IS_PROJECT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>having_IPhaving_IP_Address</th>\n",
       "      <th>URLURL_Length</th>\n",
       "      <th>Shortining_Service</th>\n",
       "      <th>having_At_Symbol</th>\n",
       "      <th>double_slash_redirecting</th>\n",
       "      <th>Prefix_Suffix</th>\n",
       "      <th>having_Sub_Domain</th>\n",
       "      <th>SSLfinal_State</th>\n",
       "      <th>Domain_registeration_length</th>\n",
       "      <th>...</th>\n",
       "      <th>popUpWidnow</th>\n",
       "      <th>Iframe</th>\n",
       "      <th>age_of_domain</th>\n",
       "      <th>DNSRecord</th>\n",
       "      <th>web_traffic</th>\n",
       "      <th>Page_Rank</th>\n",
       "      <th>Google_Index</th>\n",
       "      <th>Links_pointing_to_page</th>\n",
       "      <th>Statistical_report</th>\n",
       "      <th>Result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>...</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  having_IPhaving_IP_Address  URLURL_Length  Shortining_Service  \\\n",
       "0      1                          -1              1                   1   \n",
       "1      2                           1              1                   1   \n",
       "2      3                           1              0                   1   \n",
       "3      4                           1              0                   1   \n",
       "4      5                           1              0                  -1   \n",
       "\n",
       "   having_At_Symbol  double_slash_redirecting  Prefix_Suffix  \\\n",
       "0                 1                        -1             -1   \n",
       "1                 1                         1             -1   \n",
       "2                 1                         1             -1   \n",
       "3                 1                         1             -1   \n",
       "4                 1                         1             -1   \n",
       "\n",
       "   having_Sub_Domain  SSLfinal_State  Domain_registeration_length  ...  \\\n",
       "0                 -1              -1                           -1  ...   \n",
       "1                  0               1                           -1  ...   \n",
       "2                 -1              -1                           -1  ...   \n",
       "3                 -1              -1                            1  ...   \n",
       "4                  1               1                           -1  ...   \n",
       "\n",
       "   popUpWidnow  Iframe  age_of_domain  DNSRecord  web_traffic  Page_Rank  \\\n",
       "0            1       1             -1         -1           -1         -1   \n",
       "1            1       1             -1         -1            0         -1   \n",
       "2            1       1              1         -1            1         -1   \n",
       "3            1       1             -1         -1            1         -1   \n",
       "4           -1       1             -1         -1            0         -1   \n",
       "\n",
       "   Google_Index  Links_pointing_to_page  Statistical_report  Result  \n",
       "0             1                       1                  -1      -1  \n",
       "1             1                       1                   1      -1  \n",
       "2             1                       0                  -1      -1  \n",
       "3             1                      -1                   1      -1  \n",
       "4             1                       1                   1       1  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11055, 32)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 1    6157\n",
       "-1    4898\n",
       "Name: Result, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Result'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data['Result'].replace({1 :'Legitimate', -1 :'Phishing'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Phishing\n",
       "1          Phishing\n",
       "2          Phishing\n",
       "3          Phishing\n",
       "4        Legitimate\n",
       "            ...    \n",
       "11050    Legitimate\n",
       "11051      Phishing\n",
       "11052      Phishing\n",
       "11053      Phishing\n",
       "11054      Phishing\n",
       "Name: Result, Length: 11055, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data['Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18607c26b08>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#sns.countplot(data['Result'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.replace({-1:2}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x18608427288>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(data.isnull())\n",
    "#this heatmap represents the presence of no null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11055 entries, 0 to 11054\n",
      "Data columns (total 32 columns):\n",
      "index                          11055 non-null int64\n",
      "having_IPhaving_IP_Address     11055 non-null int64\n",
      "URLURL_Length                  11055 non-null int64\n",
      "Shortining_Service             11055 non-null int64\n",
      "having_At_Symbol               11055 non-null int64\n",
      "double_slash_redirecting       11055 non-null int64\n",
      "Prefix_Suffix                  11055 non-null int64\n",
      "having_Sub_Domain              11055 non-null int64\n",
      "SSLfinal_State                 11055 non-null int64\n",
      "Domain_registeration_length    11055 non-null int64\n",
      "Favicon                        11055 non-null int64\n",
      "port                           11055 non-null int64\n",
      "HTTPS_token                    11055 non-null int64\n",
      "Request_URL                    11055 non-null int64\n",
      "URL_of_Anchor                  11055 non-null int64\n",
      "Links_in_tags                  11055 non-null int64\n",
      "SFH                            11055 non-null int64\n",
      "Submitting_to_email            11055 non-null int64\n",
      "Abnormal_URL                   11055 non-null int64\n",
      "Redirect                       11055 non-null int64\n",
      "on_mouseover                   11055 non-null int64\n",
      "RightClick                     11055 non-null int64\n",
      "popUpWidnow                    11055 non-null int64\n",
      "Iframe                         11055 non-null int64\n",
      "age_of_domain                  11055 non-null int64\n",
      "DNSRecord                      11055 non-null int64\n",
      "web_traffic                    11055 non-null int64\n",
      "Page_Rank                      11055 non-null int64\n",
      "Google_Index                   11055 non-null int64\n",
      "Links_pointing_to_page         11055 non-null int64\n",
      "Statistical_report             11055 non-null int64\n",
      "Result                         11055 non-null object\n",
      "dtypes: int64(31), object(1)\n",
      "memory usage: 2.7+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop('Result', axis=1)\n",
    "y = data['Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "########################################  DATA PRE-PROCESSING #####################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_selection import SelectKBest, chi2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = SelectKBest(score_func=chi2, k='all')\n",
    "fs.fit(X_train, y_train)\n",
    "X_train_fs = fs.transform(X_train)\n",
    "X_test_fs = fs.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature 0: 916.345903\n",
      "Feature 1: 11.191841\n",
      "Feature 2: 1.220830\n",
      "Feature 3: 2.565374\n",
      "Feature 4: 2.366921\n",
      "Feature 5: 0.698187\n",
      "Feature 6: 56.722424\n",
      "Feature 7: 33.428590\n",
      "Feature 8: 138.979136\n",
      "Feature 9: 51.286348\n",
      "Feature 10: 0.003062\n",
      "Feature 11: 1.181231\n",
      "Feature 12: 1.364872\n",
      "Feature 13: 88.415885\n",
      "Feature 14: 2238.666182\n",
      "Feature 15: 197.181616\n",
      "Feature 16: 51.419478\n",
      "Feature 17: 0.533514\n",
      "Feature 18: 2.449819\n",
      "Feature 19: 4.116745\n",
      "Feature 20: 1.334219\n",
      "Feature 21: 0.058313\n",
      "Feature 22: 0.002525\n",
      "Feature 23: 0.059811\n",
      "Feature 24: 19.736243\n",
      "Feature 25: 6.873871\n",
      "Feature 26: 1.249762\n",
      "Feature 27: 9.553555\n",
      "Feature 28: 14.340002\n",
      "Feature 29: 35.398027\n",
      "Feature 30: 5.145421\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(fs.scores_)):\n",
    "    print('Feature %d: %f' % (i, fs.scores_[i]))\n",
    "# plot the scores\n",
    "plt.figure(figsize = (15,8))\n",
    "plt.bar([i for i in range(len(fs.scores_))], fs.scores_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6,7,8,9,13,14,15,16,24,29 (Selected features)\n",
    "new_data = data.filter(['Prefix_Suffix','having_Sub_Domain','SSLfinal_State','Domain_registeration_length','Request_URL',\n",
    "                        'URL_of_Anchor','Links_in_tags','SFH','age_of_domain','Links_pointing_to_page','Result'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11055 entries, 0 to 11054\n",
      "Data columns (total 11 columns):\n",
      "Prefix_Suffix                  11055 non-null int64\n",
      "having_Sub_Domain              11055 non-null int64\n",
      "SSLfinal_State                 11055 non-null int64\n",
      "Domain_registeration_length    11055 non-null int64\n",
      "Request_URL                    11055 non-null int64\n",
      "URL_of_Anchor                  11055 non-null int64\n",
      "Links_in_tags                  11055 non-null int64\n",
      "SFH                            11055 non-null int64\n",
      "age_of_domain                  11055 non-null int64\n",
      "Links_pointing_to_page         11055 non-null int64\n",
      "Result                         11055 non-null object\n",
      "dtypes: int64(10), object(1)\n",
      "memory usage: 950.2+ KB\n"
     ]
    }
   ],
   "source": [
    "new_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = new_data.drop('Result', axis=1)\n",
    "y = new_data['Result']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################################  LOGISTIC REGRESSION  #######################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "logmodel = LogisticRegression()\n",
    "classifier = logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = logmodel.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Legitimate       0.82      0.86      0.84      1818\n",
      "    Phishing       0.82      0.77      0.80      1499\n",
      "\n",
      "    accuracy                           0.82      3317\n",
      "   macro avg       0.82      0.82      0.82      3317\n",
      "weighted avg       0.82      0.82      0.82      3317\n",
      "\n",
      "[[1572  246]\n",
      " [ 346 1153]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix\n",
    "print(classification_report(y_test,pred))\n",
    "print(confusion_matrix(y_test,pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8215254748266506\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "print(accuracy_score(y_test, pred, normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1860c6a9a08>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import plot_confusion_matrix\n",
    "plot_confusion_matrix(classifier, X_test, y_test, cmap='Blues',values_format= '.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "###############################################  BAGGING  ##############################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#number of trees\n",
    "params_rf = {'n_estimators': [50, 100, 200]}\n",
    "rf_gs = GridSearchCV(rf, params_rf, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': 50}\n"
     ]
    }
   ],
   "source": [
    "#best estimator\n",
    "rf_best = rf_gs.best_estimator_\n",
    "print(rf_gs.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "rpred = rf_gs.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Legitimate       0.94      0.95      0.95      1818\n",
      "    Phishing       0.94      0.93      0.94      1499\n",
      "\n",
      "    accuracy                           0.94      3317\n",
      "   macro avg       0.94      0.94      0.94      3317\n",
      "weighted avg       0.94      0.94      0.94      3317\n",
      "\n",
      "[[1735   83]\n",
      " [ 103 1396]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,rpred))\n",
    "print(confusion_matrix(y_test,rpred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9439252336448598\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, rpred, normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x18609271e48>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(rf_gs, X_test, y_test, cmap='Greens',values_format= '.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#################################################  BOOSTING  ###########################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xgboost import XGBClassifier\n",
    "model = XGBClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(X_train, y_train)\n",
    "model_pred = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Legitimate       0.92      0.96      0.94      1818\n",
      "    Phishing       0.95      0.90      0.93      1499\n",
      "\n",
      "    accuracy                           0.93      3317\n",
      "   macro avg       0.94      0.93      0.93      3317\n",
      "weighted avg       0.93      0.93      0.93      3317\n",
      "\n",
      "[[1745   73]\n",
      " [ 146 1353]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,model_pred))\n",
    "print(confusion_matrix(y_test,model_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9339764847753994\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, model_pred, normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1860b153c08>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model, X_test, y_test, cmap='YlOrRd',values_format= '.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#########################################################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "model2 = AdaBoostClassifier(n_estimators = 30, random_state= 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "model2.fit(X_train, y_train)\n",
    "model2_pred= model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "  Legitimate       0.92      0.95      0.93      1818\n",
      "    Phishing       0.94      0.89      0.92      1499\n",
      "\n",
      "    accuracy                           0.93      3317\n",
      "   macro avg       0.93      0.92      0.93      3317\n",
      "weighted avg       0.93      0.93      0.93      3317\n",
      "\n",
      "[[1733   85]\n",
      " [ 158 1341]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,model2_pred))\n",
    "print(confusion_matrix(y_test,model2_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9267410310521556\n"
     ]
    }
   ],
   "source": [
    "print(accuracy_score(y_test, model2_pred, normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1860c66e848>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model2, X_test, y_test, cmap='gray',values_format= '.2f')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}