class\"].value_counts())\n",
+        "df[\"Class\"].value_counts().plot(kind=\"bar\")\n",
+        "plt.title(\"Class Distribution\")\n",
+        "plt.xlabel(\"Class\")\n",
+        "plt.ylabel(\"Count\")\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "yUnxxiQlVBiw",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 540
+        },
+        "outputId": "be7bdac0-cd41-4aca-e9fb-9a898b8816d3"
+      },
+      "execution_count": 148,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Class\n",
+            "-1    7595\n",
+            "1      254\n",
+            "Name: count, dtype: int64\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "y = dataset.target.copy()\n",
+        "\n",
+        "# Convert category to string\n",
+        "y = y.astype(str)\n",
+        "\n",
+        "# Map labels\n",
+        "y = y.map({\n",
+        "    '-1': 0,\n",
+        "    '1': 1\n",
+        "})\n",
+        "\n",
+        "print(y.unique())\n",
+        "print(y.value_counts())\n",
+        "print(y.isna().sum())"
+      ],
+      "metadata": {
+        "id": "jpfEjyTRVn3z",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "792c46ae-8169-4568-8d90-7e935eb337ef"
+      },
+      "execution_count": 149,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[0 1]\n",
+            "class\n",
+            "0    10923\n",
+            "1      260\n",
+            "Name: count, dtype: int64\n",
+            "0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "\n",
+        "X_train, X_test, y_train, y_test = train_test_split(\n",
+        "    X, y,\n",
+        "    test_size=0.2,       # 20% test, 80% train\n",
+        "    stratify=y,          # ensures class balance in both sets\n",
+        "    random_state=42      # reproducibility\n",
+        ")\n",
+        "\n",
+        "\n"
+      ],
+      "metadata": {
+        "id": "8OfydWotU6cA"
+      },
+      "execution_count": 150,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "scaler = StandardScaler()\n",
+        "\n",
+        "X_train_scaled = scaler.fit_transform(X_train)\n",
+        "X_test_scaled = scaler.transform(X_test)"
+      ],
+      "metadata": {
+        "id": "43oQeMaGReuW"
+      },
+      "execution_count": 151,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model = IsolationForest(\n",
+        "    n_estimators=300,\n",
+        "    contamination=0.03,\n",
+        "    max_samples='auto',\n",
+        "    random_state=42\n",
+        ")\n",
+        "\n",
+        "model.fit(X_train_scaled)"
+      ],
+      "metadata": {
+        "id": "-0AZ3irTaDC9",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 80
+        },
+        "outputId": "7aafd8cb-01f1-410d-9e02-fb406a647c9a"
+      },
+      "execution_count": 161,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "IsolationForest(contamination=0.03, n_estimators=300, random_state=42)"
+            ],
+            "text/html": [
+              "<style>#sk-container-id-10 {\n",
+              "  /* Definition of color scheme common for light and dark mode */\n",
+              "  --sklearn-color-text: #000;\n",
+              "  --sklearn-color-text-muted: #666;\n",
+              "  --sklearn-color-line: gray;\n",
+              "  /* Definition of color scheme for unfitted estimators */\n",
+              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+              "  --sklearn-color-unfitted-level-3: chocolate;\n",
+              "  /* Definition of color scheme for fitted estimators */\n",
+              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+              "\n",
+              "  /* Specific color for light theme */\n",
+              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+              "  --sklearn-color-icon: #696969;\n",
+              "\n",
+              "  @media (prefers-color-scheme: dark) {\n",
+              "    /* Redefinition of color scheme for dark theme */\n",
+              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+              "    --sklearn-color-icon: #878787;\n",
+              "  }\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 pre {\n",
+              "  padding: 0;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 input.sk-hidden--visually {\n",
+              "  border: 0;\n",
+              "  clip: rect(1px 1px 1px 1px);\n",
+              "  clip: rect(1px, 1px, 1px, 1px);\n",
+              "  height: 1px;\n",
+              "  margin: -1px;\n",
+              "  overflow: hidden;\n",
+              "  padding: 0;\n",
+              "  position: absolute;\n",
+              "  width: 1px;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-dashed-wrapped {\n",
+              "  border: 1px dashed var(--sklearn-color-line);\n",
+              "  margin: 0 0.4em 0.5em 0.4em;\n",
+              "  box-sizing: border-box;\n",
+              "  padding-bottom: 0.4em;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-container {\n",
+              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+              "     so we also need the `!important` here to be able to override the\n",
+              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+              "  display: inline-block !important;\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-text-repr-fallback {\n",
+              "  display: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-parallel-item,\n",
+              "div.sk-serial,\n",
+              "div.sk-item {\n",
+              "  /* draw centered vertical line to link estimators */\n",
+              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+              "  background-size: 2px 100%;\n",
+              "  background-repeat: no-repeat;\n",
+              "  background-position: center center;\n",
+              "}\n",
+              "\n",
+              "/* Parallel-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-10 div.sk-parallel-item::after {\n",
+              "  content: \"\";\n",
+              "  width: 100%;\n",
+              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+              "  flex-grow: 1;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-parallel {\n",
+              "  display: flex;\n",
+              "  align-items: stretch;\n",
+              "  justify-content: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  position: relative;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-parallel-item {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
+              "  align-self: flex-end;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
+              "  align-self: flex-start;\n",
+              "  width: 50%;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
+              "  width: 0;\n",
+              "}\n",
+              "\n",
+              "/* Serial-specific style estimator block */\n",
+              "\n",
+              "#sk-container-id-10 div.sk-serial {\n",
+              "  display: flex;\n",
+              "  flex-direction: column;\n",
+              "  align-items: center;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  padding-right: 1em;\n",
+              "  padding-left: 1em;\n",
+              "}\n",
+              "\n",
+              "\n",
+              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+              "clickable and can be expanded/collapsed.\n",
+              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+              "*/\n",
+              "\n",
+              "/* Pipeline and ColumnTransformer style (default) */\n",
+              "\n",
+              "#sk-container-id-10 div.sk-toggleable {\n",
+              "  /* Default theme specific background. It is overwritten whether we have a\n",
+              "  specific estimator or a Pipeline/ColumnTransformer */\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable label */\n",
+              "#sk-container-id-10 label.sk-toggleable__label {\n",
+              "  cursor: pointer;\n",
+              "  display: flex;\n",
+              "  width: 100%;\n",
+              "  margin-bottom: 0;\n",
+              "  padding: 0.5em;\n",
+              "  box-sizing: border-box;\n",
+              "  text-align: center;\n",
+              "  align-items: start;\n",
+              "  justify-content: space-between;\n",
+              "  gap: 0.5em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
+              "  font-size: 0.6rem;\n",
+              "  font-weight: lighter;\n",
+              "  color: var(--sklearn-color-text-muted);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
+              "  /* Arrow on the left of the label */\n",
+              "  content: \"▸\";\n",
+              "  float: left;\n",
+              "  margin-right: 0.25em;\n",
+              "  color: var(--sklearn-color-icon);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "}\n",
+              "\n",
+              "/* Toggleable content - dropdown */\n",
+              "\n",
+              "#sk-container-id-10 div.sk-toggleable__content {\n",
+              "  max-height: 0;\n",
+              "  max-width: 0;\n",
+              "  overflow: hidden;\n",
+              "  text-align: left;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-toggleable__content pre {\n",
+              "  margin: 0.2em;\n",
+              "  border-radius: 0.25em;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+              "  /* Expand drop-down */\n",
+              "  max-height: 200px;\n",
+              "  max-width: 100%;\n",
+              "  overflow: auto;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+              "  content: \"▾\";\n",
+              "}\n",
+              "\n",
+              "/* Pipeline/ColumnTransformer-specific style */\n",
+              "\n",
+              "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific style */\n",
+              "\n",
+              "/* Colorize estimator box */\n",
+              "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
+              "#sk-container-id-10 div.sk-label label {\n",
+              "  /* The background is the default theme color */\n",
+              "  color: var(--sklearn-color-text-on-default-background);\n",
+              "}\n",
+              "\n",
+              "/* On hover, darken the color of the background */\n",
+              "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Label box, darken color on hover, fitted */\n",
+              "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Estimator label */\n",
+              "\n",
+              "#sk-container-id-10 div.sk-label label {\n",
+              "  font-family: monospace;\n",
+              "  font-weight: bold;\n",
+              "  display: inline-block;\n",
+              "  line-height: 1.2em;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-label-container {\n",
+              "  text-align: center;\n",
+              "}\n",
+              "\n",
+              "/* Estimator-specific */\n",
+              "#sk-container-id-10 div.sk-estimator {\n",
+              "  font-family: monospace;\n",
+              "  border: 1px dotted var(--sklearn-color-border-box);\n",
+              "  border-radius: 0.25em;\n",
+              "  box-sizing: border-box;\n",
+              "  margin-bottom: 0.5em;\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-estimator.fitted {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-0);\n",
+              "}\n",
+              "\n",
+              "/* on hover */\n",
+              "#sk-container-id-10 div.sk-estimator:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-2);\n",
+              "}\n",
+              "\n",
+              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+              "\n",
+              "/* Common style for \"i\" and \"?\" */\n",
+              "\n",
+              ".sk-estimator-doc-link,\n",
+              "a:link.sk-estimator-doc-link,\n",
+              "a:visited.sk-estimator-doc-link {\n",
+              "  float: right;\n",
+              "  font-size: smaller;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1em;\n",
+              "  height: 1em;\n",
+              "  width: 1em;\n",
+              "  text-decoration: none !important;\n",
+              "  margin-left: 0.5em;\n",
+              "  text-align: center;\n",
+              "  /* unfitted */\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted,\n",
+              "a:link.sk-estimator-doc-link.fitted,\n",
+              "a:visited.sk-estimator-doc-link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+              ".sk-estimator-doc-link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover,\n",
+              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+              ".sk-estimator-doc-link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "/* Span, style for the box shown on hovering the info icon */\n",
+              ".sk-estimator-doc-link span {\n",
+              "  display: none;\n",
+              "  z-index: 9999;\n",
+              "  position: relative;\n",
+              "  font-weight: normal;\n",
+              "  right: .2ex;\n",
+              "  padding: .5ex;\n",
+              "  margin: .5ex;\n",
+              "  width: min-content;\n",
+              "  min-width: 20ex;\n",
+              "  max-width: 50ex;\n",
+              "  color: var(--sklearn-color-text);\n",
+              "  box-shadow: 2pt 2pt 4pt #999;\n",
+              "  /* unfitted */\n",
+              "  background: var(--sklearn-color-unfitted-level-0);\n",
+              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link.fitted span {\n",
+              "  /* fitted */\n",
+              "  background: var(--sklearn-color-fitted-level-0);\n",
+              "  border: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "\n",
+              ".sk-estimator-doc-link:hover span {\n",
+              "  display: block;\n",
+              "}\n",
+              "\n",
+              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+              "\n",
+              "#sk-container-id-10 a.estimator_doc_link {\n",
+              "  float: right;\n",
+              "  font-size: 1rem;\n",
+              "  line-height: 1em;\n",
+              "  font-family: monospace;\n",
+              "  background-color: var(--sklearn-color-background);\n",
+              "  border-radius: 1rem;\n",
+              "  height: 1rem;\n",
+              "  width: 1rem;\n",
+              "  text-decoration: none;\n",
+              "  /* unfitted */\n",
+              "  color: var(--sklearn-color-unfitted-level-1);\n",
+              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
+              "  /* fitted */\n",
+              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+              "  color: var(--sklearn-color-fitted-level-1);\n",
+              "}\n",
+              "\n",
+              "/* On hover */\n",
+              "#sk-container-id-10 a.estimator_doc_link:hover {\n",
+              "  /* unfitted */\n",
+              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+              "  color: var(--sklearn-color-background);\n",
+              "  text-decoration: none;\n",
+              "}\n",
+              "\n",
+              "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
+              "  /* fitted */\n",
+              "  background-color: var(--sklearn-color-fitted-level-3);\n",
+              "}\n",
+              "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest(contamination=0.03, n_estimators=300, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>IsolationForest</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.IsolationForest.html\">?<span>Documentation for IsolationForest</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>IsolationForest(contamination=0.03, n_estimators=300, random_state=42)</pre></div> </div></div></div></div>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 161
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df = df.copy()\n",
+        "# Ensure 'anomaly_score' and 'prediction' are not in df before creating X_for_prediction\n",
+        "if 'anomaly_score' in df.columns:\n",
+        "    df = df.drop('anomaly_score', axis=1)\n",
+        "if 'prediction' in df.columns:\n",
+        "    df = df.drop('prediction', axis=1)\n",
+        "\n",
+        "# Create X from the current (deduplicated) df to ensure consistent length\n",
+        "X_for_prediction = df.drop(\"Class\", axis=1)\n",
+        "# Scale X_for_prediction using the previously fitted scaler\n",
+        "X_for_prediction_scaled = scaler.transform(X_for_prediction)\n",
+        "df[\"anomaly_score\"] = model.decision_function(X_for_prediction_scaled)\n",
+        "df[\"prediction\"] = model.predict(X_for_prediction_scaled)"
+      ],
+      "metadata": {
+        "id": "FhsRNpE8a5Lg"
+      },
+      "execution_count": 165,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df[\"prediction\"].value_counts()"
+      ],
+      "metadata": {
+        "id": "KQByB_pIeQ4l",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 178
+        },
+        "outputId": "38179f50-a46f-4e54-d60f-8e27ccb02eba"
+      },
+      "execution_count": 154,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "prediction\n",
+              " 1    7513\n",
+              "-1     336\n",
+              "Name: count, dtype: int64"
+            ],
+            "text/html": [
+              "<div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>count</th>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>prediction</th>\n",
+              "      <th></th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>7513</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>-1</th>\n",
+              "      <td>336</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div><br><label><b>dtype:</b> int64</label>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 154
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Visualization of the results\n",
+        "plt.figure(figsize=(10, 5))\n",
+        "\n",
+        "# Plot normal instances\n",
+        "normal = df[df['prediction'] == 1]\n",
+        "plt.scatter(normal.index, normal['anomaly_score'], label='Normal')\n",
+        "\n",
+        "# Plot anomalies\n",
+        "anomalies = df[df['prediction'] == -1]\n",
+        "plt.scatter(anomalies.index, anomalies['anomaly_score'], label='prediction')\n",
+        "plt.xlabel(\"Instance\")\n",
+        "plt.ylabel(\"Anomaly Score\")\n",
+        "plt.legend()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "Ui8ntP7ufZiq",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 434
+        },
+        "outputId": "f0eff771-477b-4192-8b6e-8a82ce2160df"
+      },
+      "execution_count": 155,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(y.dtype)\n",
+        "print(y.unique())\n",
+        "print(y.isnull().sum())"
+      ],
+      "metadata": {
+        "id": "RuuQmWAof8U-",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "e100e385-c5d2-4ae6-bb82-6b55722d6e41"
+      },
+      "execution_count": 156,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "int64\n",
+            "[0 1]\n",
+            "0\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "pred = model.predict(X_for_prediction_scaled)\n",
+        "pred = np.where(pred == -1, 1, 0)\n",
+        "\n",
+        "# Create y_true for the deduplicated df, consistent with how y was mapped\n",
+        "y_true_deduplicated = df['Class'].astype(str).map({'-1': 0, '1': 1})\n",
+        "\n",
+        "precision = precision_score(y_true_deduplicated, pred)\n",
+        "recall = recall_score(y_true_deduplicated, pred)\n",
+        "f1 = f1_score(y_true_deduplicated, pred)\n",
+        "scores = -model.decision_function(X_for_prediction_scaled)\n",
+        "\n",
+        "roc_auc = roc_auc_score(y_true_deduplicated, scores)\n",
+        "print(\"Precision:\", precision)\n",
+        "print(\"Recall:\", recall)\n",
+        "print(\"F1 Score:\", f1)\n",
+        "print(\"ROC AUC Score:\", roc_auc)"
+      ],
+      "metadata": {
+        "id": "jN75W6ZcqIiO",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "9cfb857e-938c-4dca-e337-a3decd3e0914"
+      },
+      "execution_count": 167,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Precision: 0.18895348837209303\n",
+            "Recall: 0.2559055118110236\n",
+            "F1 Score: 0.21739130434782608\n",
+            "ROC AUC Score: 0.8213793264321222\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [],
+      "metadata": {
+        "id": "3peJOqf1sEDW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(classification_report(y_true_deduplicated,pred))"
+      ],
+      "metadata": {
+        "id": "qMBVX9rdq7OY",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "411b654b-cd3c-4fe0-8f24-290008c2d252"
+      },
+      "execution_count": 158,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "              precision    recall  f1-score   support\n",
+            "\n",
+            "           0       0.98      0.96      0.97      7595\n",
+            "           1       0.20      0.27      0.23       254\n",
+            "\n",
+            "    accuracy                           0.94      7849\n",
+            "   macro avg       0.59      0.62      0.60      7849\n",
+            "weighted avg       0.95      0.94      0.95      7849\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "cm = confusion_matrix(y_true_deduplicated,pred)\n",
+        "print(cm)"
+      ],
+      "metadata": {
+        "id": "xow1Sr4Qwhvx",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "a77de109-1bcc-42a8-bffe-a47ce7941f4e"
+      },
+      "execution_count": 159,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "[[7327  268]\n",
+            " [ 186   68]]\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.imshow(cm)\n",
+        "plt.title(\"Confusion Matrix\")\n",
+        "plt.colorbar()\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "id": "3HiSCj7BwoBi",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 452
+        },
+        "outputId": "d846c2f0-f2fc-483e-cb29-294dafe9b1da"
+      },
+      "execution_count": 160,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "HY-2EFshwyIv"
+      },
+      "execution_count": 160,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file