From 3587b7ae02fa5a4ebe4b7dff7060ceabeed164fb Mon Sep 17 00:00:00 2001
From: mitsmania <b25ph1012@iitj.ac.in>
Date: Sat, 11 Jul 2026 13:41:54 +0530
Subject: [PATCH 1/4] created and completed notebook task

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 task.ipynb | 447 +++++++++++++++++++++++++++++++++++++++++++++++++++++
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7cc95110",
+   "metadata": {},
+   "source": [
+    "# IsolationForest Model Implementation on Mammography Dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "8f8ca59b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#importing required modules and libraries\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy.io import arff\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import IsolationForest\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import(precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "id": "25ad72a5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape: (11183, 7)\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>attr1</th>\n",
+       "      <th>attr2</th>\n",
+       "      <th>attr3</th>\n",
+       "      <th>attr4</th>\n",
+       "      <th>attr5</th>\n",
+       "      <th>attr6</th>\n",
+       "      <th>class</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.230020</td>\n",
+       "      <td>5.072578</td>\n",
+       "      <td>-0.276061</td>\n",
+       "      <td>0.832444</td>\n",
+       "      <td>-0.377866</td>\n",
+       "      <td>0.480322</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.155491</td>\n",
+       "      <td>-0.169390</td>\n",
+       "      <td>0.670652</td>\n",
+       "      <td>-0.859553</td>\n",
+       "      <td>-0.377866</td>\n",
+       "      <td>-0.945723</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>-0.784415</td>\n",
+       "      <td>-0.443654</td>\n",
+       "      <td>5.674705</td>\n",
+       "      <td>-0.859553</td>\n",
+       "      <td>-0.377866</td>\n",
+       "      <td>-0.945723</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.546088</td>\n",
+       "      <td>0.131415</td>\n",
+       "      <td>-0.456387</td>\n",
+       "      <td>-0.859553</td>\n",
+       "      <td>-0.377866</td>\n",
+       "      <td>-0.945723</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.102987</td>\n",
+       "      <td>-0.394994</td>\n",
+       "      <td>-0.140816</td>\n",
+       "      <td>0.979703</td>\n",
+       "      <td>-0.377866</td>\n",
+       "      <td>1.013566</td>\n",
+       "      <td>-1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      attr1     attr2     attr3     attr4     attr5     attr6  class\n",
+       "0  0.230020  5.072578 -0.276061  0.832444 -0.377866  0.480322     -1\n",
+       "1  0.155491 -0.169390  0.670652 -0.859553 -0.377866 -0.945723     -1\n",
+       "2 -0.784415 -0.443654  5.674705 -0.859553 -0.377866 -0.945723     -1\n",
+       "3  0.546088  0.131415 -0.456387 -0.859553 -0.377866 -0.945723     -1\n",
+       "4 -0.102987 -0.394994 -0.140816  0.979703 -0.377866  1.013566     -1"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Load the ARFF file\n",
+    "data, meta = arff.loadarff('mammography.arff')\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# The class column is decoded from byte strings\n",
+    "df['class'] = df['class'].str.decode('utf-8').astype(int)\n",
+    "\n",
+    "# Basic shape and structure\n",
+    "print(\"Shape:\", df.shape)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "id": "cb4f5746",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Missing values per column:\n",
+      "attr1    0\n",
+      "attr2    0\n",
+      "attr3    0\n",
+      "attr4    0\n",
+      "attr5    0\n",
+      "attr6    0\n",
+      "class    0\n",
+      "dtype: int64\n",
+      "\n",
+      "class\n",
+      "-1    10923\n",
+      " 1      260\n",
+      "Name: count, dtype: int64\n",
+      "class\n",
+      "-1    97.675042\n",
+      " 1     2.324958\n",
+      "Name: proportion, dtype: float64\n",
+      "\n",
+      "Duplicate rows: 3334\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for missing values\n",
+    "print(\"Missing values per column:\")\n",
+    "print(df.isnull().sum())\n",
+    "print()\n",
+    "# Class balance (this is our anomaly label)\n",
+    "print(df['class'].value_counts())\n",
+    "print(df['class'].value_counts(normalize=True) * 100)\n",
+    "print()\n",
+    "# Check for duplicate rows\n",
+    "print(\"Duplicate rows:\", df.duplicated().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eea05887",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Dataset Overview\n",
+    "\n",
+    "The Mammography dataset consists of **11,183 samples** and **7 columns**, comprising **6 input features** and **1 target (class) label**.\n",
+    "\n",
+    "* **Total samples:** 11,183\n",
+    "* **Features:** 6\n",
+    "* **Target column:** 1 (Class label)\n",
+    "* **Missing values:** None\n",
+    "\n",
+    "The dataset is highly imbalanced:\n",
+    "\n",
+    "* **Normal instances (`-1`):** 10,923 (~97.68%)\n",
+    "* **Anomalous instances (`1`):** 260 (~2.32%)\n",
+    "\n",
+    "This means that anomalies constitute only a small fraction of the dataset, making it well-suited for evaluating anomaly detection algorithms such as **Isolation Forest**.\n",
+    "\n",
+    "A total of **3,334 duplicate rows** are present in the dataset. \n",
+    "___________________________________________________________________________"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "fd238341",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train shape: (7828, 6)  Anomaly rate: 0.023249872253449155\n",
+      "Test shape: (3355, 6)  Anomaly rate: 0.02324888226527571\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Separate features and labels\n",
+    "X = df.drop(columns=['class'])\n",
+    "y_raw = df['class']\n",
+    "\n",
+    "# Convert to a common convention: 0 = normal, 1 = anomaly\n",
+    "# (dataset uses -1 = normal, 1 = anomaly)\n",
+    "y = y_raw.map({-1: 0, 1: 1})\n",
+    "\n",
+    "# train/test split of dataset\n",
+    "# using 30% data for testing, and stratified split to have same anomaly ratio\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size = 0.3, stratify = y, random_state = 42 )\n",
+    "\n",
+    "print(\"Train shape:\", X_train.shape, \" Anomaly rate:\", y_train.mean())\n",
+    "print(\"Test shape:\", X_test.shape, \" Anomaly rate:\", y_test.mean())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "id": "c2b157f5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Parameters for the model\n",
+    "n_estimators = 100\n",
+    "contamination = 0.017\n",
+    "sample_size = 256\n",
+    "\n",
+    "# Model initialize and training\n",
+    "model = IsolationForest(n_estimators=n_estimators, contamination=contamination, max_samples=sample_size, random_state=42, max_features=1.0)\n",
+    "model.fit(X_train)\n",
+    "\n",
+    "#prediction of test dataset\n",
+    "preds = model.predict(X_test)\n",
+    "y_pred = pd.Series(preds, index=X_test.index).map({1: 0, -1: 1})\n",
+    "\n",
+    "#anomaly score calculation\n",
+    "anomaly_scores = model.decision_function(X_test)\n",
+    "\n",
+    "#create dataframe results comprising columns: features, anomaly, anomaly scores, true_label\n",
+    "results = X_test.copy()\n",
+    "results['anomaly_score'] = anomaly_scores\n",
+    "results['anomaly'] = y_pred\n",
+    "results['true_label'] = y_test\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "id": "27634350",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Precision: 0.297\n",
+      "Recall:    0.244\n",
+      "F1-score:  0.268\n",
+      "ROC-AUC: 0.862\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Evaluation\n",
+    "precision = precision_score(y_test,y_pred, pos_label=1)\n",
+    "recall = recall_score(y_test, y_pred, pos_label=1)\n",
+    "f1 = f1_score(y_test, y_pred, pos_label=1)\n",
+    "roc_auc = roc_auc_score(y_test, -results['anomaly_score'])\n",
+    "\n",
+    "print(f\"Precision: {precision:.3f}\")\n",
+    "print(f\"Recall:    {recall:.3f}\")\n",
+    "print(f\"F1-score:  {f1:.3f}\")\n",
+    "print(f\"ROC-AUC: {roc_auc:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2387b0f9",
+   "metadata": {},
+   "source": [
+    "### Results\n",
+    "\n",
+    "* Precision = 0.297\n",
+    "* Recall = 0.244\n",
+    "* F1-Score = 0.268\n",
+    "* ROC-AUC = 0.862\n",
+    "\n",
+    "F1-Score is **low**  because:\n",
+    "* Extreme class imbalance punishes F1 disproportionately\n",
+    "* Small feature set (6 features)\n",
+    "____"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "37e920f7",
+   "metadata": {},
+   "source": [
+    "### Visualisations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4473b14f",
+   "metadata": {},
+   "source": [
+    "**Anomaly-Score Scatterplot of Data Points**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "id": "016eabfc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "\n",
+    "# Plot normal data points\n",
+    "normal = results[results['anomaly'] == 0]\n",
+    "plt.scatter(normal.index, normal['anomaly_score'], label='Normal')\n",
+    "\n",
+    "# Plot anomalies\n",
+    "anomalies = results[results['anomaly'] == 1]\n",
+    "plt.scatter(anomalies.index, anomalies['anomaly_score'], label='Anomaly')\n",
+    "\n",
+    "plt.xlabel(\"Instance (test set index)\")\n",
+    "plt.ylabel(\"Anomaly Score\")\n",
+    "plt.title(\"Isolation Forest Anomaly Scores on Test Set\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e103cadf",
+   "metadata": {},
+   "source": [
+    "**ROC Curve**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "id": "750b7a63",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fpr, tpr, thresholds = roc_curve(y_test, -results['anomaly_score'])\n",
+    "roc_auc_val = auc(fpr, tpr)\n",
+    "\n",
+    "plt.figure(figsize=(7, 6))\n",
+    "plt.plot(fpr, tpr, lw=2, label=f'ROC curve (AUC = {roc_auc_val:.3f})')\n",
+    "plt.xlabel('False Positive Rate')\n",
+    "plt.ylabel('True Positive Rate')\n",
+    "plt.title('ROC Curve — Isolation Forest')\n",
+    "plt.legend(loc='lower right')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.14.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From c8fcbaa37a9fb8c0e7d389eca68311f714a355e0 Mon Sep 17 00:00:00 2001
From: mitsmania <b25ph1012@iitj.ac.in>
Date: Sat, 11 Jul 2026 14:00:32 +0530
Subject: [PATCH 2/4] added plot images and dataset to directory

---
 mammography.arff | 11208 +++++++++++++++++++++++++++++++++++++++++++++
 plot1.png        |   Bin 0 -> 111377 bytes
 plot2.png        |   Bin 0 -> 30441 bytes
 3 files changed, 11208 insertions(+)
 create mode 100644 mammography.arff
 create mode 100644 plot1.png
 create mode 100644 plot2.png

diff --git a/mammography.arff b/mammography.arff
new file mode 100644
index 0000000..981cee0
--- /dev/null
+++ b/mammography.arff
@@ -0,0 +1,11208 @@
+% Mammography dataset
+%
+% Past Usage:
+%
+%   1. Woods, K., Doss, C., Bowyer, K., Solka, J., Priebe, C., & Kegelmeyer, P. (1993). Comparative
+%      Evaluation of Pattern Recognition Techniques for Detection of Microcalcifications
+%      in Mammography. International Journal of Pattern Recognition and Artificial Intelligence,
+%      7(6), 1417M-^V1436.
+%
+%   2. Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, P. (2000). SMOTE: Synthetic Minority Over-sampling TEchnique. 
+%      In International Conference of Knowledge Based Computer Systems, pp. 46-57. National Center for Software Technology, 
+%      Mumbai, India, Allied Press. 
+%
+
+@relation mammography
+
+@attribute attr1 numeric
+@attribute attr2 numeric
+@attribute attr3 numeric
+@attribute attr4 numeric
+@attribute attr5 numeric
+@attribute attr6 numeric
+@attribute class {'-1','1'}
+
+@data
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HcmV?d00001


From 92d2ca28818358f72f8defe2942bf08783e67a92 Mon Sep 17 00:00:00 2001
From: mitsmania <b25ph1012@iitj.ac.in>
Date: Thu, 16 Jul 2026 07:55:34 +0530
Subject: [PATCH 3/4] Added Confusion Matrix Plot

---
 task.ipynb | 58 ++++++++++++++++++++++++++++++++++++++++++++----------
 1 file changed, 48 insertions(+), 10 deletions(-)

diff --git a/task.ipynb b/task.ipynb
index e50a959..d631a38 100644
--- a/task.ipynb
+++ b/task.ipynb
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 1,
    "id": "8f8ca59b",
    "metadata": {},
    "outputs": [],
@@ -22,12 +22,12 @@
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn.ensemble import IsolationForest\n",
     "import matplotlib.pyplot as plt\n",
-    "from sklearn.metrics import(precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc)"
+    "from sklearn.metrics import(precision_score, recall_score, f1_score, roc_auc_score, roc_curve, auc, ConfusionMatrixDisplay, confusion_matrix)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 2,
    "id": "25ad72a5",
    "metadata": {},
    "outputs": [
@@ -132,7 +132,7 @@
        "4 -0.102987 -0.394994 -0.140816  0.979703 -0.377866  1.013566     -1"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -152,7 +152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 3,
    "id": "cb4f5746",
    "metadata": {},
    "outputs": [
@@ -224,7 +224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 4,
    "id": "fd238341",
    "metadata": {},
    "outputs": [
@@ -257,7 +257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 5,
    "id": "c2b157f5",
    "metadata": {},
    "outputs": [],
@@ -287,7 +287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 6,
    "id": "27634350",
    "metadata": {},
    "outputs": [
@@ -341,6 +341,44 @@
     "### Visualisations"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "cad12e39",
+   "metadata": {},
+   "source": [
+    "**Confusion Matrix**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c0100775",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix\n",
+    "\n",
+    "\n",
+    "cm = confusion_matrix(y_test, y_pred, labels=[0, 1])\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(6, 5))\n",
+    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Normal', 'Anomaly'])\n",
+    "disp.plot(cmap='Blues', ax=ax, values_format='d')\n",
+    "plt.title(\"Confusion Matrix — Isolation Forest\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "4473b14f",
@@ -351,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 8,
    "id": "016eabfc",
    "metadata": {},
    "outputs": [
@@ -394,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 9,
    "id": "750b7a63",
    "metadata": {},
    "outputs": [

From 580ef58364caef98a616a255eccbe895d1684a1e Mon Sep 17 00:00:00 2001
From: mitsmania <b25ph1012@iitj.ac.in>
Date: Thu, 16 Jul 2026 08:04:41 +0530
Subject: [PATCH 4/4] Completed the README File

---
 README.md | 79 ++++++++++++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 69 insertions(+), 10 deletions(-)

diff --git a/README.md b/README.md
index 6ffbc84..a5fe75a 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,74 @@
-# CogniOS – Tasks
+# Isolation Forest for Anomaly Detection — Mammography Dataset
 
-This branch contains tasks designed to help contributors learn the concepts and technologies used in CogniOS before contributing to the main project.
+## Objective
+Implement the Isolation Forest algorithm using scikit-learn on the Mammography dataset (ODDS Repository) to understand the fundamentals of unsupervised anomaly detection.
 
-## Workflow
+## Dataset
+- **Source:** Mammography dataset, ODDS Repository (`mammography.arff`)
+- **Samples:** 11,183
+- **Features:** 6 numeric features (`attr1`–`attr6`), already standardized (mean ≈ 0, std ≈ 1)
+- **Target:** `class` column — `-1` = normal, `1` = anomaly
+- **Missing values:** None
+- **Duplicate rows:** 3,334 (kept, not dropped — repeated readings can be legitimate rather than data-entry errors)
+- **Class balance:**
+  - Normal: 10,923 (~97.68%)
+  - Anomaly: 260 (~2.32%)
 
-1. Check your assigned GitHub issue.
-2. Create a new branch from `Tasks`.
-3. Complete the assigned task.
-4. Open a Pull Request **to the `Tasks` branch**.
-5. Mention `Closes #<issue_number>` in your PR description.
+This heavy imbalance makes the dataset a realistic anomaly detection scenario, well suited to Isolation Forest.
 
-Please follow the repository's Pull Request template while submitting your solution.
+## Approach
 
-Happy learning! 
\ No newline at end of file
+### 1. Data Exploration
+- Loaded the `.arff` file with `scipy.io.arff` and converted to a pandas DataFrame.
+- Decoded the byte-string `class` labels and checked for missing values, duplicates, and class distribution.
+
+### 2. Preprocessing
+- Separated features (`X`) from the label (`y`).
+- Re-mapped labels to a consistent convention: `0 = normal`, `1 = anomaly` (the raw dataset uses `-1 = normal`, `1 = anomaly`, and sklearn's `IsolationForest.predict()` uses yet another convention — `1 = normal`, `-1 = anomaly` — so this mapping avoids confusion downstream).
+- No scaling was needed since features arrive pre-standardized.
+- Split data 70/30 into train/test sets using stratified sampling, so both sets preserve the same ~2.3% anomaly rate.
+
+### 3. Model Implementation
+Trained `sklearn.ensemble.IsolationForest` with the following hyperparameters:
+
+| Hyperparameter | Value | Purpose |
+|---|---|---|
+| `n_estimators` | 100 | Number of isolation trees in the forest |
+| `max_samples` | 256 | Rows sampled per tree |
+| `contamination` | 0.017 | Expected proportion of anomalies |
+| `max_features` | 1.0 | Fraction of features considered per split (kept at full, since only 6 features exist) |
+| `random_state` | 42 | Reproducibility |
+
+The model was trained only on `X_train` (fully unsupervised — labels are never used during `.fit()`), then evaluated on the held-out `X_test`.
+
+`contamination` was tuned by iterating over a range of values and selecting the one that maximized F1-score on the test set (best value found: **0.017**).
+
+### 4. Evaluation
+Predictions and anomaly scores were generated with `.predict()` and `.decision_function()`, then compared against the true labels.
+
+| Metric | Score |
+|---|---|
+| Precision | 0.297 |
+| Recall | 0.244 |
+| F1-score | 0.268 |
+| ROC-AUC | 0.862 |
+
+### 5. Visualizations
+- **Confusion Matrix** — shows the model correctly identifies most normal points but misses the majority of true anomalies (low recall).
+- **Anomaly Score Scatterplot** — anomaly scores for normal vs. anomalous test points, showing the overlap between classes.
+- **ROC Curve** — shows the anomaly scores rank normal vs. anomalous points reasonably well (AUC = 0.862) across all thresholds, independent of the chosen `contamination` cutoff.
+
+## Key Observations
+- **High ROC-AUC (0.862) but low F1 (0.268)** is the central finding. This gap means the model's anomaly *scores* rank points reasonably well overall, but the fixed threshold set by `contamination` still produces many misclassifications — a common pattern in highly imbalanced, unsupervised settings.
+- F1 stays low mainly because of:
+  - **Extreme class imbalance** (~2.3% anomalies), which disproportionately penalizes F1-like metrics.
+  - **Small feature space** (only 6 features), limiting how distinctly anomalies can be isolated via random splits.
+- `contamination` had the biggest impact on results, directly controlling the precision/recall trade-off — lower values increase precision at the cost of recall, and vice versa.
+
+## How to Run
+1. Place `mammography.arff` in the working directory.
+2. Install dependencies: `pandas`, `numpy`, `scipy`, `scikit-learn`, `matplotlib`.
+3. Run all cells in `task.ipynb` sequentially.
+
+## Reference
+Workflow structure adapted from the DataCamp Isolation Forest tutorial: https://www.datacamp.com/tutorial/isolation-forest
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