phishing_email_detection.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Safe or Phishing Email?\n",
        "\n",
        "## Connecting to Google Drive"
      ],
      "metadata": {
        "id": "CqIGjfg75rSX"
      }
    },
    {
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## NLTK settings"
      ],
      "metadata": {
        "id": "vuqwnS8v5uYc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "nltk.download('wordnet')"
      ],
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        {
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          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
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              "True"
            ]
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    {
      "cell_type": "markdown",
      "source": [
        "## Libraries importing"
      ],
      "metadata": {
        "id": "vG-Z_dwC5yZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import stopwords\n",
        "import re\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.linear_model import SGDClassifier, LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, ConfusionMatrixDisplay, f1_score\n",
        "from sklearn.svm import SVC"
      ],
      "metadata": {
        "id": "iKBEhp064G92"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dataset loading"
      ],
      "metadata": {
        "id": "qO9eVL1G5xQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_csv('/content/drive/MyDrive/.../Phishing_Email.csv')\n",
        "data = data.dropna()\n",
        "data = data.drop('Unnamed: 0', axis=1)\n",
        "\n",
        "data.info()"
      ],
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        "outputId": "5d9968ca-00c9-4f16-c8b1-b40ad2ad6959"
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 18634 entries, 0 to 18649\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Email Text  18634 non-null  object\n",
            " 1   Email Type  18634 non-null  object\n",
            "dtypes: object(2)\n",
            "memory usage: 436.7+ KB\n"
          ]
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      "source": [
        "data.head()"
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              "                                          Email Text      Email Type\n",
              "0  re : 6 . 1100 , disc : uniformitarianism , re ...      Safe Email\n",
              "1  the other side of * galicismos * * galicismo *...      Safe Email\n",
              "2  re : equistar deal tickets are you still avail...      Safe Email\n",
              "3  \\nHello I am your hot lil horny toy.\\n    I am...  Phishing Email\n",
              "4  software at incredibly low prices ( 86 % lower...  Phishing Email"
            ],
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      "source": [
        "## Data cleaning"
      ],
      "metadata": {
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      "cell_type": "code",
      "source": [
        "def text_preprocessing(text):\n",
        "  wn = WordNetLemmatizer()\n",
        "  text = re.sub('[^a-zA-Z]', ' ', text)\n",
        "  text = text.lower()\n",
        "  text = text.split()\n",
        "  text = [word for word in text if word not in stopwords.words('english')]\n",
        "  text = [wn.lemmatize(word) for word in text]\n",
        "  text = ' '.join(text)\n",
        "  return text"
      ],
      "metadata": {
        "id": "wpHV2ts54srM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data['Email Text'] = data['Email Text'].apply(text_preprocessing)"
      ],
      "metadata": {
        "id": "PBE0rz9d5YTA"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "data['Email Type'].replace('Safe Email', 0, inplace=True)\n",
        "data['Email Type'].replace('Phishing Email', 1, inplace=True)"
      ],
      "metadata": {
        "id": "m-6iMn5W-Sn6"
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      "execution_count": null,
      "outputs": []
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        "data"
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            "text/plain": [
              "                                              Email Text  Email Type\n",
              "0      disc uniformitarianism sex lang dick hudson ob...           0\n",
              "1      side galicismos galicismo spanish term name im...           0\n",
              "2      equistar deal ticket still available assist ro...           0\n",
              "3      hello hot lil horny toy one dream open minded ...           1\n",
              "4      software incredibly low price lower drapery se...           1\n",
              "...                                                  ...         ...\n",
              "18645  date lonely housewife always wanted date lonel...           1\n",
              "18646  request submitted access request anita dupont ...           0\n",
              "18647  important prc mtg hi dorn john discovered rece...           0\n",
              "18648  press clipping letter californian utility plea...           0\n",
              "18649                                              empty           1\n",
              "\n",
              "[18634 rows x 2 columns]"
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              "      <td>hello hot lil horny toy one dream open minded ...</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>18648</th>\n",
              "      <td>press clipping letter californian utility plea...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18649</th>\n",
              "      <td>empty</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>18634 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7bfb027b-5a71-4025-91c2-9081cf311e52')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7bfb027b-5a71-4025-91c2-9081cf311e52 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-7bfb027b-5a71-4025-91c2-9081cf311e52');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-665c88fc-f3d2-44c4-8efb-b2a537dbc969\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-665c88fc-f3d2-44c4-8efb-b2a537dbc969')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-665c88fc-f3d2-44c4-8efb-b2a537dbc969 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train - validation - test split"
      ],
      "metadata": {
        "id": "KtPvBxGv56N0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data['Email Text']\n",
        "y = data['Email Type']\n",
        "X_train, X_validation, y_train, y_validation = train_test_split(X, y, test_size=0.2, random_state=17)\n",
        "X_validation, X_test, y_validation, y_test = train_test_split(X_validation, y_validation, test_size=0.2, random_state=17)\n",
        "\n",
        "print('Training Data : ', X_train.shape)\n",
        "print('Validation Data : ', X_validation.shape)\n",
        "print('Test Data : ', X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izosJA1N57El",
        "outputId": "b2c5494a-660e-4987-9ef7-1b5deb979c61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training Data :  (14907,)\n",
            "Validation Data :  (2981,)\n",
            "Test Data :  (746,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Extraction - TFIDF - Training"
      ],
      "metadata": {
        "id": "qvn8JylF9m_e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "pipeline = Pipeline([ ('vect', CountVectorizer()),\n",
        "                      ('tfidf', TfidfTransformer()),\n",
        "                      ('clf', MLPClassifier(max_iter=100)),\n",
        "                    ])\n",
        "\n",
        "email_clf = pipeline.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "PnV2jGee9VM-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Validation-set performance"
      ],
      "metadata": {
        "id": "nB9sHHywQSuk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = email_clf.predict(X_validation)\n",
        "\n",
        "accuracy = accuracy_score(y_validation, y_pred)\n",
        "precision = precision_score(y_validation, y_pred)\n",
        "recall = recall_score(y_validation, y_pred)\n",
        "f1 = f1_score(y_validation, y_pred, average='weighted')\n",
        "\n",
        "print(\"Accuracy: \", accuracy)\n",
        "print(\"Precision: \", precision)\n",
        "print(\"Recall: \", recall)\n",
        "print(\"f1: \", f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MOagPqv39kXh",
        "outputId": "24e05eb7-dafe-4982-fdf0-81301d0ccc26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy:  0.9792016101979202\n",
            "Precision:  0.9653505237711523\n",
            "Recall:  0.9843878389482333\n",
            "f1:  0.9792319209469044\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Test-set performance"
      ],
      "metadata": {
        "id": "kNLg8EzcQWzf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = email_clf.predict(X_test)\n",
        "\n",
        "accuracy = accuracy_score(y_test, y_pred)\n",
        "precision = precision_score(y_test, y_pred)\n",
        "recall = recall_score(y_test, y_pred)\n",
        "\n",
        "print(\"Accuracy:\", accuracy)\n",
        "print(\"Precision:\", precision)\n",
        "\n",
        "print(\"Recall:\", recall)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "509DJAxR-CnM",
        "outputId": "86a5dc8f-0d46-41d6-8085-05d90a379a47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.9731903485254692\n",
            "Precision: 0.9562043795620438\n",
            "Recall: 0.9703703703703703\n"
          ]
        }
      ]
    }
  ]
}