AIML Phishing Attack model comparison.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e1b14e90",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "70eadbd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam = pd.read_csv('Phishing_Email.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0c0091a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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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>Unnamed: 0</th>\n",
       "      <th>Email Text</th>\n",
       "      <th>Email Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>re : 6 . 1100 , disc : uniformitarianism , re ...</td>\n",
       "      <td>Safe Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>the other side of * galicismos * * galicismo *...</td>\n",
       "      <td>Safe Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>re : equistar deal tickets are you still avail...</td>\n",
       "      <td>Safe Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>\\nHello I am your hot lil horny toy.\\n    I am...</td>\n",
       "      <td>Phishing Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>software at incredibly low prices ( 86 % lower...</td>\n",
       "      <td>Phishing Email</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                         Email Text  \\\n",
       "0           0  re : 6 . 1100 , disc : uniformitarianism , re ...   \n",
       "1           1  the other side of * galicismos * * galicismo *...   \n",
       "2           2  re : equistar deal tickets are you still avail...   \n",
       "3           3  \\nHello I am your hot lil horny toy.\\n    I am...   \n",
       "4           4  software at incredibly low prices ( 86 % lower...   \n",
       "\n",
       "       Email Type  \n",
       "0      Safe Email  \n",
       "1      Safe Email  \n",
       "2      Safe Email  \n",
       "3  Phishing Email  \n",
       "4  Phishing Email  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3da52036",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam = spam.drop(spam.columns[[0]], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "147f0b2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Email Text</th>\n",
       "      <th>Email Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>re : 6 . 1100 , disc : uniformitarianism , re ...</td>\n",
       "      <td>Safe Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>the other side of * galicismos * * galicismo *...</td>\n",
       "      <td>Safe Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>re : equistar deal tickets are you still avail...</td>\n",
       "      <td>Safe Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\nHello I am your hot lil horny toy.\\n    I am...</td>\n",
       "      <td>Phishing Email</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>software at incredibly low prices ( 86 % lower...</td>\n",
       "      <td>Phishing Email</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                          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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fc9084d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def swap_columns(df, col1, col2):\n",
    "    col_list = list(df.columns)\n",
    "    x, y = col_list.index(col1), col_list.index(col2)\n",
    "    col_list[y], col_list[x] = col_list[x], col_list[y]\n",
    "    df = df[col_list]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9ed9d971",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam = swap_columns(spam, 'Email Text', 'Email Type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8c75608d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Email Type</th>\n",
       "      <th>Email Text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Safe Email</td>\n",
       "      <td>re : 6 . 1100 , disc : uniformitarianism , re ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Safe Email</td>\n",
       "      <td>the other side of * galicismos * * galicismo *...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Safe Email</td>\n",
       "      <td>re : equistar deal tickets are you still avail...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Phishing Email</td>\n",
       "      <td>\\nHello I am your hot lil horny toy.\\n    I am...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Phishing Email</td>\n",
       "      <td>software at incredibly low prices ( 86 % lower...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Email Type                                         Email Text\n",
       "0      Safe Email  re : 6 . 1100 , disc : uniformitarianism , re ...\n",
       "1      Safe Email  the other side of * galicismos * * galicismo *...\n",
       "2      Safe Email  re : equistar deal tickets are you still avail...\n",
       "3  Phishing Email  \\nHello I am your hot lil horny toy.\\n    I am...\n",
       "4  Phishing Email  software at incredibly low prices ( 86 % lower..."
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9b08838e",
   "metadata": {},
   "outputs": [],
   "source": [
    "spam = spam.dropna(how='any',axis=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fd9a8775",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = spam['Email Text']\n",
    "y = spam[\"Email Type\"]\n",
    "z_train, z_test,y_train, y_test = train_test_split(z,y,test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9b69af97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    re : 6 . 1100 , disc : uniformitarianism , re ...\n",
       "1    the other side of * galicismos * * galicismo *...\n",
       "2    re : equistar deal tickets are you still avail...\n",
       "3    \\nHello I am your hot lil horny toy.\\n    I am...\n",
       "4    software at incredibly low prices ( 86 % lower...\n",
       "Name: Email Text, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1413b93c",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer()\n",
    "features = cv.fit_transform(z_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23c99942",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = svm.SVC()\n",
    "model.fit(features,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0bc20a61",
   "metadata": {},
   "outputs": [],
   "source": [
    "features_test = cv.transform(z_test)\n",
    "svc_acc=model.score(features_test,y_test)\n",
    "print(\"Accuracy: {}\".format(svc_acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8da86b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, ConfusionMatrixDisplay\n",
    "from sklearn.model_selection import RandomizedSearchCV, train_test_split\n",
    "from scipy.stats import randint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc3a8c20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()\n",
    "rf.fit(features, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2549cb81",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = rf.predict(features_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b6a9cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", rf_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "644766ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6c4fca0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = spam['Email Text']\n",
    "y = spam['Email Type']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8f15f21b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(max_features=1000)  # You can adjust max_features as needed\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "708d61f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb_classifier = GradientBoostingClassifier(n_estimators=100, random_state=42)\n",
    "gb_classifier.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21badbaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = gb_classifier.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8573a6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Evaluate the classifier\n",
    "grad_acc = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {grad_acc}')\n",
    "\n",
    "# Display classification report\n",
    "report = classification_report(y_test, y_pred)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b2a33745",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8f22e24a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = spam['Email Text']\n",
    "y = spam[\"Email Type\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "29491bff",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2008068",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "X_train_vec = vectorizer.fit_transform(X_train)\n",
    "X_test_vec = vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaf8afed",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = MultinomialNB()\n",
    "clf.fit(X_train_vec, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ed260e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bc99a38c",
   "metadata": {},
   "outputs": [],
   "source": [
    "NB_acc = accuracy_score(y_test, y_pred)\n",
    "classification_rep = classification_report(y_test, y_pred)\n",
    "\n",
    "print(f'Accuracy: {NB_acc}')\n",
    "print('Classification Report:')\n",
    "print(classification_rep)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a21fe57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "87e0a515",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "X_train_tfidf = tfidf_vectorizer.fit_transform(X_train)\n",
    "X_test_tfidf = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee575e44",
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 3 \n",
    "knn_classifier = KNeighborsClassifier(n_neighbors=k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c471bf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_classifier.fit(X_train_tfidf, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ce3d737",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = knn_classifier.predict(X_test_tfidf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36105693",
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_acc = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy: {knn_acc}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0c5ffb45",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, log_loss\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6d0ef553",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cf28120f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e559c028",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "X_train_vec = vectorizer.fit_transform(X_train)\n",
    "X_test_vec = vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8b9a3879",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
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      ],
      "text/plain": [
       "LogisticRegression(random_state=42)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LogisticRegression(random_state=42)\n",
    "clf.fit(X_train_vec, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "df2c9955",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81e2959d",
   "metadata": {},
   "outputs": [],
   "source": [
    "logreg_acc = accuracy_score(y_test, y_pred)\n",
    "precision = precision_score(y_test, y_pred, average='weighted')\n",
    "\n",
    "# Print evaluation metrics\n",
    "print('Accuracy:', logreg_acc)\n",
    "print('Precision:', precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff77e66",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Support Vector\",svc_acc)\n",
    "print(\"Random Forest\",rf_acc)\n",
    "print(\"Grad Boost\",grad_acc)\n",
    "print(\"Naive Bayes\",NB_acc)\n",
    "print(\"KNN\",knn_acc)\n",
    "print(\"Log reg\",logreg_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18408f95",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9adebbcd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tabulate import tabulate\n",
    "\n",
    "data = [\n",
    "    [\"Support Vector\", svc_acc*100],\n",
    "    [\"Random Forest\", rf_acc*100],\n",
    "    [\"Grad Boost\", grad_acc*100],\n",
    "    [\"Naive Bayes\", NB_acc*100],\n",
    "    [\"KNN\", knn_acc*100],\n",
    "    [\"Log reg\", logreg_acc*100]\n",
    "]\n",
    "\n",
    "headers = [\"Algorithm\", \"Accuracy\"]\n",
    "\n",
    "table = tabulate(data, headers, tablefmt=\"grid\")\n",
    "\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b17c83d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['model.pkl']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import joblib\n",
    "model = clf.fit(X_train_vec, y_train)\n",
    "\n",
    "joblib.dump(vectorizer, 'vectorizer.pkl')\n",
    "joblib.dump(model, 'model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8c2b4375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Safe Email' 'Phishing Email' 'Phishing Email' 'Safe Email']\n"
     ]
    }
   ],
   "source": [
    "X_test=[\"Today is Monday\",\"Hello wolrd buy our credit card\",\"This is a safe email.\",\"Your meeting is at 10 pm.\"]\n",
    "\n",
    "X_test_vec = vectorizer.transform(X_test)\n",
    "\n",
    "print(clf.predict(X_test_vec))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "251187fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict(X_test_vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00cbef7e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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