exploration_kaggle.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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1781, 21)\n"
     ]
    },
    {
     "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>URL</th>\n",
       "      <th>URL_LENGTH</th>\n",
       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
       "      <th>CHARSET</th>\n",
       "      <th>SERVER</th>\n",
       "      <th>CONTENT_LENGTH</th>\n",
       "      <th>WHOIS_COUNTRY</th>\n",
       "      <th>WHOIS_STATEPRO</th>\n",
       "      <th>WHOIS_REGDATE</th>\n",
       "      <th>WHOIS_UPDATED_DATE</th>\n",
       "      <th>...</th>\n",
       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
       "      <th>REMOTE_IPS</th>\n",
       "      <th>APP_BYTES</th>\n",
       "      <th>SOURCE_APP_PACKETS</th>\n",
       "      <th>REMOTE_APP_PACKETS</th>\n",
       "      <th>SOURCE_APP_BYTES</th>\n",
       "      <th>REMOTE_APP_BYTES</th>\n",
       "      <th>APP_PACKETS</th>\n",
       "      <th>DNS_QUERY_TIMES</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M0_109</td>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>iso-8859-1</td>\n",
       "      <td>nginx</td>\n",
       "      <td>263.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>10/10/2015 18:21</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>700</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1153</td>\n",
       "      <td>832</td>\n",
       "      <td>9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B0_2314</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>utf-8</td>\n",
       "      <td>Apache/2.4.10</td>\n",
       "      <td>15087.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1230</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>1265</td>\n",
       "      <td>1230</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B0_911</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>us-ascii</td>\n",
       "      <td>Microsoft-HTTPAPI/2.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B0_113</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>iso-8859-1</td>\n",
       "      <td>nginx</td>\n",
       "      <td>162.0</td>\n",
       "      <td>US</td>\n",
       "      <td>AK</td>\n",
       "      <td>7/10/1997 4:00</td>\n",
       "      <td>12/09/2013 0:45</td>\n",
       "      <td>...</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>3812</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>18784</td>\n",
       "      <td>4380</td>\n",
       "      <td>39</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B0_403</td>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>utf-8</td>\n",
       "      <td>None</td>\n",
       "      <td>124140.0</td>\n",
       "      <td>US</td>\n",
       "      <td>TX</td>\n",
       "      <td>12/05/1996 0:00</td>\n",
       "      <td>11/04/2017 0:00</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4278</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>129889</td>\n",
       "      <td>4586</td>\n",
       "      <td>61</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       URL  URL_LENGTH  NUMBER_SPECIAL_CHARACTERS     CHARSET  \\\n",
       "0   M0_109          16                          7  iso-8859-1   \n",
       "1  B0_2314          16                          6       utf-8   \n",
       "2   B0_911          16                          6    us-ascii   \n",
       "3   B0_113          17                          6  iso-8859-1   \n",
       "4   B0_403          17                          6       utf-8   \n",
       "\n",
       "                  SERVER  CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO  \\\n",
       "0                  nginx           263.0          None           None   \n",
       "1          Apache/2.4.10         15087.0          None           None   \n",
       "2  Microsoft-HTTPAPI/2.0           324.0          None           None   \n",
       "3                  nginx           162.0            US             AK   \n",
       "4                   None        124140.0            US             TX   \n",
       "\n",
       "      WHOIS_REGDATE WHOIS_UPDATED_DATE  ...   DIST_REMOTE_TCP_PORT  \\\n",
       "0  10/10/2015 18:21               None  ...                      0   \n",
       "1              None               None  ...                      7   \n",
       "2              None               None  ...                      0   \n",
       "3    7/10/1997 4:00    12/09/2013 0:45  ...                     22   \n",
       "4   12/05/1996 0:00    11/04/2017 0:00  ...                      2   \n",
       "\n",
       "   REMOTE_IPS  APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  \\\n",
       "0           2        700                   9                  10   \n",
       "1           4       1230                  17                  19   \n",
       "2           0          0                   0                   0   \n",
       "3           3       3812                  39                  37   \n",
       "4           5       4278                  61                  62   \n",
       "\n",
       "   SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  Type  \n",
       "0              1153               832            9              2.0     1  \n",
       "1              1265              1230           17              0.0     0  \n",
       "2                 0                 0            0              0.0     0  \n",
       "3             18784              4380           39              8.0     0  \n",
       "4            129889              4586           61              4.0     0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seeing the data\n",
    "data = pd.read_csv(\"dataset.csv\")\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1565\n",
       "1     216\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#in the dataset, there is 21 columns, the last column is the class label\n",
    "#lets check whether the dataset is imbalance\n",
    "data[\"Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset is highly imbalanced. Only 216 datapoints are malicious or phishing. i.e. only 12% of the data has class label 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature exploration\n",
    "#### 1. URL_LENGTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique values in URL_LENGTH 142\n",
      "mean of URL_LENGTH 56.96125772038181\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of unique values in URL_LENGTH\",len(data[\"URL_LENGTH\"].value_counts()))\n",
    "print(\"mean of URL_LENGTH\",data[\"URL_LENGTH\"].mean())\n",
    "plt.scatter(data[\"URL_LENGTH\"], data[\"Type\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9, 21)\n",
      "1    5\n",
      "0    4\n",
      "Name: Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#from the above plot we can see that if the length of the url is greater than 170, very few are good urls\n",
    "#lets check that stats\n",
    "length_greater_170 = data[data[\"URL_LENGTH\"] > 170]\n",
    "print(length_greater_170.shape)\n",
    "print(length_greater_170[\"Type\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the URL_LENGTH is too large then the chances of that website being suspicious is high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. NUMBER OF SPECIAL CHARACTERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of unique special characters: 31\n",
      "mean of NUMBER OF SPECIAL CHARACTERS 11.11173498034812\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Number of unique special characters:\", len(data[\"NUMBER_SPECIAL_CHARACTERS\"].value_counts()))\n",
    "print(\"mean of NUMBER OF SPECIAL CHARACTERS\",data[\"NUMBER_SPECIAL_CHARACTERS\"].mean())\n",
    "plt.scatter(data[\"NUMBER_SPECIAL_CHARACTERS\"], data[\"Type\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28, 21)\n",
      "1    19\n",
      "0     9\n",
      "Name: Type, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#from the above plot we can see that if the NUMBER OF SPECIAL CHARACTERS is greater than 25, very few are good urls\n",
    "#lets check that stats\n",
    "SPCL_CHARS_25 = data[data[\"NUMBER_SPECIAL_CHARACTERS\"] > 25]\n",
    "print(SPCL_CHARS_25.shape)\n",
    "print(SPCL_CHARS_25[\"Type\"].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If the number of special characters in an url is more than 25, most of cases the url turns out not to be good"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 402.375x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#lets check how these two feature together behaves with the class label\n",
    "sns.FacetGrid(data, hue=\"Type\", size=5) \\\n",
    "   .map(plt.scatter, \"URL_LENGTH\", \"NUMBER_SPECIAL_CHARACTERS\") \\\n",
    "   .add_legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. One thing to be noticed here is, if the URL_LENGTH is greater than 150 and NUMBER OF SPECIAL CHARACTERS is between 15 to 20, then the website is not legitimate.\n",
    "2. Also, if the URL LENGTH is greater than 120 and NUMBER OF SPECIAL CHARACTERS is between 22 to 30, a remarkable number of malicious url are there.\n",
    "Lets check that stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    27\n",
       "0     8\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_120 = data[data[\"URL_LENGTH\"] > 120]\n",
    "data_160 = data_120[data_120[\"URL_LENGTH\"] < 155]\n",
    "data_22 = data_160[data_160[\"NUMBER_SPECIAL_CHARACTERS\"] > 22 ]\n",
    "data_30 = data_22[data_22[\"NUMBER_SPECIAL_CHARACTERS\"] <= 30]\n",
    "data_30[\"Type\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that,\n",
    ">if url_length > 120 && url_length < 155 && number of character > 22 && number of characters < 30:\n",
    ">>                \"77 percent of time the url is malicious\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. CHARSET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique values:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "utf-8           1055\n",
       "iso-8859-1       561\n",
       "us-ascii         155\n",
       "None               7\n",
       "windows-1252       1\n",
       "windows-1251       1\n",
       "ISO-8859           1\n",
       "Name: CHARSET, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"unique values:\")\n",
    "data[\"CHARSET\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "utf-8 (array([0, 1], dtype=int64), array([903, 152], dtype=int64))\n",
      "us-ascii (array([0, 1], dtype=int64), array([151,   4], dtype=int64))\n",
      "iso-8859-1 (array([0, 1], dtype=int64), array([503,  58], dtype=int64))\n",
      "None (array([0, 1], dtype=int64), array([6, 1], dtype=int64))\n",
      "ISO-8859 (array([0], dtype=int64), array([1], dtype=int64))\n",
      "windows-1251 (array([1], dtype=int64), array([1], dtype=int64))\n",
      "windows-1252 (array([0], dtype=int64), array([1], dtype=int64))\n"
     ]
    }
   ],
   "source": [
    "# r_temp1 = data[data[\"CHARSET\"]=='UTF-8']\n",
    "# print(\"UTF-8 : \",np.unique(r_temp1['Type'],return_counts=True))\n",
    "# r_temp1 = data[data[\"CHARSET\"]=='ISO-8859-1']\n",
    "# print('ISO-8859-1',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1,y1=[],[]\n",
    "r_temp1 = data[data[\"CHARSET\"]=='utf-8']\n",
    "print('utf-8',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1.append(np.unique(r_temp1['Type'],return_counts=True)[1][0])\n",
    "y1.append(np.unique(r_temp1['Type'],return_counts=True)[1][1])\n",
    "r_temp1 = data[data[\"CHARSET\"]=='us-ascii']\n",
    "print('us-ascii',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1.append(np.unique(r_temp1['Type'],return_counts=True)[1][0])\n",
    "y1.append(np.unique(r_temp1['Type'],return_counts=True)[1][1])\n",
    "r_temp1 = data[data[\"CHARSET\"]=='iso-8859-1']\n",
    "print('iso-8859-1',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1.append(np.unique(r_temp1['Type'],return_counts=True)[1][0])\n",
    "y1.append(np.unique(r_temp1['Type'],return_counts=True)[1][1])\n",
    "r_temp1 = data[data[\"CHARSET\"]=='None']\n",
    "print('None',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1.append(np.unique(r_temp1['Type'],return_counts=True)[1][0])\n",
    "y1.append(np.unique(r_temp1['Type'],return_counts=True)[1][1])\n",
    "r_temp1 = data[data[\"CHARSET\"]=='ISO-8859']\n",
    "print('ISO-8859',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1.append(np.unique(r_temp1['Type'],return_counts=True)[1][0])\n",
    "y1.append(0)\n",
    "r_temp1 = data[data[\"CHARSET\"]=='windows-1251']\n",
    "print('windows-1251',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1.append(0)\n",
    "y1.append(np.unique(r_temp1['Type'],return_counts=True)[1][0])\n",
    "r_temp1 = data[data[\"CHARSET\"]=='windows-1252']\n",
    "print('windows-1252',np.unique(r_temp1['Type'],return_counts=True))\n",
    "x1.append(np.unique(r_temp1['Type'],return_counts=True)[1][0])\n",
    "y1.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "good_url = data[data[\"Type\"] == 0][\"CHARSET\"]\n",
    "bad_url = data[data[\"Type\"] == 1][\"CHARSET\"]\n",
    "plt.figure(figsize = (15,10))\n",
    "plt.hist([good_url , bad_url])\n",
    "plt.xlabel(\"CHARSET\")\n",
    "plt.ylabel(\"COUNTS\")\n",
    "plt.legend(('Good_url', 'BAD_url'))\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. SERVER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in server:\n",
      "239\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique values in server:\")\n",
    "print(data.SERVER.nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Apache                   386\n",
       "nginx                    211\n",
       "None                     175\n",
       "Microsoft-HTTPAPI/2.0    113\n",
       "cloudflare-nginx          94\n",
       "Name: SERVER, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"SERVER\"].value_counts()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. CONTENT LENGTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eHWD3k4Bb02yyBuD2iPiupGeBxZI+DfwMuDm1vxn4WpoUsIVygBARz0i6HXgW6ASuiIiuNK4PA0uBErAoIp5Jx/p4H32YmVkBsryAbSjwQeBvgIeAWRHxfJaDR8RTwJlV6qvYN8usst4OXNzHsa4DrqtSvxu4O2sfZmZWjCz3cF6gfFbxBWAN8DZJb+vZGBH/WaOxmZlZHckSOPdRniTwtvSpFIADx8zMBpTlHs6f5zAOMzOrc1meFv2FiuWP9tp2Sw3GZGZmdSjLtOjfqVie3WvbWwdxLGZmVscO9NE2fsmamZkdlCyTBhokjaIcTj3LPcFTqtnIzMysrmQJnOOBJ9gXMj+t2OZHwZiZWSZZAud3I2J1zUdiZmZ1Lcs9nG/XfBRmZlb3av4+HDMzM8h2SW28pPl9bYyIjwzieMzMrE5lCZxdlCcNmJmZHbQsgfNSRNxa85GYmVldy3IPp6PmozAzs7qX5QznfZIm9rUxItYM4njMzKxOZQmc71H+Bc/K2WoBjAXG4acNmJlZBlleT3BG5bqkSZRf3/xu4PqajMrMzOpOlns4AEianF5HcA/lWWtTIuJfazUwMzOrLwOe4Ug6HfgE8Bbgs8CciOiq9cDMzKy+ZDnD+TnwTuBB4GzgRknzez797SjpZEkPSFoh6ZmeF7hJOkFSs6SV6XtUqisdt0XSU5LOqjjW7NR+paTZFfV3SHo67TNfkvrrw8zMipElcD4IXAM8RvlSWu9PfzqB/x0RvwlMBa6QNAW4Grg/IiYD96d1gJnA5PSZCyyAcnikMZxDOfSuqQiQBaltz34zUr2vPszMrABZJg3s/aVPSceUS7Ejy8Ejog1oS8vbJa0AxgOzgHNTs1uBZZQnIswCbouIAB6RNFLSSaltc0RsSeNoBmZIWgYcFxEPp/ptwEWU7zP11YeZmRUg06QBSZdLWgOsBtZIWi3prw+kozS77UzgUeDEFEY9oTQuNRsPrK3YrTXV+qu3VqnTTx9mZlaAAQNH0v8BLgTOjYjRETEaOA+YmbYNKJ0Z3QlcFRGv9Ne0Sq337wBlqWcmaa6k5ZKWb9q06UB2NTOzA5DlDOf9wHsjYlVPIS2/D7hsoJ0lDaUcNv8eEf+ZyhvSpTLS98ZUbwVOrth9ArB+gPqEKvX++thPRCyMiKaIaBo7duxAP46ZmR2kTJfUIqK9Sm0X0N3ffmnG2M3Aioj4fMWmJUDPTLPZwF0V9cvSbLWpwMvpcthSYLqkUWmywHRgadq2XdLU1NdlvY5VrQ8zMytAlkfbtEqaFhH3VxYlTSNNCOjHb1M+Q3pa0pOp9o/APOB2SXOANcDFadvdwAVAC7AT+ABARGyRdC3weGr3qZ4JBMDlwC3ACMqTBe5J9b76MDOzAmQJnI8Ad0l6iPI06AB+i3KYzOpvx4h4iL7fGDqtSvsArujjWIuARVXqy4HTq9RfqtaHmZkVI0vg7Ab+HHgT5acNCPgR5Utlr7nUZmZmVk2WwPkC8I/pDGMvSU1p24W1GJiZmdWXLJMGJkXEU72L6VLWpEEfkZmZ1aUsgTO8n20jBmsgZmZW37IEzuOSPtS7mGZ/DfQsNTMzMyDbPZyrgG9L+lP2BUwT0Ai8p1YDMzOz+pLl4Z0bgP8h6Tz2TT/+XkT8oKYjMzOzupLlDAeAiHgAeKCGYzEzszqW+RXTZmZmh8KBY2ZmuXDgmJlZLhw4ZmaWCweOmZnlwoFjZma5cOCYmVkuHDhmZpYLB46ZmeXCgWNmZrlw4JiZWS4cOGZmlouaBo6kRZI2SvpFRe0ESc2SVqbvUakuSfMltUh6StJZFfvMTu1XSppdUX+HpKfTPvMlqb8+zMysOLU+w7kFmNGrdjVwf0RMBu5P6wAzgcnpMxdYAOXwAK4BzgHOBq6pCJAFqW3PfjMG6MPMzApS08CJiB8BW3qVZwG3puVbgYsq6rdF2SPASEknAecDzRGxJSK2As3AjLTtuIh4OCICuK3Xsar1YWZmBSniHs6JEdEGkL7Hpfp4YG1Fu9ZU66/eWqXeXx9mZlaQw2nSgKrU4iDqB9apNFfScknLN23adKC7m5lZRkUEzoZ0OYz0vTHVW4GTK9pNANYPUJ9Qpd5fH68REQsjoikimsaOHXvQP5SZmfWviMBZAvTMNJsN3FVRvyzNVpsKvJwuhy0FpksalSYLTAeWpm3bJU1Ns9Mu63Wsan2YmVlBhtTy4JK+CZwLjJHUSnm22TzgdklzgDXAxan53cAFQAuwE/gAQERskXQt8Hhq96mI6JmIcDnlmXAjgHvSh376MDOzgtQ0cCLi0j42TavSNoAr+jjOImBRlfpy4PQq9Zeq9WFmZsU5nCYNmJlZHXPg2KArn6yame3PgWODrr29veghmNlhyIFjg27nzp1FD8HMDkMOHBt0lYHT2dlZ4EjM7HDiwLFBt2vXrr3LPtsxsx4OHBt0O3bs2LvswDGzHg4cG3SVgfPqq68WOBIzO5w4cGzQbdmy740U27ZtK3AkZnY4ceDYoNu8efPeZT+B28x6OHBs0LW1tXHinj0MSctmZlDjZ6nZkal1zRrG7dpFQwTr1q0rejhmdphw4Nig2r17N+va2ji9vZ3G7m5eaGkpekhmdpjwJTUbVCtWrCAiaB0+nJN37WLTli28+OKLRQ/LzA4DDhwbVM3NzTREsKtU4qxXXkHAfffdV/SwzPZ6+umnmT9/Pi+//HLRQzniOHBs0DzxxBM89OCDHNvZiYBRe/YwZft2lnznO7zwwgtFD8+MDRs28IlPfILm5mauv/7TRQ/niOPAsUHR0tLCvOuv5/Xt7Yzu6Nhb/+N16xi2cyef/Kd/YuPGjQWO0AyGDBmy9/UZRx99dMGjOfI4cOyQdHV18Z3vfIe//9jHOGrXLv7qhRf2+0M1qrOTy1etYufWrXz0yitZtmyZ35djhRk9ejQjRowA4MILZxU8miOPA8cO2rp167j64x/n5ptv5s1bt/K3K1dy35gxtA4fTuvw4Xzx1FO583Wv4/W7d/N3K1cy5qWXuOGGG7j+uuvYunVr0cO3I9Bzzz239+Gy//qvX/Q/fnLmwLED1t3dzZIlS/jIlVey5rnnuGztWj60ejXHd3bSOmIE7aUS7aUSLUcfTWv61+SJHR38zfPPc1FbG8sffZQrLr+cBx980P/DWy527NjBsmXL+OIXb6ShAS64YAObNr3EvHnzeOaZZ+jq6ip6iEeEuv49HEkzgC8CJeCmiJhX8JB+7W3cuJEbPvc5nl2xgtO3b+eSdes4PuM7bxqAaS+9xFu2b+frEyfy2c9+locefJAPX3klxx57bG0HbkeUjRs3smzZMlpaWli9+gXa2l4kAo46qovXvW4Pv/zlMZxwQgePPvpjfvKTn9DYOJRTTpnIpEmn8eY3v5nzzjuPoUOHFv1j1J26DRxJJeBLwO8BrcDjkpZExLPFjuzX28KFC3n+2Wf5s3XrOHvbNtRr+66GBoYPH8706dO5995793s3To/XdXTwNy0t/GDMGL738MOMO/FE5syZk88PYIe9iKCzs5Ouri66urr2Lnd2dtLZ2cmePXvYs2cPnZ2ddHR00NHRwe7du9m9eze7du3i1Vdf5etf/zoAY8Z08vrX7+DMM9uZPHkH3/3uibS07JsscNppOzj33JdYteoo1q/fSnPz8zQ3N/O1r32N97znPRx11FGMGDGCYcOG0djYSGNjI0OHDqWxsZEhQ4a85lMqlSiVSgwZMoSGhgak3v+HHNnqNnCAs4GWiFgFIGkxMAtw4ByClc89x1u3beOcPp4CvatUYvr06XzoQx8iIvjxnXdWbVcCfm/zZp4+/nhW/upXNRyxQfkv8WXLltHW1oak/S5l9qxHBF1dXXR3d+/9y77n01PL8t3d3bNP5b6dvY69r115uZuurm46O7sG7TLrW9/6Cn/xF2uo/Dt/1679/0G0e/cuzjzzFc488xUAXnyxkZtuOpUNG7bx1a9+9ZDHUCo1UCo10NDQsDeMyrXSfrWGhgYaGkqUSkP21svrldsbqi73Pla1Y/cEX89/65717u5u3vnOd3Laaacd8s+aRT0HznhgbcV6K3BO70aS5gJzASZOnJjPyH5NdXV18erOnfzquOP4xSuvcPr27a9pM6Kri3vvvZeIoLm5mdF9XBsP4OFRo2gbPpwxfoVBzT311FN8/vOfL3oYB6xU6mbo0KCxsZvGxmDo0G6GDu1m2LDyZ/jw8veIEV0MH97NMcd0cswxnQwbVt5v7drhDB0alErlENuxY/9/ED344J1s3NhIBHR1iY6OBv74j9fS2dnAzp0ltm8vsXNniV27SrS3l9i9u4H29gZ2725gz54GOjqUvsvLnZ0NdHfvS7iurnKQHs4WL17Mf/3Xf+XSVz0HTrVz2df80ykiFgILAZqamnwHux+lUol/mTeP+TfeyJdLJZq2beM9bW0cVxEqI7q7aW9v3/sHeET3a/9n29TYyOLx4/nV0Udz+pQpXPnRj+b2MxypzjjjDK666ira2tro7u7u83JP5dnN/mct3a9Z772t96fnLKfnrGfftq69ZzT7L792e1dXNx0d3bS3D97/mpX/IGpvb+Taa980KMftOZtpbGyoOAvZt7z/GUq1s5bKM53Sfvv19endrvIsp/fZTaWI2Pvn4JxzXvPv8Jqp58BpBU6uWJ8ArC9oLHXjTW96EzfOn88dd9zBtxYv5qmRI/mdTZuYtnkzx3R1MaHXPZvK9S1Dh/L9sWN59IQTGDZsGH89Zw7nn38+DQ2eLFlrDQ0NTJs2rehhHLSevyB7X+qrvL/T1dW1997Onj179t7f6ejo2DsL7YEHHmDt2rX88Ic/5Nhjj2XKlCmcd955AHvvzfTcp+n59L4/U3mfpvIvfN+vGZjqdVqqpCHAr4BpwDrgceBPIuKZvvZpamqK5cuX5zTCX3/r16/nG9/4Bj/64Q9pjODcTZt49+bNDO91VvNKqcQ948bx8OjRqFRixsyZXHzxxZxwwgkFjdzMBpOkJyKiacB29Ro4AJIuAL5A+R71ooi4rr/2DpyDs2bNGr75jW/w0I9/zPjdu/nLF15gVJoqvW7YMP7faaexfehQpp9/Pu973/sYM2ZMwSM2s8HkwDkIDpxD89Of/pR5119P486dXNjWRkdDA0tOOomjRo3inz75Sd7whjcUPUQzq4GsgeOL5zZozjrrLD77uc8xdOxYvj5hAre//vWcNGkSN9x4o8PGzOp60oAVYNKkSSz48pfZsGEDAOPHj2fIEP8xMzMHjtXA8OHDOeWUU4oehpkdZnxJzczMcuHAMTOzXDhwzMwsFw4cMzPLhQPHzMxy4cAxM7NcOHDMzCwXfrRNBUmbgNVFj6NOjAE2Fz0Isz74z+fgOiUixg7UyIFjNSFpeZZnK5kVwX8+i+FLamZmlgsHjpmZ5cKBY7WysOgBmPXDfz4L4Hs4ZmaWC5/hmJlZLhw4NugkzZD0S0ktkq4uejxmPSQtkrRR0i+KHsuRyIFjg0pSCfgSMBOYAlwqaUqxozLb6xZgRtGDOFI5cGywnQ20RMSqiOgAFgOzCh6TGQAR8SNgS9HjOFI5cGywjQfWVqy3ppqZHeEcODbYVKXmqZBm5sCxQdcKnFyxPgFYX9BYzOww4sCxwfY4MFnSqZIagUuAJQWPycwOAw4cG1QR0Ql8GFgKrABuj4hnih2VWZmkbwIPA78hqVXSnKLHdCTxkwbMzCwXPsMxM7NcOHDMzCwXDhwzM8uFA8fMzHLhwDEzs1wMKXoAZkciSaOB+9Pq64AuYFNaPzs9h86srnhatFnBJH0SeDUiPlf0WMxqyZfUzA4jkv5F0hUV65+R9NeS3i3pAUnfkfSspC9JUmozU9LDkn4q6VuSji7uJzDrmwPH7PByE/DnsPfdQhcD30zbzgGuAs4AfhOYJWkccDUwLSLOAp4CPprzmM0y8T0cs8NIRDwvabukM4BTgMciYms6mXkkIv4bQNJi4H+m3aYAP0ltGoGHch+4WQYOHLPDz82Uz3ImAV+uqPe+4RqUXwfx/Yh4fy4jMzsEvqRmdvi5E7gQeDtwX0V9qqSJ6VLb+yifyfwE+F1JpwFIOlrS5LwHbJaFz3DMDjMR0S7pR8CLEdFdseknwA3wcIjgAAAAdklEQVTAW4BlwJKIiPTE42+l10EA/COwMs8xm2XhadFmhxlJDcCTwEURsSrV3g18OCIuKnRwZofAl9TMDiNpssDzlO/LrCp6PGaDyWc4ZmaWC5/hmJlZLhw4ZmaWCweOmZnlwoFjZma5cOCYmVkuHDhmZpaL/w96lMqm7bU/LgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "sns.violinplot(x=\"Type\", y=\"CONTENT_LENGTH\",  palette={\"red\",\"yellow\"}, data=data, size=6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows the distribution of the content length of good and bad urls. \n",
    "1. The content length is significantly lesser in bad urls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1dcb5260f98>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "DATA = data[data[\"CONTENT_LENGTH\"] != np.nan]\n",
    "x1 = DATA[DATA[\"Type\"] == 0][\"CONTENT_LENGTH\"]\n",
    "x2 = DATA[DATA[\"Type\"] == 1][\"CONTENT_LENGTH\"]\n",
    "plt.figure(figsize = (15,6))\n",
    "sns.kdeplot(x1, clip = (1000,100000))\n",
    "sns.kdeplot(x2,  clip = (1000,100000))\n",
    "plt.legend((\"Good Url\", \"Bad Url\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The important observation from the above plot is for the bad urls, the content length is mostly less, if the content length is very high, the chance of the url being good is high"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6.  WHOIS_COUNTRY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in whois_country:\n",
      "49\n",
      "--------------------\n",
      "Unique values         counts\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique values in whois_country:\")\n",
    "print(data.WHOIS_COUNTRY.nunique())\n",
    "print(\"--------------------\")\n",
    "print(\"Unique values         counts\")\n",
    "# print(data[\"WHOIS_COUNTRY\"].value_counts().shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>URL</th>\n",
       "      <th>URL_LENGTH</th>\n",
       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
       "      <th>CHARSET</th>\n",
       "      <th>SERVER</th>\n",
       "      <th>CONTENT_LENGTH</th>\n",
       "      <th>WHOIS_COUNTRY</th>\n",
       "      <th>WHOIS_STATEPRO</th>\n",
       "      <th>WHOIS_REGDATE</th>\n",
       "      <th>WHOIS_UPDATED_DATE</th>\n",
       "      <th>...</th>\n",
       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
       "      <th>REMOTE_IPS</th>\n",
       "      <th>APP_BYTES</th>\n",
       "      <th>SOURCE_APP_PACKETS</th>\n",
       "      <th>REMOTE_APP_PACKETS</th>\n",
       "      <th>SOURCE_APP_BYTES</th>\n",
       "      <th>REMOTE_APP_BYTES</th>\n",
       "      <th>APP_PACKETS</th>\n",
       "      <th>DNS_QUERY_TIMES</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1781</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781</td>\n",
       "      <td>1780</td>\n",
       "      <td>969.000000</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781</td>\n",
       "      <td>...</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1.781000e+03</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1.781000e+03</td>\n",
       "      <td>1.781000e+03</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1780.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>1781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>239</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>182</td>\n",
       "      <td>891</td>\n",
       "      <td>594</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>B0_745</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>utf-8</td>\n",
       "      <td>Apache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1055</td>\n",
       "      <td>386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1103</td>\n",
       "      <td>372</td>\n",
       "      <td>127</td>\n",
       "      <td>139</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>56.961258</td>\n",
       "      <td>11.111735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11726.927761</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.472768</td>\n",
       "      <td>3.060640</td>\n",
       "      <td>2.982339e+03</td>\n",
       "      <td>18.540146</td>\n",
       "      <td>18.746210</td>\n",
       "      <td>1.589255e+04</td>\n",
       "      <td>3.155599e+03</td>\n",
       "      <td>18.540146</td>\n",
       "      <td>2.263483</td>\n",
       "      <td>0.121280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27.555586</td>\n",
       "      <td>4.549896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36391.809051</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>21.807327</td>\n",
       "      <td>3.386975</td>\n",
       "      <td>5.605057e+04</td>\n",
       "      <td>41.627173</td>\n",
       "      <td>46.397969</td>\n",
       "      <td>6.986193e+04</td>\n",
       "      <td>5.605378e+04</td>\n",
       "      <td>41.627173</td>\n",
       "      <td>2.930853</td>\n",
       "      <td>0.326544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>324.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1853.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.720000e+02</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.790000e+02</td>\n",
       "      <td>7.350000e+02</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11323.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.328000e+03</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>9.806000e+03</td>\n",
       "      <td>2.701000e+03</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>249.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>649263.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.362906e+06</td>\n",
       "      <td>1198.000000</td>\n",
       "      <td>1284.000000</td>\n",
       "      <td>2.060012e+06</td>\n",
       "      <td>2.362906e+06</td>\n",
       "      <td>1198.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           URL   URL_LENGTH  NUMBER_SPECIAL_CHARACTERS CHARSET  SERVER  \\\n",
       "count     1781  1781.000000                1781.000000    1781    1780   \n",
       "unique    1781          NaN                        NaN       7     239   \n",
       "top     B0_745          NaN                        NaN   utf-8  Apache   \n",
       "freq         1          NaN                        NaN    1055     386   \n",
       "mean       NaN    56.961258                  11.111735     NaN     NaN   \n",
       "std        NaN    27.555586                   4.549896     NaN     NaN   \n",
       "min        NaN    16.000000                   5.000000     NaN     NaN   \n",
       "25%        NaN    39.000000                   8.000000     NaN     NaN   \n",
       "50%        NaN    49.000000                  10.000000     NaN     NaN   \n",
       "75%        NaN    68.000000                  13.000000     NaN     NaN   \n",
       "max        NaN   249.000000                  43.000000     NaN     NaN   \n",
       "\n",
       "        CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO WHOIS_REGDATE  \\\n",
       "count       969.000000          1781           1781          1781   \n",
       "unique             NaN            49            182           891   \n",
       "top                NaN            US             CA          None   \n",
       "freq               NaN          1103            372           127   \n",
       "mean      11726.927761           NaN            NaN           NaN   \n",
       "std       36391.809051           NaN            NaN           NaN   \n",
       "min           0.000000           NaN            NaN           NaN   \n",
       "25%         324.000000           NaN            NaN           NaN   \n",
       "50%        1853.000000           NaN            NaN           NaN   \n",
       "75%       11323.000000           NaN            NaN           NaN   \n",
       "max      649263.000000           NaN            NaN           NaN   \n",
       "\n",
       "       WHOIS_UPDATED_DATE     ...       DIST_REMOTE_TCP_PORT   REMOTE_IPS  \\\n",
       "count                1781     ...                1781.000000  1781.000000   \n",
       "unique                594     ...                        NaN          NaN   \n",
       "top                  None     ...                        NaN          NaN   \n",
       "freq                  139     ...                        NaN          NaN   \n",
       "mean                  NaN     ...                   5.472768     3.060640   \n",
       "std                   NaN     ...                  21.807327     3.386975   \n",
       "min                   NaN     ...                   0.000000     0.000000   \n",
       "25%                   NaN     ...                   0.000000     0.000000   \n",
       "50%                   NaN     ...                   0.000000     2.000000   \n",
       "75%                   NaN     ...                   5.000000     5.000000   \n",
       "max                   NaN     ...                 708.000000    17.000000   \n",
       "\n",
       "           APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  \\\n",
       "count   1.781000e+03         1781.000000         1781.000000   \n",
       "unique           NaN                 NaN                 NaN   \n",
       "top              NaN                 NaN                 NaN   \n",
       "freq             NaN                 NaN                 NaN   \n",
       "mean    2.982339e+03           18.540146           18.746210   \n",
       "std     5.605057e+04           41.627173           46.397969   \n",
       "min     0.000000e+00            0.000000            0.000000   \n",
       "25%     0.000000e+00            0.000000            0.000000   \n",
       "50%     6.720000e+02            8.000000            9.000000   \n",
       "75%     2.328000e+03           26.000000           25.000000   \n",
       "max     2.362906e+06         1198.000000         1284.000000   \n",
       "\n",
       "        SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  \\\n",
       "count       1.781000e+03      1.781000e+03  1781.000000      1780.000000   \n",
       "unique               NaN               NaN          NaN              NaN   \n",
       "top                  NaN               NaN          NaN              NaN   \n",
       "freq                 NaN               NaN          NaN              NaN   \n",
       "mean        1.589255e+04      3.155599e+03    18.540146         2.263483   \n",
       "std         6.986193e+04      5.605378e+04    41.627173         2.930853   \n",
       "min         0.000000e+00      0.000000e+00     0.000000         0.000000   \n",
       "25%         0.000000e+00      0.000000e+00     0.000000         0.000000   \n",
       "50%         5.790000e+02      7.350000e+02     8.000000         0.000000   \n",
       "75%         9.806000e+03      2.701000e+03    26.000000         4.000000   \n",
       "max         2.060012e+06      2.362906e+06  1198.000000        20.000000   \n",
       "\n",
       "               Type  \n",
       "count   1781.000000  \n",
       "unique          NaN  \n",
       "top             NaN  \n",
       "freq            NaN  \n",
       "mean       0.121280  \n",
       "std        0.326544  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  \n",
       "\n",
       "[11 rows x 21 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop('URL', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL_LENGTH                     0\n",
      "NUMBER_SPECIAL_CHARACTERS      0\n",
      "CHARSET                        0\n",
      "SERVER                         1\n",
      "CONTENT_LENGTH               812\n",
      "WHOIS_COUNTRY                  0\n",
      "WHOIS_STATEPRO                 0\n",
      "WHOIS_REGDATE                  0\n",
      "WHOIS_UPDATED_DATE             0\n",
      "TCP_CONVERSATION_EXCHANGE      0\n",
      "DIST_REMOTE_TCP_PORT           0\n",
      "REMOTE_IPS                     0\n",
      "APP_BYTES                      0\n",
      "SOURCE_APP_PACKETS             0\n",
      "REMOTE_APP_PACKETS             0\n",
      "SOURCE_APP_BYTES               0\n",
      "REMOTE_APP_BYTES               0\n",
      "APP_PACKETS                    0\n",
      "DNS_QUERY_TIMES                1\n",
      "Type                           0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Take a look at any null values to clean up data\n",
    "# Likely need to do something with these empty datasets\n",
    "print(data.isnull().sum())\n",
    "# data[pd.isnull(data).any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "URL_LENGTH                   0\n",
      "NUMBER_SPECIAL_CHARACTERS    0\n",
      "CHARSET                      0\n",
      "SERVER                       1\n",
      "CONTENT_LENGTH               0\n",
      "WHOIS_COUNTRY                0\n",
      "WHOIS_STATEPRO               0\n",
      "WHOIS_REGDATE                0\n",
      "WHOIS_UPDATED_DATE           0\n",
      "TCP_CONVERSATION_EXCHANGE    0\n",
      "DIST_REMOTE_TCP_PORT         0\n",
      "REMOTE_IPS                   0\n",
      "APP_BYTES                    0\n",
      "SOURCE_APP_PACKETS           0\n",
      "REMOTE_APP_PACKETS           0\n",
      "SOURCE_APP_BYTES             0\n",
      "REMOTE_APP_BYTES             0\n",
      "APP_PACKETS                  0\n",
      "DNS_QUERY_TIMES              0\n",
      "Type                         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "data = data.interpolate()\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['SERVER'].fillna('RARE_VALUE', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>URL_LENGTH</th>\n",
       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
       "      <th>CHARSET</th>\n",
       "      <th>SERVER</th>\n",
       "      <th>CONTENT_LENGTH</th>\n",
       "      <th>WHOIS_COUNTRY</th>\n",
       "      <th>WHOIS_STATEPRO</th>\n",
       "      <th>WHOIS_REGDATE</th>\n",
       "      <th>WHOIS_UPDATED_DATE</th>\n",
       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
       "      <th>REMOTE_IPS</th>\n",
       "      <th>APP_BYTES</th>\n",
       "      <th>SOURCE_APP_PACKETS</th>\n",
       "      <th>REMOTE_APP_PACKETS</th>\n",
       "      <th>SOURCE_APP_BYTES</th>\n",
       "      <th>REMOTE_APP_BYTES</th>\n",
       "      <th>APP_PACKETS</th>\n",
       "      <th>DNS_QUERY_TIMES</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1.781000e+03</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1.781000e+03</td>\n",
       "      <td>1.781000e+03</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "      <td>1781.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>240</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49</td>\n",
       "      <td>182</td>\n",
       "      <td>891</td>\n",
       "      <td>594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>utf-8</td>\n",
       "      <td>Apache</td>\n",
       "      <td>NaN</td>\n",
       "      <td>US</td>\n",
       "      <td>CA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1055</td>\n",
       "      <td>386</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1103</td>\n",
       "      <td>372</td>\n",
       "      <td>127</td>\n",
       "      <td>139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.961258</td>\n",
       "      <td>11.111735</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13497.243964</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.261089</td>\n",
       "      <td>5.472768</td>\n",
       "      <td>3.060640</td>\n",
       "      <td>2.982339e+03</td>\n",
       "      <td>18.540146</td>\n",
       "      <td>18.746210</td>\n",
       "      <td>1.589255e+04</td>\n",
       "      <td>3.155599e+03</td>\n",
       "      <td>18.540146</td>\n",
       "      <td>2.263335</td>\n",
       "      <td>0.121280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>27.555586</td>\n",
       "      <td>4.549896</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38415.552697</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.500975</td>\n",
       "      <td>21.807327</td>\n",
       "      <td>3.386975</td>\n",
       "      <td>5.605057e+04</td>\n",
       "      <td>41.627173</td>\n",
       "      <td>46.397969</td>\n",
       "      <td>6.986193e+04</td>\n",
       "      <td>5.605378e+04</td>\n",
       "      <td>41.627173</td>\n",
       "      <td>2.930036</td>\n",
       "      <td>0.326544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>16.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>603.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4714.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.720000e+02</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>5.790000e+02</td>\n",
       "      <td>7.350000e+02</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>68.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12578.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.328000e+03</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>9.806000e+03</td>\n",
       "      <td>2.701000e+03</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>249.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>649263.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1194.000000</td>\n",
       "      <td>708.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>2.362906e+06</td>\n",
       "      <td>1198.000000</td>\n",
       "      <td>1284.000000</td>\n",
       "      <td>2.060012e+06</td>\n",
       "      <td>2.362906e+06</td>\n",
       "      <td>1198.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         URL_LENGTH  NUMBER_SPECIAL_CHARACTERS CHARSET  SERVER  \\\n",
       "count   1781.000000                1781.000000    1781    1781   \n",
       "unique          NaN                        NaN       7     240   \n",
       "top             NaN                        NaN   utf-8  Apache   \n",
       "freq            NaN                        NaN    1055     386   \n",
       "mean      56.961258                  11.111735     NaN     NaN   \n",
       "std       27.555586                   4.549896     NaN     NaN   \n",
       "min       16.000000                   5.000000     NaN     NaN   \n",
       "25%       39.000000                   8.000000     NaN     NaN   \n",
       "50%       49.000000                  10.000000     NaN     NaN   \n",
       "75%       68.000000                  13.000000     NaN     NaN   \n",
       "max      249.000000                  43.000000     NaN     NaN   \n",
       "\n",
       "        CONTENT_LENGTH WHOIS_COUNTRY WHOIS_STATEPRO WHOIS_REGDATE  \\\n",
       "count      1781.000000          1781           1781          1781   \n",
       "unique             NaN            49            182           891   \n",
       "top                NaN            US             CA          None   \n",
       "freq               NaN          1103            372           127   \n",
       "mean      13497.243964           NaN            NaN           NaN   \n",
       "std       38415.552697           NaN            NaN           NaN   \n",
       "min           0.000000           NaN            NaN           NaN   \n",
       "25%         603.000000           NaN            NaN           NaN   \n",
       "50%        4714.750000           NaN            NaN           NaN   \n",
       "75%       12578.500000           NaN            NaN           NaN   \n",
       "max      649263.000000           NaN            NaN           NaN   \n",
       "\n",
       "       WHOIS_UPDATED_DATE  TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  \\\n",
       "count                1781                1781.000000           1781.000000   \n",
       "unique                594                        NaN                   NaN   \n",
       "top                  None                        NaN                   NaN   \n",
       "freq                  139                        NaN                   NaN   \n",
       "mean                  NaN                  16.261089              5.472768   \n",
       "std                   NaN                  40.500975             21.807327   \n",
       "min                   NaN                   0.000000              0.000000   \n",
       "25%                   NaN                   0.000000              0.000000   \n",
       "50%                   NaN                   7.000000              0.000000   \n",
       "75%                   NaN                  22.000000              5.000000   \n",
       "max                   NaN                1194.000000            708.000000   \n",
       "\n",
       "         REMOTE_IPS     APP_BYTES  SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  \\\n",
       "count   1781.000000  1.781000e+03         1781.000000         1781.000000   \n",
       "unique          NaN           NaN                 NaN                 NaN   \n",
       "top             NaN           NaN                 NaN                 NaN   \n",
       "freq            NaN           NaN                 NaN                 NaN   \n",
       "mean       3.060640  2.982339e+03           18.540146           18.746210   \n",
       "std        3.386975  5.605057e+04           41.627173           46.397969   \n",
       "min        0.000000  0.000000e+00            0.000000            0.000000   \n",
       "25%        0.000000  0.000000e+00            0.000000            0.000000   \n",
       "50%        2.000000  6.720000e+02            8.000000            9.000000   \n",
       "75%        5.000000  2.328000e+03           26.000000           25.000000   \n",
       "max       17.000000  2.362906e+06         1198.000000         1284.000000   \n",
       "\n",
       "        SOURCE_APP_BYTES  REMOTE_APP_BYTES  APP_PACKETS  DNS_QUERY_TIMES  \\\n",
       "count       1.781000e+03      1.781000e+03  1781.000000      1781.000000   \n",
       "unique               NaN               NaN          NaN              NaN   \n",
       "top                  NaN               NaN          NaN              NaN   \n",
       "freq                 NaN               NaN          NaN              NaN   \n",
       "mean        1.589255e+04      3.155599e+03    18.540146         2.263335   \n",
       "std         6.986193e+04      5.605378e+04    41.627173         2.930036   \n",
       "min         0.000000e+00      0.000000e+00     0.000000         0.000000   \n",
       "25%         0.000000e+00      0.000000e+00     0.000000         0.000000   \n",
       "50%         5.790000e+02      7.350000e+02     8.000000         0.000000   \n",
       "75%         9.806000e+03      2.701000e+03    26.000000         4.000000   \n",
       "max         2.060012e+06      2.362906e+06  1198.000000        20.000000   \n",
       "\n",
       "               Type  \n",
       "count   1781.000000  \n",
       "unique          NaN  \n",
       "top             NaN  \n",
       "freq            NaN  \n",
       "mean       0.121280  \n",
       "std        0.326544  \n",
       "min        0.000000  \n",
       "25%        0.000000  \n",
       "50%        0.000000  \n",
       "75%        0.000000  \n",
       "max        1.000000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>URL_LENGTH</th>\n",
       "      <th>NUMBER_SPECIAL_CHARACTERS</th>\n",
       "      <th>CONTENT_LENGTH</th>\n",
       "      <th>TCP_CONVERSATION_EXCHANGE</th>\n",
       "      <th>DIST_REMOTE_TCP_PORT</th>\n",
       "      <th>REMOTE_IPS</th>\n",
       "      <th>APP_BYTES</th>\n",
       "      <th>SOURCE_APP_PACKETS</th>\n",
       "      <th>REMOTE_APP_PACKETS</th>\n",
       "      <th>SOURCE_APP_BYTES</th>\n",
       "      <th>...</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/09/2015 0:00</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/09/2015 20:47</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/09/2016 0:00</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/10/2015 0:00</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/11/2015 0:00</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/11/2016 0:00</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/12/2015 0:00</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/12/2015 14:43</th>\n",
       "      <th>WHOIS_UPDATED_DATE--9/12/2016 0:00</th>\n",
       "      <th>WHOIS_UPDATED_DATE--None</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "      <td>263.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>700</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1153</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>15087.0</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1230</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>1265</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>324.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>162.0</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>3812</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>18784</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>124140.0</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4278</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>129889</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1977 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  CONTENT_LENGTH  \\\n",
       "0          16                          7           263.0   \n",
       "1          16                          6         15087.0   \n",
       "2          16                          6           324.0   \n",
       "3          17                          6           162.0   \n",
       "4          17                          6        124140.0   \n",
       "\n",
       "   TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  \\\n",
       "0                          7                     0           2        700   \n",
       "1                         17                     7           4       1230   \n",
       "2                          0                     0           0          0   \n",
       "3                         31                    22           3       3812   \n",
       "4                         57                     2           5       4278   \n",
       "\n",
       "   SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
       "0                   9                  10              1153   \n",
       "1                  17                  19              1265   \n",
       "2                   0                   0                 0   \n",
       "3                  39                  37             18784   \n",
       "4                  61                  62            129889   \n",
       "\n",
       "             ...             WHOIS_UPDATED_DATE--9/09/2015 0:00  \\\n",
       "0            ...                                              0   \n",
       "1            ...                                              0   \n",
       "2            ...                                              0   \n",
       "3            ...                                              0   \n",
       "4            ...                                              0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/09/2015 20:47  WHOIS_UPDATED_DATE--9/09/2016 0:00  \\\n",
       "0                                    0                                   0   \n",
       "1                                    0                                   0   \n",
       "2                                    0                                   0   \n",
       "3                                    0                                   0   \n",
       "4                                    0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/10/2015 0:00  WHOIS_UPDATED_DATE--9/11/2015 0:00  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/11/2016 0:00  WHOIS_UPDATED_DATE--9/12/2015 0:00  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/12/2015 14:43  WHOIS_UPDATED_DATE--9/12/2016 0:00  \\\n",
       "0                                    0                                   0   \n",
       "1                                    0                                   0   \n",
       "2                                    0                                   0   \n",
       "3                                    0                                   0   \n",
       "4                                    0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--None  \n",
       "0                         1  \n",
       "1                         1  \n",
       "2                         1  \n",
       "3                         0  \n",
       "4                         0  \n",
       "\n",
       "[5 rows x 1977 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categorical columns to numbered categorical columns\n",
    "dataset_with_dummies = pd.get_dummies(data,prefix_sep='--')\n",
    "dataset_with_dummies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>15087.0</td>\n",
       "      <td>17</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>1230</td>\n",
       "      <td>17</td>\n",
       "      <td>19</td>\n",
       "      <td>1265</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>324.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>162.0</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>3</td>\n",
       "      <td>3812</td>\n",
       "      <td>39</td>\n",
       "      <td>37</td>\n",
       "      <td>18784</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "      <td>124140.0</td>\n",
       "      <td>57</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4278</td>\n",
       "      <td>61</td>\n",
       "      <td>62</td>\n",
       "      <td>129889</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1976 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   URL_LENGTH  NUMBER_SPECIAL_CHARACTERS  CONTENT_LENGTH  \\\n",
       "0          16                          7           263.0   \n",
       "1          16                          6         15087.0   \n",
       "2          16                          6           324.0   \n",
       "3          17                          6           162.0   \n",
       "4          17                          6        124140.0   \n",
       "\n",
       "   TCP_CONVERSATION_EXCHANGE  DIST_REMOTE_TCP_PORT  REMOTE_IPS  APP_BYTES  \\\n",
       "0                          7                     0           2        700   \n",
       "1                         17                     7           4       1230   \n",
       "2                          0                     0           0          0   \n",
       "3                         31                    22           3       3812   \n",
       "4                         57                     2           5       4278   \n",
       "\n",
       "   SOURCE_APP_PACKETS  REMOTE_APP_PACKETS  SOURCE_APP_BYTES  \\\n",
       "0                   9                  10              1153   \n",
       "1                  17                  19              1265   \n",
       "2                   0                   0                 0   \n",
       "3                  39                  37             18784   \n",
       "4                  61                  62            129889   \n",
       "\n",
       "             ...             WHOIS_UPDATED_DATE--9/09/2015 0:00  \\\n",
       "0            ...                                              0   \n",
       "1            ...                                              0   \n",
       "2            ...                                              0   \n",
       "3            ...                                              0   \n",
       "4            ...                                              0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/09/2015 20:47  WHOIS_UPDATED_DATE--9/09/2016 0:00  \\\n",
       "0                                    0                                   0   \n",
       "1                                    0                                   0   \n",
       "2                                    0                                   0   \n",
       "3                                    0                                   0   \n",
       "4                                    0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/10/2015 0:00  WHOIS_UPDATED_DATE--9/11/2015 0:00  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/11/2016 0:00  WHOIS_UPDATED_DATE--9/12/2015 0:00  \\\n",
       "0                                   0                                   0   \n",
       "1                                   0                                   0   \n",
       "2                                   0                                   0   \n",
       "3                                   0                                   0   \n",
       "4                                   0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--9/12/2015 14:43  WHOIS_UPDATED_DATE--9/12/2016 0:00  \\\n",
       "0                                    0                                   0   \n",
       "1                                    0                                   0   \n",
       "2                                    0                                   0   \n",
       "3                                    0                                   0   \n",
       "4                                    0                                   0   \n",
       "\n",
       "   WHOIS_UPDATED_DATE--None  \n",
       "0                         1  \n",
       "1                         1  \n",
       "2                         1  \n",
       "3                         0  \n",
       "4                         0  \n",
       "\n",
       "[5 rows x 1976 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Separate predictors and response\n",
    "X = dataset_with_dummies.drop('Type',axis=1) #Predictors\n",
    "y = dataset_with_dummies['Type']\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get a training and test dataset\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Accuracy Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "# Train a Random Forest Regressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# n_estimators is the number of random forests to use\n",
    "# n_jobs says to use all processors available\n",
    "rf = RandomForestClassifier(n_estimators=100, n_jobs=-1, max_depth=80, criterion = 'entropy')\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "print('Training Accuracy Score: {}'.format(rf.score(X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test\n",
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test results:\n",
      "\n",
      "Accuracy Score: 0.9607\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      1.00      0.98       462\n",
      "           1       0.98      0.73      0.83        73\n",
      "\n",
      "   micro avg       0.96      0.96      0.96       535\n",
      "   macro avg       0.97      0.86      0.91       535\n",
      "weighted avg       0.96      0.96      0.96       535\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[461   1]\n",
      " [ 20  53]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_predict, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix,classification_report,accuracy_score\n",
    "\n",
    "# Visualize our results\n",
    "def print_score(classifier,X_train,y_train,X_test,y_test,train=True):\n",
    "\n",
    "    print(\"Test results:\\n\")\n",
    "    print('Accuracy Score: {0:.4f}\\n'.format(accuracy_score(y_test,classifier.predict(X_test))))\n",
    "    print('Classification Report:\\n{}\\n'.format(classification_report(y_test,classifier.predict(X_test))))\n",
    "    print('Confusion Matrix:\\n{}\\n'.format(confusion_matrix(y_test,classifier.predict(X_test))))\n",
    "\n",
    "print_score(rf,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test results:\n",
      "\n",
      "Accuracy Score: 0.8766\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.96      0.93       462\n",
      "           1       0.57      0.37      0.45        73\n",
      "\n",
      "   micro avg       0.88      0.88      0.88       535\n",
      "   macro avg       0.74      0.66      0.69       535\n",
      "weighted avg       0.86      0.88      0.86       535\n",
      "\n",
      "\n",
      "Confusion Matrix:\n",
      "[[442  20]\n",
      " [ 46  27]]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(n_neighbors = 3)\n",
    "clf.fit(X_train, y_train)\n",
    "pred = clf.predict(X_test)\n",
    "# prediction[\"KNN\"] = pred\n",
    "\n",
    "print_score(clf,X_train,y_train,X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "clf = KNeighborsClassifier(n_neighbors = 3)\n",
    "clf.fit(X_train, y_train)\n",
    "pred = clf.predict(X_test)\n",
    "# prediction[\"KNN\"] = pred\n",
    "\n",
    "print_score(clf,X_train,y_train,X_test,y_test)"
   ]
  }
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