Malware_Detection_Using_ai.ipynb

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      "source": [
        "# **Phase 1: Dataset Exploration**"
      ],
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        "id": "CEtEH5WHFB_n"
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      "source": [
        "# Step 1: Importing Libraries\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Load the Dataset\n",
        "dataset = pd.read_csv('MalwareDataset.csv')"
      ],
      "metadata": {
        "id": "Zz6MzVdPgUMY"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "# Step 3: Display the First Few Rows\n",
        "print(\"First 5 Rows of the Dataset:\")\n",
        "dataset.head()"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First 5 Rows of the Dataset:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "   AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
              "0                10407                   9                  6   \n",
              "1                 5354                   9                  6   \n",
              "2                58807                   9                  6   \n",
              "3                25166                   9                  6   \n",
              "4                70387                   9                  6   \n",
              "\n",
              "   MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
              "0                            6               33088              262144   \n",
              "1                            6               33088              262144   \n",
              "2                            6               33088              262144   \n",
              "3                            6               33088              262144   \n",
              "4                            6               33088              262144   \n",
              "\n",
              "   NumberOfSections  ResourceSize  legitimate  \n",
              "0                 4           952           1  \n",
              "1                 4           952           1  \n",
              "2                 4        136490           1  \n",
              "3                 4          1940           1  \n",
              "4                 4         83098           1  "
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        "# Step 4: Understand the Columns and Identify the Target\n",
        "print(\"\\nDataset Information:\")\n",
        "dataset.info()  # Gives an overview of columns and data types\n",
        "print(\"\\nUnique values in the 'legitimate' column:\")\n",
        "dataset['legitimate'].value_counts()  # Confirm the target column distribution"
      ],
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            "\n",
            "Dataset Information:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 137444 entries, 0 to 137443\n",
            "Data columns (total 9 columns):\n",
            " #   Column                       Non-Null Count   Dtype\n",
            "---  ------                       --------------   -----\n",
            " 0   AddressOfEntryPoint          137444 non-null  int64\n",
            " 1   MajorLinkerVersion           137444 non-null  int64\n",
            " 2   MajorImageVersion            137444 non-null  int64\n",
            " 3   MajorOperatingSystemVersion  137444 non-null  int64\n",
            " 4   DllCharacteristics           137444 non-null  int64\n",
            " 5   SizeOfStackReserve           137444 non-null  int64\n",
            " 6   NumberOfSections             137444 non-null  int64\n",
            " 7   ResourceSize                 137444 non-null  int64\n",
            " 8   legitimate                   137444 non-null  int64\n",
            "dtypes: int64(9)\n",
            "memory usage: 9.4 MB\n",
            "\n",
            "Unique values in the 'legitimate' column:\n"
          ]
        },
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              "legitimate\n",
              "0    96526\n",
              "1    40918\n",
              "Name: count, dtype: int64"
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              "      <th>legitimate</th>\n",
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      "source": [
        "# Step 5: Descriptive Statistics\n",
        "print(\"\\nDescriptive Statistics:\")\n",
        "dataset.describe()"
      ],
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          "text": [
            "\n",
            "Descriptive Statistics:\n"
          ]
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              "       AddressOfEntryPoint  MajorLinkerVersion  MajorImageVersion  \\\n",
              "count         1.374440e+05       137444.000000      137444.000000   \n",
              "mean          1.722186e+05            8.620784          68.731876   \n",
              "std           3.438014e+06            4.095635        1185.709873   \n",
              "min           0.000000e+00            0.000000           0.000000   \n",
              "25%           1.272100e+04            8.000000           0.000000   \n",
              "50%           5.300800e+04            9.000000           0.000000   \n",
              "75%           6.157800e+04           10.000000           6.000000   \n",
              "max           1.074484e+09          255.000000       28619.000000   \n",
              "\n",
              "       MajorOperatingSystemVersion  DllCharacteristics  SizeOfStackReserve  \\\n",
              "count                137444.000000       137444.000000        1.374440e+05   \n",
              "mean                      5.098738        22301.043436        9.306841e+05   \n",
              "std                      99.437584        15444.753219        5.553175e+05   \n",
              "min                       0.000000            0.000000        0.000000e+00   \n",
              "25%                       4.000000          320.000000        1.048576e+06   \n",
              "50%                       5.000000        33088.000000        1.048576e+06   \n",
              "75%                       5.000000        33088.000000        1.048576e+06   \n",
              "max                   36868.000000        49504.000000        3.355443e+07   \n",
              "\n",
              "       NumberOfSections  ResourceSize     legitimate  \n",
              "count     137444.000000  1.374440e+05  137444.000000  \n",
              "mean           4.997119  2.474766e+05       0.297707  \n",
              "std            1.917237  2.129516e+07       0.457252  \n",
              "min            1.000000  0.000000e+00       0.000000  \n",
              "25%            4.000000  2.216000e+03       0.000000  \n",
              "50%            5.000000  9.640000e+03       0.000000  \n",
              "75%            5.000000  2.376250e+04       1.000000  \n",
              "max           40.000000  4.294903e+09       1.000000  "
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AddressOfEntryPoint</th>\n",
              "      <th>MajorLinkerVersion</th>\n",
              "      <th>MajorImageVersion</th>\n",
              "      <th>MajorOperatingSystemVersion</th>\n",
              "      <th>DllCharacteristics</th>\n",
              "      <th>SizeOfStackReserve</th>\n",
              "      <th>NumberOfSections</th>\n",
              "      <th>ResourceSize</th>\n",
              "      <th>legitimate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>1.374440e+05</td>\n",
              "      <td>137444.000000</td>\n",
              "      <td>137444.000000</td>\n",
              "      <td>137444.000000</td>\n",
              "      <td>137444.000000</td>\n",
              "      <td>1.374440e+05</td>\n",
              "      <td>137444.000000</td>\n",
              "      <td>1.374440e+05</td>\n",
              "      <td>137444.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.722186e+05</td>\n",
              "      <td>8.620784</td>\n",
              "      <td>68.731876</td>\n",
              "      <td>5.098738</td>\n",
              "      <td>22301.043436</td>\n",
              "      <td>9.306841e+05</td>\n",
              "      <td>4.997119</td>\n",
              "      <td>2.474766e+05</td>\n",
              "      <td>0.297707</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>3.438014e+06</td>\n",
              "      <td>4.095635</td>\n",
              "      <td>1185.709873</td>\n",
              "      <td>99.437584</td>\n",
              "      <td>15444.753219</td>\n",
              "      <td>5.553175e+05</td>\n",
              "      <td>1.917237</td>\n",
              "      <td>2.129516e+07</td>\n",
              "      <td>0.457252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1.272100e+04</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>320.000000</td>\n",
              "      <td>1.048576e+06</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>2.216000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.300800e+04</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>33088.000000</td>\n",
              "      <td>1.048576e+06</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>9.640000e+03</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.157800e+04</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>33088.000000</td>\n",
              "      <td>1.048576e+06</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>2.376250e+04</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.074484e+09</td>\n",
              "      <td>255.000000</td>\n",
              "      <td>28619.000000</td>\n",
              "      <td>36868.000000</td>\n",
              "      <td>49504.000000</td>\n",
              "      <td>3.355443e+07</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>4.294903e+09</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"AddressOfEntryPoint\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 379693680.5827585,\n        \"min\": 0.0,\n        \"max\": 1074484297.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          172218.58133494368,\n          53008.0,\n          137444.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MajorLinkerVersion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48578.98464946322,\n        \"min\": 0.0,\n        \"max\": 137444.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.620783737376676,\n          9.0,\n          137444.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MajorImageVersion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48124.34666737728,\n        \"min\": 0.0,\n        \"max\": 137444.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          137444.0,\n          68.7318762550566,\n          28619.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MajorOperatingSystemVersion\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48472.173224345446,\n        \"min\": 0.0,\n        \"max\": 137444.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          137444.0,\n          5.0987383952737115,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DllCharacteristics\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 44188.18971305185,\n        \"min\": 0.0,\n        \"max\": 137444.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          137444.0,\n          22301.043435872063,\n          33088.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SizeOfStackReserve\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11630038.324066976,\n        \"min\": 0.0,\n        \"max\": 33554432.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          137444.0,\n          930684.1102412619,\n          33554432.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"NumberOfSections\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48590.666757075836,\n        \"min\": 1.0,\n        \"max\": 137444.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          137444.0,\n          4.997118826576642,\n          5.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ResourceSize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1517398756.192639,\n        \"min\": 0.0,\n        \"max\": 4294902624.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          247476.5888798347,\n          9640.0,\n          137444.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"legitimate\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 48593.653072083194,\n        \"min\": 0.0,\n        \"max\": 137444.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.29770670236605457,\n          1.0,\n          0.457251509485966\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Check for Missing Values\n",
        "print(\"\\nMissing Values in Each Column:\")\n",
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "o7e4lHmK_fM8",
        "outputId": "38ddfa28-88d5-47df-a755-3c0588630f4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing Values in Each Column:\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AddressOfEntryPoint            0\n",
              "MajorLinkerVersion             0\n",
              "MajorImageVersion              0\n",
              "MajorOperatingSystemVersion    0\n",
              "DllCharacteristics             0\n",
              "SizeOfStackReserve             0\n",
              "NumberOfSections               0\n",
              "ResourceSize                   0\n",
              "legitimate                     0\n",
              "dtype: int64"
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
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              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>AddressOfEntryPoint</th>\n",
              "      <td>0</td>\n",
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              "      <th>MajorLinkerVersion</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>MajorImageVersion</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MajorOperatingSystemVersion</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DllCharacteristics</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SizeOfStackReserve</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumberOfSections</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
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              "      <th>ResourceSize</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>legitimate</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**1) How many legitimate and malicious files are in the dataset?**\n",
        "\n",
        "* **Legitimate files:** 40,918\n",
        "* **Malicious files:** 96,526\n",
        "\n",
        "**2) Which features seem to differentiate legitimate files from malware?**\n",
        "\n",
        " By analyzing the descriptive statistics:\n",
        "\n",
        "* **ResourceSize:** The mean for ResourceSize is 247,476 but varies widely. Malware may have more extreme values in this feature compared to legitimate files.\n",
        "* **DllCharacteristics:** Legitimate files might have more specific and consistent characteristics, while malware could show more variation.\n",
        "* **MajorImageVersion and MajorOperatingSystemVersion:** Many files have a 0 value for these features, potentially hinting at default or missing metadata that might be more common in malware.\n",
        "* **NumberOfSections:** Malware files may have anomalous values here compared to legitimate ones.\n",
        "* **AddressOfEntryPoint:** The range and mean values are significantly large, potentially differentiating benign files from malware if the distribution of entry points is noticeably different."
      ],
      "metadata": {
        "id": "MxT3zqHMGb-Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Phase 2: Data Preparation**"
      ],
      "metadata": {
        "id": "4aDozpWERcSc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Split the dataset into features (X) and target (y)\n",
        "X = dataset_cleaned.drop(columns=['legitimate'])  # Features\n",
        "y = dataset_cleaned['legitimate']  # Target"
      ],
      "metadata": {
        "id": "knCwaGWHQWgv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Divide the data into training (80%) and testing (20%) sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
      ],
      "metadata": {
        "id": "WDldEexfQbxu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Standardize the numerical data\n",
        "scaler = StandardScaler()\n",
        "X_train_scaled = scaler.fit_transform(X_train)\n",
        "X_test_scaled = scaler.transform(X_test)"
      ],
      "metadata": {
        "id": "jlDUc7f8Qiwm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Verify scaling (mean ~0 and std ~1 for training data)\n",
        "print(\"\\nFirst 5 rows of standardized training data:\")\n",
        "print(X_train_scaled[:5])\n",
        "\n",
        "print(\"\\nFeature means (training set):\", X_train_scaled.mean(axis=0))\n",
        "print(\"Feature standard deviations (training set):\", X_train_scaled.std(axis=0))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkYE5S-cQrOp",
        "outputId": "e0c4d027-d752-45c9-c83c-7a3be5fa94ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "First 5 rows of standardized training data:\n",
            "[[-3.96330267e-02  5.60448982e-01 -5.77508438e-02 -1.47553480e-03\n",
            "   6.98777598e-01  2.14132343e-01 -1.04240747e+00  1.32093737e-02]\n",
            " [-3.30981899e-02 -1.56001329e+00 -5.26696332e-02 -1.04705134e-02\n",
            "   6.78063304e-01  2.14132343e-01  1.56744452e+00 -1.09426786e-02]\n",
            " [-5.05134816e-03 -1.46371776e-01 -5.77508438e-02 -1.04705134e-02\n",
            "  -1.35608042e+00  2.14132343e-01 -1.04240747e+00 -1.09978509e-02]\n",
            " [-4.22390771e-02  3.24842062e-01 -5.26696332e-02 -1.47553480e-03\n",
            "   7.65063341e-01  2.14132343e-01  5.23503720e-01 -7.97498777e-03]\n",
            " [-2.82469309e-02  5.60448982e-01 -5.77508438e-02 -1.47553480e-03\n",
            "   6.94634739e-01  2.14132343e-01 -5.20437076e-01 -1.08729872e-02]]\n",
            "\n",
            "Feature means (training set): [ 2.26174305e-18  3.78357302e-17 -3.21813726e-17 -2.11634529e-18\n",
            " -9.07928283e-18 -2.57192496e-17  1.57998908e-16 -6.46212301e-19]\n",
            "Feature standard deviations (training set): [1. 1. 1. 1. 1. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Phase 3: Model Creation and Training**"
      ],
      "metadata": {
        "id": "A4Xv22TERq7F"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part A: Supervised Models**"
      ],
      "metadata": {
        "id": "R_LFjNMIRxgH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.metrics import accuracy_score, classification_report"
      ],
      "metadata": {
        "id": "L2UeIVQ3R2uO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Train a Decision Tree model\n",
        "dt_model = DecisionTreeClassifier(random_state=42)\n",
        "dt_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "9sIqvRCDR5o5",
        "outputId": "61263d23-fdf6-44a1-99c9-ff9cb23881e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-1 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-1 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-1 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-1 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-1 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-1 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-1 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>DecisionTreeClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.tree.DecisionTreeClassifier.html\">?<span>Documentation for DecisionTreeClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>DecisionTreeClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions using the Decision Tree model\n",
        "dt_predictions = dt_model.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "epMRWOAyR-ZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Decision Tree model\n",
        "dt_accuracy = accuracy_score(y_test, dt_predictions)\n",
        "print(f\"Decision Tree Accuracy: {dt_accuracy:.4f}\")\n",
        "print(\"\\nDecision Tree Classification Report:\")\n",
        "print(classification_report(y_test, dt_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1nQ-QEAKSCVl",
        "outputId": "8d01819d-2a31-4882-f582-4e820670fcbc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Accuracy: 0.9879\n",
            "\n",
            "Decision Tree Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99     19305\n",
            "           1       0.98      0.98      0.98      8184\n",
            "\n",
            "    accuracy                           0.99     27489\n",
            "   macro avg       0.98      0.99      0.99     27489\n",
            "weighted avg       0.99      0.99      0.99     27489\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Train a Random Forest model\n",
        "rf_model = RandomForestClassifier(random_state=42, n_estimators=100)  # Default: 100 trees\n",
        "rf_model.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "2wLjOU7oSGkR",
        "outputId": "d96fe2c9-7cf5-40cf-b196-dc6dd4d29d9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-2 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-2 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-2 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-2 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-2 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-2 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-2 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions using the Random Forest model\n",
        "rf_predictions = rf_model.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "sp-1SqraSMqY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the Random Forest model\n",
        "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
        "print(f\"\\nRandom Forest Accuracy: {rf_accuracy:.4f}\")\n",
        "print(\"\\nRandom Forest Classification Report:\")\n",
        "print(classification_report(y_test, rf_predictions))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUEtox31SPX6",
        "outputId": "40265396-bd41-4b18-af11-6435107ce5fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Accuracy: 0.9919\n",
            "\n",
            "Random Forest Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99     19305\n",
            "           1       0.99      0.99      0.99      8184\n",
            "\n",
            "    accuracy                           0.99     27489\n",
            "   macro avg       0.99      0.99      0.99     27489\n",
            "weighted avg       0.99      0.99      0.99     27489\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Comparing the Performance of Decision Tree and Random Forest**\n",
        "\n",
        "**Accuracy:**\n",
        "* Decision Tree: 98.79%\n",
        "* Random Forest: 99.19%\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "Random Forest slightly outperforms the Decision Tree in terms of accuracy. This is expected as Random Forest leverages multiple decision trees to reduce overfitting and improve generalization."
      ],
      "metadata": {
        "id": "g8yGhtdUTnbS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Classification report**\n",
        "\n",
        "**Precision:**\n",
        "\n",
        "* Decision Tree (Class 0 - Malware): 0.99 | Class 1 - Legitimate: 0.98\n",
        "* Random Forest (Class 0 - Malware): 0.99 | Class 1 - Legitimate: 0.99\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "Both models perform well in terms of precision, but Random Forest performs better for identifying legitimate files (Class 1), with a slightly higher precision score.\n",
        "\n",
        "**Recall:**\n",
        "\n",
        "* Decision Tree (Class 0 - Malware): 0.99 | Class 1 - Legitimate: 0.98\n",
        "* Random Forest (Class 0 - Malware): 0.99 | Class 1 - Legitimate: 0.99\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "Both models have very high recall for both classes, but Random Forest slightly outperforms the Decision Tree when it comes to identifying legitimate files (Class 1).\n",
        "\n",
        "**F1-Score:**\n",
        "\n",
        "* Decision Tree (Class 0 - Malware): 0.99 | Class 1 - Legitimate: 0.98\n",
        "* Random Forest (Class 0 - Malware): 0.99 | Class 1 - Legitimate: 0.99\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "F1-score is essentially the harmonic mean of precision and recall, and both models perform similarly in terms of F1-score. However, Random Forest again shows a slight edge for legitimate files (Class 1) due to its improved recall and precision.\n"
      ],
      "metadata": {
        "id": "1ucmp0tik6-M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Confusion Matrix:**"
      ],
      "metadata": {
        "id": "RCods876T5ub"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay"
      ],
      "metadata": {
        "id": "8z7uHNFVWHQX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Decision Tree Confusion Matrix\n",
        "dt_cm = confusion_matrix(y_test, dt_predictions)\n",
        "print(\"\\nDecision Tree Confusion Matrix:\")\n",
        "print(dt_cm)\n",
        "ConfusionMatrixDisplay(confusion_matrix=dt_cm, display_labels=['Malware', 'Legitimate']).plot(cmap='Blues')\n",
        "plt.title(\"Decision Tree Confusion Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "ZkkDnvqESTRN",
        "outputId": "fe03f219-6273-4d35-9317-7ac6bb68cf5e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Decision Tree Confusion Matrix:\n",
            "[[19120   185]\n",
            " [  147  8037]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Random Forest Confusion Matrix\n",
        "rf_cm = confusion_matrix(y_test, rf_predictions)\n",
        "print(\"\\nRandom Forest Confusion Matrix:\")\n",
        "print(rf_cm)\n",
        "ConfusionMatrixDisplay(confusion_matrix=rf_cm, display_labels=['Malware', 'Legitimate']).plot(cmap='Greens')\n",
        "plt.title(\"Random Forest Confusion Matrix\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 549
        },
        "id": "dj0eaHYjWMyx",
        "outputId": "572a0c91-849e-4437-ab61-bb0e6ae29371"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Confusion Matrix:\n",
            "[[19184   121]\n",
            " [  102  8082]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**True Negatives:**\n",
        "* Decision Tree: 19120\n",
        "* Random Forest: 19184\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "Random Forest performs slightly better at detecting malware, as it has more True Negatives, correctly identifying more malicious files.\n",
        "\n",
        "**False Positives:**\n",
        "* Decision Tree: 185\n",
        "* Random Forest: 121\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "Random Forest produces fewer false alarms, meaning it misclassifies fewer malware files as legitimate compared to the Decision Tree.\n",
        "\n",
        "**False Negatives:**\n",
        "\n",
        "* Decision Tree: 147\n",
        "* Random Forest: 102\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "Random Forest misses fewer legitimate files, leading to a lower False Negative rate and improving its accuracy in identifying legitimate files.\n",
        "\n",
        "**True Positives:**\n",
        "\n",
        "* Decision Tree: 8037\n",
        "* Random Forest: 8082\n",
        "\n",
        "**Observation:**\n",
        "\n",
        "Random Forest identifies more legitimate files as legitimate, contributing to its overall better performance."
      ],
      "metadata": {
        "id": "LzSK4csjWqSj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Overall Comparison**\n",
        "\n",
        "Random Forest outperforms Decision Tree in every metric, though the differences are minimal. It is better at detecting legitimate files, with a slightly lower false positive and false negative rate compared to the Decision Tree."
      ],
      "metadata": {
        "id": "S2BUDSwid5FQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part B: Unsupervised Model**"
      ],
      "metadata": {
        "id": "xA_NdWe_n3P0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics import adjusted_rand_score\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "lvlhHmiMoBeS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Implement KMeans with 2 clusters\n",
        "kmeans = KMeans(n_clusters=2, random_state=42)\n",
        "kmeans.fit(X_train_scaled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "o4SFSaR6oEjl",
        "outputId": "73bdbd05-2c29-4eaa-b895-4c214b07fa97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KMeans(n_clusters=2, random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-10 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-10 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-10 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-10 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-10 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-10 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-10 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-10 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMeans(n_clusters=2, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>KMeans</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.cluster.KMeans.html\">?<span>Documentation for KMeans</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>KMeans(n_clusters=2, random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Get the predicted cluster labels\n",
        "cluster_labels = kmeans.labels_"
      ],
      "metadata": {
        "id": "iRo6tP99oIbq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 3: Compare with actual labels (y_train)\n",
        "cm = confusion_matrix(y_train, cluster_labels)\n",
        "if cm[0, 1] > cm[1, 0]:\n",
        "    cluster_labels = 1 - cluster_labels  # Swap the cluster labels"
      ],
      "metadata": {
        "id": "8NJssjosoNF9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 4: Compute Adjusted Rand Index (ARI) for comparison\n",
        "ari_score = adjusted_rand_score(y_train, cluster_labels)\n",
        "print(f\"Adjusted Rand Index (ARI): {ari_score:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hT7ltfLKog56",
        "outputId": "2f7c1305-8b41-4288-9b66-3607eeedb92e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Adjusted Rand Index (ARI): 0.4770\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Visualize the clusters\n",
        "plt.figure(figsize=(8, 6))\n",
        "\n",
        "# Scatter plot of the first two features colored by predicted cluster labels\n",
        "plt.scatter(X_train_scaled[:, 0], X_train_scaled[:, 1], c=cluster_labels, cmap='viridis', alpha=0.6)\n",
        "plt.title(\"KMeans Clustering of Training Data (2 Clusters)\")\n",
        "plt.xlabel(\"Feature 1 (scaled)\")\n",
        "plt.ylabel(\"Feature 2 (scaled)\")\n",
        "plt.colorbar(label='Cluster Label')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "cz9NyklnokeW",
        "outputId": "8a04c36d-60cd-4d88-c26e-dd72a18fed8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Confusion Matrix for Cluster Labels vs Actual Labels\n",
        "ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Malware', 'Legitimate']).plot(cmap='Blues')\n",
        "plt.title(\"Confusion Matrix of KMeans Clustering vs Actual Labels\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "hRkIvrDLoxyI",
        "outputId": "4b99b096-3414-4ee4-da50-aeda15aa0707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Part C: Deep Learning Model**"
      ],
      "metadata": {
        "id": "bWp7Ss1U3GHf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.losses import BinaryCrossentropy\n",
        "from tensorflow.keras.metrics import AUC\n",
        "from tensorflow.keras.layers import Input"
      ],
      "metadata": {
        "id": "g0oi9bCz284E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Create the model\n",
        "model = Sequential()"
      ],
      "metadata": {
        "id": "Cy0btkwG3Vs4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Input layer: Specify the input shape using Input layer\n",
        "model.add(Input(shape=(X_train_scaled.shape[1],)))  # input shape is (number of features,)"
      ],
      "metadata": {
        "id": "POEaZiWw3ZPp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hidden layer 1: Dense layer with 64 neurons, ReLU activation\n",
        "model.add(Dense(64, activation='relu'))"
      ],
      "metadata": {
        "id": "eAOj_t67AEVr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Hidden layer 2: Dense layer with 32 neurons, ReLU activation\n",
        "model.add(Dense(32, activation='relu'))"
      ],
      "metadata": {
        "id": "RNnG2gnsAHLw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Output layer: 1 neuron, sigmoid activation function for binary classification\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "NsZrdsfQAJnD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compile the model\n",
        "model.compile(optimizer=Adam(),\n",
        "              loss=BinaryCrossentropy(),\n",
        "              metrics=[AUC()])  # AUC (Area Under Curve) for evaluation"
      ],
      "metadata": {
        "id": "ejNwG5SvAMYP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model on the training data\n",
        "history = model.fit(X_train_scaled, y_train, epochs=20, batch_size=32, validation_data=(X_test_scaled, y_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "deOaX3Q5APfr",
        "outputId": "0ef82154-f7b0-4c4f-d05c-189e55923392"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - auc: 0.9761 - loss: 0.1772 - val_auc: 0.9896 - val_loss: 0.1141\n",
            "Epoch 2/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - auc: 0.9884 - loss: 0.1215 - val_auc: 0.9907 - val_loss: 0.1060\n",
            "Epoch 3/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m8s\u001b[0m 2ms/step - auc: 0.9907 - loss: 0.1118 - val_auc: 0.9924 - val_loss: 0.1001\n",
            "Epoch 4/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - auc: 0.9918 - loss: 0.1023 - val_auc: 0.9925 - val_loss: 0.0968\n",
            "Epoch 5/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - auc: 0.9923 - loss: 0.0968 - val_auc: 0.9932 - val_loss: 0.0895\n",
            "Epoch 6/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - auc: 0.9934 - loss: 0.0907 - val_auc: 0.9941 - val_loss: 0.0880\n",
            "Epoch 7/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 2ms/step - auc: 0.9935 - loss: 0.0899 - val_auc: 0.9944 - val_loss: 0.0862\n",
            "Epoch 8/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 3ms/step - auc: 0.9939 - loss: 0.0876 - val_auc: 0.9942 - val_loss: 0.0907\n",
            "Epoch 9/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - auc: 0.9945 - loss: 0.0831 - val_auc: 0.9949 - val_loss: 0.0803\n",
            "Epoch 10/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - auc: 0.9946 - loss: 0.0827 - val_auc: 0.9948 - val_loss: 0.0814\n",
            "Epoch 11/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - auc: 0.9947 - loss: 0.0816 - val_auc: 0.9947 - val_loss: 0.0829\n",
            "Epoch 12/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - auc: 0.9948 - loss: 0.0794 - val_auc: 0.9953 - val_loss: 0.0760\n",
            "Epoch 13/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 5ms/step - auc: 0.9951 - loss: 0.0787 - val_auc: 0.9954 - val_loss: 0.0804\n",
            "Epoch 14/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 3ms/step - auc: 0.9950 - loss: 0.0801 - val_auc: 0.9951 - val_loss: 0.0760\n",
            "Epoch 15/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m11s\u001b[0m 3ms/step - auc: 0.9950 - loss: 0.0773 - val_auc: 0.9952 - val_loss: 0.0757\n",
            "Epoch 16/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 3ms/step - auc: 0.9951 - loss: 0.0763 - val_auc: 0.9956 - val_loss: 0.0725\n",
            "Epoch 17/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - auc: 0.9953 - loss: 0.0749 - val_auc: 0.9959 - val_loss: 0.0731\n",
            "Epoch 18/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 3ms/step - auc: 0.9951 - loss: 0.0757 - val_auc: 0.9951 - val_loss: 0.0746\n",
            "Epoch 19/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m9s\u001b[0m 2ms/step - auc: 0.9952 - loss: 0.0743 - val_auc: 0.9956 - val_loss: 0.0734\n",
            "Epoch 20/20\n",
            "\u001b[1m3437/3437\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 3ms/step - auc: 0.9956 - loss: 0.0729 - val_auc: 0.9957 - val_loss: 0.0718\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Evaluate the model on the test set\n",
        "test_loss, test_auc = model.evaluate(X_test_scaled, y_test)\n",
        "print(f\"Test Loss: {test_loss:.4f}, Test AUC: {test_auc:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGQfoYd5BTxC",
        "outputId": "92e124da-b50f-4e2b-ed4c-81beeff2ff9b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m860/860\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - auc: 0.9959 - loss: 0.0708\n",
            "Test Loss: 0.0718, Test AUC: 0.9957\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Plot the training and validation AUC\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.plot(history.history['auc'], label='Train AUC')\n",
        "plt.plot(history.history['val_auc'], label='Validation AUC')\n",
        "plt.title('Model AUC')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('AUC')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "xDb_XQ5GBYXn",
        "outputId": "989449b9-8a27-4fc4-f774-7e8f1c6cf511"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Phase 4: Cross-Validation**"
      ],
      "metadata": {
        "id": "oxaBjOkEBmOb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "metadata": {
        "id": "kLU0VKDxBolg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Initialize the models\n",
        "decision_tree = DecisionTreeClassifier(random_state=42)\n",
        "random_forest = RandomForestClassifier(random_state=42, n_estimators=100)"
      ],
      "metadata": {
        "id": "Oqp5D7yFBs3v"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Perform 5-fold cross-validation\n",
        "# Cross-validation for Decision Tree\n",
        "dt_cv_scores = cross_val_score(decision_tree, X_train_scaled, y_train, cv=5, scoring='accuracy')\n",
        "print(f\"Decision Tree Cross-Validation Scores: {dt_cv_scores}\")\n",
        "print(f\"Decision Tree Average Accuracy: {dt_cv_scores.mean():.4f}\")\n",
        "print(f\"Decision Tree Standard Deviation: {dt_cv_scores.std():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_KZlKldBxKc",
        "outputId": "a6c0ffb9-55bc-4425-a0b0-f5e566244c68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Cross-Validation Scores: [0.98685826 0.98776772 0.98635806 0.98508481 0.98667637]\n",
            "Decision Tree Average Accuracy: 0.9865\n",
            "Decision Tree Standard Deviation: 0.0009\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cross-validation for Random Forest\n",
        "rf_cv_scores = cross_val_score(random_forest, X_train_scaled, y_train, cv=5, scoring='accuracy')\n",
        "print(f\"Random Forest Cross-Validation Scores: {rf_cv_scores}\")\n",
        "print(f\"Random Forest Average Accuracy: {rf_cv_scores.mean():.4f}\")\n",
        "print(f\"Random Forest Standard Deviation: {rf_cv_scores.std():.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ghcrAQZ3B1Iz",
        "outputId": "455aa0b0-c6f5-4a0b-d3e1-069c0b7542e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest Cross-Validation Scores: [0.99108726 0.99104179 0.98995043 0.98981401 0.99013233]\n",
            "Random Forest Average Accuracy: 0.9904\n",
            "Random Forest Standard Deviation: 0.0005\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Compare the average scores\n",
        "if rf_cv_scores.mean() > dt_cv_scores.mean():\n",
        "    print(\"Random Forest generalizes better on this dataset.\")\n",
        "else:\n",
        "    print(\"Decision Tree generalizes better on this dataset.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7F63ss3gCE7o",
        "outputId": "74812cbc-828a-4163-8d31-6b790f769b97"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Random Forest generalizes better on this dataset.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Phase 5: Model Evaluation and Comparison**"
      ],
      "metadata": {
        "id": "XB6UczPSCPkj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
      ],
      "metadata": {
        "id": "8K9vffM-CRh8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Train the Decision Tree and Random Forest models on the entire training set\n",
        "decision_tree.fit(X_train_scaled, y_train)\n",
        "random_forest.fit(X_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "id": "1ajQdZEuCZ8N",
        "outputId": "d5973fbb-f34b-413c-a220-b612ff56010c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(random_state=42)"
            ],
            "text/html": [
              "<style>#sk-container-id-8 {\n",
              "  /* Definition of color scheme common for light and dark mode */\n",
              "  --sklearn-color-text: #000;\n",
              "  --sklearn-color-text-muted: #666;\n",
              "  --sklearn-color-line: gray;\n",
              "  /* Definition of color scheme for unfitted estimators */\n",
              "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
              "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
              "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
              "  --sklearn-color-unfitted-level-3: chocolate;\n",
              "  /* Definition of color scheme for fitted estimators */\n",
              "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
              "  --sklearn-color-fitted-level-1: #d4ebff;\n",
              "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
              "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
              "\n",
              "  /* Specific color for light theme */\n",
              "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
              "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
              "  --sklearn-color-icon: #696969;\n",
              "\n",
              "  @media (prefers-color-scheme: dark) {\n",
              "    /* Redefinition of color scheme for dark theme */\n",
              "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
              "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
              "    --sklearn-color-icon: #878787;\n",
              "  }\n",
              "}\n",
              "\n",
              "#sk-container-id-8 {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 pre {\n",
              "  padding: 0;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-hidden--visually {\n",
              "  border: 0;\n",
              "  clip: rect(1px 1px 1px 1px);\n",
              "  clip: rect(1px, 1px, 1px, 1px);\n",
              "  height: 1px;\n",
              "  margin: -1px;\n",
              "  overflow: hidden;\n",
              "  padding: 0;\n",
              "  position: absolute;\n",
              "  width: 1px;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-dashed-wrapped {\n",
              "  border: 1px dashed var(--sklearn-color-line);\n",
              "  margin: 0 0.4em 0.5em 0.4em;\n",
              "  box-sizing: border-box;\n",
              "  padding-bottom: 0.4em;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-container {\n",
              "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
              "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
              "     so we also need the `!important` here to be able to override the\n",
              "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
              "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
              "  display: inline-block !important;\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-text-repr-fallback {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              "div.sk-parallel-item,\n",
              "div.sk-serial,\n",
              "div.sk-item {\n",
              "  /* draw centered vertical line to link estimators */\n",
              "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
              "  background-size: 2px 100%;\n",
              "  background-repeat: no-repeat;\n",
              "  background-position: center center;\n",
              "}\n",
              "\n",
              "/* Parallel-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item::after {\n",
              "  content: \"\";\n",
              "  width: 100%;\n",
              "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
              "  flex-grow: 1;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel {\n",
              "  display: flex;\n",
              "  align-items: stretch;\n",
              "  justify-content: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  position: relative;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
              "  align-self: flex-end;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
              "  align-self: flex-start;\n",
              "  width: 50%;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
              "  width: 0;\n",
              "}\n",
              "\n",
              "/* Serial-specific style estimator block */\n",
              "\n",
              "#sk-container-id-8 div.sk-serial {\n",
              "  display: flex;\n",
              "  flex-direction: column;\n",
              "  align-items: center;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  padding-right: 1em;\n",
              "  padding-left: 1em;\n",
              "}\n",
              "\n",
              "\n",
              "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
              "clickable and can be expanded/collapsed.\n",
              "- Pipeline and ColumnTransformer use this feature and define the default style\n",
              "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
              "*/\n",
              "\n",
              "/* Pipeline and ColumnTransformer style (default) */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable {\n",
              "  /* Default theme specific background. It is overwritten whether we have a\n",
              "  specific estimator or a Pipeline/ColumnTransformer */\n",
              "  background-color: var(--sklearn-color-background);\n",
              "}\n",
              "\n",
              "/* Toggleable label */\n",
              "#sk-container-id-8 label.sk-toggleable__label {\n",
              "  cursor: pointer;\n",
              "  display: flex;\n",
              "  width: 100%;\n",
              "  margin-bottom: 0;\n",
              "  padding: 0.5em;\n",
              "  box-sizing: border-box;\n",
              "  text-align: center;\n",
              "  align-items: start;\n",
              "  justify-content: space-between;\n",
              "  gap: 0.5em;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label .caption {\n",
              "  font-size: 0.6rem;\n",
              "  font-weight: lighter;\n",
              "  color: var(--sklearn-color-text-muted);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
              "  /* Arrow on the left of the label */\n",
              "  content: \"▸\";\n",
              "  float: left;\n",
              "  margin-right: 0.25em;\n",
              "  color: var(--sklearn-color-icon);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
              "  color: var(--sklearn-color-text);\n",
              "}\n",
              "\n",
              "/* Toggleable content - dropdown */\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content {\n",
              "  max-height: 0;\n",
              "  max-width: 0;\n",
              "  overflow: hidden;\n",
              "  text-align: left;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content pre {\n",
              "  margin: 0.2em;\n",
              "  border-radius: 0.25em;\n",
              "  color: var(--sklearn-color-text);\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
              "  /* Expand drop-down */\n",
              "  max-height: 200px;\n",
              "  max-width: 100%;\n",
              "  overflow: auto;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
              "  content: \"▾\";\n",
              "}\n",
              "\n",
              "/* Pipeline/ColumnTransformer-specific style */\n",
              "\n",
              "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator-specific style */\n",
              "\n",
              "/* Colorize estimator box */\n",
              "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  /* The background is the default theme color */\n",
              "  color: var(--sklearn-color-text-on-default-background);\n",
              "}\n",
              "\n",
              "/* On hover, darken the color of the background */\n",
              "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "/* Label box, darken color on hover, fitted */\n",
              "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
              "  color: var(--sklearn-color-text);\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Estimator label */\n",
              "\n",
              "#sk-container-id-8 div.sk-label label {\n",
              "  font-family: monospace;\n",
              "  font-weight: bold;\n",
              "  display: inline-block;\n",
              "  line-height: 1.2em;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-label-container {\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              "/* Estimator-specific */\n",
              "#sk-container-id-8 div.sk-estimator {\n",
              "  font-family: monospace;\n",
              "  border: 1px dotted var(--sklearn-color-border-box);\n",
              "  border-radius: 0.25em;\n",
              "  box-sizing: border-box;\n",
              "  margin-bottom: 0.5em;\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-0);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-0);\n",
              "}\n",
              "\n",
              "/* on hover */\n",
              "#sk-container-id-8 div.sk-estimator:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-2);\n",
              "}\n",
              "\n",
              "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-2);\n",
              "}\n",
              "\n",
              "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
              "\n",
              "/* Common style for \"i\" and \"?\" */\n",
              "\n",
              ".sk-estimator-doc-link,\n",
              "a:link.sk-estimator-doc-link,\n",
              "a:visited.sk-estimator-doc-link {\n",
              "  float: right;\n",
              "  font-size: smaller;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1em;\n",
              "  height: 1em;\n",
              "  width: 1em;\n",
              "  text-decoration: none !important;\n",
              "  margin-left: 0.5em;\n",
              "  text-align: center;\n",
              "  /* unfitted */\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted,\n",
              "a:link.sk-estimator-doc-link.fitted,\n",
              "a:visited.sk-estimator-doc-link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
              ".sk-estimator-doc-link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover,\n",
              "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
              ".sk-estimator-doc-link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "/* Span, style for the box shown on hovering the info icon */\n",
              ".sk-estimator-doc-link span {\n",
              "  display: none;\n",
              "  z-index: 9999;\n",
              "  position: relative;\n",
              "  font-weight: normal;\n",
              "  right: .2ex;\n",
              "  padding: .5ex;\n",
              "  margin: .5ex;\n",
              "  width: min-content;\n",
              "  min-width: 20ex;\n",
              "  max-width: 50ex;\n",
              "  color: var(--sklearn-color-text);\n",
              "  box-shadow: 2pt 2pt 4pt #999;\n",
              "  /* unfitted */\n",
              "  background: var(--sklearn-color-unfitted-level-0);\n",
              "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link.fitted span {\n",
              "  /* fitted */\n",
              "  background: var(--sklearn-color-fitted-level-0);\n",
              "  border: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "\n",
              ".sk-estimator-doc-link:hover span {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link {\n",
              "  float: right;\n",
              "  font-size: 1rem;\n",
              "  line-height: 1em;\n",
              "  font-family: monospace;\n",
              "  background-color: var(--sklearn-color-background);\n",
              "  border-radius: 1rem;\n",
              "  height: 1rem;\n",
              "  width: 1rem;\n",
              "  text-decoration: none;\n",
              "  /* unfitted */\n",
              "  color: var(--sklearn-color-unfitted-level-1);\n",
              "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
              "  /* fitted */\n",
              "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
              "  color: var(--sklearn-color-fitted-level-1);\n",
              "}\n",
              "\n",
              "/* On hover */\n",
              "#sk-container-id-8 a.estimator_doc_link:hover {\n",
              "  /* unfitted */\n",
              "  background-color: var(--sklearn-color-unfitted-level-3);\n",
              "  color: var(--sklearn-color-background);\n",
              "  text-decoration: none;\n",
              "}\n",
              "\n",
              "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
              "  /* fitted */\n",
              "  background-color: var(--sklearn-color-fitted-level-3);\n",
              "}\n",
              "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" checked><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow\"><div><div>RandomForestClassifier</div></div><div><a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.6/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></div></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 2. Make predictions on the test set\n",
        "dt_predictions = decision_tree.predict(X_test_scaled)\n",
        "rf_predictions = random_forest.predict(X_test_scaled)"
      ],
      "metadata": {
        "id": "YgMWe7ssCj34"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 3. Evaluate Decision Tree\n",
        "print(\"Decision Tree Evaluation:\")\n",
        "dt_accuracy = accuracy_score(y_test, dt_predictions)\n",
        "dt_conf_matrix = confusion_matrix(y_test, dt_predictions)\n",
        "dt_class_report = classification_report(y_test, dt_predictions)\n",
        "\n",
        "print(f\"Accuracy: {dt_accuracy:.4f}\")\n",
        "print(f\"Confusion Matrix:\\n{dt_conf_matrix}\")\n",
        "print(f\"Classification Report:\\n{dt_class_report}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k8ZDPr6lCoiZ",
        "outputId": "d33da21c-0e4d-4676-e79a-2d98c7852a36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decision Tree Evaluation:\n",
            "Accuracy: 0.9879\n",
            "Confusion Matrix:\n",
            "[[19120   185]\n",
            " [  147  8037]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99     19305\n",
            "           1       0.98      0.98      0.98      8184\n",
            "\n",
            "    accuracy                           0.99     27489\n",
            "   macro avg       0.98      0.99      0.99     27489\n",
            "weighted avg       0.99      0.99      0.99     27489\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 4. Evaluate Random Forest\n",
        "print(\"\\nRandom Forest Evaluation:\")\n",
        "rf_accuracy = accuracy_score(y_test, rf_predictions)\n",
        "rf_conf_matrix = confusion_matrix(y_test, rf_predictions)\n",
        "rf_class_report = classification_report(y_test, rf_predictions)\n",
        "\n",
        "print(f\"Accuracy: {rf_accuracy:.4f}\")\n",
        "print(f\"Confusion Matrix:\\n{rf_conf_matrix}\")\n",
        "print(f\"Classification Report:\\n{rf_class_report}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u52OeB1TCxAY",
        "outputId": "e93c5e97-4bba-41dc-8bc5-e2dca9e407ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Random Forest Evaluation:\n",
            "Accuracy: 0.9919\n",
            "Confusion Matrix:\n",
            "[[19184   121]\n",
            " [  102  8082]]\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.99      0.99      0.99     19305\n",
            "           1       0.99      0.99      0.99      8184\n",
            "\n",
            "    accuracy                           0.99     27489\n",
            "   macro avg       0.99      0.99      0.99     27489\n",
            "weighted avg       0.99      0.99      0.99     27489\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 5. Evaluate KMeans clustering model\n",
        "from sklearn.metrics import adjusted_rand_score\n",
        "\n",
        "kmeans_predictions = kmeans.predict(X_test_scaled)\n",
        "kmeans_adjusted_rand = adjusted_rand_score(y_test, kmeans_predictions)\n",
        "\n",
        "print(\"\\nKMeans Clustering Evaluation:\")\n",
        "print(f\"Adjusted Rand Index (ARI): {kmeans_adjusted_rand:.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkZTbBMKC0_s",
        "outputId": "f254746f-9a72-4a49-f5a6-23c02bd06cfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "KMeans Clustering Evaluation:\n",
            "Adjusted Rand Index (ARI): 0.4811\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Comparison:**"
      ],
      "metadata": {
        "id": "e9HX0ZpXRhkj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Best Overall Model:**\n",
        "\n",
        "* The Random Forest achieves the best overall accuracy (99.19%) among the models.\n",
        "\n",
        "**Consistency of Results Cross-Validation vs. Train-Test Split:**\n",
        "\n",
        "The Decision Tree and Random Forest both show consistent performance between simple data splits and 5-fold cross-validation.\n",
        "\n",
        "1. Decision Tree:\n",
        "\n",
        "* Simple split accuracy: 98.79% (0.9879)\n",
        "* Cross-validation average accuracy: 98.65% (0.9865)\n",
        "\n",
        "2. Random Forest:\n",
        "\n",
        "* Simple split accuracy: 99.19% (0.9919)\n",
        "* Cross-validation average accuracy: 99.04% (0.9904)\n",
        "\n",
        "The results are consistent between the simple data split and cross-validation for both models. This confirms their robustness and reliable performance on the dataset.\n",
        "\n",
        "**KMeans Effectiveness:**\n",
        "\n",
        "* KMeans does not effectively differentiate between malware and legitimate files compared to supervised models. This is expected because KMeans relies solely on feature distribution, and the data likely has overlapping regions that are better separated by supervised learning.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "sKEkySCurCug"
      }
    }
  ]
}