FakeProfileDetection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sklearn \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "real_users = pd.read_csv('data/users.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>screen_name</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>created_at</th>\n",
       "      <th>url</th>\n",
       "      <th>...</th>\n",
       "      <th>profile_sidebar_fill_color</th>\n",
       "      <th>profile_background_image_url</th>\n",
       "      <th>profile_background_color</th>\n",
       "      <th>profile_link_color</th>\n",
       "      <th>utc_offset</th>\n",
       "      <th>protected</th>\n",
       "      <th>verified</th>\n",
       "      <th>description</th>\n",
       "      <th>updated</th>\n",
       "      <th>dataset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3610511</td>\n",
       "      <td>Davide Dellacasa</td>\n",
       "      <td>braddd</td>\n",
       "      <td>20370</td>\n",
       "      <td>5470</td>\n",
       "      <td>2385</td>\n",
       "      <td>145</td>\n",
       "      <td>52</td>\n",
       "      <td>Fri Apr 06 10:58:22 +0000 2007</td>\n",
       "      <td>http://braddd.tumblr.com</td>\n",
       "      <td>...</td>\n",
       "      <td>FFF7CC</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>BADFCD</td>\n",
       "      <td>FF0000</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Founder of http://www.screenweek.it &amp; http://w...</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5656162</td>\n",
       "      <td>Simone Economo</td>\n",
       "      <td>eKoeS</td>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>Mon Apr 30 15:08:42 +0000 2007</td>\n",
       "      <td>http://www.lineheight.net/</td>\n",
       "      <td>...</td>\n",
       "      <td>DDEEF6</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>C0DEED</td>\n",
       "      <td>0084B4</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BSc degree (cum laude) in Computer Engineering...</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5682702</td>\n",
       "      <td>tacone</td>\n",
       "      <td>tacone_</td>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>Tue May 01 11:53:40 +0000 2007</td>\n",
       "      <td>http://t.co/LKrl1dZE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>1A1B1F</td>\n",
       "      <td>2FC2EF</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6067292</td>\n",
       "      <td>alesaura</td>\n",
       "      <td>alesstar</td>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>Tue May 15 16:55:16 +0000 2007</td>\n",
       "      <td>http://alesstar.wordpress.com/</td>\n",
       "      <td>...</td>\n",
       "      <td>95E8EC</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme4/bg.gif</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6015122</td>\n",
       "      <td>Angelo</td>\n",
       "      <td>PerDiletto</td>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun May 13 19:52:00 +0000 2007</td>\n",
       "      <td>http://www.flickr.com/per_diletto</td>\n",
       "      <td>...</td>\n",
       "      <td>F6F6F6</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme18/bg.gif</td>\n",
       "      <td>ACDED6</td>\n",
       "      <td>38543</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Je me souviens</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id              name screen_name  statuses_count  followers_count  \\\n",
       "0  3610511  Davide Dellacasa      braddd           20370             5470   \n",
       "1  5656162    Simone Economo       eKoeS            3131              506   \n",
       "2  5682702            tacone     tacone_            4024              264   \n",
       "3  6067292          alesaura    alesstar           40586              640   \n",
       "4  6015122            Angelo  PerDiletto            2016               62   \n",
       "\n",
       "   friends_count  favourites_count  listed_count  \\\n",
       "0           2385               145            52   \n",
       "1            381                 9            40   \n",
       "2             87               323            16   \n",
       "3            622              1118            32   \n",
       "4             64                13             0   \n",
       "\n",
       "                       created_at                                url  ...  \\\n",
       "0  Fri Apr 06 10:58:22 +0000 2007           http://braddd.tumblr.com  ...   \n",
       "1  Mon Apr 30 15:08:42 +0000 2007         http://www.lineheight.net/  ...   \n",
       "2  Tue May 01 11:53:40 +0000 2007               http://t.co/LKrl1dZE  ...   \n",
       "3  Tue May 15 16:55:16 +0000 2007     http://alesstar.wordpress.com/  ...   \n",
       "4  Sun May 13 19:52:00 +0000 2007  http://www.flickr.com/per_diletto  ...   \n",
       "\n",
       "  profile_sidebar_fill_color  \\\n",
       "0                     FFF7CC   \n",
       "1                     DDEEF6   \n",
       "2                          0   \n",
       "3                     95E8EC   \n",
       "4                     F6F6F6   \n",
       "\n",
       "                        profile_background_image_url profile_background_color  \\\n",
       "0  http://a0.twimg.com/profile_background_images/...                   BADFCD   \n",
       "1    http://a0.twimg.com/images/themes/theme1/bg.png                   C0DEED   \n",
       "2  http://a0.twimg.com/profile_background_images/...                   1A1B1F   \n",
       "3    http://a0.twimg.com/images/themes/theme4/bg.gif                   0099B9   \n",
       "4   http://a0.twimg.com/images/themes/theme18/bg.gif                   ACDED6   \n",
       "\n",
       "   profile_link_color  utc_offset  protected verified  \\\n",
       "0              FF0000      3600.0        NaN      NaN   \n",
       "1              0084B4      3600.0        NaN      NaN   \n",
       "2              2FC2EF      3600.0        NaN      NaN   \n",
       "3              0099B9      3600.0        NaN      NaN   \n",
       "4               38543      3600.0        NaN      NaN   \n",
       "\n",
       "                                         description          updated dataset  \n",
       "0  Founder of http://www.screenweek.it & http://w...  2/14/2015 10:54     E13  \n",
       "1  BSc degree (cum laude) in Computer Engineering...  2/14/2015 10:54     E13  \n",
       "2                             Cogito ergo bestemmio.  2/14/2015 10:54     E13  \n",
       "3               Se la vita ti dà sarde, scapocciale!  2/14/2015 10:54     E13  \n",
       "4                                     Je me souviens  2/14/2015 10:54     E13  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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    }
   ],
   "source": [
    "real_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1481 entries, 0 to 1480\n",
      "Data columns (total 34 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   id                                  1481 non-null   int64  \n",
      " 1   name                                1481 non-null   object \n",
      " 2   screen_name                         1481 non-null   object \n",
      " 3   statuses_count                      1481 non-null   int64  \n",
      " 4   followers_count                     1481 non-null   int64  \n",
      " 5   friends_count                       1481 non-null   int64  \n",
      " 6   favourites_count                    1481 non-null   int64  \n",
      " 7   listed_count                        1481 non-null   int64  \n",
      " 8   created_at                          1481 non-null   object \n",
      " 9   url                                 463 non-null    object \n",
      " 10  lang                                1481 non-null   object \n",
      " 11  time_zone                           1063 non-null   object \n",
      " 12  location                            989 non-null    object \n",
      " 13  default_profile                     397 non-null    float64\n",
      " 14  default_profile_image               4 non-null      float64\n",
      " 15  geo_enabled                         719 non-null    float64\n",
      " 16  profile_image_url                   1481 non-null   object \n",
      " 17  profile_banner_url                  985 non-null    object \n",
      " 18  profile_use_background_image        1423 non-null   float64\n",
      " 19  profile_background_image_url_https  1481 non-null   object \n",
      " 20  profile_text_color                  1481 non-null   object \n",
      " 21  profile_image_url_https             1481 non-null   object \n",
      " 22  profile_sidebar_border_color        1481 non-null   object \n",
      " 23  profile_background_tile             486 non-null    float64\n",
      " 24  profile_sidebar_fill_color          1481 non-null   object \n",
      " 25  profile_background_image_url        1481 non-null   object \n",
      " 26  profile_background_color            1481 non-null   object \n",
      " 27  profile_link_color                  1481 non-null   object \n",
      " 28  utc_offset                          1063 non-null   float64\n",
      " 29  protected                           0 non-null      float64\n",
      " 30  verified                            0 non-null      float64\n",
      " 31  description                         1463 non-null   object \n",
      " 32  updated                             1481 non-null   object \n",
      " 33  dataset                             1481 non-null   object \n",
      "dtypes: float64(8), int64(6), object(20)\n",
      "memory usage: 393.5+ KB\n"
     ]
    }
   ],
   "source": [
    "real_users.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>screen_name</th>\n",
       "      <th>statuses_count</th>\n",
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       "      <th>0</th>\n",
       "      <td>370098498</td>\n",
       "      <td>pirfectmoses</td>\n",
       "      <td>pirfectmoses</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>588</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Thu Sep 08 13:20:35 +0000 2011</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>C6E2EE</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/14/2015 10:40</td>\n",
       "      <td>INT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37384589</td>\n",
       "      <td>SAK Nair</td>\n",
       "      <td>bsknair1967</td>\n",
       "      <td>656</td>\n",
       "      <td>57</td>\n",
       "      <td>693</td>\n",
       "      <td>597</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun May 03 07:35:13 +0000 2009</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>in Kuwait with my buetiful family.</td>\n",
       "      <td>2/14/2015 10:40</td>\n",
       "      <td>INT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72110028</td>\n",
       "      <td>Deepak</td>\n",
       "      <td>dedjven</td>\n",
       "      <td>1234</td>\n",
       "      <td>15</td>\n",
       "      <td>104</td>\n",
       "      <td>1150</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun Sep 06 19:50:08 +0000 2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>EFEFEF</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme14/bg.gif</td>\n",
       "      <td>131516</td>\n",
       "      <td>9999</td>\n",
       "      <td>-39600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/14/2015 10:40</td>\n",
       "      <td>INT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>82885728</td>\n",
       "      <td>Marcos Vinicius</td>\n",
       "      <td>BrowAlves</td>\n",
       "      <td>573</td>\n",
       "      <td>14</td>\n",
       "      <td>227</td>\n",
       "      <td>530</td>\n",
       "      <td>0</td>\n",
       "      <td>Fri Oct 16 14:02:48 +0000 2009</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>DDEEF6</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>C0DEED</td>\n",
       "      <td>0084B4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/14/2015 10:40</td>\n",
       "      <td>INT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>110120789</td>\n",
       "      <td>Shri Kant Kanaujia</td>\n",
       "      <td>kanaujiask</td>\n",
       "      <td>675</td>\n",
       "      <td>18</td>\n",
       "      <td>519</td>\n",
       "      <td>653</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun Jan 31 12:08:41 +0000 2010</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>DDEEF6</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>C0DEED</td>\n",
       "      <td>0084B4</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2/14/2015 10:40</td>\n",
       "      <td>INT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id                name   screen_name  statuses_count  \\\n",
       "0  370098498        pirfectmoses  pirfectmoses              24   \n",
       "1   37384589            SAK Nair   bsknair1967             656   \n",
       "2   72110028              Deepak       dedjven            1234   \n",
       "3   82885728     Marcos Vinicius     BrowAlves             573   \n",
       "4  110120789  Shri Kant Kanaujia    kanaujiask             675   \n",
       "\n",
       "   followers_count  friends_count  favourites_count  listed_count  \\\n",
       "0                4            588                16             0   \n",
       "1               57            693               597             0   \n",
       "2               15            104              1150             0   \n",
       "3               14            227               530             0   \n",
       "4               18            519               653             0   \n",
       "\n",
       "                       created_at  url  ... profile_sidebar_fill_color  \\\n",
       "0  Thu Sep 08 13:20:35 +0000 2011  NaN  ...                     DDEEF6   \n",
       "1  Sun May 03 07:35:13 +0000 2009  NaN  ...                     DDEEF6   \n",
       "2  Sun Sep 06 19:50:08 +0000 2009  NaN  ...                     EFEFEF   \n",
       "3  Fri Oct 16 14:02:48 +0000 2009  NaN  ...                     DDEEF6   \n",
       "4  Sun Jan 31 12:08:41 +0000 2010  NaN  ...                     DDEEF6   \n",
       "\n",
       "                        profile_background_image_url profile_background_color  \\\n",
       "0  http://a0.twimg.com/profile_background_images/...                   C6E2EE   \n",
       "1    http://a0.twimg.com/images/themes/theme1/bg.png                   C0DEED   \n",
       "2   http://a0.twimg.com/images/themes/theme14/bg.gif                   131516   \n",
       "3  http://a0.twimg.com/profile_background_images/...                   C0DEED   \n",
       "4    http://a0.twimg.com/images/themes/theme1/bg.png                   C0DEED   \n",
       "\n",
       "   profile_link_color  utc_offset  protected verified  \\\n",
       "0              1F98C7         NaN        NaN      NaN   \n",
       "1              0084B4         NaN        NaN      NaN   \n",
       "2                9999    -39600.0        NaN      NaN   \n",
       "3              0084B4         NaN        NaN      NaN   \n",
       "4              0084B4     19800.0        NaN      NaN   \n",
       "\n",
       "                          description          updated dataset  \n",
       "0                                 NaN  2/14/2015 10:40     INT  \n",
       "1  in Kuwait with my buetiful family.  2/14/2015 10:40     INT  \n",
       "2                                 NaN  2/14/2015 10:40     INT  \n",
       "3                                 NaN  2/14/2015 10:40     INT  \n",
       "4                                 NaN  2/14/2015 10:40     INT  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fake_users = pd.read_csv('data/fusers.csv')\n",
    "fake_users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's combine the two datasets \n",
    "data = pd.concat([real_users,fake_users])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>id</th>\n",
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       "      <th>screen_name</th>\n",
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       "      <th>followers_count</th>\n",
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       "      <th>listed_count</th>\n",
       "      <th>created_at</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3610511</td>\n",
       "      <td>Davide Dellacasa</td>\n",
       "      <td>braddd</td>\n",
       "      <td>20370</td>\n",
       "      <td>5470</td>\n",
       "      <td>2385</td>\n",
       "      <td>145</td>\n",
       "      <td>52</td>\n",
       "      <td>Fri Apr 06 10:58:22 +0000 2007</td>\n",
       "      <td>http://braddd.tumblr.com</td>\n",
       "      <td>...</td>\n",
       "      <td>FFF7CC</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>BADFCD</td>\n",
       "      <td>FF0000</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Founder of http://www.screenweek.it &amp; http://w...</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5656162</td>\n",
       "      <td>Simone Economo</td>\n",
       "      <td>eKoeS</td>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>Mon Apr 30 15:08:42 +0000 2007</td>\n",
       "      <td>http://www.lineheight.net/</td>\n",
       "      <td>...</td>\n",
       "      <td>DDEEF6</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>C0DEED</td>\n",
       "      <td>0084B4</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BSc degree (cum laude) in Computer Engineering...</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5682702</td>\n",
       "      <td>tacone</td>\n",
       "      <td>tacone_</td>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>Tue May 01 11:53:40 +0000 2007</td>\n",
       "      <td>http://t.co/LKrl1dZE</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>1A1B1F</td>\n",
       "      <td>2FC2EF</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6067292</td>\n",
       "      <td>alesaura</td>\n",
       "      <td>alesstar</td>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>Tue May 15 16:55:16 +0000 2007</td>\n",
       "      <td>http://alesstar.wordpress.com/</td>\n",
       "      <td>...</td>\n",
       "      <td>95E8EC</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme4/bg.gif</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6015122</td>\n",
       "      <td>Angelo</td>\n",
       "      <td>PerDiletto</td>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun May 13 19:52:00 +0000 2007</td>\n",
       "      <td>http://www.flickr.com/per_diletto</td>\n",
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       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Je me souviens</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id              name screen_name  statuses_count  followers_count  \\\n",
       "0  3610511  Davide Dellacasa      braddd           20370             5470   \n",
       "1  5656162    Simone Economo       eKoeS            3131              506   \n",
       "2  5682702            tacone     tacone_            4024              264   \n",
       "3  6067292          alesaura    alesstar           40586              640   \n",
       "4  6015122            Angelo  PerDiletto            2016               62   \n",
       "\n",
       "   friends_count  favourites_count  listed_count  \\\n",
       "0           2385               145            52   \n",
       "1            381                 9            40   \n",
       "2             87               323            16   \n",
       "3            622              1118            32   \n",
       "4             64                13             0   \n",
       "\n",
       "                       created_at                                url  ...  \\\n",
       "0  Fri Apr 06 10:58:22 +0000 2007           http://braddd.tumblr.com  ...   \n",
       "1  Mon Apr 30 15:08:42 +0000 2007         http://www.lineheight.net/  ...   \n",
       "2  Tue May 01 11:53:40 +0000 2007               http://t.co/LKrl1dZE  ...   \n",
       "3  Tue May 15 16:55:16 +0000 2007     http://alesstar.wordpress.com/  ...   \n",
       "4  Sun May 13 19:52:00 +0000 2007  http://www.flickr.com/per_diletto  ...   \n",
       "\n",
       "  profile_sidebar_fill_color  \\\n",
       "0                     FFF7CC   \n",
       "1                     DDEEF6   \n",
       "2                          0   \n",
       "3                     95E8EC   \n",
       "4                     F6F6F6   \n",
       "\n",
       "                        profile_background_image_url profile_background_color  \\\n",
       "0  http://a0.twimg.com/profile_background_images/...                   BADFCD   \n",
       "1    http://a0.twimg.com/images/themes/theme1/bg.png                   C0DEED   \n",
       "2  http://a0.twimg.com/profile_background_images/...                   1A1B1F   \n",
       "3    http://a0.twimg.com/images/themes/theme4/bg.gif                   0099B9   \n",
       "4   http://a0.twimg.com/images/themes/theme18/bg.gif                   ACDED6   \n",
       "\n",
       "   profile_link_color  utc_offset  protected verified  \\\n",
       "0              FF0000      3600.0        NaN      NaN   \n",
       "1              0084B4      3600.0        NaN      NaN   \n",
       "2              2FC2EF      3600.0        NaN      NaN   \n",
       "3              0099B9      3600.0        NaN      NaN   \n",
       "4               38543      3600.0        NaN      NaN   \n",
       "\n",
       "                                         description          updated dataset  \n",
       "0  Founder of http://www.screenweek.it & http://w...  2/14/2015 10:54     E13  \n",
       "1  BSc degree (cum laude) in Computer Engineering...  2/14/2015 10:54     E13  \n",
       "2                             Cogito ergo bestemmio.  2/14/2015 10:54     E13  \n",
       "3               Se la vita ti dà sarde, scapocciale!  2/14/2015 10:54     E13  \n",
       "4                                     Je me souviens  2/14/2015 10:54     E13  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2818 entries, 0 to 1336\n",
      "Data columns (total 34 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   id                                  2818 non-null   int64  \n",
      " 1   name                                2818 non-null   object \n",
      " 2   screen_name                         2818 non-null   object \n",
      " 3   statuses_count                      2818 non-null   int64  \n",
      " 4   followers_count                     2818 non-null   int64  \n",
      " 5   friends_count                       2818 non-null   int64  \n",
      " 6   favourites_count                    2818 non-null   int64  \n",
      " 7   listed_count                        2818 non-null   int64  \n",
      " 8   created_at                          2818 non-null   object \n",
      " 9   url                                 463 non-null    object \n",
      " 10  lang                                2818 non-null   object \n",
      " 11  time_zone                           1069 non-null   object \n",
      " 12  location                            2271 non-null   object \n",
      " 13  default_profile                     1728 non-null   float64\n",
      " 14  default_profile_image               8 non-null      float64\n",
      " 15  geo_enabled                         721 non-null    float64\n",
      " 16  profile_image_url                   2818 non-null   object \n",
      " 17  profile_banner_url                  987 non-null    object \n",
      " 18  profile_use_background_image        2760 non-null   float64\n",
      " 19  profile_background_image_url_https  2818 non-null   object \n",
      " 20  profile_text_color                  2818 non-null   object \n",
      " 21  profile_image_url_https             2818 non-null   object \n",
      " 22  profile_sidebar_border_color        2818 non-null   object \n",
      " 23  profile_background_tile             489 non-null    float64\n",
      " 24  profile_sidebar_fill_color          2818 non-null   object \n",
      " 25  profile_background_image_url        2818 non-null   object \n",
      " 26  profile_background_color            2818 non-null   object \n",
      " 27  profile_link_color                  2818 non-null   object \n",
      " 28  utc_offset                          1069 non-null   float64\n",
      " 29  protected                           0 non-null      float64\n",
      " 30  verified                            0 non-null      float64\n",
      " 31  description                         2547 non-null   object \n",
      " 32  updated                             2818 non-null   object \n",
      " 33  dataset                             2818 non-null   object \n",
      "dtypes: float64(8), int64(6), object(20)\n",
      "memory usage: 770.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#label users added from real profiles as 1 and fake profiles as 0\n",
    "labels = pd.DataFrame(len(fake_users)*[0] + len(real_users)*[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1481\n",
       "0    1337\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Fake_Profile'] = labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 1481 real profiles and 1337 fake profiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2818 entries, 0 to 1336\n",
      "Data columns (total 35 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   id                                  2818 non-null   int64  \n",
      " 1   name                                2818 non-null   object \n",
      " 2   screen_name                         2818 non-null   object \n",
      " 3   statuses_count                      2818 non-null   int64  \n",
      " 4   followers_count                     2818 non-null   int64  \n",
      " 5   friends_count                       2818 non-null   int64  \n",
      " 6   favourites_count                    2818 non-null   int64  \n",
      " 7   listed_count                        2818 non-null   int64  \n",
      " 8   created_at                          2818 non-null   object \n",
      " 9   url                                 463 non-null    object \n",
      " 10  lang                                2818 non-null   object \n",
      " 11  time_zone                           1069 non-null   object \n",
      " 12  location                            2271 non-null   object \n",
      " 13  default_profile                     1728 non-null   float64\n",
      " 14  default_profile_image               8 non-null      float64\n",
      " 15  geo_enabled                         721 non-null    float64\n",
      " 16  profile_image_url                   2818 non-null   object \n",
      " 17  profile_banner_url                  987 non-null    object \n",
      " 18  profile_use_background_image        2760 non-null   float64\n",
      " 19  profile_background_image_url_https  2818 non-null   object \n",
      " 20  profile_text_color                  2818 non-null   object \n",
      " 21  profile_image_url_https             2818 non-null   object \n",
      " 22  profile_sidebar_border_color        2818 non-null   object \n",
      " 23  profile_background_tile             489 non-null    float64\n",
      " 24  profile_sidebar_fill_color          2818 non-null   object \n",
      " 25  profile_background_image_url        2818 non-null   object \n",
      " 26  profile_background_color            2818 non-null   object \n",
      " 27  profile_link_color                  2818 non-null   object \n",
      " 28  utc_offset                          1069 non-null   float64\n",
      " 29  protected                           0 non-null      float64\n",
      " 30  verified                            0 non-null      float64\n",
      " 31  description                         2547 non-null   object \n",
      " 32  updated                             2818 non-null   object \n",
      " 33  dataset                             2818 non-null   object \n",
      " 34  Fake_Profile                        2818 non-null   int64  \n",
      "dtypes: float64(8), int64(7), object(20)\n",
      "memory usage: 792.6+ KB\n"
     ]
    }
   ],
   "source": [
    "#Let's have a look at data again\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>name</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>Davide Dellacasa</td>\n",
       "      <td>braddd</td>\n",
       "      <td>20370</td>\n",
       "      <td>5470</td>\n",
       "      <td>2385</td>\n",
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       "      <td>Fri Apr 06 10:58:22 +0000 2007</td>\n",
       "      <td>http://braddd.tumblr.com</td>\n",
       "      <td>it</td>\n",
       "      <td>...</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>BADFCD</td>\n",
       "      <td>FF0000</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Founder of http://www.screenweek.it &amp; http://w...</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Simone Economo</td>\n",
       "      <td>eKoeS</td>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>Mon Apr 30 15:08:42 +0000 2007</td>\n",
       "      <td>http://www.lineheight.net/</td>\n",
       "      <td>en</td>\n",
       "      <td>...</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>C0DEED</td>\n",
       "      <td>0084B4</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BSc degree (cum laude) in Computer Engineering...</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tacone</td>\n",
       "      <td>tacone_</td>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>Tue May 01 11:53:40 +0000 2007</td>\n",
       "      <td>http://t.co/LKrl1dZE</td>\n",
       "      <td>en</td>\n",
       "      <td>...</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>1A1B1F</td>\n",
       "      <td>2FC2EF</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>alesaura</td>\n",
       "      <td>alesstar</td>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>Tue May 15 16:55:16 +0000 2007</td>\n",
       "      <td>http://alesstar.wordpress.com/</td>\n",
       "      <td>en</td>\n",
       "      <td>...</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme4/bg.gif</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angelo</td>\n",
       "      <td>PerDiletto</td>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun May 13 19:52:00 +0000 2007</td>\n",
       "      <td>http://www.flickr.com/per_diletto</td>\n",
       "      <td>it</td>\n",
       "      <td>...</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme18/bg.gif</td>\n",
       "      <td>ACDED6</td>\n",
       "      <td>38543</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Je me souviens</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               name screen_name  statuses_count  followers_count  \\\n",
       "0  Davide Dellacasa      braddd           20370             5470   \n",
       "1    Simone Economo       eKoeS            3131              506   \n",
       "2            tacone     tacone_            4024              264   \n",
       "3          alesaura    alesstar           40586              640   \n",
       "4            Angelo  PerDiletto            2016               62   \n",
       "\n",
       "   friends_count  favourites_count  listed_count  \\\n",
       "0           2385               145            52   \n",
       "1            381                 9            40   \n",
       "2             87               323            16   \n",
       "3            622              1118            32   \n",
       "4             64                13             0   \n",
       "\n",
       "                       created_at                                url lang  \\\n",
       "0  Fri Apr 06 10:58:22 +0000 2007           http://braddd.tumblr.com   it   \n",
       "1  Mon Apr 30 15:08:42 +0000 2007         http://www.lineheight.net/   en   \n",
       "2  Tue May 01 11:53:40 +0000 2007               http://t.co/LKrl1dZE   en   \n",
       "3  Tue May 15 16:55:16 +0000 2007     http://alesstar.wordpress.com/   en   \n",
       "4  Sun May 13 19:52:00 +0000 2007  http://www.flickr.com/per_diletto   it   \n",
       "\n",
       "   ...                       profile_background_image_url  \\\n",
       "0  ...  http://a0.twimg.com/profile_background_images/...   \n",
       "1  ...    http://a0.twimg.com/images/themes/theme1/bg.png   \n",
       "2  ...  http://a0.twimg.com/profile_background_images/...   \n",
       "3  ...    http://a0.twimg.com/images/themes/theme4/bg.gif   \n",
       "4  ...   http://a0.twimg.com/images/themes/theme18/bg.gif   \n",
       "\n",
       "  profile_background_color  profile_link_color  utc_offset  protected  \\\n",
       "0                   BADFCD              FF0000      3600.0        NaN   \n",
       "1                   C0DEED              0084B4      3600.0        NaN   \n",
       "2                   1A1B1F              2FC2EF      3600.0        NaN   \n",
       "3                   0099B9              0099B9      3600.0        NaN   \n",
       "4                   ACDED6               38543      3600.0        NaN   \n",
       "\n",
       "  verified                                        description  \\\n",
       "0      NaN  Founder of http://www.screenweek.it & http://w...   \n",
       "1      NaN  BSc degree (cum laude) in Computer Engineering...   \n",
       "2      NaN                             Cogito ergo bestemmio.   \n",
       "3      NaN               Se la vita ti dà sarde, scapocciale!   \n",
       "4      NaN                                     Je me souviens   \n",
       "\n",
       "           updated dataset Fake_Profile  \n",
       "0  2/14/2015 10:54     E13            0  \n",
       "1  2/14/2015 10:54     E13            0  \n",
       "2  2/14/2015 10:54     E13            0  \n",
       "3  2/14/2015 10:54     E13            0  \n",
       "4  2/14/2015 10:54     E13            0  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Profile IDs are artibitrarily assigned, we won't be using that\n",
    "data.drop(['id'],axis=1,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Name on it's own may not be helpful, let's extract gender from it.\n",
    "import gender_guesser.detector as gender\n",
    "\n",
    "sex_predictor = gender.Detector(case_sensitive=False)\n",
    "\n",
    "name = list(data['name'])\n",
    "sex = []\n",
    "for i in name:\n",
    "    first_name = i.split(' ')[0]\n",
    "    sex.append(sex_predictor.get_gender(first_name))\n",
    "\n",
    "data['sex'] = sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>screen_name</th>\n",
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       "      <td>braddd</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>eKoeS</td>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
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       "      <td>Mon Apr 30 15:08:42 +0000 2007</td>\n",
       "      <td>http://www.lineheight.net/</td>\n",
       "      <td>en</td>\n",
       "      <td>Rome</td>\n",
       "      <td>...</td>\n",
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       "      <td>BSc degree (cum laude) in Computer Engineering...</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tacone_</td>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>Tue May 01 11:53:40 +0000 2007</td>\n",
       "      <td>http://t.co/LKrl1dZE</td>\n",
       "      <td>en</td>\n",
       "      <td>Rome</td>\n",
       "      <td>...</td>\n",
       "      <td>1A1B1F</td>\n",
       "      <td>2FC2EF</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>alesstar</td>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>Tue May 15 16:55:16 +0000 2007</td>\n",
       "      <td>http://alesstar.wordpress.com/</td>\n",
       "      <td>en</td>\n",
       "      <td>Rome</td>\n",
       "      <td>...</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PerDiletto</td>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun May 13 19:52:00 +0000 2007</td>\n",
       "      <td>http://www.flickr.com/per_diletto</td>\n",
       "      <td>it</td>\n",
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       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Je me souviens</td>\n",
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       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  screen_name  statuses_count  followers_count  friends_count  \\\n",
       "0      braddd           20370             5470           2385   \n",
       "1       eKoeS            3131              506            381   \n",
       "2     tacone_            4024              264             87   \n",
       "3    alesstar           40586              640            622   \n",
       "4  PerDiletto            2016               62             64   \n",
       "\n",
       "   favourites_count  listed_count                      created_at  \\\n",
       "0               145            52  Fri Apr 06 10:58:22 +0000 2007   \n",
       "1                 9            40  Mon Apr 30 15:08:42 +0000 2007   \n",
       "2               323            16  Tue May 01 11:53:40 +0000 2007   \n",
       "3              1118            32  Tue May 15 16:55:16 +0000 2007   \n",
       "4                13             0  Sun May 13 19:52:00 +0000 2007   \n",
       "\n",
       "                                 url lang time_zone  ...  \\\n",
       "0           http://braddd.tumblr.com   it      Rome  ...   \n",
       "1         http://www.lineheight.net/   en      Rome  ...   \n",
       "2               http://t.co/LKrl1dZE   en      Rome  ...   \n",
       "3     http://alesstar.wordpress.com/   en      Rome  ...   \n",
       "4  http://www.flickr.com/per_diletto   it      Rome  ...   \n",
       "\n",
       "  profile_background_color  profile_link_color  utc_offset  protected  \\\n",
       "0                   BADFCD              FF0000      3600.0        NaN   \n",
       "1                   C0DEED              0084B4      3600.0        NaN   \n",
       "2                   1A1B1F              2FC2EF      3600.0        NaN   \n",
       "3                   0099B9              0099B9      3600.0        NaN   \n",
       "4                   ACDED6               38543      3600.0        NaN   \n",
       "\n",
       "  verified                                        description  \\\n",
       "0      NaN  Founder of http://www.screenweek.it & http://w...   \n",
       "1      NaN  BSc degree (cum laude) in Computer Engineering...   \n",
       "2      NaN                             Cogito ergo bestemmio.   \n",
       "3      NaN               Se la vita ti dà sarde, scapocciale!   \n",
       "4      NaN                                     Je me souviens   \n",
       "\n",
       "           updated dataset Fake_Profile sex  \n",
       "0  2/14/2015 10:54     E13            0   2  \n",
       "1  2/14/2015 10:54     E13            0  -2  \n",
       "2  2/14/2015 10:54     E13            0   0  \n",
       "3  2/14/2015 10:54     E13            0   0  \n",
       "4  2/14/2015 10:54     E13            0   2  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assign codes to sex\n",
    "sex_dict = {'female': -2, 'mostly_female': -1,'unknown':0,'mostly_male':1, 'male': 2, 'andy':0}\n",
    "data.replace(sex_dict,inplace=True)\n",
    "data.drop(['name'],axis=1,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1A1B1F</td>\n",
       "      <td>2FC2EF</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>Tue May 15 16:55:16 +0000 2007</td>\n",
       "      <td>http://alesstar.wordpress.com/</td>\n",
       "      <td>en</td>\n",
       "      <td>Rome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>Sun May 13 19:52:00 +0000 2007</td>\n",
       "      <td>http://www.flickr.com/per_diletto</td>\n",
       "      <td>it</td>\n",
       "      <td>Rome</td>\n",
       "      <td>iPhone: 44.069630,12.569966</td>\n",
       "      <td>...</td>\n",
       "      <td>ACDED6</td>\n",
       "      <td>38543</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Je me souviens</td>\n",
       "      <td>2/14/2015 10:54</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   statuses_count  followers_count  friends_count  favourites_count  \\\n",
       "0           20370             5470           2385               145   \n",
       "1            3131              506            381                 9   \n",
       "2            4024              264             87               323   \n",
       "3           40586              640            622              1118   \n",
       "4            2016               62             64                13   \n",
       "\n",
       "   listed_count                      created_at  \\\n",
       "0            52  Fri Apr 06 10:58:22 +0000 2007   \n",
       "1            40  Mon Apr 30 15:08:42 +0000 2007   \n",
       "2            16  Tue May 01 11:53:40 +0000 2007   \n",
       "3            32  Tue May 15 16:55:16 +0000 2007   \n",
       "4             0  Sun May 13 19:52:00 +0000 2007   \n",
       "\n",
       "                                 url lang time_zone  \\\n",
       "0           http://braddd.tumblr.com   it      Rome   \n",
       "1         http://www.lineheight.net/   en      Rome   \n",
       "2               http://t.co/LKrl1dZE   en      Rome   \n",
       "3     http://alesstar.wordpress.com/   en      Rome   \n",
       "4  http://www.flickr.com/per_diletto   it      Rome   \n",
       "\n",
       "                      location  ...  profile_background_color  \\\n",
       "0                         Roma  ...                    BADFCD   \n",
       "1                  Rome, Italy  ...                    C0DEED   \n",
       "2                    Internets  ...                    1A1B1F   \n",
       "3                          NaN  ...                    0099B9   \n",
       "4  iPhone: 44.069630,12.569966  ...                    ACDED6   \n",
       "\n",
       "   profile_link_color  utc_offset protected verified  \\\n",
       "0              FF0000      3600.0       NaN      NaN   \n",
       "1              0084B4      3600.0       NaN      NaN   \n",
       "2              2FC2EF      3600.0       NaN      NaN   \n",
       "3              0099B9      3600.0       NaN      NaN   \n",
       "4               38543      3600.0       NaN      NaN   \n",
       "\n",
       "                                         description          updated dataset  \\\n",
       "0  Founder of http://www.screenweek.it & http://w...  2/14/2015 10:54     E13   \n",
       "1  BSc degree (cum laude) in Computer Engineering...  2/14/2015 10:54     E13   \n",
       "2                             Cogito ergo bestemmio.  2/14/2015 10:54     E13   \n",
       "3               Se la vita ti dà sarde, scapocciale!  2/14/2015 10:54     E13   \n",
       "4                                     Je me souviens  2/14/2015 10:54     E13   \n",
       "\n",
       "  Fake_Profile sex  \n",
       "0            0   2  \n",
       "1            0  -2  \n",
       "2            0   0  \n",
       "3            0   0  \n",
       "4            0   2  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We already have gender from name, we will not be considering screen_name at this point.\n",
    "    #Length of the screen name does not have much to do with real/fake profiles\n",
    "    #Real users do sometimes have names that aren't the same as their profile names\n",
    "    #Drop \n",
    "\n",
    "data.drop(['screen_name'], axis=1, inplace = True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Null Values : \n",
      "Status count: 0\n",
      "follower count: 0\n",
      "favorite count: 0\n",
      "friends count: 0\n",
      "listed count: 0\n"
     ]
    }
   ],
   "source": [
    "#Let's check if the next couple rows have missing values\n",
    "print('Null Values : ')\n",
    "print('Status count:', data['statuses_count'].isnull().sum())\n",
    "print('follower count:', data['followers_count'].isnull().sum())\n",
    "print('favorite count:', data['friends_count'].isnull().sum())\n",
    "print('friends count:', data['favourites_count'].isnull().sum())\n",
    "print('listed count:', data['listed_count'].isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, let's leave those as they are.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Created at and updated are two columns with dates and times. \n",
    "#Perhaps we can subrtact the time between the year it was created and year it was last updated  and store it in a variable.\n",
    "\n",
    "# data['created_at'].value_counts()\n",
    "# data['updated'].value_counts()\n",
    "\n",
    "data['created_at']  = pd.to_datetime(data['created_at']).dt.year\n",
    "data['updated'] = pd.to_datetime(data['updated']).dt.year\n",
    "year_cr = list(data['created_at'])\n",
    "year_up = list(data['updated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_update = []\n",
    "for i in range(len(year_cr)):\n",
    "    last_update.append(year_cr[i] - year_up[i])\n",
    "\n",
    "data['last_update'] = last_update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['created_at', 'updated'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>statuses_count</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>url</th>\n",
       "      <th>lang</th>\n",
       "      <th>time_zone</th>\n",
       "      <th>location</th>\n",
       "      <th>default_profile</th>\n",
       "      <th>...</th>\n",
       "      <th>profile_background_color</th>\n",
       "      <th>profile_link_color</th>\n",
       "      <th>utc_offset</th>\n",
       "      <th>protected</th>\n",
       "      <th>verified</th>\n",
       "      <th>description</th>\n",
       "      <th>dataset</th>\n",
       "      <th>Fake_Profile</th>\n",
       "      <th>sex</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20370</td>\n",
       "      <td>5470</td>\n",
       "      <td>2385</td>\n",
       "      <td>145</td>\n",
       "      <td>52</td>\n",
       "      <td>http://braddd.tumblr.com</td>\n",
       "      <td>it</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Roma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>BADFCD</td>\n",
       "      <td>FF0000</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Founder of http://www.screenweek.it &amp; http://w...</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>http://www.lineheight.net/</td>\n",
       "      <td>en</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Rome, Italy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>C0DEED</td>\n",
       "      <td>0084B4</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BSc degree (cum laude) in Computer Engineering...</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>http://t.co/LKrl1dZE</td>\n",
       "      <td>en</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Internets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1A1B1F</td>\n",
       "      <td>2FC2EF</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>http://alesstar.wordpress.com/</td>\n",
       "      <td>en</td>\n",
       "      <td>Rome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>http://www.flickr.com/per_diletto</td>\n",
       "      <td>it</td>\n",
       "      <td>Rome</td>\n",
       "      <td>iPhone: 44.069630,12.569966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>ACDED6</td>\n",
       "      <td>38543</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Je me souviens</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   statuses_count  followers_count  friends_count  favourites_count  \\\n",
       "0           20370             5470           2385               145   \n",
       "1            3131              506            381                 9   \n",
       "2            4024              264             87               323   \n",
       "3           40586              640            622              1118   \n",
       "4            2016               62             64                13   \n",
       "\n",
       "   listed_count                                url lang time_zone  \\\n",
       "0            52           http://braddd.tumblr.com   it      Rome   \n",
       "1            40         http://www.lineheight.net/   en      Rome   \n",
       "2            16               http://t.co/LKrl1dZE   en      Rome   \n",
       "3            32     http://alesstar.wordpress.com/   en      Rome   \n",
       "4             0  http://www.flickr.com/per_diletto   it      Rome   \n",
       "\n",
       "                      location  default_profile  ...  \\\n",
       "0                         Roma              NaN  ...   \n",
       "1                  Rome, Italy              NaN  ...   \n",
       "2                    Internets              NaN  ...   \n",
       "3                          NaN              NaN  ...   \n",
       "4  iPhone: 44.069630,12.569966              NaN  ...   \n",
       "\n",
       "   profile_background_color  profile_link_color utc_offset protected  \\\n",
       "0                    BADFCD              FF0000     3600.0       NaN   \n",
       "1                    C0DEED              0084B4     3600.0       NaN   \n",
       "2                    1A1B1F              2FC2EF     3600.0       NaN   \n",
       "3                    0099B9              0099B9     3600.0       NaN   \n",
       "4                    ACDED6               38543     3600.0       NaN   \n",
       "\n",
       "   verified                                        description dataset  \\\n",
       "0       NaN  Founder of http://www.screenweek.it & http://w...     E13   \n",
       "1       NaN  BSc degree (cum laude) in Computer Engineering...     E13   \n",
       "2       NaN                             Cogito ergo bestemmio.     E13   \n",
       "3       NaN               Se la vita ti dà sarde, scapocciale!     E13   \n",
       "4       NaN                                     Je me souviens     E13   \n",
       "\n",
       "  Fake_Profile sex  last_update  \n",
       "0            0   2           -8  \n",
       "1            0  -2           -8  \n",
       "2            0   0           -8  \n",
       "3            0   0           -8  \n",
       "4            0   2           -8  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2355"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's look at url\n",
    "\n",
    "data['url'].isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['url'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "en    1502\n",
       "it    1297\n",
       "es      11\n",
       "fr       3\n",
       "de       2\n",
       "gl       1\n",
       "tr       1\n",
       "nl       1\n",
       "Name: lang, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Language\n",
    "\n",
    "data['lang'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1502\n",
       "1    1297\n",
       "2      11\n",
       "3       3\n",
       "4       2\n",
       "7       1\n",
       "5       1\n",
       "6       1\n",
       "Name: lang, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Assign numerical codes to languages\n",
    "data.replace({'en':0, 'it':1, 'es':2, 'fr':3, 'de':4, 'gl':5, 'tr':6, 'nl':7},inplace=True)\n",
    "data['lang'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20370</td>\n",
       "      <td>5470</td>\n",
       "      <td>2385</td>\n",
       "      <td>145</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Roma</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>BADFCD</td>\n",
       "      <td>FF0000</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Founder of http://www.screenweek.it &amp; http://w...</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Rome, Italy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>C0DEED</td>\n",
       "      <td>0084B4</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BSc degree (cum laude) in Computer Engineering...</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>Rome</td>\n",
       "      <td>Internets</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1A1B1F</td>\n",
       "      <td>2FC2EF</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>Rome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>0099B9</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Rome</td>\n",
       "      <td>iPhone: 44.069630,12.569966</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>ACDED6</td>\n",
       "      <td>38543</td>\n",
       "      <td>3600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Je me souviens</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   statuses_count  followers_count  friends_count  favourites_count  \\\n",
       "0           20370             5470           2385               145   \n",
       "1            3131              506            381                 9   \n",
       "2            4024              264             87               323   \n",
       "3           40586              640            622              1118   \n",
       "4            2016               62             64                13   \n",
       "\n",
       "   listed_count  lang time_zone                     location  default_profile  \\\n",
       "0            52     1      Rome                         Roma              NaN   \n",
       "1            40     0      Rome                  Rome, Italy              NaN   \n",
       "2            16     0      Rome                    Internets              NaN   \n",
       "3            32     0      Rome                          NaN              NaN   \n",
       "4             0     1      Rome  iPhone: 44.069630,12.569966              NaN   \n",
       "\n",
       "   default_profile_image  ...  profile_background_color profile_link_color  \\\n",
       "0                    NaN  ...                    BADFCD             FF0000   \n",
       "1                    NaN  ...                    C0DEED             0084B4   \n",
       "2                    NaN  ...                    1A1B1F             2FC2EF   \n",
       "3                    NaN  ...                    0099B9             0099B9   \n",
       "4                    NaN  ...                    ACDED6              38543   \n",
       "\n",
       "  utc_offset  protected verified  \\\n",
       "0     3600.0        NaN      NaN   \n",
       "1     3600.0        NaN      NaN   \n",
       "2     3600.0        NaN      NaN   \n",
       "3     3600.0        NaN      NaN   \n",
       "4     3600.0        NaN      NaN   \n",
       "\n",
       "                                         description dataset Fake_Profile  \\\n",
       "0  Founder of http://www.screenweek.it & http://w...     E13            0   \n",
       "1  BSc degree (cum laude) in Computer Engineering...     E13            0   \n",
       "2                             Cogito ergo bestemmio.     E13            0   \n",
       "3               Se la vita ti dà sarde, scapocciale!     E13            0   \n",
       "4                                     Je me souviens     E13            0   \n",
       "\n",
       "   sex last_update  \n",
       "0    2          -8  \n",
       "1   -2          -8  \n",
       "2    0          -8  \n",
       "3    0          -8  \n",
       "4    2          -8  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop columns with more null values and urls\n",
    "data['time_zone'].isnull().sum()\n",
    "data.drop(['time_zone', 'location', 'default_profile', 'default_profile_image', 'profile_background_color', 'profile_link_color', 'utc_offset', 'protected', 'verified'], axis=1, 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>statuses_count</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>lang</th>\n",
       "      <th>geo_enabled</th>\n",
       "      <th>profile_image_url</th>\n",
       "      <th>profile_banner_url</th>\n",
       "      <th>profile_use_background_image</th>\n",
       "      <th>...</th>\n",
       "      <th>profile_image_url_https</th>\n",
       "      <th>profile_sidebar_border_color</th>\n",
       "      <th>profile_background_tile</th>\n",
       "      <th>profile_sidebar_fill_color</th>\n",
       "      <th>profile_background_image_url</th>\n",
       "      <th>description</th>\n",
       "      <th>dataset</th>\n",
       "      <th>Fake_Profile</th>\n",
       "      <th>sex</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20370</td>\n",
       "      <td>5470</td>\n",
       "      <td>2385</td>\n",
       "      <td>145</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://a0.twimg.com/profile_images/1575057050/...</td>\n",
       "      <td>https://si0.twimg.com/profile_banners/3610511/...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>https://si0.twimg.com/profile_images/157505705...</td>\n",
       "      <td>F2E195</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FFF7CC</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>Founder of http://www.screenweek.it &amp; http://w...</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://a0.twimg.com/profile_images/1901298312/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>https://si0.twimg.com/profile_images/190129831...</td>\n",
       "      <td>FFFFFF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DDEEF6</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme1/bg.png</td>\n",
       "      <td>BSc degree (cum laude) in Computer Engineering...</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://a0.twimg.com/profile_images/1640620850/...</td>\n",
       "      <td>https://si0.twimg.com/profile_banners/5682702/...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>https://si0.twimg.com/profile_images/164062085...</td>\n",
       "      <td>181A1E</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>http://a0.twimg.com/profile_background_images/...</td>\n",
       "      <td>Cogito ergo bestemmio.</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://a0.twimg.com/profile_images/2797534662/...</td>\n",
       "      <td>https://si0.twimg.com/profile_banners/6067292/...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>https://si0.twimg.com/profile_images/279753466...</td>\n",
       "      <td>FFFFFF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95E8EC</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme4/bg.gif</td>\n",
       "      <td>Se la vita ti dà sarde, scapocciale!</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://a0.twimg.com/profile_images/1073412966/...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>https://si0.twimg.com/profile_images/107341296...</td>\n",
       "      <td>EEEEEE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>F6F6F6</td>\n",
       "      <td>http://a0.twimg.com/images/themes/theme18/bg.gif</td>\n",
       "      <td>Je me souviens</td>\n",
       "      <td>E13</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   statuses_count  followers_count  friends_count  favourites_count  \\\n",
       "0           20370             5470           2385               145   \n",
       "1            3131              506            381                 9   \n",
       "2            4024              264             87               323   \n",
       "3           40586              640            622              1118   \n",
       "4            2016               62             64                13   \n",
       "\n",
       "   listed_count  lang  geo_enabled  \\\n",
       "0            52     1          NaN   \n",
       "1            40     0          NaN   \n",
       "2            16     0          NaN   \n",
       "3            32     0          1.0   \n",
       "4             0     1          1.0   \n",
       "\n",
       "                                   profile_image_url  \\\n",
       "0  http://a0.twimg.com/profile_images/1575057050/...   \n",
       "1  http://a0.twimg.com/profile_images/1901298312/...   \n",
       "2  http://a0.twimg.com/profile_images/1640620850/...   \n",
       "3  http://a0.twimg.com/profile_images/2797534662/...   \n",
       "4  http://a0.twimg.com/profile_images/1073412966/...   \n",
       "\n",
       "                                  profile_banner_url  \\\n",
       "0  https://si0.twimg.com/profile_banners/3610511/...   \n",
       "1                                                NaN   \n",
       "2  https://si0.twimg.com/profile_banners/5682702/...   \n",
       "3  https://si0.twimg.com/profile_banners/6067292/...   \n",
       "4                                                NaN   \n",
       "\n",
       "   profile_use_background_image  ...  \\\n",
       "0                           1.0  ...   \n",
       "1                           1.0  ...   \n",
       "2                           1.0  ...   \n",
       "3                           1.0  ...   \n",
       "4                           1.0  ...   \n",
       "\n",
       "                             profile_image_url_https  \\\n",
       "0  https://si0.twimg.com/profile_images/157505705...   \n",
       "1  https://si0.twimg.com/profile_images/190129831...   \n",
       "2  https://si0.twimg.com/profile_images/164062085...   \n",
       "3  https://si0.twimg.com/profile_images/279753466...   \n",
       "4  https://si0.twimg.com/profile_images/107341296...   \n",
       "\n",
       "  profile_sidebar_border_color profile_background_tile  \\\n",
       "0                       F2E195                     NaN   \n",
       "1                       FFFFFF                     NaN   \n",
       "2                       181A1E                     1.0   \n",
       "3                       FFFFFF                     NaN   \n",
       "4                       EEEEEE                     NaN   \n",
       "\n",
       "  profile_sidebar_fill_color  \\\n",
       "0                     FFF7CC   \n",
       "1                     DDEEF6   \n",
       "2                          0   \n",
       "3                     95E8EC   \n",
       "4                     F6F6F6   \n",
       "\n",
       "                        profile_background_image_url  \\\n",
       "0  http://a0.twimg.com/profile_background_images/...   \n",
       "1    http://a0.twimg.com/images/themes/theme1/bg.png   \n",
       "2  http://a0.twimg.com/profile_background_images/...   \n",
       "3    http://a0.twimg.com/images/themes/theme4/bg.gif   \n",
       "4   http://a0.twimg.com/images/themes/theme18/bg.gif   \n",
       "\n",
       "                                         description dataset Fake_Profile sex  \\\n",
       "0  Founder of http://www.screenweek.it & http://w...     E13            0   2   \n",
       "1  BSc degree (cum laude) in Computer Engineering...     E13            0  -2   \n",
       "2                             Cogito ergo bestemmio.     E13            0   0   \n",
       "3               Se la vita ti dà sarde, scapocciale!     E13            0   0   \n",
       "4                                     Je me souviens     E13            0   2   \n",
       "\n",
       "   last_update  \n",
       "0           -8  \n",
       "1           -8  \n",
       "2           -8  \n",
       "3           -8  \n",
       "4           -8  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2818 entries, 0 to 1336\n",
      "Data columns (total 22 columns):\n",
      " #   Column                              Non-Null Count  Dtype  \n",
      "---  ------                              --------------  -----  \n",
      " 0   statuses_count                      2818 non-null   int64  \n",
      " 1   followers_count                     2818 non-null   int64  \n",
      " 2   friends_count                       2818 non-null   int64  \n",
      " 3   favourites_count                    2818 non-null   int64  \n",
      " 4   listed_count                        2818 non-null   int64  \n",
      " 5   lang                                2818 non-null   int64  \n",
      " 6   geo_enabled                         721 non-null    float64\n",
      " 7   profile_image_url                   2818 non-null   object \n",
      " 8   profile_banner_url                  987 non-null    object \n",
      " 9   profile_use_background_image        2760 non-null   float64\n",
      " 10  profile_background_image_url_https  2818 non-null   object \n",
      " 11  profile_text_color                  2818 non-null   object \n",
      " 12  profile_image_url_https             2818 non-null   object \n",
      " 13  profile_sidebar_border_color        2818 non-null   object \n",
      " 14  profile_background_tile             489 non-null    float64\n",
      " 15  profile_sidebar_fill_color          2818 non-null   object \n",
      " 16  profile_background_image_url        2818 non-null   object \n",
      " 17  description                         2547 non-null   object \n",
      " 18  dataset                             2818 non-null   object \n",
      " 19  Fake_Profile                        2818 non-null   int64  \n",
      " 20  sex                                 2818 non-null   int64  \n",
      " 21  last_update                         2818 non-null   int64  \n",
      "dtypes: float64(3), int64(9), object(10)\n",
      "memory usage: 506.4+ KB\n"
     ]
    }
   ],
   "source": [
    "#Check data info again\n",
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['statuses_count', 'followers_count', 'friends_count',\n",
       "       'favourites_count', 'listed_count', 'lang', 'geo_enabled',\n",
       "       'profile_image_url', 'profile_banner_url',\n",
       "       'profile_use_background_image', 'profile_background_image_url_https',\n",
       "       'profile_text_color', 'profile_image_url_https',\n",
       "       'profile_sidebar_border_color', 'profile_background_tile',\n",
       "       'profile_sidebar_fill_color', 'profile_background_image_url',\n",
       "       'description', 'dataset', 'Fake_Profile', 'sex', 'last_update'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Drop the cols with many null values \n",
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.drop(['geo_enabled', 'profile_image_url', 'profile_banner_url',\n",
    "        'profile_use_background_image', 'profile_background_image_url_https',\n",
    "        'profile_text_color', 'profile_image_url_https','profile_sidebar_border_color', \n",
    "        'profile_background_tile','profile_sidebar_fill_color', 'profile_background_image_url', \n",
    "        'dataset'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    2547\n",
       "True      271\n",
       "Name: description, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's look at description\n",
    "data['description'].isnull().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace rows with description with 1 and no description with 0\n",
    "data['description'] = data['description'].notnull().astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>statuses_count</th>\n",
       "      <th>followers_count</th>\n",
       "      <th>friends_count</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>listed_count</th>\n",
       "      <th>lang</th>\n",
       "      <th>description</th>\n",
       "      <th>Fake_Profile</th>\n",
       "      <th>sex</th>\n",
       "      <th>last_update</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20370</td>\n",
       "      <td>5470</td>\n",
       "      <td>2385</td>\n",
       "      <td>145</td>\n",
       "      <td>52</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3131</td>\n",
       "      <td>506</td>\n",
       "      <td>381</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4024</td>\n",
       "      <td>264</td>\n",
       "      <td>87</td>\n",
       "      <td>323</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40586</td>\n",
       "      <td>640</td>\n",
       "      <td>622</td>\n",
       "      <td>1118</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>62</td>\n",
       "      <td>64</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   statuses_count  followers_count  friends_count  favourites_count  \\\n",
       "0           20370             5470           2385               145   \n",
       "1            3131              506            381                 9   \n",
       "2            4024              264             87               323   \n",
       "3           40586              640            622              1118   \n",
       "4            2016               62             64                13   \n",
       "\n",
       "   listed_count  lang  description  Fake_Profile  sex  last_update  \n",
       "0            52     1            1             0    2           -8  \n",
       "1            40     0            1             0   -2           -8  \n",
       "2            16     0            1             0    0           -8  \n",
       "3            32     0            1             0    0           -8  \n",
       "4             0     1            1             0    2           -8  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a0f589ed0>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,18))\n",
    "sns.heatmap(data.corr(), annot = True, cmap = 'viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1a10bdd590>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1686.75x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data,hue='Fake_Profile',palette='bwr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x1a17bfac90>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAagAAAGoCAYAAAATsnHAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAdBUlEQVR4nO3df9xmdV3n8dfbGVAiC4mxVZgRMKLYMrF7HbN2l9IEf4Ft2gJu/sjiUa26ZbELD324au6uSo+2XEmiH2uaoGgszRo6bYZaFhNT/EZHJ0RmwF1BA33gJMz42T+uc8PF7XXPfV1wnbm+9329no/H9bjP+Z7vOedznZn7vOf8mHNSVUiS1JpHzLoASZJGMaAkSU0yoCRJTTKgJElNMqAkSU1aP+sCHiJvPZS0lmTWBbTIIyhJUpMMKElSk1brKb6H5KJtt45sP3PzpgNciSRpJXMVUMsZFVyGliTNlqf4JElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTVo/6wJaddG2W0e2n7l50wGuRJLmk0dQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCatn3UBq81F2279prYzN2+aQSWStLZ5BCVJapIBJUlqkqf4pmDUab/9GXVKcLllePpQ0rxKVc26hokl+QhwxEOY9QjgzimX0yfr7Zf19st6x3dnVZ0yo3U3a1UG1EOVZHtVLcy6jnFZb7+st1/Wq4fLa1CSpCYZUJKkJs1bQF046wImZL39st5+Wa8elrm6BiVJWj3m7QhKkrRKGFCSpCYZUJKkJhlQkqQmGVCSpCatyoA65ZRTCvDjx4+ftfIZyxre9420KgPqzjtX0+O9JGk65m3ftyoDSpK09hlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCYZUJKkJhlQkqQmGVCSpCb1GlBJ/iDJF5PcsMz0JHl7kp1JrkvylD7rkSStHut7Xv67gHcA715m+rOB47rPZuCd3c+pOvqcP33Yyzj04HVUFV+77xsjp69LOGPzRhaecDjnbd3B7Xft4fGHHcLZJx/PC048EoDXXXY9F2/bxb6htxgvzvfmF3z/RPVcdvVty65nFhbrue2uPaxL2FfFkQ3UNVxbK9tK0nh6Daiq+kSSo/fT5TTg3TV47/yVSQ5L8riq+sK0aphGOAHcc+++/U7fV8UfXXkrF115K4sRdttdezj30usB2P75L/NHV9667HzA2CF12dW3ce6l17Pnvn3ftJ5Z7HiX1rMYwLOua1RtLdQkaTyzvgZ1JLBraHx317ZqLT2+2nPfPs7buoOLt+0a2X/RStOHnbd1x/073KXrmYVR9SyaZV3Q3raSNL6+T/GtJCPaRj56PclZwFkAmzZt6rOmqbv9rj0rPk9/+LTfOMubpL1vK613VnXtb92zrEmaxPC+74h/diQXbRuccTlz8+raDz4Usz6C2g1sHBo/Crh9VMequrCqFqpqYcOGDQekuGl5/GGHsC6jsvgBK01furxJ2vu20npnVdf+1j3LmqRJDO/7Hn3Y4bMu54CadUBtAV7S3c33NODuaV5/moWlG/SQg9Zx9snHc8bmjSP7L1pp+rCzTz6eQw5aN3I9szCqnkWzrAva21aSxtfrKb4kFwMnAUck2Q38Z+AggKq6ALgceA6wE/ga8PJp13DLW57bxF18ixfkp3EX3+KyWrkzbbie1u7ia21bSRpfaoJrH61YWFio7du3z7oMSZqWsc7xH/u9T6o3v+tDwJq7BjXy+8/6JglJ0pgOP/TgtRZM+zXra1CSJI1kQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmmRASZKaZEBJkppkQEmSmtR7QCU5JcmOJDuTnDNi+qYkVyS5Osl1SZ7Td02SpPb1GlBJ1gHnA88GTgDOSHLCkm6vAy6pqhOB04Hf7rMmSdLq0PcR1FOBnVV1c1XdC7wPOG1JnwK+rRv+duD2nmuSJK0C63te/pHArqHx3cDmJX3eAPxZklcBhwLP7LkmSdIq0PcRVEa01ZLxM4B3VdVRwHOA9yT5prqSnJVke5Ltd9xxRw+lSlJ75nnf13dA7QY2Do0fxTefwnsFcAlAVf0N8CjgiKULqqoLq2qhqhY2bNjQU7mS1JZ53vf1HVBXAcclOSbJwQxugtiypM+twDMAknwvg4Car38mSJK+Sa8BVVV7gVcCW4FPMbhb78Ykb0pyatftV4CfS3ItcDHwsqpaehpQkjRn+r5Jgqq6HLh8Sdvrh4ZvAn647zokSauLT5KQJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1yYCSJDXJgJIkNcmAkiQ1qfeASnJKkh1JdiY5Z5k+P5XkpiQ3Jrmo75okSe1b3+fCk6wDzgd+HNgNXJVkS1XdNNTnOOBc4Ier6h+TPLbPmiRJq0PfR1BPBXZW1c1VdS/wPuC0JX1+Dji/qv4RoKq+2HNNkqRVoO+AOhLYNTS+u2sb9t3Adyf5ZJIrk5zSc02SpFWg11N8QEa01YgajgNOAo4C/jLJ91XVXQ9aUHIWcBbApk2bpl+pJDVonvd9Ex1BJTkkyfETzLIb2Dg0fhRw+4g+f1JV91XV54AdDALrQarqwqpaqKqFDRs2TFK2JK1a87zvGzugkjwfuAb4SDf+5CRbVpjtKuC4JMckORg4HVg6z2XAj3bLPILBKb+bx61LkrQ2TXIE9QYGNz3cBVBV1wBH72+GqtoLvBLYCnwKuKSqbkzypiSndt22Al9KchNwBXB2VX1pki8hSVp7JrkGtbeq7k5GXVZaXlVdDly+pO31Q8MFvKb7SJIETBZQNyQ5E1jX/d+lVwN/3U9ZkqR5N8kpvlcB/xz4OnAx8BXgl/ooSpKksY+gquprwGu7jyRJvVoxoJL8b775/y7dr6pOXW6aJEkP1ThHUL/eexWSJC2xYkBV1ccPRCGSJA0b5xTfJVX1U0muZ8Spvqp6Ui+VSZLm2jin+Bbv1Hten4VIkjRsnID6EPAU4M1V9dM91yNJEjBeQB2c5KXA05P8m6UTq+rS6ZclSZp34wTUzwMvBg4Dnr9kWgEGlCRp6sa5i++vgL9Ksr2qfv8A1CRJ0kTP4ntPklcD/6ob/zhwQVXdN/2yJEnzbpKA+m3goO4nwE8D7wR+dtpFSZI0SUD9i6r6gaHxv0hy7bQLkiQJJnua+b4kT1wcSXIssG/6JUmSNNkR1K8CVyS5GQjwBODlvVQlSZp7YwVUknXADwDHAcczCKhPV9XXe6xNkjTHxjrFV1X7gFOr6utVdV1VXWs4SZL6NMkpvr9O8g7g/cA9i41V9fdTr0qSNPcmCaindz/fNNRWwI9NrxxJkgYmCagXVdWdvVUiSdKQFa9BJXl+kjuA65LsTvL0leaRJOnhGucmif8C/Muqejzwk8B/67ckSZLGC6i9VfVpgKraBjy635IkSRrvGtRjk7xmufGq+o3plyVJmnfjBNTv8uCjpqXjkiRN3Tjvg3rjOAtKcm5VeX1KkjQVkzwsdiUvmuKyJElzbpoBlSkuS5I056YZUDXFZUmS5pxHUJKkJk0zoD4wxWVJkubc2AGV5LuTfDTJDd34k5K8bnF6Vf3XPgqUJM2nSY6gfhc4F7gPoKquA07voyhJkiYJqG+pqr9d0rZ3msVIkrRokoC6M8kT6e7WS/JC4Au9VCVJmnuTvA/q3wMXAt+T5Dbgc8CLe6lKkjT3Jgmox1TVM5McCjyiqr6a5PnA53uqTZI0xya6SSLJ91fVPV04nQ68bsW5JEl6CCY5gnoh8MEkLwZ+BHgJ8KxeqpIkzb2xA6qqbu6Omi4DdgHPqqo9vVUmSZprKwZUkut58HP2DgfWAduSUFVP6qs4SdL8GucI6nm9VyFJ0hLjvLDwQXfpJXks8KjeKpIkicmexXdqks8y+P9PHwduAT7cU12SpDk3yW3mvwY8DfhMVR0DPAP4ZC9VSZLm3iQBdV9VfQl4RJJHVNUVwJN7qkuSNOcmCai7knwr8AngvUl+izEeFpvklCQ7kuxMcs5++r0wSSVZmKAmSdIatWJAJdnUDZ4GfA34ZeAjwD8Az19h3nXA+cCzgROAM5KcMKLfo4FXA9smKV6StHaNcwR1GUBV3QN8oKr2VtUfVtXbu1N++/NUYGdV3VxV9wLvYxB0S/0a8DbgnyaoXZK0ho0TUBkaPnbC5R/J4KkTi3Z3bQ8sPDkR2FhVH5pw2ZKkNWycgKplhseREW33LyPJI4D/DvzKigtKzkqyPcn2O+64Y8IyJGl1mud93zgB9QNJvpLkq8CTuuGvJPlqkq+sMO9uYOPQ+FHA7UPjjwa+D/hYklsY3Ma+ZdSNElV1YVUtVNXChg0bxihbkla/ed73jfMkiXUPY/lXAcclOQa4DTgdOHNo2XcDRyyOJ/kY8KtVtf1hrFOStAZMcpv5xKpqL/BKYCvwKeCSqroxyZuSnNrnuiVJq9sk74N6SKrqcuDyJW2vX6bvSX3XI0laHXo9gpIk6aEyoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElN6j2gkpySZEeSnUnOGTH9NUluSnJdko8meULfNUmS2tdrQCVZB5wPPBs4ATgjyQlLul0NLFTVk4APAm/rsyZJ0urQ9xHUU4GdVXVzVd0LvA84bbhDVV1RVV/rRq8Ejuq5JknSKtB3QB0J7Boa3921LecVwId7rUiStCqs73n5GdFWIzsm/w5YAP71MtPPAs4C2LRp07Tqk6SmzfO+r+8jqN3AxqHxo4Dbl3ZK8kzgtcCpVfX1UQuqqguraqGqFjZs2NBLsZLUmnne9/UdUFcBxyU5JsnBwOnAluEOSU4EfodBOH2x53okSatErwFVVXuBVwJbgU8Bl1TVjUnelOTUrtt5wLcCH0hyTZItyyxOkjRH+r4GRVVdDly+pO31Q8PP7LsGSdLq45MkJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNWj/rAiRJ4/nyPfdy0bZbZ13GVJy5edOKfTyCkiQ1qfeASnJKkh1JdiY5Z8T0RyZ5fzd9W5Kj+65JktS+Xk/xJVkHnA/8OLAbuCrJlqq6aajbK4B/rKrvSnI68Fbg306zjqPP+dNpLm4mbnnLc+8fvuzq2zhv6w5uv2sPjz/sEM4++XhecOKR909/3WXXc/G2XeyrYl3CGZs38uYXfP9+511pmUtN2n/aRq0fmGlNml+z/n1Yq/q+BvVUYGdV3QyQ5H3AacBwQJ0GvKEb/iDwjiSpqppGAWshnGDwPW55y3O57OrbOPfS69lz3z4AbrtrD+deej0ALzjxSF532fX80ZUPnKPeV3X/+MITDh857/bPf5k//rvbll3mUivV0LdR6z/7g9dCwX3fqJnUpPk169+HtazvU3xHAruGxnd3bSP7VNVe4G7gO3qua9U6b+uO+38RFu25bx/nbd0BwMXbdo2ajYu37Vp23ou37drvMietoW+j1n/fvro/nGZRk+bXrH8f1rK+j6Ayom3pkdE4fUhyFnAWwKZNK9/9sVbdftee/bbvW+bAc1/VsvMuN89K6xq3fdomWc+Bqknzq+/fh6X7vnHuflsr+j6C2g1sHBo/Crh9uT5J1gPfDnx56YKq6sKqWqiqhQ0bNvRUbvsef9gh+21fl1F5P2hfbt7l5llpXeO2T9sk6zlQNWl+9f37MM/7vr4D6irguCTHJDkYOB3YsqTPFuCl3fALgb+Y1vWntejsk4/nkIPWPajtkIPW3X+TwBmbN46ajTM2b1x23jM2b9zvMietoW+j1n/QunDQIx4ctAeyJs2vWf8+rGW9nuKrqr1JXglsBdYBf1BVNyZ5E7C9qrYAvw+8J8lOBkdOp0+zhlve8tw1caPE4l18ixddl7tjaPFuveXu4ltu3oUnHD72XUgr1dC35dY/y5o0v2b9+7CWZTUerCwsLNT27dtnXYYkTcvo8+xLrOF938jv75MkJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTTKgJElNMqAkSU0yoCRJTVqVT5JIcgfw+Ycw6xHAnVMup0/W2y/r7Zf1ju/OqjplpU5JPjJOv7ViVQbUQ5Vke1UtzLqOcVlvv6y3X9arh8tTfJKkJhlQkqQmzVtAXTjrAiZkvf2y3n5Zrx6WuboGJUlaPebtCEqStEoYUJKkJs1NQCU5JcmOJDuTnDOjGjYmuSLJp5LcmOQ/dO2HJ/k/ST7b/XxM154kb+9qvi7JU4aW9dKu/2eTvLTnutcluTrJh7rxY5Js69b9/iQHd+2P7MZ3dtOPHlrGuV37jiQn91jrYUk+mOTT3Xb+oZa3b5Jf7v4u3JDk4iSPamn7JvmDJF9McsNQ29S2Z5IfTHJ9N8/bk4z1ZtkJ6z2v+/twXZL/leSwoWkjt9ty+4vl/mzUk6pa8x9gHfAPwLHAwcC1wAkzqONxwFO64UcDnwFOAN4GnNO1nwO8tRt+DvBhBq9DfhqwrWs/HLi5+/mYbvgxPdb9GuAi4EPd+CXA6d3wBcAvdMO/CFzQDZ8OvL8bPqHb5o8Ejun+LNb1VOsfAj/bDR8MHNbq9gWOBD4HHDK0XV/W0vYF/hXwFOCGobapbU/gb4Ef6ub5MPDsHup9FrC+G37rUL0jtxv72V8s92fjp5/PzAs4IF9y8AuwdWj8XODcBur6E+DHgR3A47q2xwE7uuHfAc4Y6r+jm34G8DtD7Q/qN+UajwI+CvwY8KFuR3Ln0C/8/dsW2Ar8UDe8vuuXpdt7uN+Ua/02Bjv8LGlvcvsyCKhd3Y57fbd9T25t+wJHL9nhT2V7dtM+PdT+oH7TqnfJtJ8A3tsNj9xuLLO/2N/ffT/9fOblFN/ijmDR7q5tZrrTMycC24DvrKovAHQ/H9t1W67uA/l9fhP4j8A3uvHvAO6qqr0j1n1/Xd30u7v+B6reY4E7gP/ZnZL8vSSH0uj2rarbgF8HbgW+wGB7/R3tbt9F09qeR3bDS9v79DMMjtRYoa5R7fv7u68ezEtAjTqvPbP765N8K/DHwC9V1Vf213VEW+2nfaqSPA/4YlX93Rg17W/agdr+6xmc3nlnVZ0I3MPgFNRyZr19HwOcxuD00uOBQ4Fn72fds96+K5m0vgNad5LXAnuB9y42TVhXK9t5bsxLQO0GNg6NHwXcPotCkhzEIJzeW1WXds3/L8njuumPA77YtS9X94H6Pj8MnJrkFuB9DE7z/SZwWJL1I9Z9f13d9G8HvnwA690N7K6qbd34BxkEVqvb95nA56rqjqq6D7gUeDrtbt9F09qeu7vhpe1T192Y8TzgxdWdn3sI9d7J8n826sG8BNRVwHHdHTgHM7jAvOVAF9HdofT7wKeq6jeGJm0BFu9seimDa1OL7S/p7o56GnB3d0plK/CsJI/p/hX+rK5tqqrq3Ko6qqqOZrDN/qKqXgxcAbxwmXoXv8cLu/7VtZ/e3YV2DHAcg4vj0673/wK7khzfNT0DuIlGty+DU3tPS/It3d+NxXqb3L5DprI9u2lfTfK07vu/ZGhZU5PkFOA/AadW1deWfI9R223k/qLb1sv92agPs74IdqA+DO4w+gyDu3NeO6MafoTBKYHrgGu6z3MYnNv+KPDZ7ufhXf8A53c1Xw8sDC3rZ4Cd3eflB6D2k3jgLr5jGfwi7wQ+ADyya39UN76zm37s0Pyv7b7HDh7mnVor1PlkYHu3jS9jcNdYs9sXeCPwaeAG4D0M7ihrZvsCFzO4PnYfgyOLV0xzewIL3Xf/B+AdLLnBZUr17mRwTWnxd+6ClbYby+wvlvuz8dPPx0cdSZKaNC+n+CRJq4wBJUlqkgElSWqSASVJapIBJUlqkgElSWqSAaVmJXl1Bq/MeO+S9oUkb5/SOl6W5B3TWNa0ZPDKkF+cdR3SrK1fuYs0M7/I4D9Pfm6xIcn6qtrO4D/jrlWHMfjuvz3rQqRZ8ghKTUpyAYP/tb8lyd1JLkzyZ8C7k5yUB16eeGj3krqruieYn9a1vyzJpUk+0r1c7m1Dy355ks8k+TiD5w0utr8ogxcHXpvkE/upbV2SX+9etHddkld17c/oari+q+mRXfstSY7ohheSfKwbfkPX72NJbk7y6m4VbwGemOSaJOdNbaNKq4xHUGpSVf189wy1HwVeCTwf+JGq2pPkpKGur2XwTLqfyeBNqX+b5M+7aU9m8EqTrwM7kvwPBk+zfiPwgwxeV3EFcHXX//XAyVV1W4beujrCWQyeQH5iVe3N4A2zjwLeBTyjqj6T5N3ALzB4uO7+fE/3HR/d1fhOBk9g/76qevIK80prmkdQWi22VNWeEe3PAs5Jcg3wMQbPq9vUTftoVd1dVf/E4CGsTwA2Ax+rwRPE7wXeP7SsTwLvSvJzDN6qupxnMnie216AqvoycDyDJ5N/puvzhwze7rqSP62qr1fVnQyeCv6dY8wjzQWPoLRa3LNMe4CfrKodD2pMNjM4clq0jwf+vo98AGV31LYZeC5wTZInV9WXllnn0mWMelfQor088I/BRy2ZtlyN0tzzCEqr3VbgVd3rGkhy4gr9twEnJfmODN7N9aLFCUmeWFXbqur1DN79s3GZZfwZ8POL7wVKcjiDJ5IfneS7uj4/DXy8G76FwSlFgJ8c4zt9lcEpP2muGVBa7X4NOAi4LskN3fiyavAOojcAfwP8OfD3Q5PP625wuAH4BHDtMov5PQbvcrouybXAmd1pxJcDH0hyPfAN4IKu/xuB30rylwyOkvarO2r7ZHfDhjdJaG75ug1JUpM8gpIkNckLstIykpwMvHVJ8+eq6idmUY80bzzFJ0lqkqf4JElNMqAkSU0yoCRJTTKgJElN+v8dFGoZy69OcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='friends_count',y='Fake_Profile',data=data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['statuses_count', 'followers_count', 'friends_count',\n",
       "       'favourites_count', 'listed_count', 'lang', 'description',\n",
       "       'Fake_Profile', 'sex', 'last_update'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Split the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Let's split the data\n",
    "\n",
    "x = data[['statuses_count', 'followers_count', 'friends_count','favourites_count', \n",
    "           'listed_count', 'lang', 'sex', 'last_update']]\n",
    "y = data['Fake_Profile']\n",
    "\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=0.20, random_state=44)\n",
    "\n",
    "y = data['Fake_Profile']\n",
    "\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=0.20, random_state=44)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find out important features\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1756195  0.16803492 0.19847425 0.15014024 0.09554705 0.06034322\n",
      " 0.06143054 0.09041028]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Find out what features are important using ExtraTreesRegressor\n",
    "from sklearn.ensemble import ExtraTreesRegressor\n",
    "selection = ExtraTreesRegressor()\n",
    "selection.fit(x,y)\n",
    "print(selection.feature_importances_)\n",
    "plt.figure(figsize = (12,8))\n",
    "feat_importances = pd.Series(selection.feature_importances_, index=x.columns)\n",
    "feat_importances.nlargest(20).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
       "                       criterion='gini', 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=40,\n",
       "                       n_jobs=None, oob_score=True, random_state=None,\n",
       "                       verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train the model \n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier(n_estimators=40,oob_score=True)\n",
    "rf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make the predictions on test data\n",
    "\n",
    "y_pred = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.95      1.00      0.97       535\n",
      "           1       0.50      0.03      0.06        29\n",
      "\n",
      "    accuracy                           0.95       564\n",
      "   macro avg       0.73      0.52      0.52       564\n",
      "weighted avg       0.93      0.95      0.93       564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Defining functions to evaluate model performance\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, learning_curve\n",
    "import sklearn.metrics as metrics\n",
    "\n",
    "def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,\n",
    "                        n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.title(title)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(*ylim)\n",
    "    plt.xlabel(\"Training examples\")\n",
    "    plt.ylabel(\"Score\")\n",
    "    train_sizes, train_scores, test_scores = learning_curve(\n",
    "        estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)\n",
    "    train_scores_mean = np.mean(train_scores, axis=1)\n",
    "    train_scores_std = np.std(train_scores, axis=1)\n",
    "    test_scores_mean = np.mean(test_scores, axis=1)\n",
    "    test_scores_std = np.std(test_scores, axis=1)\n",
    "    plt.grid()\n",
    "\n",
    "    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,\n",
    "                     train_scores_mean + train_scores_std, alpha=0.1,\n",
    "                     color=\"r\")\n",
    "    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,\n",
    "                     test_scores_mean + test_scores_std, alpha=0.1, color=\"g\")\n",
    "    plt.plot(train_sizes, train_scores_mean, 'o-', color=\"r\",\n",
    "             label=\"Training score\")\n",
    "    plt.plot(train_sizes, test_scores_mean, 'o-', color=\"g\",\n",
    "             label=\"Cross-validation score\")\n",
    "\n",
    "    plt.legend(loc=\"best\")\n",
    "    return plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(cm, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    target_names=['Fake','Genuine']\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(target_names))\n",
    "    plt.xticks(tick_marks, target_names, rotation=45)\n",
    "    plt.yticks(tick_marks, target_names)\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(y_test, y_pred):\n",
    "    false_positive_rate, true_positive_rate, thresholds = metrics.roc_curve(y_test, y_pred)\n",
    "\n",
    "    print(\"False Positive rate: \",false_positive_rate)\n",
    "    print(\"True Positive rate: \",true_positive_rate)\n",
    "\n",
    "\n",
    "    roc_auc = metrics.auc(false_positive_rate, true_positive_rate)\n",
    "\n",
    "    plt.title('Receiver Operating Characteristic')\n",
    "    plt.plot(false_positive_rate, true_positive_rate, 'b',\n",
    "    label='AUC = %0.2f'% roc_auc)\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.plot([0,1],[0,1],'r--')\n",
    "    plt.xlim([-0.1,1.2])\n",
    "    plt.ylim([-0.1,1.2])\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Cross Validatin Score:  0.9454279379157426\n"
     ]
    }
   ],
   "source": [
    "cl_scores = cross_val_score(rf, X_train,y_train, cv=5)\n",
    "print(\"Average Cross Validatin Score: \", cl_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Learning Curve'\n",
    "plot_learning_curve(rf, title, X_train, y_train, cv=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[534   1]\n",
      " [ 28   1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "print('Confusion matrix, without normalization')\n",
    "print(cm)\n",
    "plot_confusion_matrix(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[0.99813084 0.00186916]\n",
      " [0.96551724 0.03448276]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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DMTPrTEdzvqyqumNJ0lhgDDCoY1tEXNWsoMzMivprcx4ASV8jzWgyhjTS/2DgbsBJ1Mz6RHlTaHVX548EPgj8MSJOAnYG1m9qVGZmWcdg+2perVBNc355RLwpaZWkYcBiwIPtzazP9OvmPDBb0sbAZaQr9q8B9zc1KjOzghLn0KrunT8zL14iaTowLCIebm5YZmaJUKkfmdzdYPvdutsXEXOaE1JJtLXDoCGtjsI68cZKT2e7TlE/HScKXNDNvgA+0OBYzMw6Vc0V8FbpbrD9/n0ZiJlZZ0T/v7BkZtZSJW7NO4maWbmVfVJmJ1EzK70S59CqZraXpOMlnZfXR0sa1/zQzMwSqbpXK1Rz0etiYDxwTF5/FZjctIjMzAo6HplczasVqmnO7xkRu0l6ECAiXsyPTjYz6xPtJW7OV5NEV0pqJz8SRNKmwJtNjcrMLFMLa5nVqKY5/33gl8AISd8kTYP3raZGZWZWUOY+0Wrunb9G0gOk6fAEHB4Rv296ZGZmWZmvzlczKfNo4HXg18VtEbGgmYGZmcFbF5bKqpo+0Vt464F1g4CtgceAnZoYl5lZImgv8c3z1TTn31Ncz7M7ndbF4WZmDacSPyCk13csRcQcSXs0Ixgzs0r9/mmfkj5fWG0DdgOWNC0iM7MK/TqJAkMLy6tIfaS/aE44ZmZrEv14ApI8yH5IRHyxj+IxM1tTA8eASpoI/DvQDlweEf/cxXFHAtcDe0TE7O7K7O7xIAMiYlV3jwkxM+sLjRjilCuFk4EDgUXALElTI2JexXFDgbOB+6opt7ua6P2k/s+5kqaSsvKyjp0RcWOvPoGZWQ0aeGFpHDA/Ip4AkHQdcBgwr+K4bwD/CpxTTaHV9IluAjxPeqZSx3jRAJxEzawPiPbqa6LDJRWb31MiYkpe3gJYWNi3CNhzjTNJuwKjIuJmSXUn0RH5yvzveCt5dohqCjczq1d6xlLVhy+NiN27KarSX3KZpDbgQuDEXoTXbRJtB4b0dGIzs6ZSw5rzi4BRhfWRwLOF9aHAWGBmfjDeO4Cpkg7t7uJSd0n0uYiYVHu8ZmaN0aB752cB20vaGngGOBo4tmNnRLwMDO9YlzQTOKfmq/N0XgM1M+tTjRonmkcbnQXMILW0r4iIRyVNAmZHxNRayu0uiX6wlgLNzBqtUeNEI2IaMK1i23ldHDuhmjK7TKIR8UJvgjMzawZR3ezxreJHJptZuSk9IqSsnETNrNQEvRkn2uecRM2s9MqbQp1EzawfKHFF1EnUzMpO7hM1M6uV+0TNzOpU3hTqJGpmZechTmZmtfNgezOzOrkmamZWhxI/p85J1MzKLTXny5tFnUTNrPRK3Jp3EjWzshNyTdTMrDYebG9mVg+5OW9mVhcnUTOzOrhP1MysRu4TNTOrU4lzqJOomZVfmZvzTb2vX9Jmkn4q6QlJD0i6V9JHG3yOQyV9uZFlmll5iHTbZzWvVmhaTVRpxoCbgB9HxLF525bAoY08T0RMBaY2skwzKxGJthK355tZE/0AsCIiLunYEBFPR8QPJLVL+o6kWZIelnQagKQJkmZKukHSHyRdk5Mxkp6SNDwv7y5pZl4+UdJFeflKSd+X9P9z7ffIjnNL+mLhfOc38XObWYOpylcrNLNPdCdgThf7TgZejog9JK0P3CPp1rxv1/zeZ4F7gL2Au3tx3s2BvYF3k2qoN0g6CNgeGEf6rqdK2jci7iy+UdKpwKkADBzWi1OaWbOk5nx5a6J9dmFJ0mRSclsBPA28t1BT3IiU5FYA90fEovyeucBW9C6J3hQRbwLzJG2Wtx2UXw/m9SH5fGsk0YiYAkwBaBuyefTm85lZ85Q3hTY3iT4KHNGxEhF/l5vjs4EFwGciYkbxDZImAG8UNq0uxLiKt7ofBnVz3uL7Vfj/tyPi0l5+BjMrgTJPytzMPtHbgUGSzihs2zD/fwZwhqT1ACS9S9LgHsp7CnhfXj6im+M6MwP4tKQh+XxbSBrRyzLMrEWk6l6t0LSaaESEpMOBCyV9CVgCLAP+Abie1Eyfky8cLQEO76HI84EfSfoKcF8vY7lV0o7Avfkv2mvA8cDi3pRjZq1R3nooKMJdf51pG7J5rP+eT7U6DOvEH2//ZqtDsG5svOGAByJi90aVt+N7do2rps6s6thx22zc0HNXw3csmVmppeFL5a2LOomaWbm18G6kapT5cc5mZkmDRttLmijpMUnzO7tdXNLnJc3LN+Xclu+y7JaTqJmVnKr+r9tSpHZgMnAwMAY4RtKYisMeBHaPiPcCNwD/2lN0TqJmVnoNGuI0DpgfEU9ExArgOuCw4gERcUdEvJ5XfwuM7KlQJ1EzKzXRqyQ6XNLswuvUQlFbAAsL64vytq6cDPxnT/H5wpKZlV4vrs4v7WaIU2eFdDrGU9LxwO7Afj2d0EnUzEqvQXcjLQJGFdZHkiY6qjiXDgC+CuwXEW9U7q/k5ryZlV6DLs7PAraXtLWkgcDRVMxFLGlX4FLg0Iio6o5G10TNrNzUmAlIImKVpLNIc2m0A1dExKOSJgGz8wTv3yHN8nZ9PueCiOh2InknUTMrtY4LS40QEdOAaRXbzissH9DbMp1Ezaz0SnzDkpOomfUDJc6iTqJmVnp+PIiZWR3Km0KdRM2sPyhxFnUSNbNS83yiZmb1KPl8ok6iZlZ+TqJmZrXqea7QVnISNbPSK/EIJydRMyu3KicXaRknUTMrvUZMQNIsTqJmVnolzqFOomZWfiXOoU6iZlZy1T2ErmWcRM2s1NJ8ouXNok6iZlZ65U2hTqJm1g+UuCLqJGpm5ec7lszM6uCaqJlZjeSr82Zm9XFz3sysHuXNoU6iZlZ+npTZzKxmnk/UzKxm6Y6lVkfRtbZWB2Bm1p+5JmpmpddW4qqok6iZlZvHiZqZ1c6PBzEzq1eJs6iTqJmVnvtEzczqUN4U6iRqZv1BibOok6iZlV6Z71hSRLQ6hlKStAR4utVxNNBwYGmrg7BOrW0/my0jYtNGFSZpOuk7qsbSiJjYqHNXw0l0HSFpdkTs3uo47K/5Z9O/+bZPM7M6OImamdXBSXTdMaXVAViX/LPpx9wnamZWB9dEzczq4CRqZlYHJ1Ezszo4ia5jpBLP5GDWDzmJrkMkKfKVREltktZrdUzWM//hKzdfnV8HSTobGAsMBaZExB0tDsmqIOlYYDtgLvBwRDzV2ogMXBNd50g6FTgUOB8YAZzY0oCsKpLOAM4Efg/8ANi/tRFZByfRtZykyp/xIOCTwMeBN4CTJQ2UNKLPg7OqSNoEeA9wCLAB8N/AVblLZv2WBmdOomu7iHgTUlNQ0hhgK+B2YFxEHBIRq4CTgZMktbcuUutQ2QcaES+QZnm6GTguIg6MiNXAGYAnLmkxJ9G1lKS/kfTFwqZjSb+I5wLLgcX5uFOAs4Cb8i+mtVDFxb+xkt6bdz0CBHBh3nc0cDrwp5YEan/hSZnXXqtItcs3I+ICYAjwzoiYK+lDwDWSrgS2BI6KiMdaGKuRul4KLYfPkpLkC5IejojTJW0NnJL3DQeOiYj5LQzZcBJda0XEbEknAJdIeh24G3hd0jsj4llJJ5EnAo6I11oZq4GkgRGxIi//DTAe2BNYBjwk6aKIOEvSYNIV+mcjYknrIrYOHuK0FinWZArbxgEXA7sBvwDeBvyZ9NSaoyPi1T4P1NYgaQfgYOAiYHPgMlJX28kRsTCP550N/E9EfKx1kVpnnETXEhV9aZ8gNd+fiIg7JO1C6ku7MyK+lo95R0T8sXURW4fc77mQlEAXAjsDnwN+CdwWEc9JGgjcARwFPBf+xS0NX1haC+SE2JFAzyL9Ar4OTJd0fETMBb4AnJD708AXJFqu4yp8RDwMrAD+njR+90Hgh6Ta6QGSRkbEiojYKyKedQItFyfRfi5fJJoqaYSknYEjgInAJsDvgEmSzoyIOcDHgF8B+Bex9Qp/+PYndbF8H1gN/CNwL/Aj0njevSW1+/bPcnJzvh+TNBH4KvDNiJiet7WR7kj6bERMyBeXfgwcGRE3ti5a64ykIaSZ7VcCJwE7AKeQboT4FvA+YH5EPNOyIK1bron2U/kulmnABRExXdJ2kn4MDAQGk+5qAXgVuJbURLTyWUb6Q/hnUjJ9DLgceDupC+ZOJ9Byc020H8tN+W+Q7n+/ELglIv5N0j6ku5CGAlsDH/NkFeWSWwgRET/J66OAScArpD7tdwEvRoT7rkvO40T7sYi4RdJq0qw+X4mIf8u77gVeA3YEZjuBtl5x9ET2KjBZ0sqI+BnwDDCdlEhXRcQXWhGn9Z5romsBSQeSZvbZMyJebnU8tqaK4WfvAxZGxGJJE4ArSH8Ar5P0cWAMcHlELGpdxNYbTqJrCUkHA98DxucJK6wEKhLomcBngBeAK0l9n/uQrso/AuwNHBQRj7cmWquFm/NriYj4zzwg+zeSdk+b/Bey1QoJ9DBSktwFOBD4MGlau8nAh4B3AP/orpf+xzXRtYykIb4XvlzyXK2XAltExLi87RDgMOB/gKsj4rkWhmh18BCntYwTaOsVB8VLGhARi0ljPl+S9E2AiJhGupD0TtLwJuunXBM1axJJp5FmXFoC3EB6HMtZpDkNzsvHDI6IZa2L0urlmqhZgxQfxZKnGjyeNCPTV0n9ng8A/w7sIuncfOjrfR2nNZZromYNIGlv0gD5h/NcrheQrsDvSkqmH4qIlZIGkcbvLvadSGsHX503q1Oew+DbpLvGhuXNT5OGnK2OiIPycV8lNeWvbUmg1hROomZ1kLQfaTLl4yLivsKuYaRbOC+VtCHpSZ1HAcf0fZTWTE6iZvXZFfhBMYFK+hbpwYCrSI/4+CywPnBCRPy+JVFa0ziJmtWgcCfStsDLhe0HA6OBI4GfkGaq/z7p+oPvJFsL+eq8WQ0Kd4PdBOwpabe8/hvg0xExG7gaeCMiXnQCXXs5iZrV57fAPcDRksZFxMqIWCHpGFI/6L2tDc+azUOczOokaQvS/K0fIE1+vZzUnD88Iua1MjZrPidRswaQtAHpsdQHkuYGnenZmNYNTqJmZnVwn6iZWR2cRM3M6uAkamZWBydRM7M6OImamdXBSdTMrA5OotYjSaslzZX0O0nX51mJai3rSklH5uXLJY3p5tgJkt5fwzmekjS82u0Vx/Tq8SqSvi7pnN7GaGsPJ1GrxvKI2CUixgIrgNOLOyW111JoRJzSwx09E4BeJ1GzvuQkar11F7BdriXeIemnwCOS2iV9R9IsSQ/n5wuh5CJJ8yTdQnrOEHnfzPx4ZyRNlDRH0kOSbpO0FSlZfy7XgveRtKmkX+RzzJK0V37v2yXdKulBSZcCogeSbpL0gKRHJZ1ase+CHMttkjbN27aVND2/5y5J727El2n9n6fCs6pJGgAcTHpKJcA4YGxEPJkT0csRsYek9YF7JN1Kmm9zB+A9wGbAPOCKinI3JT2LaN9c1iYR8YKkS4DXIuK7+bifAhdGxN2SRgMzSI/a+Bpwd0RMkvQhYI2k2IVP53NsAMyS9IuIeB4YDMyJiC9IOi+XfRYwBTg9Ih6XtCdwMeleeVvHOYlaNTaQNDcv3wX8iNTMvj8inszbDwLe29HfCWwEbA/sC1wbEauBZyXd3kn5fwPc2VFWN9PGHQCMKTyReJikofkcH8vvvUXSi1V8prMlfTQvj8qxPg+8Cfwsb/8JcKOkIfnzXl849/pVnMPWAU6iVo3lEbFLcUNOJsVH/Qr4TETMqDjuEKCnCRpUxTGQup/GR8TyTmKpehIISRNICXl8RLwuaSYwqIvDI5/3pcrvwAzcJ2qNMwM4Q9J6AJLeJWkwcCdprs12SZsD+3fy3nuB/SRtnd+7Sd7+KjC0cNytpKY1+biOpHYncFzedjDwth5i3Qh4MSfQd5Nqwh3aSNPYQXrEx90R8QrwpKSj8jkkaecezmHrCCdRa5TLSf2dcyT9DriU1NL5JfA48AjwQ+C/Kt8YEUtI/Zg3SnqIt5rTvwY+2nFhCTgb2D1fuJrHW6MEzgf2lTSH1K2woIdYpwMDJD0MfIM0sXKHZcBOkh4g9XlOytuPA07O8T0KHFbFd2LrAE+FZ2ZWB9dEzczq4CRqZlYHJ1Ezszo4iZqZ1cFJ1MysDk6iZmZ1cB3R+1cAAAALSURBVBI1M6vD/wLGuL7PfvUyJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "print('Normalized confusion matrix')\n",
    "print(cm_normalized)\n",
    "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Fake       0.95      1.00      0.97       535\n",
      "     Genuine       0.50      0.03      0.06        29\n",
      "\n",
      "    accuracy                           0.95       564\n",
      "   macro avg       0.73      0.52      0.52       564\n",
      "weighted avg       0.93      0.95      0.93       564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred, target_names=['Fake','Genuine']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Positive rate:  [0.         0.00186916 1.        ]\n",
      "True Positive rate:  [0.         0.03448276 1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gradient Boosting Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "model2 = GradientBoostingClassifier(random_state=1, learning_rate=0.01)\n",
    "model2.fit(X_train,y_train)\n",
    "y_pred_xgb = model2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Cross Validatin Score:  0.948979551613698\n"
     ]
    }
   ],
   "source": [
    "cl_scores = cross_val_score(model2, X_train,y_train, cv=5)\n",
    "print(\"Average Cross Validatin Score: \", cl_scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = 'Learning Curve'\n",
    "plot_learning_curve(model2, title, X_train, y_train, cv=5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix, without normalization\n",
      "[[534   1]\n",
      " [ 28   1]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm = metrics.confusion_matrix(y_test, y_pred)\n",
    "print('Confusion matrix, without normalization')\n",
    "print(cm)\n",
    "plot_confusion_matrix(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Normalized confusion matrix\n",
      "[[0.99813084 0.00186916]\n",
      " [0.96551724 0.03448276]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "print('Normalized confusion matrix')\n",
    "print(cm_normalized)\n",
    "plot_confusion_matrix(cm_normalized, title='Normalized confusion matrix')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "        Fake       0.95      1.00      0.97       535\n",
      "     Genuine       0.50      0.03      0.06        29\n",
      "\n",
      "    accuracy                           0.95       564\n",
      "   macro avg       0.73      0.52      0.52       564\n",
      "weighted avg       0.93      0.95      0.93       564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test, y_pred, target_names=['Fake','Genuine']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False Positive rate:  [0.         0.00186916 1.        ]\n",
      "True Positive rate:  [0.         0.03448276 1.        ]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Net Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['statuses_count', 'followers_count', 'friends_count',\n",
       "       'favourites_count', 'listed_count', 'lang', 'sex', 'last_update'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_cols = []\n",
    "\n",
    "for col in x.columns:\n",
    "    feat_cols.append(tf.feature_column.numeric_column(col))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[NumericColumn(key='statuses_count', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='followers_count', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='friends_count', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='favourites_count', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='listed_count', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='lang', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='sex', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None),\n",
       " NumericColumn(key='last_update', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_func = tf.compat.v1.estimator.inputs.pandas_input_fn(x=X_train,y=y_train,batch_size=10,num_epochs=5,shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "WARNING:tensorflow:Using temporary folder as model directory: /var/folders/gs/xvw6z9pn1sx6bs5q5cxj_10c0000gn/T/tmp2zdjkfsg\n",
      "INFO:tensorflow:Using config: {'_model_dir': '/var/folders/gs/xvw6z9pn1sx6bs5q5cxj_10c0000gn/T/tmp2zdjkfsg', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': allow_soft_placement: true\n",
      "graph_options {\n",
      "  rewrite_options {\n",
      "    meta_optimizer_iterations: ONE\n",
      "  }\n",
      "}\n",
      ", '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_train_distribute': None, '_device_fn': None, '_protocol': None, '_eval_distribute': None, '_experimental_distribute': None, '_experimental_max_worker_delay_secs': None, '_session_creation_timeout_secs': 7200, '_service': None, '_cluster_spec': ClusterSpec({}), '_task_type': 'worker', '_task_id': 0, '_global_id_in_cluster': 0, '_master': '', '_evaluation_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "classifier = tf.estimator.DNNClassifier(hidden_units=[10, 20, 10], n_classes=3,feature_columns=feat_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n",
      "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0...\n",
      "INFO:tensorflow:Saving checkpoints for 0 into /var/folders/gs/xvw6z9pn1sx6bs5q5cxj_10c0000gn/T/tmp2zdjkfsg/model.ckpt.\n",
      "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0...\n",
      "INFO:tensorflow:loss = 104.80631, step = 0\n",
      "INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 50...\n",
      "INFO:tensorflow:Saving checkpoints for 50 into /var/folders/gs/xvw6z9pn1sx6bs5q5cxj_10c0000gn/T/tmp2zdjkfsg/model.ckpt.\n",
      "INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 50...\n",
      "INFO:tensorflow:Loss for final step: 28.230328.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow_estimator.python.estimator.canned.dnn.DNNClassifierV2 at 0x1a3d624810>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier.train(input_fn=input_func,steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(x=X_test,batch_size=len(X_test),shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Calling model_fn.\n",
      "INFO:tensorflow:Done calling model_fn.\n",
      "INFO:tensorflow:Graph was finalized.\n",
      "INFO:tensorflow:Restoring parameters from /var/folders/gs/xvw6z9pn1sx6bs5q5cxj_10c0000gn/T/tmp2zdjkfsg/model.ckpt-50\n",
      "INFO:tensorflow:Running local_init_op.\n",
      "INFO:tensorflow:Done running local_init_op.\n"
     ]
    }
   ],
   "source": [
    "note_predictions = list(classifier.predict(input_fn=pred_fn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'logits': array([ 690.82947,  -96.02232, 1124.7654 ], dtype=float32),\n",
       " 'probabilities': array([0., 0., 1.], dtype=float32),\n",
       " 'class_ids': array([2]),\n",
       " 'classes': array([b'2'], dtype=object),\n",
       " 'all_class_ids': array([0, 1, 2], dtype=int32),\n",
       " 'all_classes': array([b'0', b'1', b'2'], dtype=object)}"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "note_predictions[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_preds  = []\n",
    "for pred in note_predictions:\n",
    "    final_preds.append(pred['class_ids'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 47 216 272]\n",
      " [  8   2  19]\n",
      " [  0   0   0]]\n"
     ]
    }
   ],
   "source": [
    "print(metrics.confusion_matrix(y_test,final_preds))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.09      0.16       535\n",
      "           1       0.01      0.07      0.02        29\n",
      "           2       0.00      0.00      0.00         0\n",
      "\n",
      "    accuracy                           0.09       564\n",
      "   macro avg       0.29      0.05      0.06       564\n",
      "weighted avg       0.81      0.09      0.15       564\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(metrics.classification_report(y_test,final_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keras Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " ...\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]]\n"
     ]
    }
   ],
   "source": [
    "n_classes = 2\n",
    "y_train_e = keras.utils.to_categorical(y_train, n_classes)\n",
    "y_test_e = keras.utils.to_categorical(y_test, n_classes)\n",
    "print(y_train_e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "# for l2 regularizer. this doesn't perform as good as dropouts\n",
    "# model.add(Dense(32, activation='relu', input_dim=4, kernel_regularizer=regularizers.l2(0.01))) \n",
    "# tanh is better than relu in this case\n",
    "model.add(Dense(64, activation='relu', input_shape=(8,)))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(64, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(2, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1 = model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "38/38 [==============================] - 0s 8ms/step - loss: 89.5517 - accuracy: 0.9015 - val_loss: 72.9295 - val_accuracy: 0.9486\n",
      "Epoch 2/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 58.1828 - accuracy: 0.9166 - val_loss: 56.0175 - val_accuracy: 0.9486\n",
      "Epoch 3/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 45.9750 - accuracy: 0.9148 - val_loss: 46.9744 - val_accuracy: 0.9486\n",
      "Epoch 4/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 44.1645 - accuracy: 0.9166 - val_loss: 44.0853 - val_accuracy: 0.9486\n",
      "Epoch 5/150\n",
      "38/38 [==============================] - 0s 5ms/step - loss: 44.5308 - accuracy: 0.9144 - val_loss: 33.6859 - val_accuracy: 0.9486\n",
      "Epoch 6/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 51.0458 - accuracy: 0.9219 - val_loss: 25.6177 - val_accuracy: 0.9486\n",
      "Epoch 7/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 32.1979 - accuracy: 0.9295 - val_loss: 22.3235 - val_accuracy: 0.9486\n",
      "Epoch 8/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 27.7956 - accuracy: 0.9295 - val_loss: 22.4076 - val_accuracy: 0.9486\n",
      "Epoch 9/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 23.2453 - accuracy: 0.9321 - val_loss: 17.8605 - val_accuracy: 0.9486\n",
      "Epoch 10/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 16.1803 - accuracy: 0.9281 - val_loss: 14.2797 - val_accuracy: 0.9486\n",
      "Epoch 11/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 16.4357 - accuracy: 0.9099 - val_loss: 12.6852 - val_accuracy: 0.9433\n",
      "Epoch 12/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 11.1766 - accuracy: 0.9232 - val_loss: 12.1338 - val_accuracy: 0.9486\n",
      "Epoch 13/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 11.8787 - accuracy: 0.9290 - val_loss: 12.0783 - val_accuracy: 0.9486\n",
      "Epoch 14/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 14.6282 - accuracy: 0.9210 - val_loss: 10.4644 - val_accuracy: 0.9486\n",
      "Epoch 15/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 10.0484 - accuracy: 0.9241 - val_loss: 10.4420 - val_accuracy: 0.9486\n",
      "Epoch 16/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 9.2051 - accuracy: 0.9330 - val_loss: 9.9138 - val_accuracy: 0.9486\n",
      "Epoch 17/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 6.2284 - accuracy: 0.9303 - val_loss: 8.3247 - val_accuracy: 0.9486\n",
      "Epoch 18/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 7.8422 - accuracy: 0.9144 - val_loss: 7.5849 - val_accuracy: 0.9486\n",
      "Epoch 19/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 6.5715 - accuracy: 0.9193 - val_loss: 6.3581 - val_accuracy: 0.9486\n",
      "Epoch 20/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 6.6256 - accuracy: 0.9201 - val_loss: 5.9021 - val_accuracy: 0.9486\n",
      "Epoch 21/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 5.4862 - accuracy: 0.9228 - val_loss: 4.5628 - val_accuracy: 0.9486\n",
      "Epoch 22/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 4.4050 - accuracy: 0.9219 - val_loss: 3.8972 - val_accuracy: 0.9486\n",
      "Epoch 23/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 3.6888 - accuracy: 0.9215 - val_loss: 2.9531 - val_accuracy: 0.9486\n",
      "Epoch 24/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 7.4157 - accuracy: 0.9250 - val_loss: 3.0350 - val_accuracy: 0.9486\n",
      "Epoch 25/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 5.1159 - accuracy: 0.9317 - val_loss: 6.1598 - val_accuracy: 0.9486\n",
      "Epoch 26/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 2.0345 - accuracy: 0.9370 - val_loss: 5.6801 - val_accuracy: 0.9486\n",
      "Epoch 27/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 3.6778 - accuracy: 0.9312 - val_loss: 5.3183 - val_accuracy: 0.9486\n",
      "Epoch 28/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 3.6141 - accuracy: 0.9330 - val_loss: 4.8081 - val_accuracy: 0.9486\n",
      "Epoch 29/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 1.7328 - accuracy: 0.9326 - val_loss: 4.4715 - val_accuracy: 0.9486\n",
      "Epoch 30/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 2.1034 - accuracy: 0.9352 - val_loss: 3.8909 - val_accuracy: 0.9486\n",
      "Epoch 31/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 1.4429 - accuracy: 0.9348 - val_loss: 3.4219 - val_accuracy: 0.9486\n",
      "Epoch 32/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 2.4628 - accuracy: 0.9388 - val_loss: 3.8154 - val_accuracy: 0.9486\n",
      "Epoch 33/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 1.4831 - accuracy: 0.9410 - val_loss: 3.4742 - val_accuracy: 0.9486\n",
      "Epoch 34/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 1.2762 - accuracy: 0.9428 - val_loss: 2.9326 - val_accuracy: 0.9486\n",
      "Epoch 35/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 1.1654 - accuracy: 0.9401 - val_loss: 2.4478 - val_accuracy: 0.9486\n",
      "Epoch 36/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 1.2887 - accuracy: 0.9450 - val_loss: 2.1815 - val_accuracy: 0.9486\n",
      "Epoch 37/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.9724 - accuracy: 0.9406 - val_loss: 1.8525 - val_accuracy: 0.9486\n",
      "Epoch 38/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5952 - accuracy: 0.9459 - val_loss: 1.7823 - val_accuracy: 0.9486\n",
      "Epoch 39/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.7250 - accuracy: 0.9428 - val_loss: 1.7239 - val_accuracy: 0.9486\n",
      "Epoch 40/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 1.4562 - accuracy: 0.9441 - val_loss: 1.6108 - val_accuracy: 0.9486\n",
      "Epoch 41/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.5767 - accuracy: 0.9459 - val_loss: 1.4912 - val_accuracy: 0.9486\n",
      "Epoch 42/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.6150 - accuracy: 0.9463 - val_loss: 1.4358 - val_accuracy: 0.9486\n",
      "Epoch 43/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.4157 - accuracy: 0.9423 - val_loss: 1.5259 - val_accuracy: 0.9486\n",
      "Epoch 44/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.8010 - accuracy: 0.9450 - val_loss: 1.4915 - val_accuracy: 0.9486\n",
      "Epoch 45/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.4797 - accuracy: 0.9472 - val_loss: 1.4600 - val_accuracy: 0.9486\n",
      "Epoch 46/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.8319 - accuracy: 0.9485 - val_loss: 1.3943 - val_accuracy: 0.9486\n",
      "Epoch 47/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.4292 - accuracy: 0.9468 - val_loss: 1.3019 - val_accuracy: 0.9486\n",
      "Epoch 48/150\n",
      "38/38 [==============================] - 0s 6ms/step - loss: 0.6903 - accuracy: 0.9459 - val_loss: 1.2569 - val_accuracy: 0.9486\n",
      "Epoch 49/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.4913 - accuracy: 0.9468 - val_loss: 1.1748 - val_accuracy: 0.9486\n",
      "Epoch 50/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 1.3922 - accuracy: 0.9468 - val_loss: 1.6068 - val_accuracy: 0.9486\n",
      "Epoch 51/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.4141 - accuracy: 0.9481 - val_loss: 1.6866 - val_accuracy: 0.9486\n",
      "Epoch 52/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.5268 - accuracy: 0.9490 - val_loss: 1.6650 - val_accuracy: 0.9486\n",
      "Epoch 53/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3556 - accuracy: 0.9472 - val_loss: 1.6192 - val_accuracy: 0.9486\n",
      "Epoch 54/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3347 - accuracy: 0.9468 - val_loss: 1.4782 - val_accuracy: 0.9486\n",
      "Epoch 55/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 1.0342 - accuracy: 0.9476 - val_loss: 2.0277 - val_accuracy: 0.9486\n",
      "Epoch 56/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3323 - accuracy: 0.9490 - val_loss: 2.0032 - val_accuracy: 0.9486\n",
      "Epoch 57/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.5458 - accuracy: 0.9472 - val_loss: 1.7454 - val_accuracy: 0.9486\n",
      "Epoch 58/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2650 - accuracy: 0.9463 - val_loss: 1.7022 - val_accuracy: 0.9486\n",
      "Epoch 59/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.4139 - accuracy: 0.9476 - val_loss: 1.6648 - val_accuracy: 0.9486\n",
      "Epoch 60/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3374 - accuracy: 0.9476 - val_loss: 1.6373 - val_accuracy: 0.9486\n",
      "Epoch 61/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.6866 - accuracy: 0.9468 - val_loss: 1.6377 - val_accuracy: 0.9486\n",
      "Epoch 62/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.5004 - accuracy: 0.9485 - val_loss: 1.4764 - val_accuracy: 0.9486\n",
      "Epoch 63/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2941 - accuracy: 0.9476 - val_loss: 1.4239 - val_accuracy: 0.9486\n",
      "Epoch 64/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3789 - accuracy: 0.9468 - val_loss: 1.4152 - val_accuracy: 0.9486\n",
      "Epoch 65/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2406 - accuracy: 0.9476 - val_loss: 1.3858 - val_accuracy: 0.9486\n",
      "Epoch 66/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2843 - accuracy: 0.9485 - val_loss: 1.2976 - val_accuracy: 0.9486\n",
      "Epoch 67/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2794 - accuracy: 0.9490 - val_loss: 1.2671 - val_accuracy: 0.9486\n",
      "Epoch 68/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 3.7183 - accuracy: 0.9490 - val_loss: 1.8890 - val_accuracy: 0.9486\n",
      "Epoch 69/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.9916 - accuracy: 0.9481 - val_loss: 2.5744 - val_accuracy: 0.9486\n",
      "Epoch 70/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2760 - accuracy: 0.9485 - val_loss: 2.4424 - val_accuracy: 0.9486\n",
      "Epoch 71/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2547 - accuracy: 0.9476 - val_loss: 2.4347 - val_accuracy: 0.9486\n",
      "Epoch 72/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 1.1160 - accuracy: 0.9485 - val_loss: 2.2541 - val_accuracy: 0.9486\n",
      "Epoch 73/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.4614 - accuracy: 0.9503 - val_loss: 2.0865 - val_accuracy: 0.9486\n",
      "Epoch 74/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.4310 - accuracy: 0.9476 - val_loss: 2.0413 - val_accuracy: 0.9486\n",
      "Epoch 75/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.3649 - accuracy: 0.9485 - val_loss: 2.0342 - val_accuracy: 0.9486\n",
      "Epoch 76/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2547 - accuracy: 0.9472 - val_loss: 1.9714 - val_accuracy: 0.9486\n",
      "Epoch 77/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2180 - accuracy: 0.9481 - val_loss: 1.9643 - val_accuracy: 0.9486\n",
      "Epoch 78/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.3017 - accuracy: 0.9481 - val_loss: 1.9184 - val_accuracy: 0.9486\n",
      "Epoch 79/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.2428 - accuracy: 0.9468 - val_loss: 1.8872 - val_accuracy: 0.9486\n",
      "Epoch 80/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.2551 - accuracy: 0.9481 - val_loss: 1.8065 - val_accuracy: 0.9486\n",
      "Epoch 81/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.3526 - accuracy: 0.9481 - val_loss: 1.7800 - val_accuracy: 0.9486\n",
      "Epoch 82/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2157 - accuracy: 0.9490 - val_loss: 1.7744 - val_accuracy: 0.9486\n",
      "Epoch 83/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2238 - accuracy: 0.9485 - val_loss: 1.7418 - val_accuracy: 0.9486\n",
      "Epoch 84/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.3134 - accuracy: 0.9481 - val_loss: 1.6749 - val_accuracy: 0.9486\n",
      "Epoch 85/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2647 - accuracy: 0.9481 - val_loss: 1.5782 - val_accuracy: 0.9486\n",
      "Epoch 86/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.3427 - accuracy: 0.9485 - val_loss: 1.5791 - val_accuracy: 0.9486\n",
      "Epoch 87/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2656 - accuracy: 0.9485 - val_loss: 1.5856 - val_accuracy: 0.9486\n",
      "Epoch 88/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2370 - accuracy: 0.9476 - val_loss: 1.5841 - val_accuracy: 0.9486\n",
      "Epoch 89/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.4416 - accuracy: 0.9481 - val_loss: 1.5286 - val_accuracy: 0.9486\n",
      "Epoch 90/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2093 - accuracy: 0.9490 - val_loss: 1.5225 - val_accuracy: 0.9486\n",
      "Epoch 91/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2091 - accuracy: 0.9490 - val_loss: 1.3835 - val_accuracy: 0.9486\n",
      "Epoch 92/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1996 - accuracy: 0.9468 - val_loss: 1.3229 - val_accuracy: 0.9486\n",
      "Epoch 93/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2224 - accuracy: 0.9485 - val_loss: 1.3294 - val_accuracy: 0.9486\n",
      "Epoch 94/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.4146 - accuracy: 0.9481 - val_loss: 1.3237 - val_accuracy: 0.9486\n",
      "Epoch 95/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3823 - accuracy: 0.9472 - val_loss: 1.3143 - val_accuracy: 0.9486\n",
      "Epoch 96/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.4302 - accuracy: 0.9485 - val_loss: 1.2703 - val_accuracy: 0.9486\n",
      "Epoch 97/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2461 - accuracy: 0.9490 - val_loss: 1.2496 - val_accuracy: 0.9486\n",
      "Epoch 98/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1988 - accuracy: 0.9481 - val_loss: 1.2499 - val_accuracy: 0.9486\n",
      "Epoch 99/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.5094 - accuracy: 0.9490 - val_loss: 1.2025 - val_accuracy: 0.9486\n",
      "Epoch 100/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3777 - accuracy: 0.9490 - val_loss: 1.1916 - val_accuracy: 0.9486\n",
      "Epoch 101/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 2.4758 - accuracy: 0.9485 - val_loss: 1.0625 - val_accuracy: 0.9486\n",
      "Epoch 102/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2020 - accuracy: 0.9485 - val_loss: 1.0605 - val_accuracy: 0.9486\n",
      "Epoch 103/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2767 - accuracy: 0.9485 - val_loss: 1.0672 - val_accuracy: 0.9486\n",
      "Epoch 104/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2831 - accuracy: 0.9485 - val_loss: 0.9912 - val_accuracy: 0.9486\n",
      "Epoch 105/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.3333 - accuracy: 0.9481 - val_loss: 0.9848 - val_accuracy: 0.9486\n",
      "Epoch 106/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2147 - accuracy: 0.9490 - val_loss: 0.9725 - val_accuracy: 0.9486\n",
      "Epoch 107/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2851 - accuracy: 0.9476 - val_loss: 0.9712 - val_accuracy: 0.9486\n",
      "Epoch 108/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1842 - accuracy: 0.9490 - val_loss: 0.9713 - val_accuracy: 0.9486\n",
      "Epoch 109/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2227 - accuracy: 0.9476 - val_loss: 0.9789 - val_accuracy: 0.9486\n",
      "Epoch 110/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1960 - accuracy: 0.9481 - val_loss: 0.9817 - val_accuracy: 0.9486\n",
      "Epoch 111/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2067 - accuracy: 0.9490 - val_loss: 0.9863 - val_accuracy: 0.9486\n",
      "Epoch 112/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3964 - accuracy: 0.9494 - val_loss: 0.9934 - val_accuracy: 0.9486\n",
      "Epoch 113/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2147 - accuracy: 0.9485 - val_loss: 0.9947 - val_accuracy: 0.9486\n",
      "Epoch 114/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2671 - accuracy: 0.9481 - val_loss: 0.9845 - val_accuracy: 0.9486\n",
      "Epoch 115/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2355 - accuracy: 0.9490 - val_loss: 0.9839 - val_accuracy: 0.9486\n",
      "Epoch 116/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.2189 - accuracy: 0.9485 - val_loss: 0.9033 - val_accuracy: 0.9486\n",
      "Epoch 117/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2332 - accuracy: 0.9472 - val_loss: 0.8738 - val_accuracy: 0.9486\n",
      "Epoch 118/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.2222 - accuracy: 0.9485 - val_loss: 0.8611 - val_accuracy: 0.9486\n",
      "Epoch 119/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1902 - accuracy: 0.9490 - val_loss: 0.8586 - val_accuracy: 0.9486\n",
      "Epoch 120/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1790 - accuracy: 0.9490 - val_loss: 0.8597 - val_accuracy: 0.9486\n",
      "Epoch 121/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.4861 - accuracy: 0.9485 - val_loss: 0.8635 - val_accuracy: 0.9486\n",
      "Epoch 122/150\n",
      "38/38 [==============================] - 0s 5ms/step - loss: 0.1875 - accuracy: 0.9490 - val_loss: 0.8667 - val_accuracy: 0.9486\n",
      "Epoch 123/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2361 - accuracy: 0.9481 - val_loss: 0.8612 - val_accuracy: 0.9486\n",
      "Epoch 124/150\n",
      "38/38 [==============================] - 0s 6ms/step - loss: 0.2204 - accuracy: 0.9490 - val_loss: 0.8346 - val_accuracy: 0.9486\n",
      "Epoch 125/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1967 - accuracy: 0.9490 - val_loss: 0.7171 - val_accuracy: 0.9486\n",
      "Epoch 126/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.4595 - accuracy: 0.9476 - val_loss: 0.7108 - val_accuracy: 0.9486\n",
      "Epoch 127/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1898 - accuracy: 0.9490 - val_loss: 0.7091 - val_accuracy: 0.9486\n",
      "Epoch 128/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2046 - accuracy: 0.9481 - val_loss: 0.7116 - val_accuracy: 0.9486\n",
      "Epoch 129/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2036 - accuracy: 0.9494 - val_loss: 0.7138 - val_accuracy: 0.9486\n",
      "Epoch 130/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1768 - accuracy: 0.9485 - val_loss: 0.7153 - val_accuracy: 0.9486\n",
      "Epoch 131/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.3986 - accuracy: 0.9490 - val_loss: 0.7101 - val_accuracy: 0.9486\n",
      "Epoch 132/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2984 - accuracy: 0.9490 - val_loss: 0.7040 - val_accuracy: 0.9486\n",
      "Epoch 133/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1815 - accuracy: 0.9481 - val_loss: 0.7047 - val_accuracy: 0.9486\n",
      "Epoch 134/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2855 - accuracy: 0.9476 - val_loss: 0.6535 - val_accuracy: 0.9486\n",
      "Epoch 135/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2628 - accuracy: 0.9481 - val_loss: 0.5953 - val_accuracy: 0.9486\n",
      "Epoch 136/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1954 - accuracy: 0.9490 - val_loss: 0.5860 - val_accuracy: 0.9486\n",
      "Epoch 137/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2765 - accuracy: 0.9490 - val_loss: 0.5815 - val_accuracy: 0.9486\n",
      "Epoch 138/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1938 - accuracy: 0.9485 - val_loss: 0.5050 - val_accuracy: 0.9486\n",
      "Epoch 139/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2012 - accuracy: 0.9490 - val_loss: 0.5087 - val_accuracy: 0.9486\n",
      "Epoch 140/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1853 - accuracy: 0.9490 - val_loss: 0.4979 - val_accuracy: 0.9486\n",
      "Epoch 141/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1932 - accuracy: 0.9490 - val_loss: 0.4896 - val_accuracy: 0.9486\n",
      "Epoch 142/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.1741 - accuracy: 0.9490 - val_loss: 0.4915 - val_accuracy: 0.9486\n",
      "Epoch 143/150\n",
      "38/38 [==============================] - 0s 4ms/step - loss: 0.2843 - accuracy: 0.9490 - val_loss: 0.4921 - val_accuracy: 0.9486\n",
      "Epoch 144/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1817 - accuracy: 0.9485 - val_loss: 0.4948 - val_accuracy: 0.9486\n",
      "Epoch 145/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1810 - accuracy: 0.9476 - val_loss: 0.4972 - val_accuracy: 0.9486\n",
      "Epoch 146/150\n",
      "38/38 [==============================] - 0s 3ms/step - loss: 0.2858 - accuracy: 0.9485 - val_loss: 0.4995 - val_accuracy: 0.9486\n",
      "Epoch 147/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1920 - accuracy: 0.9485 - val_loss: 0.4988 - val_accuracy: 0.9486\n",
      "Epoch 148/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1916 - accuracy: 0.9490 - val_loss: 0.5010 - val_accuracy: 0.9486\n",
      "Epoch 149/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.2188 - accuracy: 0.9490 - val_loss: 0.5038 - val_accuracy: 0.9486\n",
      "Epoch 150/150\n",
      "38/38 [==============================] - 0s 2ms/step - loss: 0.1843 - accuracy: 0.9485 - val_loss: 0.5067 - val_accuracy: 0.9486\n"
     ]
    }
   ],
   "source": [
    "epoch_n = 150\n",
    "history = model.fit(X_train, y_train_e, batch_size=60, epochs=epoch_n, verbose=1,\\\n",
    " validation_data=(X_test, y_test_e))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(history.history.keys())\n",
    "# summarize history for accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.plot(history.history['val_accuracy'])\n",
    "plt.title('model accuracy')\n",
    "plt.ylabel('accuracy')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()\n",
    "# summarize history for loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('model loss')\n",
    "plt.ylabel('loss')\n",
    "plt.xlabel('epoch')\n",
    "plt.legend(['train', 'test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_22\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_46 (Dense)             (None, 64)                576       \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_47 (Dense)             (None, 64)                4160      \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 64)                0         \n",
      "_________________________________________________________________\n",
      "dense_48 (Dense)             (None, 2)                 130       \n",
      "=================================================================\n",
      "Total params: 4,866\n",
      "Trainable params: 4,866\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_keras = model.predict(X_test).ravel()\n",
    "\n",
    "pred = list()\n",
    "for i in range(len(y_pred_keras)):\n",
    "    pred.append(np.argmax(y_pred_keras[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat_probs = model.predict(X_test, verbose=0)\n",
    "# predict crisp classes for test set\n",
    "yhat_classes = model.predict_classes(X_test, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "yhat_probs = yhat_probs[:]\n",
    "yhat_classes = yhat_classes[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.948582\n"
     ]
    }
   ],
   "source": [
    "# accuracy: (tp + tn) / (p + n)\n",
    "accuracy = metrics.accuracy_score(y_test, yhat_classes)\n",
    "print('Accuracy: %f' % accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}