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        "id": "J648rbYAUXK1",
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          "text": [
            " Loading file: kdd_test.csv\n",
            "Shape: (22545, 42)\n",
            "\n",
            "First 5 rows:\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/tmp/ipython-input-3261762481.py:22: DtypeWarning: Columns (0,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  df = pd.read_csv(found, header=None)\n"
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              "         0              1         2     3          4          5     6   \\\n",
              "0  duration  protocol_type   service  flag  src_bytes  dst_bytes  land   \n",
              "1         5            tcp      smtp    SF       2429        475     0   \n",
              "2         0            udp  domain_u    SF         45        134     0   \n",
              "3         0            udp  domain_u    SF         45         80     0   \n",
              "4      1979            udp     other    SF        145        105     0   \n",
              "\n",
              "               7       8    9   ...                  32  \\\n",
              "0  wrong_fragment  urgent  hot  ...  dst_host_srv_count   \n",
              "1               0       0    0  ...                  57   \n",
              "2               0       0    0  ...                 155   \n",
              "3               0       0    0  ...                 255   \n",
              "4               0       0    0  ...                   2   \n",
              "\n",
              "                       33                      34  \\\n",
              "0  dst_host_same_srv_rate  dst_host_diff_srv_rate   \n",
              "1                    0.11                    0.02   \n",
              "2                     0.8                    0.02   \n",
              "3                     1.0                     0.0   \n",
              "4                    0.01                    0.84   \n",
              "\n",
              "                            35                           36  \\\n",
              "0  dst_host_same_src_port_rate  dst_host_srv_diff_host_rate   \n",
              "1                         0.01                         0.04   \n",
              "2                         0.01                          0.0   \n",
              "3                         0.01                          0.0   \n",
              "4                          1.0                          0.0   \n",
              "\n",
              "                     37                        38                    39  \\\n",
              "0  dst_host_serror_rate  dst_host_srv_serror_rate  dst_host_rerror_rate   \n",
              "1                   0.0                       0.0                  0.87   \n",
              "2                   0.0                       0.0                   0.0   \n",
              "3                   0.0                       0.0                   0.0   \n",
              "4                   0.0                       0.0                   0.0   \n",
              "\n",
              "                         40      41  \n",
              "0  dst_host_srv_rerror_rate  labels  \n",
              "1                       0.0  normal  \n",
              "2                       0.0  normal  \n",
              "3                       0.0  normal  \n",
              "4                       0.0  normal  \n",
              "\n",
              "[5 rows x 42 columns]"
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>36</th>\n",
              "      <th>37</th>\n",
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              "      <th>39</th>\n",
              "      <th>40</th>\n",
              "      <th>41</th>\n",
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              "      <th>0</th>\n",
              "      <td>duration</td>\n",
              "      <td>protocol_type</td>\n",
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              "      <td>flag</td>\n",
              "      <td>src_bytes</td>\n",
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              "      <td>land</td>\n",
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              "      <td>dst_host_srv_serror_rate</td>\n",
              "      <td>dst_host_rerror_rate</td>\n",
              "      <td>dst_host_srv_rerror_rate</td>\n",
              "      <td>labels</td>\n",
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              "      <th>1</th>\n",
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              "      <td>SF</td>\n",
              "      <td>2429</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
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              "    <tr>\n",
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              "      <td>udp</td>\n",
              "      <td>domain_u</td>\n",
              "      <td>SF</td>\n",
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              "      <td>normal</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1979</td>\n",
              "      <td>udp</td>\n",
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              "      <td>SF</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>normal</td>\n",
              "    </tr>\n",
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              "<p>5 rows × 42 columns</p>\n",
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              "        async function quickchart(key) {\n",
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          "metadata": {}
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          "name": "stdout",
          "text": [
            "\n",
            "Sample of label column values (last column):\n",
            "['labels' 'normal' 'portsweep' 'neptune' 'smurf' 'satan']\n"
          ]
        }
      ],
      "source": [
        "# detect and load dataset\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "candidates = [\n",
        "    'kdd_test.csv'\n",
        "]\n",
        "\n",
        "found = None\n",
        "for f in candidates:\n",
        "    if os.path.exists(f):\n",
        "        found = f\n",
        "        break\n",
        "\n",
        "if found is None:\n",
        "    print(\" No NSL-KDD file found. Uploaded files:\")\n",
        "    for f in os.listdir():\n",
        "        print(\"   \", f)\n",
        "    raise FileNotFoundError(\"Upload your NSL-KDD dataset file\")\n",
        "\n",
        "print(\" Loading file:\", found)\n",
        "df = pd.read_csv(found, header=None)\n",
        "print(\"Shape:\", df.shape)\n",
        "print(\"\\nFirst 5 rows:\")\n",
        "display(df.head())\n",
        "\n",
        "print(\"\\nSample of label column values (last column):\")\n",
        "print(df.iloc[:20, -1].unique())\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reload with header row and clean\n",
        "import pandas as pd\n",
        "\n",
        "# Reload correctly (first row as header)\n",
        "df = pd.read_csv('kdd_test.csv', header=0, low_memory=False)\n",
        "\n",
        "# Rename label column to a consistent name\n",
        "df.rename(columns={df.columns[-1]: 'label'}, inplace=True)\n",
        "\n",
        "print(\"Dataset loaded with headers.\")\n",
        "print(\"Shape:\", df.shape)\n",
        "print(\"\\nColumns sample:\", df.columns[:10].tolist(), \"...\", df.columns[-3:].tolist())\n",
        "\n",
        "# Convert numeric columns to floats where possible\n",
        "num_cols = df.columns.drop(['protocol_type', 'service', 'flag', 'label'])\n",
        "df[num_cols] = df[num_cols].apply(pd.to_numeric, errors='coerce')\n",
        "\n",
        "print(\"\\nAny missing values after conversion?\")\n",
        "print(df.isna().sum().sum())\n",
        "\n",
        "print(\"\\nClass distribution (top 10 labels):\")\n",
        "print(df['label'].value_counts().head(10))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z91JLr5hYFcg",
        "outputId": "3db32276-1afc-4dcb-b375-6487351b500a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset loaded with headers.\n",
            "Shape: (22544, 42)\n",
            "\n",
            "Columns sample: ['duration', 'protocol_type', 'service', 'flag', 'src_bytes', 'dst_bytes', 'land', 'wrong_fragment', 'urgent', 'hot'] ... ['dst_host_rerror_rate', 'dst_host_srv_rerror_rate', 'label']\n",
            "\n",
            "Any missing values after conversion?\n",
            "0\n",
            "\n",
            "Class distribution (top 10 labels):\n",
            "label\n",
            "normal          11245\n",
            "neptune          6654\n",
            "satan             698\n",
            "smurf             540\n",
            "ipsweep           479\n",
            "guess_passwd      411\n",
            "portsweep         371\n",
            "mscan             310\n",
            "warezmaster       279\n",
            "apache2           228\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# map fine-grained attacks to main categories\n",
        "\n",
        "# Define category mapping\n",
        "attack_category = {\n",
        "    # DOS\n",
        "    'back':'dos','land':'dos','neptune':'dos','pod':'dos','smurf':'dos','teardrop':'dos',\n",
        "    'mailbomb':'dos','apache2':'dos','processtable':'dos','udpstorm':'dos',\n",
        "\n",
        "    # PROBE\n",
        "    'satan':'probe','ipsweep':'probe','nmap':'probe','portsweep':'probe','mscan':'probe','saint':'probe',\n",
        "\n",
        "    # R2L\n",
        "    'guess_passwd':'r2l','ftp_write':'r2l','imap':'r2l','phf':'r2l','multihop':'r2l',\n",
        "    'warezmaster':'r2l','warezclient':'r2l','spy':'r2l','xlock':'r2l','xsnoop':'r2l','snmpguess':'r2l',\n",
        "    'snmpgetattack':'r2l','httptunnel':'r2l','sendmail':'r2l','named':'r2l',\n",
        "\n",
        "    # U2R\n",
        "    'buffer_overflow':'u2r','loadmodule':'u2r','rootkit':'u2r','perl':'u2r','sqlattack':'u2r',\n",
        "    'xterm':'u2r','ps':'u2r'\n",
        "}\n",
        "\n",
        "# Apply mapping\n",
        "df['attack_category'] = df['label'].replace(attack_category)\n",
        "df.loc[df['attack_category'] == 'label', 'attack_category'] = df['label']  # fix any unmapped\n",
        "df.loc[df['attack_category'].isin(['normal']), 'attack_category'] = 'normal'\n",
        "\n",
        "# Check new column\n",
        "print(\"Added new column: 'attack_category'\")\n",
        "print(\"\\nCategory distribution:\")\n",
        "print(df['attack_category'].value_counts())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JPQt47rlYYJL",
        "outputId": "4b36331b-1d0d-4a51-f12b-89c80a80da5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Added new column: 'attack_category'\n",
            "\n",
            "Category distribution:\n",
            "attack_category\n",
            "normal    11245\n",
            "dos        8095\n",
            "probe      2157\n",
            "r2l        1009\n",
            "u2r          38\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# preprocessing\n",
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Separate features and labels\n",
        "X = df.drop(['label', 'attack_category'], axis=1)\n",
        "y = df['attack_category']\n",
        "\n",
        "# Identify categorical and numeric columns\n",
        "categorical_features = ['protocol_type', 'service', 'flag']\n",
        "numeric_features = X.columns.drop(categorical_features)\n",
        "\n",
        "# Define preprocessing pipeline\n",
        "preprocessor = ColumnTransformer([\n",
        "    ('onehot', OneHotEncoder(handle_unknown='ignore'), categorical_features),\n",
        "    ('scaler', StandardScaler(), numeric_features)\n",
        "])\n",
        "\n",
        "# Split data\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    X, y, test_size=0.3, random_state=42, stratify=y\n",
        ")\n",
        "\n",
        "print(\" Data split complete.\")\n",
        "print(\"Training samples:\", X_train.shape[0])\n",
        "print(\"Testing samples:\", X_test.shape[0])\n",
        "print(\"Features before encoding:\", X.shape[1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "snWpTIMYYnWs",
        "outputId": "47355911-2a0e-4814-c0f1-f5032b83acaf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Data split complete.\n",
            "Training samples: 15780\n",
            "Testing samples: 6764\n",
            "Features before encoding: 41\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest model\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.model_selection import RandomizedSearchCV\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "import numpy as np\n",
        "\n",
        "# Build full pipeline preprocessing + model\n",
        "rf_pipeline = Pipeline([\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('model', RandomForestClassifier(random_state=42))\n",
        "])\n",
        "\n",
        "# Define parameter grid for RandomizedSearch\n",
        "param_dist = {\n",
        "    'model__n_estimators': [100, 200, 300, 400, 500],\n",
        "    'model__max_depth': [None, 10, 20, 30, 40],\n",
        "    'model__min_samples_split': [2, 5, 10],\n",
        "    'model__min_samples_leaf': [1, 2, 4],\n",
        "    'model__bootstrap': [True, False]\n",
        "}\n",
        "\n",
        "# Randomized search\n",
        "rf_search = RandomizedSearchCV(\n",
        "    rf_pipeline, param_distributions=param_dist,\n",
        "    n_iter=10, cv=3, scoring='accuracy', verbose=2,\n",
        "    n_jobs=-1, random_state=42\n",
        ")\n",
        "\n",
        "print(\"Training Random Forest (this may take 1–3 minutes)...\")\n",
        "rf_search.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate\n",
        "y_pred_rf = rf_search.predict(X_test)\n",
        "acc_rf = accuracy_score(y_test, y_pred_rf)\n",
        "\n",
        "print(\"\\nRandom Forest Training Complete!\")\n",
        "print(\"Best Parameters:\", rf_search.best_params_)\n",
        "print(f\"Accuracy on Test Set: {acc_rf:.4f}\\n\")\n",
        "print(\"Classification Report:\\n\")\n",
        "print(classification_report(y_test, y_pred_rf))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NRdmWSCIY2X3",
        "outputId": "b2cb0afa-9d83-49a9-c8c5-b4423b0266e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Random Forest (this may take 1–3 minutes)...\n",
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
            "\n",
            "Random Forest Training Complete!\n",
            "Best Parameters: {'model__n_estimators': 300, 'model__min_samples_split': 2, 'model__min_samples_leaf': 1, 'model__max_depth': 40, 'model__bootstrap': False}\n",
            "Accuracy on Test Set: 0.9905\n",
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         dos       1.00      1.00      1.00      2429\n",
            "      normal       0.99      0.99      0.99      3374\n",
            "       probe       0.99      0.99      0.99       647\n",
            "         r2l       0.96      0.90      0.93       303\n",
            "         u2r       1.00      0.55      0.71        11\n",
            "\n",
            "    accuracy                           0.99      6764\n",
            "   macro avg       0.99      0.89      0.92      6764\n",
            "weighted avg       0.99      0.99      0.99      6764\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Gradient Boosting Classifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "\n",
        "# Build pipeline\n",
        "gb_pipeline = Pipeline([\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('model', GradientBoostingClassifier(random_state=42))\n",
        "])\n",
        "\n",
        "# Define parameter grid\n",
        "param_grid_gb = {\n",
        "    'model__n_estimators': [100, 200, 300],\n",
        "    'model__learning_rate': [0.05, 0.1, 0.2],\n",
        "    'model__max_depth': [3, 4, 5]\n",
        "}\n",
        "\n",
        "gb_search = RandomizedSearchCV(\n",
        "    gb_pipeline, param_distributions=param_grid_gb,\n",
        "    n_iter=5, cv=3, scoring='accuracy', verbose=2,\n",
        "    n_jobs=-1, random_state=42\n",
        ")\n",
        "\n",
        "print(\" Training Gradient Boosting Classifier (may take a few minutes)...\")\n",
        "gb_search.fit(X_train, y_train)\n",
        "\n",
        "# Evaluate\n",
        "y_pred_gb = gb_search.predict(X_test)\n",
        "acc_gb = accuracy_score(y_test, y_pred_gb)\n",
        "\n",
        "print(\"\\n Gradient Boosting Training Complete!\")\n",
        "print(\"Best Parameters:\", gb_search.best_params_)\n",
        "print(f\"Accuracy on Test Set: {acc_gb:.4f}\\n\")\n",
        "print(\"Classification Report:\\n\")\n",
        "print(classification_report(y_test, y_pred_gb))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u_sE6oA3Zk0X",
        "outputId": "0f477d55-fb0c-4d73-8542-17fb9eb51966"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Training Gradient Boosting Classifier (may take a few minutes)...\n",
            "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
            "\n",
            " Gradient Boosting Training Complete!\n",
            "Best Parameters: {'model__n_estimators': 200, 'model__max_depth': 4, 'model__learning_rate': 0.1}\n",
            "Accuracy on Test Set: 0.9907\n",
            "\n",
            "Classification Report:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         dos       1.00      1.00      1.00      2429\n",
            "      normal       0.99      0.99      0.99      3374\n",
            "       probe       0.98      0.99      0.99       647\n",
            "         r2l       0.98      0.91      0.94       303\n",
            "         u2r       0.70      0.64      0.67        11\n",
            "\n",
            "    accuracy                           0.99      6764\n",
            "   macro avg       0.93      0.91      0.92      6764\n",
            "weighted avg       0.99      0.99      0.99      6764\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Feature Importance Visualization\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Random Forest Feature Importance\n",
        "rf_best = rf_search.best_estimator_.named_steps['model']\n",
        "rf_pre = rf_search.best_estimator_.named_steps['preprocessor']\n",
        "\n",
        "# Get feature names after preprocessing\n",
        "cat_features = rf_pre.transformers_[0][2]\n",
        "num_features = rf_pre.transformers_[1][2]\n",
        "encoded_cat = rf_pre.named_transformers_['onehot'].get_feature_names_out(cat_features)\n",
        "all_features = np.concatenate([encoded_cat, num_features])\n",
        "\n",
        "# Random Forest importances\n",
        "rf_importances = rf_best.feature_importances_\n",
        "sorted_idx = np.argsort(rf_importances)[-15:]\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.barh(range(len(sorted_idx)), rf_importances[sorted_idx], align='center')\n",
        "plt.yticks(range(len(sorted_idx)), all_features[sorted_idx])\n",
        "plt.title(\"Top 15 Important Features - Random Forest\")\n",
        "plt.xlabel(\"Importance Score\")\n",
        "plt.show()\n",
        "\n",
        "# Gradient Boosting Feature Importance\n",
        "gb_best = gb_search.best_estimator_.named_steps['model']\n",
        "gb_importances = gb_best.feature_importances_\n",
        "sorted_idx_gb = np.argsort(gb_importances)[-15:]\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.barh(range(len(sorted_idx_gb)), gb_importances[sorted_idx_gb], align='center', color='orange')\n",
        "plt.yticks(range(len(sorted_idx_gb)), all_features[sorted_idx_gb])\n",
        "plt.title(\"Top 15 Important Features - Gradient Boosting\")\n",
        "plt.xlabel(\"Importance Score\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "NXpZSkBPZb8h",
        "outputId": "52cd9dcc-6717-4edb-824f-6f39ce160000"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "# Random Forest timing\n",
        "start_rf_train = time.time()\n",
        "rf_search.fit(X_train, y_train)\n",
        "end_rf_train = time.time()\n",
        "\n",
        "start_rf_pred = time.time()\n",
        "rf_pred_time = rf_search.predict(X_test)\n",
        "end_rf_pred = time.time()\n",
        "\n",
        "rf_train_time = end_rf_train - start_rf_train\n",
        "rf_pred_time = end_rf_pred - start_rf_pred\n",
        "\n",
        "# Gradient Boosting timing\n",
        "start_gb_train = time.time()\n",
        "gb_search.fit(X_train, y_train)\n",
        "end_gb_train = time.time()\n",
        "\n",
        "start_gb_pred = time.time()\n",
        "gb_pred_time = gb_search.predict(X_test)\n",
        "end_gb_pred = time.time()\n",
        "\n",
        "gb_train_time = end_gb_train - start_gb_train\n",
        "gb_pred_time = end_gb_pred - start_gb_pred\n",
        "\n",
        "print(f\"Random Forest - Train Time: {rf_train_time:.2f} sec, Predict Time: {rf_pred_time:.2f} sec\")\n",
        "print(f\"Gradient Boosting - Train Time: {gb_train_time:.2f} sec, Predict Time: {gb_pred_time:.2f} sec\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bfGB6psjdU_f",
        "outputId": "1a141ce2-c592-437c-b745-447cd345fa20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
            "Fitting 3 folds for each of 5 candidates, totalling 15 fits\n",
            "Random Forest - Train Time: 96.07 sec, Predict Time: 0.30 sec\n",
            "Gradient Boosting - Train Time: 647.91 sec, Predict Time: 0.14 sec\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Predict again for safety\n",
        "y_pred_rf = rf_search.predict(X_test)\n",
        "y_pred_gb = gb_search.predict(X_test)\n",
        "\n",
        "# Confusion Matrix for Random Forest\n",
        "cm_rf = confusion_matrix(y_test, y_pred_rf, labels=rf_search.classes_)\n",
        "disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf, display_labels=rf_search.classes_)\n",
        "plt.figure(figsize=(10,8))\n",
        "disp_rf.plot(cmap='Blues', ax=plt.gca())\n",
        "plt.title(\"Confusion Matrix - Random Forest\")\n",
        "plt.show()\n",
        "\n",
        "# Confusion Matrix for Gradient Boosting\n",
        "cm_gb = confusion_matrix(y_test, y_pred_gb, labels=gb_search.classes_)\n",
        "disp_gb = ConfusionMatrixDisplay(confusion_matrix=cm_gb, display_labels=gb_search.classes_)\n",
        "plt.figure(figsize=(10,8))\n",
        "disp_gb.plot(cmap='Oranges', ax=plt.gca())\n",
        "plt.title(\"Confusion Matrix - Gradient Boosting\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "13d6NE2ygZ2q",
        "outputId": "550c60a2-dcc0-4a00-9e4f-b79066d0c3fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install imbalanced-learn\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APhBszuNgo_2",
        "outputId": "401bbb84-a5af-4ea0-e760-c3887ba117c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.12/dist-packages (0.14.0)\n",
            "Requirement already satisfied: numpy<3,>=1.25.2 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (2.0.2)\n",
            "Requirement already satisfied: scipy<2,>=1.11.4 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (1.16.3)\n",
            "Requirement already satisfied: scikit-learn<2,>=1.4.2 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (1.6.1)\n",
            "Requirement already satisfied: joblib<2,>=1.2.0 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (1.5.2)\n",
            "Requirement already satisfied: threadpoolctl<4,>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from imbalanced-learn) (3.6.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import SMOTE\n",
        "\n",
        "\n",
        "# Preprocess training data (encoding, scaling)\n",
        "X_train_processed = preprocessor.fit_transform(X_train)\n",
        "\n",
        "print(f\"Shape before SMOTE: {X_train_processed.shape}\")\n",
        "\n",
        "# Apply SMOTE\n",
        "smote = SMOTE(random_state=42)\n",
        "X_train_bal, y_train_bal = smote.fit_resample(X_train_processed, y_train)\n",
        "\n",
        "print(f\"Shape after SMOTE: {X_train_bal.shape}\")\n",
        "\n",
        "# Now train Random Forest again on balanced data\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "rf_smote = RandomForestClassifier(\n",
        "    n_estimators=300,\n",
        "    max_depth=40,\n",
        "    min_samples_split=2,\n",
        "    min_samples_leaf=1,\n",
        "    bootstrap=False,\n",
        "    random_state=42\n",
        ")\n",
        "\n",
        "print(\"Training Random Forest on SMOTE balanced data...\")\n",
        "rf_smote.fit(X_train_bal, y_train_bal)\n",
        "\n",
        "# Evaluate on original test seT\n",
        "X_test_processed = preprocessor.transform(X_test)\n",
        "y_pred_smote = rf_smote.predict(X_test_processed)\n",
        "\n",
        "from sklearn.metrics import classification_report, accuracy_score\n",
        "\n",
        "print(f\"Accuracy on Test Set after SMOTE: {accuracy_score(y_test, y_pred_smote):.4f}\")\n",
        "print(\"Classification Report after SMOTE:\\n\")\n",
        "print(classification_report(y_test, y_pred_smote))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RlTKQqYZgyxa",
        "outputId": "febb5330-e0f6-450f-ba0f-a68b40a78ade"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape before SMOTE: (15780, 118)\n",
            "Shape after SMOTE: (39355, 118)\n",
            "Training Random Forest on SMOTE balanced data...\n",
            "Accuracy on Test Set after SMOTE: 0.9910\n",
            "Classification Report after SMOTE:\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "         dos       1.00      1.00      1.00      2429\n",
            "      normal       0.99      0.99      0.99      3374\n",
            "       probe       0.99      0.99      0.99       647\n",
            "         r2l       0.96      0.91      0.93       303\n",
            "         u2r       1.00      0.73      0.84        11\n",
            "\n",
            "    accuracy                           0.99      6764\n",
            "   macro avg       0.99      0.92      0.95      6764\n",
            "weighted avg       0.99      0.99      0.99      6764\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Confusion Matrix for Random Forest\n",
        "cm_rf = confusion_matrix(y_test, y_pred_smote, labels=rf_search.classes_)\n",
        "disp_rf = ConfusionMatrixDisplay(confusion_matrix=cm_rf, display_labels=rf_search.classes_)\n",
        "plt.figure(figsize=(10,8))\n",
        "disp_rf.plot(cmap='Blues', ax=plt.gca())\n",
        "plt.title(\"Confusion Matrix - Random Forest\")\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "7uDgLAh9hImI",
        "outputId": "9dc2df76-012d-46ac-b383-9f3c8e2a9d46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}