diff --git a/PULL_REQUEST_TEMPLATE.md b/PULL_REQUEST_TEMPLATE.md
index 4b4d87f..0316096 100644
--- a/PULL_REQUEST_TEMPLATE.md
+++ b/PULL_REQUEST_TEMPLATE.md
@@ -2,7 +2,7 @@
 
 ## Related Issue
 
-Closes #
+Closes #16
 
 ---
 
@@ -11,14 +11,17 @@ Closes #
 Provide a brief overview of your implementation.
 
 - What did you implement?
+- I trained a model on the shuttle ODDS dataset using the isolation forest to detect the the anomalies in the dataset. 
 - What approach did you follow?
+- Firstly after importing the data and converted the data into the DataFrame i looked for the missing valeus(NaN) luckily there weren't any so there was almost no preprocessing required.
+  Major Challenge i faced  during training of the model was recalling the correct syntax and some libs, I took some help from the medium page.To decide the best parameters values like         n_estimators,contamination and sample size i did some hard yakka, by running the loops to decide the n_estimators in range(1,700,50) luckily i got the highest f1 scores for values 101 and 151 but i took 100 because it had higher f1_score as well as auc_value both tending to one if i took approximaiton upto 2 decimal places.
 
 ---
 
 ## Dataset
 
 - [ ] Mammography
-- [ ] Shuttle
+- [x] Shuttle
 
 Dataset Source:
 
@@ -28,11 +31,10 @@ Dataset Source:
 
 Describe any preprocessing performed.
 
-Examples:
-- Missing value handling
-- Feature scaling
-- Encoding
-- Feature selection
+- Missing value handling - No,It has nil NaN values confirmed it by df.isnull().sum()==0
+- Feature scaling - No isolation forest can handle without scaling.
+- Encoding - No data contained the numeric values so not needed.
+- Feature selection - No verly low dimensionality.
 
 ---
 
@@ -42,11 +44,11 @@ List the important hyperparameters used.
 
 | Hyperparameter | Value |
 |---------------|-------|
-| n_estimators | |
-| contamination | |
-| max_samples | |
-| max_features | |
-| random_state | |
+| n_estimators |100|
+| contamination |0.075|
+| max_samples |256|
+| max_features |1|
+| random_state |42|
 
 ---
 
@@ -54,10 +56,10 @@ List the important hyperparameters used.
 
 | Metric | Value |
 |--------|-------|
-| Precision | |
-| Recall | |
-| F1-score | |
-| ROC-AUC (Optional) | |
+| Precision |0.9332|
+| Recall |0.9789|
+| F1-score |0.955|
+| ROC-AUC (Optional) |0.9975|
 
 ---
 
@@ -65,14 +67,11 @@ List the important hyperparameters used.
 
 Attach **at least 2 plots** from your analysis.
 
-Examples:
-- PCA visualization
-- Anomaly score distribution
-- Confusion Matrix
-- Correlation heatmap
-- Feature distributions
-- Hyperparameter comparison
-- Precision/Recall/F1 comparison
+<img width="3063" height="2957" alt="anomalyscore" src="https://github.com/user-attachments/assets/435f65ba-3713-4eee-a5f8-229dcdef624f" />
+<img width="1591" height="1403" alt="confusionmatrix" src="https://github.com/user-attachments/assets/54dbf8b6-1e82-4dab-97b6-4145b6f24558" />
+<img width="1872" height="1403" alt="roc_auc_curve" src="https://github.com/user-attachments/assets/c5072c8e-060b-46ea-9f78-674df0df1436" />
+<img width="1191" height="180" alt="Screenshot From 2026-07-13 02-28-36" src="https://github.com/user-attachments/assets/7cacdab4-2405-4c2f-97e0-99eb5f9eb483" />
+
 
 ---
 
@@ -81,17 +80,19 @@ Examples:
 Briefly summarize:
 
 - What worked well?
+ - Preprocessing there wasn't any.
 - Which hyperparameter had the biggest impact?
-- Any interesting findings?
+ - Contamination value.increasing it slightly helped me achieved the good f1_score,recall slight more auc_val.
 - Challenges faced (if any)
+ - Since new to Isolation forest faced slight issues with syntax and libs.
 
 ---
 
 ## Checklist
 
-- [ ] Code runs successfully
-- [ ] Notebook (`.ipynb`) included
-- [ ] Code is well-commented
-- [ ] README/documentation updated
-- [ ] At least **2 plots** included
-- [ ] PR is linked to the corresponding issue
\ No newline at end of file
+- [x] Code runs successfully
+- [x] Notebook (`.ipynb`) included
+- [x] Code is well-commented
+- [x] README/documentation updated
+- [x] At least **2 plots** included
+- [x] PR is linked to the corresponding issue
diff --git a/Screenshot From 2026-07-13 02-28-36.png b/Screenshot From 2026-07-13 02-28-36.png
new file mode 100644
index 0000000..23045ec
Binary files /dev/null and b/Screenshot From 2026-07-13 02-28-36.png differ
diff --git a/anomalyscore.png b/anomalyscore.png
new file mode 100644
index 0000000..04b982a
Binary files /dev/null and b/anomalyscore.png differ
diff --git a/confusionmatrix.png b/confusionmatrix.png
new file mode 100644
index 0000000..f70e42c
Binary files /dev/null and b/confusionmatrix.png differ
diff --git a/main.py b/main.py
new file mode 100644
index 0000000..c546bdd
--- /dev/null
+++ b/main.py
@@ -0,0 +1,6 @@
+def main():
+    print("Hello from cognios!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/ml_task_shuttle.ipynb b/ml_task_shuttle.ipynb
new file mode 100644
index 0000000..5e94c4a
--- /dev/null
+++ b/ml_task_shuttle.ipynb
@@ -0,0 +1,2231 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6cefcec7-7505-4d93-a903-07d03bc2914c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Defaulting to user installation because normal site-packages is not writeable\n",
+      "Requirement already satisfied: ucimlrepo in /home/nikhil/.local/lib/python3.14/site-packages (0.0.7)\n",
+      "Requirement already satisfied: pandas>=1.0.0 in /home/nikhil/.local/lib/python3.14/site-packages (from ucimlrepo) (3.0.3)\n",
+      "Requirement already satisfied: certifi>=2020.12.5 in /home/nikhil/.local/lib/python3.14/site-packages (from ucimlrepo) (2026.5.20)\n",
+      "Requirement already satisfied: numpy>=2.3.3 in /home/nikhil/.local/lib/python3.14/site-packages (from pandas>=1.0.0->ucimlrepo) (2.4.6)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.2 in /home/nikhil/.local/lib/python3.14/site-packages (from pandas>=1.0.0->ucimlrepo) (2.9.0.post0)\n",
+      "Requirement already satisfied: six>=1.5 in /home/nikhil/.local/lib/python3.14/site-packages (from python-dateutil>=2.8.2->pandas>=1.0.0->ucimlrepo) (1.17.0)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install ucimlrepo --break-system-packages\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.ensemble import IsolationForest\n",
+    "from ucimlrepo import fetch_ucirepo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "77fd9056-768a-4fb5-9c15-997dbcac5a5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Success! ---\n",
+      "Features shape (X): (49097, 9)\n",
+      "Labels shape (y): (49097,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import io\n",
+    "import urllib.request\n",
+    "import scipy.io\n",
+    "mat_data = scipy.io.loadmat('shuttle.mat')\n",
+    "#data from \n",
+    "X = mat_data['X']\n",
+    "y = mat_data['y'].ravel()\n",
+    "print(\"--- Success! ---\")\n",
+    "print(f\"Features shape (X): {X.shape}\")\n",
+    "print(f\"Labels shape (y): {y.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "5a80aafc-855e-4e90-a809-599320a38d54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "feature_columns = [f'feature_{i+1}' for i in range(X.shape[1])]\n",
+    "X_df = pd.DataFrame(X, columns=feature_columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "41d13edd-4f4b-4b1b-9490-70c7a10be88b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "      <th></th>\n",
+       "      <th>feature_1</th>\n",
+       "      <th>feature_2</th>\n",
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+       "      <th>feature_4</th>\n",
+       "      <th>feature_5</th>\n",
+       "      <th>feature_6</th>\n",
+       "      <th>feature_7</th>\n",
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+       "      <th>feature_9</th>\n",
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+       "      <td>0</td>\n",
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+       "      <td>-26</td>\n",
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+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>85</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88</td>\n",
+       "      <td>-4</td>\n",
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+       "      <td>1</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49092</th>\n",
+       "      <td>39</td>\n",
+       "      <td>-2</td>\n",
+       "      <td>80</td>\n",
+       "      <td>-4</td>\n",
+       "      <td>38</td>\n",
+       "      <td>0</td>\n",
+       "      <td>41</td>\n",
+       "      <td>41</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49093</th>\n",
+       "      <td>43</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>1</td>\n",
+       "      <td>42</td>\n",
+       "      <td>-9</td>\n",
+       "      <td>37</td>\n",
+       "      <td>39</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49094</th>\n",
+       "      <td>49</td>\n",
+       "      <td>0</td>\n",
+       "      <td>87</td>\n",
+       "      <td>0</td>\n",
+       "      <td>46</td>\n",
+       "      <td>-12</td>\n",
+       "      <td>38</td>\n",
+       "      <td>41</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49095</th>\n",
+       "      <td>80</td>\n",
+       "      <td>0</td>\n",
+       "      <td>84</td>\n",
+       "      <td>0</td>\n",
+       "      <td>-36</td>\n",
+       "      <td>-29</td>\n",
+       "      <td>4</td>\n",
+       "      <td>120</td>\n",
+       "      <td>116</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>49096</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>103</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>-16</td>\n",
+       "      <td>66</td>\n",
+       "      <td>85</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>49097 rows × 9 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
+       "0             50         21         77          0         28          0   \n",
+       "1             53          0         82          0         52         -5   \n",
+       "2             37          0         76          0         28         18   \n",
+       "3             37          0         79          0         34        -26   \n",
+       "4             85          0         88         -4          6          1   \n",
+       "...          ...        ...        ...        ...        ...        ...   \n",
+       "49092         39         -2         80         -4         38          0   \n",
+       "49093         43          0         81          1         42         -9   \n",
+       "49094         49          0         87          0         46        -12   \n",
+       "49095         80          0         84          0        -36        -29   \n",
+       "49096         37          0        103          0         18        -16   \n",
+       "\n",
+       "       feature_7  feature_8  feature_9  \n",
+       "0             27         48         22  \n",
+       "1             29         30          2  \n",
+       "2             40         48          8  \n",
+       "3             43         46          2  \n",
+       "4              3         83         80  \n",
+       "...          ...        ...        ...  \n",
+       "49092         41         41          0  \n",
+       "49093         37         39          2  \n",
+       "49094         38         41          2  \n",
+       "49095          4        120        116  \n",
+       "49096         66         85         20  \n",
+       "\n",
+       "[49097 rows x 9 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "18a2b6e6-17c9-4185-8a0b-3a1b07e5ec41",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
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+       "        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>feature_1</th>\n",
+       "      <th>feature_2</th>\n",
+       "      <th>feature_3</th>\n",
+       "      <th>feature_4</th>\n",
+       "      <th>feature_5</th>\n",
+       "      <th>feature_6</th>\n",
+       "      <th>feature_7</th>\n",
+       "      <th>feature_8</th>\n",
+       "      <th>feature_9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "      <td>49097.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>46.932399</td>\n",
+       "      <td>-0.063955</td>\n",
+       "      <td>85.123124</td>\n",
+       "      <td>0.213231</td>\n",
+       "      <td>36.871784</td>\n",
+       "      <td>2.160030</td>\n",
+       "      <td>38.200725</td>\n",
+       "      <td>48.288592</td>\n",
+       "      <td>10.261930</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>12.875159</td>\n",
+       "      <td>84.674481</td>\n",
+       "      <td>8.877517</td>\n",
+       "      <td>37.579171</td>\n",
+       "      <td>19.963113</td>\n",
+       "      <td>218.324964</td>\n",
+       "      <td>13.446306</td>\n",
+       "      <td>20.572064</td>\n",
+       "      <td>23.751024</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>27.000000</td>\n",
+       "      <td>-4821.000000</td>\n",
+       "      <td>21.000000</td>\n",
+       "      <td>-3939.000000</td>\n",
+       "      <td>-188.000000</td>\n",
+       "      <td>-26739.000000</td>\n",
+       "      <td>-48.000000</td>\n",
+       "      <td>-353.000000</td>\n",
+       "      <td>-356.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>37.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>79.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>-4.000000</td>\n",
+       "      <td>33.000000</td>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>44.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>83.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>42.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>39.000000</td>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>50.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>88.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>46.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>43.000000</td>\n",
+       "      <td>55.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>126.000000</td>\n",
+       "      <td>5075.000000</td>\n",
+       "      <td>149.000000</td>\n",
+       "      <td>3830.000000</td>\n",
+       "      <td>436.000000</td>\n",
+       "      <td>15164.000000</td>\n",
+       "      <td>105.000000</td>\n",
+       "      <td>270.000000</td>\n",
+       "      <td>266.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          feature_1     feature_2     feature_3     feature_4     feature_5  \\\n",
+       "count  49097.000000  49097.000000  49097.000000  49097.000000  49097.000000   \n",
+       "mean      46.932399     -0.063955     85.123124      0.213231     36.871784   \n",
+       "std       12.875159     84.674481      8.877517     37.579171     19.963113   \n",
+       "min       27.000000  -4821.000000     21.000000  -3939.000000   -188.000000   \n",
+       "25%       37.000000      0.000000     79.000000      0.000000     30.000000   \n",
+       "50%       44.000000      0.000000     83.000000      0.000000     42.000000   \n",
+       "75%       50.000000      0.000000     88.000000      0.000000     46.000000   \n",
+       "max      126.000000   5075.000000    149.000000   3830.000000    436.000000   \n",
+       "\n",
+       "          feature_6     feature_7     feature_8     feature_9  \n",
+       "count  49097.000000  49097.000000  49097.000000  49097.000000  \n",
+       "mean       2.160030     38.200725     48.288592     10.261930  \n",
+       "std      218.324964     13.446306     20.572064     23.751024  \n",
+       "min   -26739.000000    -48.000000   -353.000000   -356.000000  \n",
+       "25%       -4.000000     33.000000     35.000000      0.000000  \n",
+       "50%        0.000000     39.000000     41.000000      2.000000  \n",
+       "75%        5.000000     43.000000     55.000000      6.000000  \n",
+       "max    15164.000000    105.000000    270.000000    266.000000  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "aa362e14-598c-4fda-ba98-79e54dab7877",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "feature_1    0\n",
+      "feature_2    0\n",
+      "feature_3    0\n",
+      "feature_4    0\n",
+      "feature_5    0\n",
+      "feature_6    0\n",
+      "feature_7    0\n",
+      "feature_8    0\n",
+      "feature_9    0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_df.isna().sum())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "d68b47d7-8cea-49bf-80b4-d3f603f2867f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [feature_1, feature_2, feature_3, feature_4, feature_5, feature_6, feature_7, feature_8, feature_9]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(X_df[X_df.isna().any(axis=1)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "afe50c84-b512-4daf-ab0a-c77f68496810",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [feature_1, feature_2, feature_3, feature_4, feature_5, feature_6, feature_7, feature_8, feature_9]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "nan_rows = X_df[X_df.isna().any(axis=1)]\n",
+    "print(nan_rows)\n",
+    "#this shows we have not any rows with NaN so replacement needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d31f792c-146b-4f60-a3e4-d2c8b48069dc",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(49097, 9)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feature_1</th>\n",
+       "      <th>feature_2</th>\n",
+       "      <th>feature_3</th>\n",
+       "      <th>feature_4</th>\n",
+       "      <th>feature_5</th>\n",
+       "      <th>feature_6</th>\n",
+       "      <th>feature_7</th>\n",
+       "      <th>feature_8</th>\n",
+       "      <th>feature_9</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>50</td>\n",
+       "      <td>21</td>\n",
+       "      <td>77</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>48</td>\n",
+       "      <td>22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>53</td>\n",
+       "      <td>0</td>\n",
+       "      <td>82</td>\n",
+       "      <td>0</td>\n",
+       "      <td>52</td>\n",
+       "      <td>-5</td>\n",
+       "      <td>29</td>\n",
+       "      <td>30</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "      <td>28</td>\n",
+       "      <td>18</td>\n",
+       "      <td>40</td>\n",
+       "      <td>48</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>37</td>\n",
+       "      <td>0</td>\n",
+       "      <td>79</td>\n",
+       "      <td>0</td>\n",
+       "      <td>34</td>\n",
+       "      <td>-26</td>\n",
+       "      <td>43</td>\n",
+       "      <td>46</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>85</td>\n",
+       "      <td>0</td>\n",
+       "      <td>88</td>\n",
+       "      <td>-4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>83</td>\n",
+       "      <td>80</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  \\\n",
+       "0         50         21         77          0         28          0   \n",
+       "1         53          0         82          0         52         -5   \n",
+       "2         37          0         76          0         28         18   \n",
+       "3         37          0         79          0         34        -26   \n",
+       "4         85          0         88         -4          6          1   \n",
+       "\n",
+       "   feature_7  feature_8  feature_9  \n",
+       "0         27         48         22  \n",
+       "1         29         30          2  \n",
+       "2         40         48          8  \n",
+       "3         43         46          2  \n",
+       "4          3         83         80  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(X_df.shape)\n",
+    "X_df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "27d8add6-64c5-4294-b0ea-603ec28f631f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_estimators=100\n",
+    "contamination=0.075\n",
+    "sample_size=256\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "131ba8c3-4192-4a18-bdb1-8c4ed04d5988",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>.sk-global {\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",
+       "\n",
+       ".sk-global.light {\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: black;\n",
+       "  --sklearn-color-background: white;\n",
+       "  --sklearn-color-border-box: black;\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "}\n",
+       "\n",
+       ".sk-global.dark {\n",
+       "  --sklearn-color-text-on-default-background: white;\n",
+       "  --sklearn-color-background: #111;\n",
+       "  --sklearn-color-border-box: white;\n",
+       "  --sklearn-color-icon: #878787;\n",
+       "}\n",
+       "\n",
+       ".sk-global {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".sk-global pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip-path: inset(100%);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       ".sk-global 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-global 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-global 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-global 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-global 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-global div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       ".sk-global 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-global 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-global 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: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".sk-global 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-global 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-global label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content {\n",
+       "  display: none;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global 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-global div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  overflow: visible;\n",
+       "}\n",
+       "\n",
+       ".sk-global input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       ".sk-global 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-global 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-global 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-global 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-global div.sk-label label.sk-toggleable__label,\n",
+       ".sk-global 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-global 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-global 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-global div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       ".sk-global div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       ".sk-global 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-global div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       ".sk-global div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".sk-global 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-unfitted-level-0);\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-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-3);\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",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-3);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-0);\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",
+       "  border: var(--sklearn-color-fitted-level-0) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-0);\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-global 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-unfitted-level-0);\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-global a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\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-global 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-global a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-top-container.sk-global {\n",
+       "  /* pydata-sphinx-theme hides overflow, so scrolling is disabled.\n",
+       "   We need to set it to !important and add tabindex=\"0\" in the HTML\n",
+       "   to allow keyboard-only users to navigate the display. */\n",
+       "  overflow-x: scroll !important;\n",
+       "  max-width: 100%;\n",
+       "}\n",
+       "\n",
+       ".estimator-table {\n",
+       "    font-family: monospace;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary {\n",
+       "    padding: .5rem;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".estimator-table summary::marker {\n",
+       "    font-size: 0.7rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table details[open] {\n",
+       "    padding-left: 0.1rem;\n",
+       "    padding-right: 0.1rem;\n",
+       "    padding-bottom: 0.3rem;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table {\n",
+       "    margin-left: auto !important;\n",
+       "    margin-right: auto !important;\n",
+       "    margin-top: 0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(odd) {\n",
+       "    background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:nth-child(even) {\n",
+       "    background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".estimator-table .parameters-table tr:hover td {\n",
+       "    background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".estimator-table table :is(td, th) {\n",
+       "    border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    `table td`is set in notebook with right text-align.\n",
+       "    We need to overwrite it.\n",
+       "*/\n",
+       ".estimator-table table td.param {\n",
+       "    text-align: left;\n",
+       "    position: relative;\n",
+       "    padding: 0;\n",
+       "}\n",
+       "\n",
+       ".user-set td {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td.value {\n",
+       "    color:rgb(255, 94, 0);\n",
+       "    background-color: transparent;\n",
+       "}\n",
+       "\n",
+       ".default td, .estimator-table th {\n",
+       "    color: black;\n",
+       "    text-align: left !important;\n",
+       "}\n",
+       "\n",
+       ".user-set td i,\n",
+       ".default td i {\n",
+       "    color: black;\n",
+       "}\n",
+       "\n",
+       "td.fitted-att-type {\n",
+       "    white-space: preserve nowrap;\n",
+       "}\n",
+       "\n",
+       "/*\n",
+       "    Styles for parameter documentation links\n",
+       "    We need styling for visited so jupyter doesn't overwrite it\n",
+       "*/\n",
+       "a.param-doc-link,\n",
+       "a.param-doc-link:link,\n",
+       "a.param-doc-link:visited {\n",
+       "    text-decoration: underline dashed;\n",
+       "    text-underline-offset: .3em;\n",
+       "    color: inherit;\n",
+       "    display: block;\n",
+       "    padding: .5em;\n",
+       "}\n",
+       "\n",
+       "@supports(anchor-name: --doc-link) {\n",
+       "    a.param-doc-link,\n",
+       "    a.param-doc-link:link,\n",
+       "    a.param-doc-link:visited {\n",
+       "    anchor-name: --doc-link;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* \"hack\" to make the entire area of the cell containing the link clickable */\n",
+       "a.param-doc-link::before {\n",
+       "    position: absolute;\n",
+       "    content: \"\";\n",
+       "    inset: 0;\n",
+       "}\n",
+       "\n",
+       ".param-doc-description {\n",
+       "    display: none;\n",
+       "    position: absolute;\n",
+       "    z-index: 9999;\n",
+       "    left: 0;\n",
+       "    padding: .5ex;\n",
+       "    margin-left: 1.5em;\n",
+       "    color: var(--sklearn-color-text);\n",
+       "    box-shadow: .3em .3em .4em #999;\n",
+       "    width: max-content;\n",
+       "    text-align: left;\n",
+       "    max-height: 10em;\n",
+       "    overflow-y: auto;\n",
+       "\n",
+       "    /* unfitted */\n",
+       "    background: var(--sklearn-color-unfitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       "@supports(position-area: center right) {\n",
+       "    .param-doc-description {\n",
+       "    position-area: center right;\n",
+       "    position: fixed;\n",
+       "    margin-left: 0;\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "/* Fitted state for parameter tooltips */\n",
+       ".fitted .param-doc-description {\n",
+       "    /* fitted */\n",
+       "    background: var(--sklearn-color-fitted-level-0);\n",
+       "    border: thin solid var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".param-doc-link:hover .param-doc-description {\n",
+       "    display: block;\n",
+       "}\n",
+       "\n",
+       ".copy-paste-icon {\n",
+       "    background-image: url(data:image/svg+xml;base64,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);\n",
+       "    background-repeat: no-repeat;\n",
+       "    background-size: 14px 14px;\n",
+       "    background-position: 0;\n",
+       "    display: inline-block;\n",
+       "    width: 14px;\n",
+       "    height: 14px;\n",
+       "    cursor: pointer;\n",
+       "}\n",
+       "\n",
+       ".features {\n",
+       "  font-family: monospace;\n",
+       "  cursor: pointer;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: .20em;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  font-size: inherit; /* Needed for jupyter */\n",
+       "}\n",
+       "\n",
+       ".features.fitted {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features summary {\n",
+       "  cursor: pointer;\n",
+       "  display: flex;\n",
+       "  margin-bottom: 0;\n",
+       "  text-align: center;\n",
+       "  align-items: center;\n",
+       "  justify-content: center;\n",
+       "  gap: 0.5em;\n",
+       "  padding: .25em;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features.fitted details[open] > summary {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "  border-radius: .20em 0 0 0;\n",
+       "}\n",
+       "\n",
+       ".features details > summary .arrow::before {\n",
+       "  content: \"▸\";\n",
+       "  color: grey;\n",
+       "}\n",
+       "\n",
+       ".features details[open] > summary .arrow::before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       ".features details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features.fitted details:hover > summary {\n",
+       "  margin: 0;\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       ".features .features-container {\n",
+       "  max-width: 15em;\n",
+       "  max-height: 10em;\n",
+       "  overflow: auto;\n",
+       "  scrollbar-width: thin;\n",
+       "  padding: .25em 0.1rem;\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "  border-radius: 0 0 .5em .5em;\n",
+       "}\n",
+       "\n",
+       ".features.fitted .features-container {\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       ".features .image-container {\n",
+       "  block-size: 1em;\n",
+       "  inline-size: 1em;\n",
+       "  padding: 0;\n",
+       "  margin: 0%;\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  align-items: center;\n",
+       "}\n",
+       "\n",
+       ".features .copy-paste-icon {\n",
+       "  background-size: 1em 1em;\n",
+       "  width: 1em;\n",
+       "  height: 1em;\n",
+       "  filter: grayscale(100%) opacity(60%);\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  width: 100%;\n",
+       "  margin: 0.01em;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(odd) {\n",
+       "  background-color: #fff;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:nth-child(even) {\n",
+       "  background-color: #f6f6f6;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table tr:hover {\n",
+       "  background-color: #e0e0e0;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table {\n",
+       "  table-layout: inherit;\n",
+       "}\n",
+       "\n",
+       ".features .features-container table td {\n",
+       "  text-align: left;\n",
+       "  padding: 0 0.5em;\n",
+       "  border: 1px solid rgba(106, 105, 104, 0.232);\n",
+       "  white-space: nowrap;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       ".total_features {\n",
+       "  display: flex;\n",
+       "  justify-content: center;\n",
+       "  margin-top: 0.5em;\n",
+       "}\n",
+       "</style><body><div id=\"sk-container-id-1\" tabindex=\"0\" class=\"sk-top-container sk-global\"><div class=\"sk-text-repr-fallback\"><pre>IsolationForest(contamination=0.075, max_samples=256, 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 sk-global\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>IsolationForest</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/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\" data-param-prefix=\"\">\n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Parameters</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                  <tbody>\n",
+       "                    \n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_samples',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-max_samples;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=max_samples,-%22auto%22%2C%20int%20or%20float%2C%20default%3D%22auto%22\">\n",
+       "            max_samples\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-max_samples;\">\n",
+       "            max_samples: &quot;auto&quot;, int or float, default=&quot;auto&quot;<br><br>The number of samples to draw from X to train each base estimator.<br><br>- If int, then draw `max_samples` samples.<br>- If float, then draw `max_samples * X.shape[0]` samples.<br>- If &quot;auto&quot;, then `max_samples=min(256, n_samples)`.<br><br>If max_samples is larger than the number of samples provided,<br>all samples will be used for all trees (no sampling).</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">256</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('contamination',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-contamination;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=contamination,-%27auto%27%20or%20float%2C%20default%3D%27auto%27\">\n",
+       "            contamination\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-contamination;\">\n",
+       "            contamination: &#x27;auto&#x27; or float, default=&#x27;auto&#x27;<br><br>The amount of contamination of the data set, i.e. the proportion<br>of outliers in the data set. Used when fitting to define the threshold<br>on the scores of the samples.<br><br>- If &#x27;auto&#x27;, the threshold is determined as in the<br>  original paper.<br>- If float, the contamination should be in the range (0, 0.5].<br><br>.. versionchanged:: 0.22<br>   The default value of ``contamination`` changed from 0.1<br>   to ``&#x27;auto&#x27;``.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0.075</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"user-set\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('random_state',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-random_state;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=random_state,-int%2C%20RandomState%20instance%20or%20None%2C%20default%3DNone\">\n",
+       "            random_state\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-random_state;\">\n",
+       "            random_state: int, RandomState instance or None, default=None<br><br>Controls the pseudo-randomness of the selection of the feature<br>and split values for each branching step and each tree in the forest.<br><br>Pass an int for reproducible results across multiple function calls.<br>See :term:`Glossary &lt;random_state&gt;`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">42</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_estimators',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_estimators;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=n_estimators,-int%2C%20default%3D100\">\n",
+       "            n_estimators\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_estimators;\">\n",
+       "            n_estimators: int, default=100<br><br>The number of base estimators in the ensemble.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">100</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('max_features',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-max_features;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=max_features,-int%20or%20float%2C%20default%3D1.0\">\n",
+       "            max_features\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-max_features;\">\n",
+       "            max_features: int or float, default=1.0<br><br>The number of features to draw from X to train each base estimator.<br><br>- If int, then draw `max_features` features.<br>- If float, then draw `max(1, int(max_features * n_features_in_))` features.<br><br>Note: using a float number less than 1.0 or integer less than number of<br>features will enable feature subsampling and leads to a longer runtime.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">1.0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('bootstrap',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-bootstrap;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=bootstrap,-bool%2C%20default%3DFalse\">\n",
+       "            bootstrap\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-bootstrap;\">\n",
+       "            bootstrap: bool, default=False<br><br>If True, individual trees are fit on random subsets of the training<br>data sampled with replacement. If False, sampling without replacement<br>is performed.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('n_jobs',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_jobs;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=n_jobs,-int%2C%20default%3DNone\">\n",
+       "            n_jobs\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_jobs;\">\n",
+       "            n_jobs: int, default=None<br><br>The number of jobs to run in parallel for :meth:`fit`. ``None`` means 1<br>unless in a :obj:`joblib.parallel_backend` context. ``-1`` means using<br>all processors. See :term:`Glossary &lt;n_jobs&gt;` for more details.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">None</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('verbose',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-verbose;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=verbose,-int%2C%20default%3D0\">\n",
+       "            verbose\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-verbose;\">\n",
+       "            verbose: int, default=0<br><br>Controls the verbosity of the tree building process.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">0</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "\n",
+       "        <tr class=\"default\">\n",
+       "            <td><i class=\"copy-paste-icon\"\n",
+       "                 onclick=\"copyToClipboard('warm_start',\n",
+       "                          this.parentElement.nextElementSibling)\"\n",
+       "            ></i></td>\n",
+       "            <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-warm_start;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=warm_start,-bool%2C%20default%3DFalse\">\n",
+       "            warm_start\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-warm_start;\">\n",
+       "            warm_start: bool, default=False<br><br>When set to ``True``, reuse the solution of the previous call to fit<br>and add more estimators to the ensemble, otherwise, just fit a whole<br>new forest. See :term:`the Glossary &lt;warm_start&gt;`.<br><br>.. versionadded:: 0.21</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "            <td class=\"value\">False</td>\n",
+       "        </tr>\n",
+       "    \n",
+       "                  </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    \n",
+       "        <div class=\"estimator-table\">\n",
+       "            <details>\n",
+       "                <summary>Fitted attributes</summary>\n",
+       "                <table class=\"parameters-table\">\n",
+       "                    <tbody>\n",
+       "                        <tr>\n",
+       "                        <th>Name</th>\n",
+       "                        <th>Type</th>\n",
+       "                        <th>Value</th>\n",
+       "                        </tr>\n",
+       "                        \n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-estimator_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=estimator_,-%3Aclass%3A~sklearn.tree.ExtraTreeRegressor%20instance\">\n",
+       "            estimator_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-estimator_;\">\n",
+       "            estimator_: :class:`~sklearn.tree.ExtraTreeRegressor` instance<br><br>The child estimator template used to create the collection of<br>fitted sub-estimators.<br><br>.. versionadded:: 1.2<br>   `base_estimator_` was renamed to `estimator_`.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ExtraTreeRegressor</td>\n",
+       "           <td>ExtraTreeRegr...ndom_state=42)</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-estimators_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=estimators_,-list%20of%20ExtraTreeRegressor%20instances\">\n",
+       "            estimators_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-estimators_;\">\n",
+       "            estimators_: list of ExtraTreeRegressor instances<br><br>The collection of fitted sub-estimators.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">list</td>\n",
+       "           <td>[ExtraTreeRegr...te=1952926171), ExtraTreeRegr...te=1761383086), ExtraTreeRegr...te=1449071958), ExtraTreeRegr...te=1910541088), ...]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-estimators_features_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=estimators_features_,-list%20of%20ndarray\">\n",
+       "            estimators_features_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-estimators_features_;\">\n",
+       "            estimators_features_: list of ndarray<br><br>The subset of drawn features for each base estimator.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">list</td>\n",
+       "           <td>[array([0, 1, ..., 5, 6, 7, 8]), array([0, 1, ..., 5, 6, 7, 8]), array([0, 1, ..., 5, 6, 7, 8]), array([0, 1, ..., 5, 6, 7, 8]), ...]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-estimators_samples_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=estimators_samples_,-list%20of%20ndarray\">\n",
+       "            estimators_samples_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-estimators_samples_;\">\n",
+       "            estimators_samples_: list of ndarray<br><br>The subset of drawn samples (i.e., the in-bag samples) for each base<br>estimator.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">list</td>\n",
+       "           <td>[array([18907,... 1456, 31410]), array([37550,... 8433,  7058]), array([ 5462,...48189, 28432]), array([35616,...31131, 46250]), ...]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-feature_names_in_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=feature_names_in_,-ndarray%20of%20shape%20%28n_features_in_%2C%29\">\n",
+       "            feature_names_in_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-feature_names_in_;\">\n",
+       "            feature_names_in_: ndarray of shape (`n_features_in_`,)<br><br>Names of features seen during :term:`fit`. Defined only when `X`<br>has feature names that are all strings.<br><br>.. versionadded:: 1.0</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">ndarray[object](9,)</td>\n",
+       "           <td>[&#x27;feature_1&#x27;,&#x27;feature_2&#x27;,&#x27;feature_3&#x27;,...,&#x27;feature_7&#x27;,&#x27;feature_8&#x27;,\n",
+       " &#x27;feature_9&#x27;]</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-max_samples_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=max_samples_,-int\">\n",
+       "            max_samples_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-max_samples_;\">\n",
+       "            max_samples_: int<br><br>The actual number of samples.</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int</td>\n",
+       "           <td>256</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-n_features_in_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=n_features_in_,-int\">\n",
+       "            n_features_in_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-n_features_in_;\">\n",
+       "            n_features_in_: int<br><br>Number of features seen during :term:`fit`.<br><br>.. versionadded:: 0.24</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">int</td>\n",
+       "           <td>9</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "\n",
+       "       <tr class=\"default\">\n",
+       "           <td class=\"param\">\n",
+       "        <a class=\"param-doc-link\"\n",
+       "            style=\"anchor-name: --doc-link-offset_;\"\n",
+       "            rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.9/modules/generated/sklearn.ensemble.IsolationForest.html#:~:text=offset_,-float\">\n",
+       "            offset_\n",
+       "            <span class=\"param-doc-description\"\n",
+       "            style=\"position-anchor: --doc-link-offset_;\">\n",
+       "            offset_: float<br><br>Offset used to define the decision function from the raw scores. We<br>have the relation: ``decision_function = score_samples - offset_``.<br>``offset_`` is defined as follows. When the contamination parameter is<br>set to &quot;auto&quot;, the offset is equal to -0.5 as the scores of inliers are<br>close to 0 and the scores of outliers are close to -1. When a<br>contamination parameter different than &quot;auto&quot; is provided, the offset<br>is defined in such a way we obtain the expected number of outliers<br>(samples with decision function &lt; 0) in training.<br><br>.. versionadded:: 0.20</span>\n",
+       "        </a>\n",
+       "    </td>\n",
+       "           <td class=\"fitted-att-type\">float64</td>\n",
+       "           <td>-0.5577</td>\n",
+       "\n",
+       "\n",
+       "       </tr>\n",
+       "    \n",
+       "                    </tbody>\n",
+       "                </table>\n",
+       "            </details>\n",
+       "        </div>\n",
+       "    </div></div></div></div></div><script>/*  Authors: The scikit-learn developers\n",
+       " SPDX-License-Identifier: BSD-3-Clause\n",
+       "*/\n",
+       "\n",
+       "function copyToClipboard(text, element) {\n",
+       "    // Get the parameter prefix from the closest toggleable content\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${text}` : text;\n",
+       "\n",
+       "    const originalStyle = element.style;\n",
+       "    const computedStyle = window.getComputedStyle(element);\n",
+       "    const originalWidth = computedStyle.width;\n",
+       "    const originalHTML = element.innerHTML.replace('Copied!', '');\n",
+       "\n",
+       "    navigator.clipboard.writeText(fullParamName)\n",
+       "        .then(() => {\n",
+       "            element.style.width = originalWidth;\n",
+       "            element.style.color = 'green';\n",
+       "            element.innerHTML = \"Copied!\";\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'red';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 2000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "\n",
+       "document.querySelectorAll('.copy-paste-icon').forEach(function(element) {\n",
+       "    const toggleableContent = element.closest('.sk-toggleable__content');\n",
+       "    const paramPrefix = toggleableContent ? toggleableContent.dataset.paramPrefix : '';\n",
+       "\n",
+       "    const parent = element.parentElement;\n",
+       "    if (!parent || !parent.nextElementSibling) {\n",
+       "        console.warn('Expected copy-paste icon is missing from the DOM structure');\n",
+       "        return;\n",
+       "    }\n",
+       "\n",
+       "    const paramName = element.parentElement.nextElementSibling\n",
+       "        .textContent.trim().split(' ')[0];\n",
+       "    const fullParamName = paramPrefix ? `${paramPrefix}${paramName}` : paramName;\n",
+       "\n",
+       "    element.setAttribute('title', fullParamName);\n",
+       "});\n",
+       "\n",
+       "/**\n",
+       " * Copy the list of feature names formatted as a Python list.\n",
+       " *\n",
+       " * @param {HTMLElement} element - The copy button inside a `.features` block; its siblings\n",
+       " *   contain a `details` element and a table containing feature named.\n",
+       " * @returns {boolean} Always returns `false` so callers can prevent the default click behavior.\n",
+       " */\n",
+       "function copyFeatureNamesToClipboard(element) {\n",
+       "    var detailsElem = element.closest('.features').querySelector('details');\n",
+       "    var wasOpen = detailsElem.open;\n",
+       "    detailsElem.open = true;\n",
+       "    var content = element.closest('.features').querySelector('tbody')\n",
+       "                  .innerText.trim();\n",
+       "    if (!wasOpen) detailsElem.open = false;\n",
+       "    const rows = content.split('\\n').map(row => `    \"${row}\"`);\n",
+       "    const formattedText = `[\\n${rows.join(',\\n')},\\n]`;\n",
+       "    const originalHTML = element.innerHTML.replace('✔', '');\n",
+       "    const originalStyle = element.style;\n",
+       "    const copyMark = document.createElement('span');\n",
+       "    copyMark.innerHTML = '✔';\n",
+       "    copyMark.style.color = 'blue';\n",
+       "    copyMark.style.fontSize = '1em';\n",
+       "\n",
+       "    navigator.clipboard.writeText(formattedText)\n",
+       "        .then(() => {\n",
+       "            element.style.display = 'none';\n",
+       "            element.parentElement.appendChild(copyMark);\n",
+       "\n",
+       "            setTimeout(() => {\n",
+       "                copyMark.remove();\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        })\n",
+       "        .catch(err => {\n",
+       "            console.error('Failed to copy:', err);\n",
+       "            element.style.color = 'orange';\n",
+       "            element.innerHTML = \"Failed!\";\n",
+       "            setTimeout(() => {\n",
+       "                element.innerHTML = originalHTML;\n",
+       "                element.style = originalStyle;\n",
+       "            }, 1000);\n",
+       "        });\n",
+       "    return false;\n",
+       "}\n",
+       "/**\n",
+       " * Adapted from Skrub\n",
+       " * https://github.com/skrub-data/skrub/blob/403466d1d5d4dc76a7ef569b3f8228db59a31dc3/skrub/_reporting/_data/templates/report.js#L789\n",
+       " * @returns \"light\" or \"dark\"\n",
+       " */\n",
+       "function detectTheme(element) {\n",
+       "    const body = document.querySelector('body');\n",
+       "\n",
+       "    // Check VSCode theme\n",
+       "    const themeKindAttr = body.getAttribute('data-vscode-theme-kind');\n",
+       "    const themeNameAttr = body.getAttribute('data-vscode-theme-name');\n",
+       "\n",
+       "    if (themeKindAttr && themeNameAttr) {\n",
+       "        const themeKind = themeKindAttr.toLowerCase();\n",
+       "        const themeName = themeNameAttr.toLowerCase();\n",
+       "\n",
+       "        if (themeKind.includes(\"dark\") || themeName.includes(\"dark\")) {\n",
+       "            return \"dark\";\n",
+       "        }\n",
+       "        if (themeKind.includes(\"light\") || themeName.includes(\"light\")) {\n",
+       "            return \"light\";\n",
+       "        }\n",
+       "    }\n",
+       "\n",
+       "    // Check Jupyter theme\n",
+       "    if (body.getAttribute('data-jp-theme-light') === 'false') {\n",
+       "        return 'dark';\n",
+       "    } else if (body.getAttribute('data-jp-theme-light') === 'true') {\n",
+       "        return 'light';\n",
+       "    }\n",
+       "\n",
+       "    // Guess based on a parent element's color\n",
+       "    const color = window.getComputedStyle(element.parentNode, null).getPropertyValue('color');\n",
+       "    const match = color.match(/^rgb\\s*\\(\\s*(\\d+)\\s*,\\s*(\\d+)\\s*,\\s*(\\d+)\\s*\\)\\s*$/i);\n",
+       "    if (match) {\n",
+       "        const [r, g, b] = [\n",
+       "            parseFloat(match[1]),\n",
+       "            parseFloat(match[2]),\n",
+       "            parseFloat(match[3])\n",
+       "        ];\n",
+       "\n",
+       "        // https://en.wikipedia.org/wiki/HSL_and_HSV#Lightness\n",
+       "        const luma = 0.299 * r + 0.587 * g + 0.114 * b;\n",
+       "\n",
+       "        if (luma > 180) {\n",
+       "            // If the text is very bright we have a dark theme\n",
+       "            return 'dark';\n",
+       "        }\n",
+       "        if (luma < 75) {\n",
+       "            // If the text is very dark we have a light theme\n",
+       "            return 'light';\n",
+       "        }\n",
+       "        // Otherwise fall back to the next heuristic.\n",
+       "    }\n",
+       "\n",
+       "    // Fallback to system preference\n",
+       "    return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light';\n",
+       "}\n",
+       "\n",
+       "\n",
+       "function forceTheme(elementId) {\n",
+       "    const estimatorElement = document.querySelector(`#${elementId}`);\n",
+       "    if (estimatorElement === null) {\n",
+       "        console.error(`Element with id ${elementId} not found.`);\n",
+       "    } else {\n",
+       "        const theme = detectTheme(estimatorElement);\n",
+       "        estimatorElement.classList.add(theme);\n",
+       "    }\n",
+       "}\n",
+       "\n",
+       "forceTheme('sk-container-id-1');</script></body>"
+      ],
+      "text/plain": [
+       "IsolationForest(contamination=0.075, max_samples=256, random_state=42)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "iso_forest=IsolationForest(n_estimators=n_estimators,contamination=contamination,max_samples=256,random_state=42)\n",
+    "iso_forest.fit(X_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d316c251-995e-45e5-99ce-b37aeb472d44",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data=X_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "87828028-7db3-4591-b7e8-4299db7f57e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "anomaly\n",
+      " 1    45414\n",
+      "-1     3683\n",
+      "Name: count, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "X_test_features = X_df\n",
+    "\n",
+    "data = X_df.copy()\n",
+    "\n",
+    "data['anomaly_score'] = iso_forest.decision_function(X_test_features)\n",
+    "data['anomaly'] = iso_forest.predict(X_test_features)\n",
+    "\n",
+    "print(data['anomaly'].value_counts())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "da64cd4c-4926-4347-873f-7a829587c81b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "feature_1  feature_2  feature_3  feature_4  feature_5  feature_6  feature_7  feature_8  feature_9  anomaly_score  anomaly\n",
+       "50          21        77          0         28          0         27         48         22         0.073395       1          1\n",
+       "53          0         82          0         52         -5         29         30         2          0.161392       1          1\n",
+       "37          0         76          0         28          18        40         48         8          0.167454       1          1\n",
+       "                      79          0         34         -26        43         46         2          0.164228       1          1\n",
+       "55         -1         95         -3         54         -4         40         41         2          0.111741       1          1\n",
+       "                                                                                                                            ..\n",
+       "38          2         79          0         38          18        42         41         0          0.172478       1          1\n",
+       "39         -2         80         -4         38          0         41         41         0          0.175143       1          1\n",
+       "43          0         81          1         42         -9         37         39         2          0.196754       1          1\n",
+       "49          0         87          0         46         -12        38         41         2          0.184996       1          1\n",
+       "37          0         103         0         18         -16        66         85         20         0.047590       1          1\n",
+       "Name: count, Length: 45414, dtype: int64"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[data['anomaly']==1].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "24fa93bc-59c7-4ce2-a644-2737bbe23582",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(12,12))\n",
+    "anomalies = data[data['anomaly_score'] < 0.0]\n",
+    "\n",
+    "# Suspicious: Borderline positive scores close to the cutoff threshold\n",
+    "sus = data[(data['anomaly_score'] >= 0.0) & (data['anomaly_score'] <= 0.05)]\n",
+    "# Normal: Securely positive scores\n",
+    "normal = data[data['anomaly_score'] > 0.05]\n",
+    "plt.scatter(normal.index,normal['anomaly_score'],label='Normal',color='green')\n",
+    "plt.scatter(sus.index,sus['anomaly_score'],label='sus',color='blue')\n",
+    "plt.scatter(anomalies.index,anomalies['anomaly_score'],label='Anomaly',color='red')\n",
+    "plt.xlabel(\"InstanceIndex\")\n",
+    "plt.ylabel(\"anomaly score\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "0d33e98c-75cf-4dc9-8ead-ff283f411bb8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['feature_1', 'feature_2', 'feature_3', 'feature_4', 'feature_5',\n",
+       "       'feature_6', 'feature_7', 'feature_8', 'feature_9', 'anomaly_score',\n",
+       "       'anomaly'],\n",
+       "      dtype='str')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "17ba1743-31d0-4f76-92e4-c9e16acb8644",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.06453682866512256"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['anomaly_score'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "91c96e4a-5898-4c14-9146-dd809366a29d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "map_predictions=np.where(data['anomaly']==-1,1,0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e8ade0e1-473d-441e-8b01-5bb8fac1ff7b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Classification Report\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "   Normal(0)       1.00      0.99      1.00     45586\n",
+      "  Anomaly(1)       0.93      0.98      0.96      3511\n",
+      "\n",
+      "    accuracy                           0.99     49097\n",
+      "   macro avg       0.97      0.99      0.98     49097\n",
+      "weighted avg       0.99      0.99      0.99     49097\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report,accuracy_score,confusion_matrix\n",
+    "print(\"Classification Report\")\n",
+    "print(classification_report(y,map_predictions,target_names=['Normal(0)','Anomaly(1)']))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "3c17e908-25f2-408a-93f8-1c0b5ea80660",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "cm=confusion_matrix(y,map_predictions)\n",
+    "plt.figure(figsize=(6,5))\n",
+    "sns.heatmap(cm,annot=True,fmt='d',cmap='Reds',xticklabels=['Predicted Normal','Predicted Anomaly'],yticklabels=['Actual Normal','Actual Anomaly'])\n",
+    "plt.title(\"Confusion Matrix\")\n",
+    "plt.ylabel(\"True Category\")\n",
+    "plt.xlabel(\"Predicted Category\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "974fff97-0ec1-4079-85b9-360680b8c5a9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ROC - AUC curve auc value 0.9975\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import roc_auc_score,roc_curve\n",
+    "scores=iso_forest.decision_function(X_df)\n",
+    "inverted_scores=-scores\n",
+    "auc_val=roc_auc_score(y,inverted_scores)\n",
+    "print(f'ROC - AUC curve auc value {auc_val:.4f}')\n",
+    "fpr,tpr,thresholds=roc_curve(y,inverted_scores)\n",
+    "plt.figure(figsize=(7,5))\n",
+    "plt.plot(fpr,tpr,color='darkorange',lw=2,label=f'ROC curve (Area curve {auc_val:.2f})')\n",
+    "plt.plot([0,1],[0,1],color='navy',lw=2,linestyle='--',label='Random Guessing Baseline')\n",
+    "plt.xlim([0.0,1.0])\n",
+    "plt.ylim([0.0,1.05])\n",
+    "plt.xlabel('False Positive Rate (FPR)')\n",
+    "plt.ylabel('True Positive Rate (TPR)')\n",
+    "plt.title('Receiver Operating Characteristic (ROC) Curve')\n",
+    "plt.legend(loc=\"lower right\")\n",
+    "plt.grid(True, alpha=0.3)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "d75bc112-f5ad-4ea6-be69-22dbcd330374",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tp=np.sum((y==1) & (map_predictions==1))\n",
+    "fp=np.sum((y==0) & (map_predictions==1))\n",
+    "tn=np.sum((y==0) & (map_predictions==0))\n",
+    "fn=np.sum((y==1) & (map_predictions==0))\n",
+    "precision=tp/(tp+fp)\n",
+    "recall=tp/(tp+fn)\n",
+    "f1_score=2*(precision*recall)/(precision+recall)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "8e230793-6332-41ab-b4a3-9207e203ba11",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision is : 0.9332\n",
+      "Recall is : 0.9789\n",
+      "F1_score is : 0.9555\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f'Precision is : {precision:.4f}')\n",
+    "print(f'Recall is : {recall:.4f}')\n",
+    "print(f'F1_score is : {f1_score:.4}')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/roc_auc_curve.png b/roc_auc_curve.png
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