Twitter_bot_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Twitter bot Detection\n",
    "## -Aayush Tyagi 2013206"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 697,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.impute import SimpleImputer\n",
    "import seaborn as sns\n",
    "import sys\n",
    "import warnings\n",
    "if not sys.warnoptions:\n",
    "    warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 698,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 699,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def Performance(actual_value , predicted_value):\n",
    "    accuracy = accuracy_score(actual_value , predicted_value) * 100\n",
    "    precision = precision_score(actual_value , predicted_value) * 100\n",
    "    recall = recall_score(actual_value , predicted_value) * 100\n",
    "    f1 = f1_score(actual_value , predicted_value, average='weighted')\n",
    "    print('Accuracy is {:.4f}%\\n Precision is {:.4f}%\\n Recall is {:.4f}%\\nF1 Score is {:.4f}\\n'.format(accuracy, precision, recall, f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 700,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set Data\n",
    "training_data = pd.read_csv(\"training_data_2_csv_UTF.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 701,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'id_str', 'screen_name', 'location', 'description', 'url',\n",
       "       'followers_count', 'friends_count', 'listed_count', 'created_at',\n",
       "       'favourites_count', 'verified', 'statuses_count', 'lang', 'status',\n",
       "       'default_profile', 'default_profile_image', 'has_extended_profile',\n",
       "       'name', 'bot'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 701,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 702,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>id_str</th>\n",
       "      <th>screen_name</th>\n",
       "      <th>location</th>\n",
       "      <th>description</th>\n",
       "      <th>url</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>created_at</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>verified</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>lang</th>\n",
       "      <th>status</th>\n",
       "      <th>default_profile</th>\n",
       "      <th>default_profile_image</th>\n",
       "      <th>has_extended_profile</th>\n",
       "      <th>name</th>\n",
       "      <th>bot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.160000e+17</td>\n",
       "      <td>\"815745789754417152\"</td>\n",
       "      <td>\"HoustonPokeMap\"</td>\n",
       "      <td>\"Houston, TX\"</td>\n",
       "      <td>\"Rare and strong PokŽmon in Houston, TX. See m...</td>\n",
       "      <td>\"https://t.co/dnWuDbFRkt\"</td>\n",
       "      <td>1291</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>\"Mon Jan 02 02:25:26 +0000 2017\"</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>78554</td>\n",
       "      <td>\"en\"</td>\n",
       "      <td>{\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>\"Houston PokŽ Alert\"</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.843621e+09</td>\n",
       "      <td>4843621225</td>\n",
       "      <td>kernyeahx</td>\n",
       "      <td>Templeville town, MD, USA</td>\n",
       "      <td>From late 2014 Socium Marketplace will make sh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>2/1/2016 7:37</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>31</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Keri Nelson</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.303727e+09</td>\n",
       "      <td>4303727112</td>\n",
       "      <td>mattlieberisbot</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Inspired by the smart, funny folks at @replyal...</td>\n",
       "      <td>https://t.co/P1e1o0m4KC</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>Fri Nov 20 18:53:22 +0000 2015</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>713</td>\n",
       "      <td>en</td>\n",
       "      <td>{'retweeted': False, 'is_quote_status': False,...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Matt Lieber Is Bot</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.063139e+09</td>\n",
       "      <td>3063139353</td>\n",
       "      <td>sc_papers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2/25/2015 20:11</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>676</td>\n",
       "      <td>en</td>\n",
       "      <td>Construction of human anti-tetanus single-chai...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>single cell papers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.955142e+09</td>\n",
       "      <td>2955142070</td>\n",
       "      <td>lucarivera16</td>\n",
       "      <td>Dublin, United States</td>\n",
       "      <td>Inspiring cooks everywhere since 1956.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11</td>\n",
       "      <td>745</td>\n",
       "      <td>0</td>\n",
       "      <td>1/1/2015 17:44</td>\n",
       "      <td>146</td>\n",
       "      <td>False</td>\n",
       "      <td>185</td>\n",
       "      <td>en</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>lucarivera16</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8.410000e+17</td>\n",
       "      <td>8.41E+17</td>\n",
       "      <td>dantheimprover</td>\n",
       "      <td>Austin, TX</td>\n",
       "      <td>Just a guy trying to do good by telling everyo...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>186</td>\n",
       "      <td>0</td>\n",
       "      <td>13/03/2017 22:53</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>11</td>\n",
       "      <td>en</td>\n",
       "      <td>Status(_api=&lt;tweepy.api.API object at 0x101927...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>dantheimprover</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2.482835e+09</td>\n",
       "      <td>2482834658</td>\n",
       "      <td>_all_of_us_</td>\n",
       "      <td>in a machine.</td>\n",
       "      <td>bot by @rubicon</td>\n",
       "      <td>NaN</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>Wed May 07 22:29:25 +0000 2014</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>6068</td>\n",
       "      <td>en</td>\n",
       "      <td>{u'contributors': None, u'truncated': False, u...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>everything always</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3.333574e+09</td>\n",
       "      <td>3333573622</td>\n",
       "      <td>KatamariItems</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[Bot rolled up by @BeachEpisode] Cataloguing e...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8227</td>\n",
       "      <td>2</td>\n",
       "      <td>89</td>\n",
       "      <td>Thu Jun 18 22:07:31 +0000 2015</td>\n",
       "      <td>26</td>\n",
       "      <td>False</td>\n",
       "      <td>2597</td>\n",
       "      <td>en</td>\n",
       "      <td>{u'contributors': None, u'truncated': False, u...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Katamari Collection</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.996105e+09</td>\n",
       "      <td>2996105102</td>\n",
       "      <td>AutophagyPapers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Twitterbot for #Autophagy papers. Curated by @...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>275</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>1/25/2015 17:34</td>\n",
       "      <td>23</td>\n",
       "      <td>False</td>\n",
       "      <td>9922</td>\n",
       "      <td>en</td>\n",
       "      <td>Feeding Schedule And Proteolysis Regulate Auto...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Autophagy Papers</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>3.271096e+09</td>\n",
       "      <td>3271095818</td>\n",
       "      <td>HSC_papers</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>7/7/2015 15:23</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>2515</td>\n",
       "      <td>en</td>\n",
       "      <td>Functional Selectivity in Cytokine Signaling R...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Hematopoiesis</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                id_str       screen_name  \\\n",
       "0  8.160000e+17  \"815745789754417152\"  \"HoustonPokeMap\"   \n",
       "1  4.843621e+09            4843621225         kernyeahx   \n",
       "2  4.303727e+09            4303727112   mattlieberisbot   \n",
       "3  3.063139e+09            3063139353         sc_papers   \n",
       "4  2.955142e+09            2955142070      lucarivera16   \n",
       "5  8.410000e+17              8.41E+17    dantheimprover   \n",
       "6  2.482835e+09            2482834658       _all_of_us_   \n",
       "7  3.333574e+09            3333573622     KatamariItems   \n",
       "8  2.996105e+09            2996105102   AutophagyPapers   \n",
       "9  3.271096e+09            3271095818        HSC_papers   \n",
       "\n",
       "                    location  \\\n",
       "0              \"Houston, TX\"   \n",
       "1  Templeville town, MD, USA   \n",
       "2                        NaN   \n",
       "3                        NaN   \n",
       "4      Dublin, United States   \n",
       "5                 Austin, TX   \n",
       "6              in a machine.   \n",
       "7                        NaN   \n",
       "8                        NaN   \n",
       "9                        NaN   \n",
       "\n",
       "                                         description  \\\n",
       "0  \"Rare and strong PokŽmon in Houston, TX. See m...   \n",
       "1  From late 2014 Socium Marketplace will make sh...   \n",
       "2  Inspired by the smart, funny folks at @replyal...   \n",
       "3                                                NaN   \n",
       "4             Inspiring cooks everywhere since 1956.   \n",
       "5  Just a guy trying to do good by telling everyo...   \n",
       "6                                    bot by @rubicon   \n",
       "7  [Bot rolled up by @BeachEpisode] Cataloguing e...   \n",
       "8  Twitterbot for #Autophagy papers. Curated by @...   \n",
       "9                                                NaN   \n",
       "\n",
       "                         url  followers_count  friends_count  listed_count  \\\n",
       "0  \"https://t.co/dnWuDbFRkt\"             1291              0            10   \n",
       "1                        NaN                1            349             0   \n",
       "2    https://t.co/P1e1o0m4KC             1086              0            14   \n",
       "3                        NaN               33              0             8   \n",
       "4                        NaN               11            745             0   \n",
       "5                        NaN                1            186             0   \n",
       "6                        NaN              193              0            19   \n",
       "7                        NaN             8227              2            89   \n",
       "8                        NaN              275              0            17   \n",
       "9                        NaN               51              3             9   \n",
       "\n",
       "                         created_at  favourites_count  verified  \\\n",
       "0  \"Mon Jan 02 02:25:26 +0000 2017\"                 0     False   \n",
       "1                     2/1/2016 7:37                38     False   \n",
       "2    Fri Nov 20 18:53:22 +0000 2015                 0     False   \n",
       "3                   2/25/2015 20:11                 0     False   \n",
       "4                    1/1/2015 17:44               146     False   \n",
       "5                  13/03/2017 22:53                 0     False   \n",
       "6    Wed May 07 22:29:25 +0000 2014                 0     False   \n",
       "7    Thu Jun 18 22:07:31 +0000 2015                26     False   \n",
       "8                   1/25/2015 17:34                23     False   \n",
       "9                    7/7/2015 15:23                 0     False   \n",
       "\n",
       "   statuses_count  lang                                             status  \\\n",
       "0           78554  \"en\"  {\\r      \"created_at\": \"Sun Mar 12 15:44:04 +0...   \n",
       "1              31    en                                                NaN   \n",
       "2             713    en  {'retweeted': False, 'is_quote_status': False,...   \n",
       "3             676    en  Construction of human anti-tetanus single-chai...   \n",
       "4             185    en                                                NaN   \n",
       "5              11    en  Status(_api=<tweepy.api.API object at 0x101927...   \n",
       "6            6068    en  {u'contributors': None, u'truncated': False, u...   \n",
       "7            2597    en  {u'contributors': None, u'truncated': False, u...   \n",
       "8            9922    en  Feeding Schedule And Proteolysis Regulate Auto...   \n",
       "9            2515    en  Functional Selectivity in Cytokine Signaling R...   \n",
       "\n",
       "   default_profile  default_profile_image has_extended_profile  \\\n",
       "0             True                  False                False   \n",
       "1             True                  False                False   \n",
       "2             True                  False                False   \n",
       "3             True                   True                False   \n",
       "4            False                  False                False   \n",
       "5             True                  False                 True   \n",
       "6            False                  False                False   \n",
       "7             True                  False                False   \n",
       "8            False                  False                False   \n",
       "9             True                  False                False   \n",
       "\n",
       "                   name  bot  \n",
       "0  \"Houston PokŽ Alert\"    1  \n",
       "1           Keri Nelson    1  \n",
       "2    Matt Lieber Is Bot    1  \n",
       "3    single cell papers    1  \n",
       "4          lucarivera16    1  \n",
       "5        dantheimprover    1  \n",
       "6     everything always    1  \n",
       "7   Katamari Collection    1  \n",
       "8      Autophagy Papers    1  \n",
       "9         Hematopoiesis    1  "
      ]
     },
     "execution_count": 702,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.head(10)"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 703,
   "metadata": {
    "scrolled": true
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>listed_count</th>\n",
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       "      <th>bot</th>\n",
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       "  <tbody>\n",
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       "      <th>count</th>\n",
       "      <td>2.797000e+03</td>\n",
       "      <td>2.797000e+03</td>\n",
       "      <td>2.797000e+03</td>\n",
       "      <td>2797.000000</td>\n",
       "      <td>2797.000000</td>\n",
       "      <td>2.797000e+03</td>\n",
       "      <td>2797.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.482460e+17</td>\n",
       "      <td>9.964245e+05</td>\n",
       "      <td>4.796493e+03</td>\n",
       "      <td>3192.299964</td>\n",
       "      <td>2035.577047</td>\n",
       "      <td>1.945170e+04</td>\n",
       "      <td>0.472292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.100578e+17</td>\n",
       "      <td>5.593714e+06</td>\n",
       "      <td>5.619217e+04</td>\n",
       "      <td>17283.423291</td>\n",
       "      <td>16073.463967</td>\n",
       "      <td>1.543729e+05</td>\n",
       "      <td>0.499321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.526000e+03</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>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.895649e+07</td>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>1.100000e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.660000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.917035e+09</td>\n",
       "      <td>3.430000e+02</td>\n",
       "      <td>1.840000e+02</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>2.240000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.326332e+09</td>\n",
       "      <td>1.372500e+04</td>\n",
       "      <td>6.690000e+02</td>\n",
       "      <td>332.000000</td>\n",
       "      <td>491.000000</td>\n",
       "      <td>1.004200e+04</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>8.410000e+17</td>\n",
       "      <td>9.632156e+07</td>\n",
       "      <td>2.056668e+06</td>\n",
       "      <td>620331.000000</td>\n",
       "      <td>714021.000000</td>\n",
       "      <td>6.863363e+06</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id  followers_count  friends_count   listed_count  \\\n",
       "count  2.797000e+03     2.797000e+03   2.797000e+03    2797.000000   \n",
       "mean   1.482460e+17     9.964245e+05   4.796493e+03    3192.299964   \n",
       "std    3.100578e+17     5.593714e+06   5.619217e+04   17283.423291   \n",
       "min    3.526000e+03     0.000000e+00   0.000000e+00       0.000000   \n",
       "25%    6.895649e+07     4.400000e+01   1.100000e+01       1.000000   \n",
       "50%    1.917035e+09     3.430000e+02   1.840000e+02      17.000000   \n",
       "75%    3.326332e+09     1.372500e+04   6.690000e+02     332.000000   \n",
       "max    8.410000e+17     9.632156e+07   2.056668e+06  620331.000000   \n",
       "\n",
       "       favourites_count  statuses_count          bot  \n",
       "count       2797.000000    2.797000e+03  2797.000000  \n",
       "mean        2035.577047    1.945170e+04     0.472292  \n",
       "std        16073.463967    1.543729e+05     0.499321  \n",
       "min            0.000000    0.000000e+00     0.000000  \n",
       "25%            0.000000    1.660000e+02     0.000000  \n",
       "50%           25.000000    2.240000e+03     0.000000  \n",
       "75%          491.000000    1.004200e+04     1.000000  \n",
       "max       714021.000000    6.863363e+06     1.000000  "
      ]
     },
     "execution_count": 703,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 704,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25048d3a7f0>"
      ]
     },
     "execution_count": 704,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['id', 'id_str', 'screen_name', 'location', 'description', 'url',\n",
    "       'followers_count', 'friends_count', 'listed_count', 'created_at',\n",
    "       'favourites_count', 'verified', 'statuses_count', 'lang', 'status',\n",
    "       'default_profile', 'default_profile_image', 'has_extended_profile',\n",
    "       'name', 'bot']\n",
    "\n",
    "mask = np.zeros_like(training_data[features].corr(), dtype = np.bool) \n",
    "mask[np.triu_indices_from(mask)] = True \n",
    "\n",
    "f, ax = plt.subplots(figsize=(16, 12))\n",
    "plt.title('Pearson Correlation Matrix',fontsize=25)\n",
    "\n",
    "sns.heatmap(training_data[features].corr(),linewidths = 0.25,vmax = 0.7,square = True,cmap = \"BuGn\", \n",
    "            linecolor = 'w',annot = True,annot_kws = {\"size\":8},mask = mask,cbar_kws = {\"shrink\": 0.9})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "metadata": {},
   "outputs": [],
   "source": [
    "training_data.drop(['id_str', 'screen_name', 'location', 'description', 'url', 'created_at', 'lang', 'status','default_profile', 'default_profile_image', 'has_extended_profile','name'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 706,
   "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>id</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>verified</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>bot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.160000e+17</td>\n",
       "      <td>1291</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>78554</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.843621e+09</td>\n",
       "      <td>1</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>38</td>\n",
       "      <td>False</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.303727e+09</td>\n",
       "      <td>1086</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>713</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.063139e+09</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>676</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.955142e+09</td>\n",
       "      <td>11</td>\n",
       "      <td>745</td>\n",
       "      <td>0</td>\n",
       "      <td>146</td>\n",
       "      <td>False</td>\n",
       "      <td>185</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             id  followers_count  friends_count  listed_count  \\\n",
       "0  8.160000e+17             1291              0            10   \n",
       "1  4.843621e+09                1            349             0   \n",
       "2  4.303727e+09             1086              0            14   \n",
       "3  3.063139e+09               33              0             8   \n",
       "4  2.955142e+09               11            745             0   \n",
       "\n",
       "   favourites_count  verified  statuses_count  bot  \n",
       "0                 0     False           78554    1  \n",
       "1                38     False              31    1  \n",
       "2                 0     False             713    1  \n",
       "3                 0     False             676    1  \n",
       "4               146     False             185    1  "
      ]
     },
     "execution_count": 706,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 707,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                  0\n",
       "followers_count     0\n",
       "friends_count       0\n",
       "listed_count        0\n",
       "favourites_count    0\n",
       "verified            0\n",
       "statuses_count      0\n",
       "bot                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 707,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 708,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2797, 8)"
      ]
     },
     "execution_count": 708,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 709,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>verified</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>bot</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.672925</td>\n",
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       "      <td>-0.439430</td>\n",
       "      <td>-0.611899</td>\n",
       "      <td>-0.451945</td>\n",
       "      <td>0.652131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>followers_count</th>\n",
       "      <td>-0.672925</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>0.896126</td>\n",
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       "      <td>-0.468430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>friends_count</th>\n",
       "      <td>-0.402346</td>\n",
       "      <td>0.375522</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.204403</td>\n",
       "      <td>0.641529</td>\n",
       "      <td>0.356452</td>\n",
       "      <td>0.111118</td>\n",
       "      <td>-0.483105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>listed_count</th>\n",
       "      <td>-0.615005</td>\n",
       "      <td>0.896126</td>\n",
       "      <td>0.204403</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.349059</td>\n",
       "      <td>0.694340</td>\n",
       "      <td>0.684976</td>\n",
       "      <td>-0.318445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favourites_count</th>\n",
       "      <td>-0.439430</td>\n",
       "      <td>0.457363</td>\n",
       "      <td>0.641529</td>\n",
       "      <td>0.349059</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.394227</td>\n",
       "      <td>0.295108</td>\n",
       "      <td>-0.526228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verified</th>\n",
       "      <td>-0.611899</td>\n",
       "      <td>0.709732</td>\n",
       "      <td>0.356452</td>\n",
       "      <td>0.694340</td>\n",
       "      <td>0.394227</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333278</td>\n",
       "      <td>-0.508555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>statuses_count</th>\n",
       "      <td>-0.451945</td>\n",
       "      <td>0.649117</td>\n",
       "      <td>0.111118</td>\n",
       "      <td>0.684976</td>\n",
       "      <td>0.295108</td>\n",
       "      <td>0.333278</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.089018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bot</th>\n",
       "      <td>0.652131</td>\n",
       "      <td>-0.468430</td>\n",
       "      <td>-0.483105</td>\n",
       "      <td>-0.318445</td>\n",
       "      <td>-0.526228</td>\n",
       "      <td>-0.508555</td>\n",
       "      <td>-0.089018</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        id  followers_count  friends_count  listed_count  \\\n",
       "id                1.000000        -0.672925      -0.402346     -0.615005   \n",
       "followers_count  -0.672925         1.000000       0.375522      0.896126   \n",
       "friends_count    -0.402346         0.375522       1.000000      0.204403   \n",
       "listed_count     -0.615005         0.896126       0.204403      1.000000   \n",
       "favourites_count -0.439430         0.457363       0.641529      0.349059   \n",
       "verified         -0.611899         0.709732       0.356452      0.694340   \n",
       "statuses_count   -0.451945         0.649117       0.111118      0.684976   \n",
       "bot               0.652131        -0.468430      -0.483105     -0.318445   \n",
       "\n",
       "                  favourites_count  verified  statuses_count       bot  \n",
       "id                       -0.439430 -0.611899       -0.451945  0.652131  \n",
       "followers_count           0.457363  0.709732        0.649117 -0.468430  \n",
       "friends_count             0.641529  0.356452        0.111118 -0.483105  \n",
       "listed_count              0.349059  0.694340        0.684976 -0.318445  \n",
       "favourites_count          1.000000  0.394227        0.295108 -0.526228  \n",
       "verified                  0.394227  1.000000        0.333278 -0.508555  \n",
       "statuses_count            0.295108  0.333278        1.000000 -0.089018  \n",
       "bot                      -0.526228 -0.508555       -0.089018  1.000000  "
      ]
     },
     "execution_count": 709,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_data.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"bot\", y=\"followers_count\", data=training_data);\n",
    "sns.catplot(x=\"bot\", y=\"friends_count\", data=training_data);\n",
    "sns.catplot(x=\"bot\", y=\"listed_count\", data=training_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 711,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(x=\"bot\", y=\"favourites_count\", data=training_data);\n",
    "sns.catplot(x=\"bot\", y=\"verified\", data=training_data);\n",
    "sns.catplot(x=\"bot\", y=\"statuses_count\", data=training_data);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 712,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = training_data.iloc[:, :-1].values\n",
    "y = training_data.iloc[:, 7].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 713,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[8.16e+17 1291 0 ... 0 0 78554]\n",
      " [4843621225.0 1 349 ... 38 0 31]\n",
      " [4303727112.0 1086 0 ... 0 0 713]\n",
      " ...\n",
      " [15485441.0 45044433 7451 ... 24 1 9606]\n",
      " [751386872.0 16 64 ... 15 0 62]\n",
      " [19929362.0 22490 308 ... 43 1 1897]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
    "Labelx=LabelEncoder()\n",
    "X[:,5]=Labelx.fit_transform(X[:,5])\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 714,
   "metadata": {},
   "outputs": [],
   "source": [
    "#feature scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc_X = StandardScaler()\n",
    "X = sc_X.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 715,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='entropy', max_depth=None, max_features='auto',\n",
       "                       max_leaf_nodes=None, max_samples=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                       n_jobs=None, oob_score=False, random_state=0, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 715,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#fitting\n",
    "from sklearn.ensemble import RandomForestClassifier as rf\n",
    "classifier= rf(n_estimators=10,criterion='entropy',random_state=0)\n",
    "classifier.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 716,
   "metadata": {},
   "outputs": [],
   "source": [
    "bots = training_data[training_data.bot==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 717,
   "metadata": {},
   "outputs": [],
   "source": [
    "B = bots.iloc[:,:-1]\n",
    "B_y = bots.iloc[:,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 718,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_pred = classifier.predict(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 719,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Confusionmatrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(B_y,B_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 720,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0]\n",
      " [528 793]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 721,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 60.0303%\n",
      " Precision is 100.0000%\n",
      " Recall is 60.0303%\n",
      "F1 Score is 0.7502\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Performance(B_y,B_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 722,
   "metadata": {},
   "outputs": [],
   "source": [
    "nbots = training_data[training_data.bot==0]\n",
    "NB = nbots.iloc[:,:-1]\n",
    "NB_y = nbots.iloc[:,7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 723,
   "metadata": {},
   "outputs": [],
   "source": [
    "NB_pred = classifier.predict(NB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 724,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0   0]\n",
      " [528 793]]\n"
     ]
    }
   ],
   "source": [
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 725,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 61.2466%\n",
      " Precision is 0.0000%\n",
      " Recall is 0.0000%\n",
      "F1 Score is 0.4653\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Performance(NB_pred,NB_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 726,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(ccp_alpha=0.0, criterion='mse', max_depth=None,\n",
       "                      max_features=None, max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                      random_state=0, splitter='best')"
      ]
     },
     "execution_count": 726,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor as DTR\n",
    "classifier= DTR(random_state=0)\n",
    "classifier.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 727,
   "metadata": {},
   "outputs": [],
   "source": [
    "B_pred = classifier.predict(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 728,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    0]\n",
      " [1313    8]]\n"
     ]
    }
   ],
   "source": [
    "#Confusionmatrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(B_y,B_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 729,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 0.6056%\n",
      " Precision is 100.0000%\n",
      " Recall is 0.6056%\n",
      "F1 Score is 0.0120\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Performance(B_y,B_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 730,
   "metadata": {},
   "outputs": [],
   "source": [
    "NB_pred = classifier.predict(NB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 731,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[831 645]\n",
      " [  0   0]]\n"
     ]
    }
   ],
   "source": [
    "#Confusionmatrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm=confusion_matrix(NB_y,NB_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 732,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy is 56.3008%\n",
      " Precision is 0.0000%\n",
      " Recall is 0.0000%\n",
      "F1 Score is 0.7204\n",
      "\n"
     ]
    }
   ],
   "source": [
    "Performance(NB_y,NB_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 733,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,\n",
       "    decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',\n",
       "    max_iter=-1, probability=False, random_state=0, shrinking=True, tol=0.001,\n",
       "    verbose=False)"
      ]
     },
     "execution_count": 733,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "classifier=SVC(kernel='linear', random_state=0)\n",
    "classifier.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 695,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                 0           1       2        3       4    5        6    7\n",
      "0     8.160000e+17      1291.0     0.0     10.0     0.0  0.0  78554.0  1.0\n",
      "1     4.843621e+09         1.0   349.0      0.0    38.0  0.0     31.0  1.0\n",
      "2     4.303727e+09      1086.0     0.0     14.0     0.0  0.0    713.0  1.0\n",
      "3     3.063139e+09        33.0     0.0      8.0     0.0  0.0    676.0  1.0\n",
      "4     2.955142e+09        11.0   745.0      0.0   146.0  0.0    185.0  1.0\n",
      "...            ...         ...     ...      ...     ...  ...      ...  ...\n",
      "2792  2.362091e+08     18998.0  2005.0    425.0  2503.0  0.0   3498.0  0.0\n",
      "2793  2.545039e+08        32.0    54.0      0.0     1.0  0.0     97.0  0.0\n",
      "2794  1.548544e+07  45044433.0  7451.0  68157.0    24.0  1.0   9606.0  0.0\n",
      "2795  7.513869e+08        16.0    64.0      1.0    15.0  0.0     62.0  0.0\n",
      "2796  1.992936e+07     22490.0   308.0   1342.0    43.0  1.0   1897.0  0.0\n",
      "\n",
      "[2797 rows x 8 columns]\n"
     ]
    }
   ],
   "source": [
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 696,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split, KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn import tree, metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.impute import SimpleImputer\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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