k_means.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /Users/akhmadramadani/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     /Users/akhmadramadani/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     /Users/akhmadramadani/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.cluster import KMeans\n",
    "import script.func as func\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>timezone</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>...</th>\n",
       "      <th>geo</th>\n",
       "      <th>source</th>\n",
       "      <th>user_rt_id</th>\n",
       "      <th>user_rt</th>\n",
       "      <th>retweet_id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>retweet_date</th>\n",
       "      <th>translate</th>\n",
       "      <th>trans_src</th>\n",
       "      <th>trans_dest</th>\n",
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       "      <td>1603569991151804417</td>\n",
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       "      <td>2022-12-16 08:54:42 WIB</td>\n",
       "      <td>2022-12-16</td>\n",
       "      <td>08:54:42</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED😎</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>3</th>\n",
       "      <td>1603569076390924289</td>\n",
       "      <td>1603569076390924289</td>\n",
       "      <td>2022-12-16 08:53:55 WIB</td>\n",
       "      <td>2022-12-16</td>\n",
       "      <td>08:53:55</td>\n",
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       "      <td>collegemenfess</td>\n",
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       "      <td>2022-12-16</td>\n",
       "      <td>08:53:03</td>\n",
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       "                    id      conversation_id               created_at  \\\n",
       "0  1603569991151804417  1603569991151804417  2022-12-16 08:57:33 WIB   \n",
       "1  1603569910097285120  1603569910097285120  2022-12-16 08:57:14 WIB   \n",
       "2  1603569275658448896  1603569275658448896  2022-12-16 08:54:42 WIB   \n",
       "3  1603569076390924289  1603569076390924289  2022-12-16 08:53:55 WIB   \n",
       "4  1603568860145528832  1603568860145528832  2022-12-16 08:53:03 WIB   \n",
       "\n",
       "         date      time  timezone              user_id        username  \\\n",
       "0  2022-12-16  08:57:33       700  1046084008742801408  collegemenfess   \n",
       "1  2022-12-16  08:57:14       700  1046084008742801408  collegemenfess   \n",
       "2  2022-12-16  08:54:42       700  1046084008742801408  collegemenfess   \n",
       "3  2022-12-16  08:53:55       700  1046084008742801408  collegemenfess   \n",
       "4  2022-12-16  08:53:03       700  1046084008742801408  collegemenfess   \n",
       "\n",
       "                  name  place  ... geo source user_rt_id user_rt retweet_id  \\\n",
       "0  COLLE | CEK PINNED😎    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "1  COLLE | CEK PINNED😎    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "2  COLLE | CEK PINNED😎    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "3  COLLE | CEK PINNED😎    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "4  COLLE | CEK PINNED😎    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "\n",
       "   reply_to  retweet_date  translate trans_src trans_dest  \n",
       "0        []           NaN        NaN       NaN        NaN  \n",
       "1        []           NaN        NaN       NaN        NaN  \n",
       "2        []           NaN        NaN       NaN        NaN  \n",
       "3        []           NaN        NaN       NaN        NaN  \n",
       "4        []           NaN        NaN       NaN        NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
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    "# Load the data\n",
    "df = pd.read_csv('data/collegemenfess.csv')\n",
    "df.head()\n"
   ]
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       "      <td>[cm] smt 5 mending ikut studi independen dulu ...</td>\n",
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       "      <th>2</th>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
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       "      <td>[CM] Dosen praktikum aku ngasih ini waktu pert...</td>\n",
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      "text/plain": [
       "                                               tweet  \\\n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...   \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...   \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...   \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...   \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...   \n",
       "\n",
       "                                          real_tweet  \n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...  \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...  \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...  \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...  \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...  "
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "# get only tweet column\n",
    "df = df[['tweet']]\n",
    "df['real_tweet'] = df['tweet']\n",
    "df.head()"
   ]
  },
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       "      <td>dosen praktikum aku ngasih ini waktu pertemua...</td>\n",
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       "                                               tweet  \\\n",
       "0   smt   mending ikut studi independen dulu apa ...   \n",
       "1   di kampus kalian mahasiswa s  wajib publish j...   \n",
       "2   bisa gk yh tugas uas ini ngerjain dirinya sen...   \n",
       "3   dosen praktikum aku ngasih ini waktu pertemua...   \n",
       "4   alhamdulilah masih bisa hidup walaupun dikasi...   \n",
       "\n",
       "                                          real_tweet  \n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...  \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...  \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...  \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...  \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...  "
      ]
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     "execution_count": 4,
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   ],
   "source": [
    "# clean the data\n",
    "df['tweet'] = df['tweet'].apply(func.cleanTxt)\n",
    "df.head()"
   ]
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       "      <td>dosen praktikum saya kasih ini waktu pertemuan...</td>\n",
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       "                                               tweet  \\\n",
       "0  semester lebih baik ikut studi independen dulu...   \n",
       "1  di kampus kalian mahasiswa s wajib publish jur...   \n",
       "2  bisa tidak iya tugas uas ini mengerjakan dirin...   \n",
       "3  dosen praktikum saya kasih ini waktu pertemuan...   \n",
       "4  alhamdulilah masih bisa hidup walaupun dikasi ...   \n",
       "\n",
       "                                          real_tweet  \n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...  \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...  \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...  \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...  \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# slang to formal\n",
    "df['tweet'] = df['tweet'].apply(func.slang_to_formal)\n",
    "df.head()"
   ]
  },
  {
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   "execution_count": 6,
   "metadata": {},
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       "      <th>1</th>\n",
       "      <td>di kampus kalian mahasiswa s wajib publish jur...</td>\n",
       "      <td>[cm] di kampus kalian mahasiswa s1 wajib publi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>bisa tidak iya tugas uas ini mengerjakan dirin...</td>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dosen praktikum saya kasih ini waktu pertemuan...</td>\n",
       "      <td>[CM] Dosen praktikum aku ngasih ini waktu pert...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alhamdulilah masih bisa hidup walaupun dikasi ...</td>\n",
       "      <td>[cm] Alhamdulilah masih bisa hidup walaupun di...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester lebih baik ikut studi independen dulu...   \n",
       "1  di kampus kalian mahasiswa s wajib publish jur...   \n",
       "2  bisa tidak iya tugas uas ini mengerjakan dirin...   \n",
       "3  dosen praktikum saya kasih ini waktu pertemuan...   \n",
       "4  alhamdulilah masih bisa hidup walaupun dikasi ...   \n",
       "\n",
       "                                          real_tweet  \n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...  \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...  \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...  \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...  \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# acronym to formal\n",
    "df['tweet'] = df['tweet'].apply(func.acronym_to_formal)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>2</th>\n",
       "      <td>iya tugas uas manja bantu kerjain</td>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dosen praktikum kasih waktu pertemuan kelas te...</td>\n",
       "      <td>[CM] Dosen praktikum aku ngasih ini waktu pert...</td>\n",
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       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal terindek...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu pertemuan kelas te...   \n",
       "4  alhamdulilah hidup dikasi include kos kuliah j...   \n",
       "\n",
       "                                          real_tweet  \n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...  \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...  \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...  \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...  \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove stopword\n",
    "df['tweet'] = df['tweet'].apply(func.removeStopWords)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>tweet</th>\n",
       "      <th>real_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
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       "      <td>iya tugas uas manja bantu kerjain</td>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dosen praktikum kasih waktu temu kelas haru ka...</td>\n",
       "      <td>[CM] Dosen praktikum aku ngasih ini waktu pert...</td>\n",
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       "    <tr>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal indeks s...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu temu kelas haru ka...   \n",
       "4  alhamdulilah hidup kasi include kos kuliah jak...   \n",
       "\n",
       "                                          real_tweet  \n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...  \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...  \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...  \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...  \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# stemming\n",
    "df['tweet'] = df['tweet'].apply(func.stemSentence)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>tweet</th>\n",
       "      <th>real_tweet</th>\n",
       "      <th>token</th>\n",
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       "      <td>[dosen, praktikum, kasih, waktu, temu, kelas, ...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alhamdulilah hidup kasi include kos kuliah jak...</td>\n",
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       "</div>"
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      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal indeks s...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu temu kelas haru ka...   \n",
       "4  alhamdulilah hidup kasi include kos kuliah jak...   \n",
       "\n",
       "                                          real_tweet  \\\n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...   \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...   \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...   \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...   \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...   \n",
       "\n",
       "                                               token  \n",
       "0  [semester, studi, independen, magang, yaa, sem...  \n",
       "1  [kampus, mahasiswa, wajib, publish, jurnal, in...  \n",
       "2           [iya, tugas, uas, manja, bantu, kerjain]  \n",
       "3  [dosen, praktikum, kasih, waktu, temu, kelas, ...  \n",
       "4  [alhamdulilah, hidup, kasi, include, kos, kuli...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# tokenization\n",
    "df['token'] = df['tweet'].apply(func.word_tokenize)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>4</th>\n",
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      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal indeks s...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu temu kelas haru ka...   \n",
       "4  alhamdulilah hidup kasi include kos kuliah jak...   \n",
       "\n",
       "                                          real_tweet  \\\n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...   \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...   \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...   \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...   \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...   \n",
       "\n",
       "                                               token  \n",
       "0  [semester, studi, independen, magang, yaa, sem...  \n",
       "1  [kampus, mahasiswa, wajib, publish, jurnal, in...  \n",
       "2           [iya, tugas, uas, manja, bantu, kerjain]  \n",
       "3  [dosen, praktikum, kasih, waktu, temu, kelas, ...  \n",
       "4  [alhamdulilah, hidup, kasi, include, kos, kuli...  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove empty tweet\n",
    "df = df[df['token'].apply(len) > 0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vector(text):\n",
    "    vector = np.zeros(100)\n",
    "    for word in text:\n",
    "        vector += model.wv[word]\n",
    "    return vector/len(text)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal indeks s...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu temu kelas haru ka...   \n",
       "4  alhamdulilah hidup kasi include kos kuliah jak...   \n",
       "\n",
       "                                          real_tweet  \\\n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...   \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...   \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...   \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...   \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...   \n",
       "\n",
       "                                               token  \\\n",
       "0  [semester, studi, independen, magang, yaa, sem...   \n",
       "1  [kampus, mahasiswa, wajib, publish, jurnal, in...   \n",
       "2           [iya, tugas, uas, manja, bantu, kerjain]   \n",
       "3  [dosen, praktikum, kasih, waktu, temu, kelas, ...   \n",
       "4  [alhamdulilah, hidup, kasi, include, kos, kuli...   \n",
       "\n",
       "                                              vector  \n",
       "0  [-0.4777812850661576, 0.731285504065454, 0.182...  \n",
       "1  [-0.3680373860730065, 0.6005052402615547, 0.14...  \n",
       "2  [-0.17908126286541423, 0.4448075814483066, 0.0...  \n",
       "3  [-0.34827083740383386, 0.5752464842051268, 0.1...  \n",
       "4  [-0.25330586758043083, 0.40945335796901156, 0....  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Vectorization using Word2Vec\n",
    "from gensim.models import Word2Vec\n",
    "model = Word2Vec(df['token'], min_count=1)\n",
    "\n",
    "# get vector\n",
    "df['vector'] = df['token'].apply(get_vector)\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>tweet</th>\n",
       "      <th>real_tweet</th>\n",
       "      <th>token</th>\n",
       "      <th>vector</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>semester studi independen magang yaa semester ...</td>\n",
       "      <td>[cm] smt 5 mending ikut studi independen dulu ...</td>\n",
       "      <td>[semester, studi, independen, magang, yaa, sem...</td>\n",
       "      <td>[-0.4777812850661576, 0.731285504065454, 0.182...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kampus mahasiswa wajib publish jurnal indeks s...</td>\n",
       "      <td>[cm] di kampus kalian mahasiswa s1 wajib publi...</td>\n",
       "      <td>[kampus, mahasiswa, wajib, publish, jurnal, in...</td>\n",
       "      <td>[-0.3680373860730065, 0.6005052402615547, 0.14...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iya tugas uas manja bantu kerjain</td>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
       "      <td>[iya, tugas, uas, manja, bantu, kerjain]</td>\n",
       "      <td>[-0.17908126286541423, 0.4448075814483066, 0.0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dosen praktikum kasih waktu temu kelas haru ka...</td>\n",
       "      <td>[CM] Dosen praktikum aku ngasih ini waktu pert...</td>\n",
       "      <td>[dosen, praktikum, kasih, waktu, temu, kelas, ...</td>\n",
       "      <td>[-0.34827083740383386, 0.5752464842051268, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alhamdulilah hidup kasi include kos kuliah jak...</td>\n",
       "      <td>[cm] Alhamdulilah masih bisa hidup walaupun di...</td>\n",
       "      <td>[alhamdulilah, hidup, kasi, include, kos, kuli...</td>\n",
       "      <td>[-0.25330586758043083, 0.40945335796901156, 0....</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal indeks s...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu temu kelas haru ka...   \n",
       "4  alhamdulilah hidup kasi include kos kuliah jak...   \n",
       "\n",
       "                                          real_tweet  \\\n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...   \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...   \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...   \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...   \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...   \n",
       "\n",
       "                                               token  \\\n",
       "0  [semester, studi, independen, magang, yaa, sem...   \n",
       "1  [kampus, mahasiswa, wajib, publish, jurnal, in...   \n",
       "2           [iya, tugas, uas, manja, bantu, kerjain]   \n",
       "3  [dosen, praktikum, kasih, waktu, temu, kelas, ...   \n",
       "4  [alhamdulilah, hidup, kasi, include, kos, kuli...   \n",
       "\n",
       "                                              vector  \n",
       "0  [-0.4777812850661576, 0.731285504065454, 0.182...  \n",
       "1  [-0.3680373860730065, 0.6005052402615547, 0.14...  \n",
       "2  [-0.17908126286541423, 0.4448075814483066, 0.0...  \n",
       "3  [-0.34827083740383386, 0.5752464842051268, 0.1...  \n",
       "4  [-0.25330586758043083, 0.40945335796901156, 0....  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df = df[df['vector'].apply(lambda x: len(x) != 0)]\n",
    "df.head()    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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QEABA9+7dYTAYcPjwYanm0KFDMBgMFjUnT55EamqqVLNjxw6oVCp07ty5xo6RiIiIbIesE8ELCgpw7tw56XlSUhLi4uKg0WjQqFEjzJgxAyNGjECvXr3Qt29fbN++HVu2bMG+ffsAAGq1Gi+99BIiIiLg4+MDjUaD6dOnIzg4GP379wdQPjI1ePBgjB07FitWrAAAjBs3DmFhYWjdujUAYODAgQgMDER4eDgWLVqE7OxsTJ8+HWPHjuXoEREREZW7q4WIqtnevXurXCdh9OjRUs2XX34pWrRoIVxcXESHDh3Epk2bLN6jqKhITJw4UWg0GuHq6irCwsLExYsXLWqysrLEqFGjhIeHh/Dw8BCjRo0SOTk5FjXJycliyJAhwtXVVWg0GjFx4kRRXFx8V8djMBgEAGEwGO7qdURERCQfa3+/a806TXVBXl4e1Go1DAYDR6iIiIhshLW/37V2ThMRERFRbcLQRERERGQFhiYiIiIiKzA0EREREVmBoYmIiIjICgxNNqCkzIzDSdnghY5ERETyYWiq5UpNZoTM343hK6JxPrNQ7naIiIjsFkNTLefkoESgXg0A2JNQ+R56REREdH8wNNmA0Db+AIDd8Rkyd0JERGS/GJpsQL/roen35BwYrpXK3A0REZF9YmiyAQEaN7TS1oPJLLD/bKbc7RAREdklhiYb0a+NFgCwJ57zmoiIiOTA0GQjQtuWn6LbdyYTZSazzN0QERHZH4YmG9EpwAtebk7IvVaKYym5crdDRERkdxiabISjgxJ9WvkB4FV0REREcmBosiH92l6f18T1moiIiO47hiYb0rulHxyUCpxJL0BK9jW52yEiIrIrDE02RO3mhC6NvQEAexJ4io6IiOh+YmiyMRVX0e1maCIiIrqvGJpsTMV6TTHns1BoLJO5GyIiIvvB0GRjmvu5o7GPG0pMZkSeuyp3O0RERHaDocnGKBQK6V50e7j0ABER0X3D0GSDQituqZKYAbNZyNwNERGRfWBoskEPNdXA3dkBmflGnLxikLsdIiIiu8DQZIOcHZXoxdXBiYiI7iuGJhslzWvi0gNERET3BUOTjerT2h8KBXDisgHpecVyt0NERFTnMTTZKD8PFTo09AIA7OVoExERUY1jaLJhoW24OjgREdH9wtBkw/pdv6VK5NmrKC41ydwNERFR3cbQZMMC63tC5+mColITYi5kyd0OERFRncbQZMMUCoU02sSr6IiIiGoWQ5ONk+Y1xWdACK4OTkREVFMYmmxcSHNfqByVuJxbhDPpBXK3Q0REVGcxNNk4V2cH9GjhCwDYnZAuczdERER1F0NTHSCtDs5bqhAREdUYhqY6oCI0Hb2Yg+zCEpm7ISIiqpsYmuoAvZcr2tb3hFkA+89wtImIiKgmMDTVETdeRUdERETVT9bQdODAAQwdOhR6vR4KhQKbNm26Ze348eOhUCiwZMkSi+1GoxGTJk2Cr68v3N3dMWzYMFy6dMmiJicnB+Hh4VCr1VCr1QgPD0dubq5FzcWLFzF06FC4u7vD19cXkydPRkmJ7Zzqqlivaf+ZTJSazDJ3Q0REVPfIGpoKCwvRoUMHLFu27LZ1mzZtwqFDh6DX6yvtmzp1KjZu3Ij169cjMjISBQUFCAsLg8n0121FRo4cibi4OGzfvh3bt29HXFwcwsPDpf0mkwlDhgxBYWEhIiMjsX79evz444+IiIiovoOtYR0aesHH3Rn5xWX4/c8cudshIiKqe0QtAUBs3Lix0vZLly6JBg0aiJMnT4rGjRuLDz/8UNqXm5srnJycxPr166Vtly9fFkqlUmzfvl0IIcTp06cFABETEyPVREdHCwAiISFBCCHEtm3bhFKpFJcvX5Zq1q1bJ1QqlTAYDFYfg8FgEADu6jXVadr3caLxzK3i3a2nZPl8IiIiW2Tt73etntNkNpsRHh6OGTNmoF27dpX2x8bGorS0FAMHDpS26fV6BAUFISoqCgAQHR0NtVqNrl27SjXdunWDWq22qAkKCrIYyRo0aBCMRiNiY2Nv2Z/RaEReXp7FQ06h10/R7eYtVYiIiKpdrQ5NCxYsgKOjIyZPnlzl/rS0NDg7O8Pb29tiu1arRVpamlTj7+9f6bX+/v4WNVqt1mK/t7c3nJ2dpZqqzJs3T5onpVarERAQcFfHV90ebukLR6UCFzILkXS1UNZeiIiI6ppaG5piY2Px0UcfYfXq1VAoFHf1WiGExWuqev291Nxs1qxZMBgM0iMlJeWu+qxuHi5O6NpMA4A38CUiIqputTY0/fbbb8jIyECjRo3g6OgIR0dHJCcnIyIiAk2aNAEA6HQ6lJSUICfHcuJzRkaGNHKk0+mQnl759iKZmZkWNTePKOXk5KC0tLTSCNSNVCoVPD09LR5y69emvN89vKUKERFRtaq1oSk8PBzHjx9HXFyc9NDr9ZgxYwZ+/fVXAEDnzp3h5OSEnTt3Sq9LTU3FyZMnERISAgDo3r07DAYDDh8+LNUcOnQIBoPBoubkyZNITU2Vanbs2AGVSoXOnTvfj8OtNhXrNR26kI384lKZuyEiIqo7HOX88IKCApw7d056npSUhLi4OGg0GjRq1Ag+Pj4W9U5OTtDpdGjdujUAQK1W46WXXkJERAR8fHyg0Wgwffp0BAcHo3///gCAtm3bYvDgwRg7dixWrFgBABg3bhzCwsKk9xk4cCACAwMRHh6ORYsWITs7G9OnT8fYsWNrxejR3Wji645mfu64kFmI385exaPB9eVuiYiIqE6QdaTp999/R6dOndCpUycAwLRp09CpUyf85z//sfo9PvzwQzz++OMYPnw4evToATc3N2zZsgUODg5SzXfffYfg4GAMHDgQAwcORPv27fHNN99I+x0cHPDzzz/DxcUFPXr0wPDhw/H444/j/fffr76DvY+4OjgREVH1UwghhNxN1BV5eXlQq9UwGAyyjlBFn8/Cs5/HwMfdGYff6A8H5d1NpCciIrIn1v5+19o5TXTvujTxhoeLI7IKS/DHpVy52yEiIqoTGJrqICcHJXq38gMA7OEpOiIiomrB0FRHcXVwIiKi6sXQVEf1buUPpQKIT83DldwiudshIiKyeQxNdZTG3RkPNCq/vQxXByciIvr7GJrqsH7XT9ExNBEREf19DE11WOj1W6ocPHcVRSUmmbshIiKybQxNdVgrbT008HKFscyMqPNX5W6HiIjIpjE01WEKhYJX0REREVUThqY6rt/1W6rsic8AF38nIiK6dwxNdVy3Zj5wdXJAWl4xTqfmyd0OERGRzWJoquNcnBzQs6UvAK4OTkRE9HcwNNmB0Dac10RERPR3MTTZgb7XQ9Mfl3KRmW+UuRsiIiLbxNBkB7SeLghuoIYQwL5EjjYRERHdC4YmOyFdRcdTdERERPeEoclOVKzXdOBMJkrKzDJ3Q0REZHsYmuxEkF4NPw8VCktMOJyULXc7RERENoehyU4olQr0a11xFV26zN0QERHZHoYmO9Kv4pYqXB2ciIjorjE02ZGeLXzh7KDExexrOJ9ZKHc7RERENoWhyY64qxzRrbkPAGAPT9ERERHdFYYmOyOtDs5bqhAREd0VhiY7U7Fe0+/JOTBcK5W5GyIiItvB0GRnAjRuaKWtB5NZYP/ZTLnbISIishkMTXaoXxstAGBPPOc1ERERWYuhyQ5VrA6+70wmykxcHZyIiMgaDE12qFOAF7zcnJB7rRTHUnLlboeIiMgmMDTZIUcHJfq08gPAq+iIiIisxdBkp/q1vT6vies1ERERWYWhyU71bukHB6UCZ9ILkJJ9Te52iIiIaj2GJjuldnNCl8beAIA9CTxFR0REdCcMTXas4iq63QxNREREd8TQZMcq1muKOZ+FQmOZzN0QERHVbgxNdqy5nzsa+7ihxGRG5LmrcrdDRERUqzE02TGFQiHdi24Plx4gIiK6LYYmOxdacUuVxAyYzULmboiIiGovhiY791BTDdydHZCZb8TJKwa52yEiIqq1GJrsnLOjEr24OjgREdEdyRqaDhw4gKFDh0Kv10OhUGDTpk3SvtLSUsycORPBwcFwd3eHXq/H888/jytXrli8h9FoxKRJk+Dr6wt3d3cMGzYMly5dsqjJyclBeHg41Go11Go1wsPDkZuba1Fz8eJFDB06FO7u7vD19cXkyZNRUlJSU4deq0jzmrj0ABER0S3JGpoKCwvRoUMHLFu2rNK+a9eu4ejRo3jzzTdx9OhRbNiwAWfOnMGwYcMs6qZOnYqNGzdi/fr1iIyMREFBAcLCwmAymaSakSNHIi4uDtu3b8f27dsRFxeH8PBwab/JZMKQIUNQWFiIyMhIrF+/Hj/++CMiIiJq7uBrkT6t/aFQACcuG5CeVyx3O0RERLWTqCUAiI0bN9625vDhwwKASE5OFkIIkZubK5ycnMT69eulmsuXLwulUim2b98uhBDi9OnTAoCIiYmRaqKjowUAkZCQIIQQYtu2bUKpVIrLly9LNevWrRMqlUoYDAarj8FgMAgAd/Wa2uKxZZGi8cytYt2hZLlbISIiuq+s/f22qTlNBoMBCoUCXl5eAIDY2FiUlpZi4MCBUo1er0dQUBCioqIAANHR0VCr1ejatatU061bN6jVaouaoKAg6PV6qWbQoEEwGo2IjY29ZT9GoxF5eXkWD1sV2oargxMREd2OzYSm4uJivPbaaxg5ciQ8PT0BAGlpaXB2doa3t7dFrVarRVpamlTj7+9f6f38/f0tarRarcV+b29vODs7SzVVmTdvnjRPSq1WIyAg4G8do5z6Xb+lSuTZqyguNd2hmoiIyP7YRGgqLS3FM888A7PZjOXLl9+xXggBhUIhPb/xv/9Ozc1mzZoFg8EgPVJSUu7YW20VWN8TOk8XFJWaEHMhS+52iIiIap1aH5pKS0sxfPhwJCUlYefOndIoEwDodDqUlJQgJyfH4jUZGRnSyJFOp0N6enql983MzLSouXlEKScnB6WlpZVGoG6kUqng6elp8bBVCoVCGm3iVXRERESV1erQVBGYzp49i127dsHHx8dif+fOneHk5ISdO3dK21JTU3Hy5EmEhIQAALp37w6DwYDDhw9LNYcOHYLBYLCoOXnyJFJTU6WaHTt2QKVSoXPnzjV5iLWKNK8pPgNCcHVwIiKiGznK+eEFBQU4d+6c9DwpKQlxcXHQaDTQ6/V46qmncPToUWzduhUmk0kaDdJoNHB2doZarcZLL72EiIgI+Pj4QKPRYPr06QgODkb//v0BAG3btsXgwYMxduxYrFixAgAwbtw4hIWFoXXr1gCAgQMHIjAwEOHh4Vi0aBGys7Mxffp0jB071qZHj+5WSHNfqByVuJxbhDPpBWit85C7JSIiotrjPlzJd0t79+4VACo9Ro8eLZKSkqrcB0Ds3btXeo+ioiIxceJEodFohKurqwgLCxMXL160+JysrCwxatQo4eHhITw8PMSoUaNETk6ORU1ycrIYMmSIcHV1FRqNRkycOFEUFxff1fHY8pIDFV5YdVg0nrlVfLL3rNytEBER3RfW/n4rhOB5mOqSl5cHtVoNg8FgsyNU38Yk49+bTqJLY2/8758hcrdDRERU46z9/a7Vc5ro/qu4pcrRiznILrSP28gQERFZg6GJLOi9XNG2vifMAth/hlfRERERVWBookpuvIqOiIiIyjE0USUV6zXtP5OJUpNZ5m6IiIhqB4YmqqRDQy/4uDsjv7gMv/+Zc+cXEBER2QGGJqrEQalAn9YVq4NXXk2diIjIHjE0UZVCr5+i281bqhAREQFgaKJbeLilLxyVClzILETS1UK52yEiIpIdQxNVycPFCV2baQDwBr5EREQAQxPdRr82WgCc10RERAQwNNFtVKzXdOhCNvKLS2XuhoiISF4MTXRLTXzd0czPHWVmgd/OXpW7HSIiIlkxNNFtcXVwIiKicgxNdFsV85r2JWbAZBYyd0NERCQfhia6rS5NvOHh4oiswhL8cSlX7naIiIhkw9BEt+XkoETvVn4AgD08RUdERHaMoYnuiKuDExERMTSRFXq38odSAcSn5uFKbpHc7RAREcmCoYnuSOPujAcaeQPg6uBERGS/GJrIKv2un6JjaCIiInvF0ERWCb2+9MDBc1dRVGKSuRsiIqL7j6GJrNJKWw8NvFxhLDMj6jxXByciIvvD0ERWUSgUvIqOiIjsGkMTWa3f9Vuq7InPgBBcHZyIiOwLQxNZrVszH7g6OSAtrxinU/PkboeIiOi+Ymgiq7k4OaBnS18AXB2ciIjsD0MT3ZXQNpzXRERE9omhie5K3+uh6Y9LucjMN8rcDRER0f3D0ER3RevpguAGaggB7EvkaBMREdkPhia6a9JVdDxFR0REdoShie5axXpNB85koqTMLHM3RERE9wdDE921IL0afh4qFJaYcDgpW+52iIiI7guGJrprSqUC/VpXXEWXLnM3RERE9wdDE92TfhW3VOHq4EREZCcYmuie9GzhC2cHJS5mX8P5zEK52yEiIqpxDE10T9xVjujW3AcAsIen6IiIyA4wNNE9k1YH5y1ViIjIDjA00T2rWK/p9+QcGK6VytwNERFRzZI1NB04cABDhw6FXq+HQqHApk2bLPYLITBnzhzo9Xq4urqiT58+OHXqlEWN0WjEpEmT4OvrC3d3dwwbNgyXLl2yqMnJyUF4eDjUajXUajXCw8ORm5trUXPx4kUMHToU7u7u8PX1xeTJk1FSUlITh11nBGjc0EpbDyazwP6zmXK3Q0REVKNkDU2FhYXo0KEDli1bVuX+hQsXYvHixVi2bBmOHDkCnU6HAQMGID8/X6qZOnUqNm7ciPXr1yMyMhIFBQUICwuDyWSSakaOHIm4uDhs374d27dvR1xcHMLDw6X9JpMJQ4YMQWFhISIjI7F+/Xr8+OOPiIiIqLmDryP6tdECAPbEc14TERHVcaKWACA2btwoPTebzUKn04n58+dL24qLi4VarRafffaZEEKI3Nxc4eTkJNavXy/VXL58WSiVSrF9+3YhhBCnT58WAERMTIxUEx0dLQCIhIQEIYQQ27ZtE0qlUly+fFmqWbdunVCpVMJgMFh9DAaDQQC4q9fYusNJWaLxzK2iw1u/itIyk9ztEBER3TVrf79r7ZympKQkpKWlYeDAgdI2lUqF3r17IyoqCgAQGxuL0tJSixq9Xo+goCCpJjo6Gmq1Gl27dpVqunXrBrVabVETFBQEvV4v1QwaNAhGoxGxsbE1epy2rlOAF7zcnJB7rRTHUnLlboeIiKjG1NrQlJaWBgDQarUW27VarbQvLS0Nzs7O8Pb2vm2Nv79/pff39/e3qLn5c7y9veHs7CzVVMVoNCIvL8/iYW8cHZTo08oPAK+iIyKiuq3WhqYKCoXC4rkQotK2m91cU1X9vdTcbN68edLkcrVajYCAgNv2VVf1a3t9XhPXayIiojqs1oYmnU4HAJVGejIyMqRRIZ1Oh5KSEuTk5Ny2Jj298o95ZmamRc3Nn5OTk4PS0tJKI1A3mjVrFgwGg/RISUm5y6OsG3q39IODUoEz6QVIyb4mdztEREQ1otaGpqZNm0Kn02Hnzp3StpKSEuzfvx8hISEAgM6dO8PJycmiJjU1FSdPnpRqunfvDoPBgMOHD0s1hw4dgsFgsKg5efIkUlNTpZodO3ZApVKhc+fOt+xRpVLB09PT4mGP1G5O6NK4/BTpngSeoiMiorrJUc4PLygowLlz56TnSUlJiIuLg0ajQaNGjTB16lTMnTsXLVu2RMuWLTF37ly4ublh5MiRAAC1Wo2XXnoJERER8PHxgUajwfTp0xEcHIz+/fsDANq2bYvBgwdj7NixWLFiBQBg3LhxCAsLQ+vWrQEAAwcORGBgIMLDw7Fo0SJkZ2dj+vTpGDt2rN0GobsV2tYfh5KysTshA6NDmsjdDhERUfW7D1fy3dLevXsFgEqP0aNHCyHKlx2YPXu20Ol0QqVSiV69eokTJ05YvEdRUZGYOHGi0Gg0wtXVVYSFhYmLFy9a1GRlZYlRo0YJDw8P4eHhIUaNGiVycnIsapKTk8WQIUOEq6ur0Gg0YuLEiaK4uPiujscelxyocDY9XzSeuVW0fH2bKCgulbsdIiIiq1n7+60QQggZM1udkpeXB7VaDYPBYHcjVEII9Hl/H5KzrmFFeGcMaqeTuyUiIiKrWPv7XWvnNJFtUSgU0r3o9nDpASIiqoMYmqjahFbcUiUxA2YzBzCJiKhuYWiiavNQUw3cnR2QmW/EySsGudshIiKqVgxNVG2cHZXoxdXBiYiojmJoomolzWviek1ERFTHMDRRterT2h8KBXDisgHpecVyt0NERFRtGJqoWvl5qNChoRcAYC9Hm4iIqA5haKJqF3r9FN1uhiYiIqpD/nZoSk5OxunTp2E2m6ujH6oD+rUtD02RZ6+iuNQkczdERETVw+rQtGbNGixZssRi27hx49CsWTMEBwcjKCgIKSkp1d0f2aDA+p7QebqgqNSEmAtZcrdDRERULawOTZ999hnUarX0fPv27Vi1ahW+/vprHDlyBF5eXnjrrbdqpEmyLQqFQhpt4lV0RERUV1gdms6cOYMuXbpIz3/66ScMGzYMo0aNwgMPPIC5c+di9+7dNdIk2R5pXlN8Bnh7QyIiqgusDk1FRUUWN7GLiopCr169pOfNmjVDWlpa9XZHNiukuS9Ujkpczi3CmfQCudshIiL626wOTY0bN0ZsbCwA4OrVqzh16hR69uwp7U9LS7M4fUf2zdXZAT1a+AIAdieky9wNERHR32d1aHr++ecxYcIEvPPOO3j66afRpk0bdO7cWdofFRWFoKCgGmmSbJO0OjhvqUJERHWAo7WFM2fOxLVr17BhwwbodDr897//tdh/8OBBPPvss9XeINmuitB09GIOsgtLoHF3lrkjIiKie6cQnKVbbfLy8qBWq2EwGCzmf9mzRz76DfGpefhwRAf8o1NDudshIiKqxNrf77+1uGVxcTHWrFmD5cuX49y5c3/nraiOuvEqOiIiIltmdWiaMWMGpkyZIj0vKSlB9+7dMXbsWLz++uvo2LEjoqOja6RJsl0V6zXtP5OJUhNXjSciIttldWj65ZdfEBoaKj3/7rvvkJycjLNnzyInJwdPP/003n333RppkmxXh4Ze8HF3Rn5xGX7/M0fudoiIiO6Z1aHp4sWLCAwMlJ7v2LEDTz31FBo3bgyFQoEpU6bg2LFjNdIk2S4HpQJ9WlesDs6lB4iIyHZZHZqUSqXFys4xMTHo1q2b9NzLyws5ORxJoMpCr5+i281bqhARkQ2zOjS1adMGW7ZsAQCcOnUKFy9eRN++faX9ycnJ0Gq11d8h2byHW/rCUanAhcxCJF0tlLsdIiKie3JXE8Ffe+01hIaGIjQ0FI8++iiaNm0q7d+2bRseeuihGmmSbJuHixO6NtMA4A18iYjIdlkdmp588kls27YN7du3xyuvvILvv//eYr+bmxv+9a9/VXuDVDf0a1M+Csl5TUREZKu4uGU14uKWt/bn1UL0eX8fHJUKHPvPAHi4OMndEhEREYAaWNzy7NmzePbZZ5GXl1dpn8FgwMiRI3HhwoV765bqvCa+7mjm544ys8DaQxflboeIiOiuWR2aFi1ahICAgCoTmFqtRkBAABYtWlStzVHd8n89mwEAPthxBqeuGGTuhoiI6O5YHZoOHDiAp59++pb7hw8fjj179lRLU1Q3PftQAPq31aLEZMaU9XEoKjHJ3RIREZHVrA5NycnJ8Pf3v+V+X19fpKSkVEtTVDcpFAoseDIYfh4qnMsowLxf4uVuiYiIyGpWhya1Wo3z58/fcv+5c+c4+ZnuyKeeCu8/3QEA8HV0MnbH82o6IiKyDVaHpl69emHp0qW33P/xxx/j4YcfrpamqG7r3coPL/YoX+Pr1f8dR0Z+scwdERER3ZnVoWnWrFn45Zdf8NRTT+Hw4cMwGAwwGAw4dOgQnnzySfz666+YNWtWTfZKdcirg1ujjc4DWYUlmPHf4+DKF0REVNtZHZo6deqE//3vfzhw4AC6d+8OjUYDjUaDkJAQ/Pbbb/jhhx/wwAMP1GSvVIe4ODng42c7QeWoxP4zmVgd9afcLREREd2W1Ytbnjt3Di1atEBRURF+/fVXnD17FkIItGrVCgMHDoSbm1tN91rrcXHLu7cm6k/M3nwKzo5KbJ7YA210/N6IiOj+svb32+rQpFQq0aBBA/Tt21d6NGnSpLr6rRMYmu6eEAIvrj6CvYmZaK31wE8Te8DFyUHutoiIyI5U+4rg+/fvx/jx43HlyhVMnDgRzZs3R9OmTfHSSy/h22+/xeXLl6ulcbIvCoUCC5/qAN96zkhMz8f8XxLkbomIiKhK93TvudLSUkRHR2Pfvn3Yt28fYmJiYDQa0aJFCyQmJtZEnzaBI033bm9CBl5YfQQAsOqFB9G39a3XBCMiIqpO1X56ripFRUWIjIzEr7/+is8//xwFBQUwmex3lWeGpr9nzuZTWB31J3zrqbB96sPwraeSuyUiIrID1X56DgCKi4uxZ88evPnmm3j44Yfh7e2NyZMno6CgAJ9++ikuXqzeG7GWlZXh3//+N5o2bQpXV1c0a9YMb7/9Nsxms1QjhMCcOXOg1+vh6uqKPn364NSpUxbvYzQaMWnSJPj6+sLd3R3Dhg3DpUuXLGpycnIQHh4OtVoNtVqN8PBw5ObmVuvx0O299kgbtNLWw9UCI2b+j8sQEBFR7WJ1aOrduzc0Gg2mTJmC7OxsTJo0CcnJyYiPj8dnn32GkSNHokGDBtXa3IIFC/DZZ59h2bJliI+Px8KFC7Fo0SKLRTYXLlyIxYsXY9myZThy5Ah0Oh0GDBiA/Px8qWbq1KnYuHEj1q9fj8jISBQUFCAsLMxiVGzkyJGIi4vD9u3bsX37dsTFxSE8PLxaj4duz8XJAR890wnODkrsTsjAtzHJcrdERET0F2ElR0dHERAQICZNmiR+/PFHkZmZae1L79mQIUPEiy++aLHtiSeeEM8995wQQgiz2Sx0Op2YP3++tL+4uFio1Wrx2WefCSGEyM3NFU5OTmL9+vVSzeXLl4VSqRTbt28XQghx+vRpAUDExMRINdHR0QKASEhIsLpfg8EgAAiDwXD3B0uSL367IBrP3CpavbFNnEnLk7sdIiKq46z9/bZ6pCk3NxcrV66Em5sbFixYgAYNGiA4OBgTJ07E//73P2RmZlZ7oOvZsyd2796NM2fOAAD++OMPREZG4tFHHwUAJCUlIS0tDQMHDpReo1Kp0Lt3b0RFRQEAYmNjUVpaalGj1+sRFBQk1URHR0OtVqNr165STbdu3aBWq6Uaun9eCGmCXq38YCwzY/L6OBjL7HeeHBER1R6O1ha6u7tj8ODBGDx4MAAgPz8fkZGR2Lt3LxYuXIhRo0ahZcuWOHnyZLU1N3PmTBgMBrRp0wYODg4wmUx477338OyzzwIA0tLSAABardbidVqtFsnJyVKNs7MzvL29K9VUvD4tLQ3+/pWv1vL395dqqmI0GmE0GqXneXl593CUdDOlUoH3n2qPwR/9hvjUPCzanoh/hwXK3RYREdm5u5oIfiN3d3fpVire3t5wdHREfHx8dfaG77//Ht9++y3Wrl2Lo0ePYs2aNXj//fexZs0aizqFQmHxXAhRadvNbq6pqv5O7zNv3jxp4rharUZAQIA1h0VW8Pd0wYIn2wMAvohMwm9nq38kk4iI6G5YHZrMZjMOHz6MhQsX4pFHHoGXlxdCQkKwfPly6HQ6fPLJJ7hw4UK1Njdjxgy89tpreOaZZxAcHIzw8HC88sormDdvHgBAp9MBQKXRoIyMDGn0SafToaSkBDk5ObetSU9Pr/T5mZmZlUaxbjRr1izpxsUGgwEpKSn3frBUyYBALZ7r1ggAEPHDH8guLJG5IyIismdWhyYvLy90794dH3/8MXx8fLB48WKcOXMGFy9exJo1azBmzBg0bty4Wpu7du0alErLFh0cHKQlB5o2bQqdToedO3dK+0tKSrB//36EhIQAADp37gwnJyeLmtTUVJw8eVKq6d69OwwGAw4fPizVHDp0CAaDQaqpikqlgqenp8WDqtcbjwaiuZ87MvKNmPkjlyEgIiL5WD2nadGiRejbty9atWpVk/1YGDp0KN577z00atQI7dq1w7Fjx7B48WK8+OKLAMpPqU2dOhVz585Fy5Yt0bJlS8ydOxdubm4YOXIkAECtVuOll15CREQEfHx8oNFoMH36dAQHB6N///4AgLZt22Lw4MEYO3YsVqxYAQAYN24cwsLC0Lp16/t2vFSZq3P5MgT/WH4QO0+nY93hFIzs2kjutoiIyB7dhyv57lleXp6YMmWKaNSokXBxcRHNmjUTb7zxhjAajVKN2WwWs2fPFjqdTqhUKtGrVy9x4sQJi/cpKioSEydOFBqNRri6uoqwsDBx8eJFi5qsrCwxatQo4eHhITw8PMSoUaNETk7OXfXLJQdqzsr950XjmVtF639vE2fT8+Vuh4iI6hBrf7//1m1UyBJvo1JzzGaB8K8O4eC5LAQ18MSGf/aAs+M9X8dAREQkqZHbqBDJRalU4IOnO8LLzQknL+fhg532e2NoIiKSB0MT2Qyd2gXznyhfhmDlgQuIOndV5o6IiMieMDSRTRkcpMOzDwVACGDaD38gh8sQEBHRfcLQRDbnzbBANPN1R1peMV7feILLEBAR0X3B0EQ2x83ZER890wmOSgV+OZmG//5+Se6WiIjIDjA0kU0KbqhGxMDyNbTmbDmFpKuFMndERER1HUMT2axxvZqhWzMNrpWYMHX9MZSazHK3REREdRhDE9ksB6UCi4d3hNrVCX9cMmDJrjNyt0RERHUYQxPZNL2XK+b+IxgAsHzfeRy6kCVzR0REVFcxNJHNG9K+Pp7u3BBCAK98HwfDtVK5WyIiojqIoYnqhNnD2qGxjxuuGIrx+iYuQ0BERNWPoYnqhHqq8mUIHJQK/Hw8FRuOXpa7JSIiqmMYmqjO6BjghVf6twQA/Oenk0jO4jIERERUfRiaqE75Z58WeKiJBoUlJkxZH8dlCIiIqNowNFGd4qBUYPGIDvBwcURcSi6W7jknd0tERFRHMDRRndPQ2w3vXV+GYNmeszjyZ7bMHRERUV3A0ER10rAOejzRqQHMApi6Pg55xVyGgIiI/h6GJqqz3nqsHQI0rricW4T/bDopdztERGTjGJqozvJwccKSEeXLEGyKu4JNx7gMARER3TuGJqrTOjf2xqR+LQAAb246iZTsazJ3REREtoqhieq8iX1boHNjb+Qby/DK93Eo4zIERER0DxiaqM5zdFBiyYiOqKdyxO/JOVi+77zcLRERkQ1iaCK7EKBxwzuPtwMAfLT7LGKTc2TuiIiIbA1DE9mNxzs2wLAOepjMAlO/P4Z8LkNARER3gaGJ7IZCocA7jwehgZcrUrKLMGfzablbIiIiG8LQRHZF7eqED0d0hFIB/Hj0Erb8cUXuloiIyEYwNJHdeaipBhP6li9D8PrGE7icWyRzR0REZAsYmsguTQ5tiY4BXsgvLl+GwGQWcrdERES1HEMT2SUnByU+eqYj3J0dcDgpG5/t5zIERER0ewxNZLca+7hjzrDyZQg+3HkGcSm58jZERES1GkMT2bWnOjfEkPb1UWYWmLr+GAqNZXK3REREtRRDE9k1hUKBuY8Ho77aBX9mXcPbW7gMARERVY2hieye2q18GQKFAvj+9xT8ciJV7paIiKgWYmgiAtCtmQ/+2bs5AOC1DSeQauAyBEREZImhiei6qf1boX1DNQxFpZj2/R8wcxkCIiK6AUMT0XXOjkosGdERrk4OiL6QhZW/XZC7JSIiqkUYmohu0MyvHuYMCwQAfLAjEScuGWTuiIiIaguGJqKbDO8SgMHtdCg1CUz5/hiulXAZAiIiYmgiqkShUGDeE8HQeqpwIbMQ72yNl7slIiKqBWp9aLp8+TKee+45+Pj4wM3NDR07dkRsbKy0XwiBOXPmQK/Xw9XVFX369MGpU6cs3sNoNGLSpEnw9fWFu7s7hg0bhkuXLlnU5OTkIDw8HGq1Gmq1GuHh4cjNzb0fh0i1kLe7MxYPL1+GYN3hi/j1VJrcLRERkcxqdWjKyclBjx494OTkhF9++QWnT5/GBx98AC8vL6lm4cKFWLx4MZYtW4YjR45Ap9NhwIAByM/Pl2qmTp2KjRs3Yv369YiMjERBQQHCwsJgMpmkmpEjRyIuLg7bt2/H9u3bERcXh/Dw8Pt5uFTL9Gjhi3EPNwMAvPbjcaTnFcvcERERyUkhhKi111W/9tprOHjwIH777bcq9wshoNfrMXXqVMycORNA+aiSVqvFggULMH78eBgMBvj5+eGbb77BiBEjAABXrlxBQEAAtm3bhkGDBiE+Ph6BgYGIiYlB165dAQAxMTHo3r07EhIS0Lp1a6v6zcvLg1qthsFggKenZzV8AyQ3Y5kJTyyPwqkreejZwhdfv/gQlEqF3G0REVE1svb3u1aPNG3evBldunTB008/DX9/f3Tq1Amff/65tD8pKQlpaWkYOHCgtE2lUqF3796IiooCAMTGxqK0tNSiRq/XIygoSKqJjo6GWq2WAhMAdOvWDWq1WqqpitFoRF5ensWD6haVowM+eqYTXJyUiDx3FV8dTJK7JSIikkmtDk0XLlzAp59+ipYtW+LXX3/Fyy+/jMmTJ+Prr78GAKSllc8z0Wq1Fq/TarXSvrS0NDg7O8Pb2/u2Nf7+/pU+39/fX6qpyrx586Q5UGq1GgEBAfd+sFRrtfCvhzfDypchWLg9EaeucBkCIiJ7VKtDk9lsxgMPPIC5c+eiU6dOGD9+PMaOHYtPP/3Uok6hsDxdIoSotO1mN9dUVX+n95k1axYMBoP0SElJseawyAaNfKgR+rfVosRkxpT1cSgqMd35RUREVKfU6tBUv359BAYGWmxr27YtLl68CADQ6XQAUGk0KCMjQxp90ul0KCkpQU5Ozm1r0tPTK31+ZmZmpVGsG6lUKnh6elo8qG5SKBRY8GQw/DxUOJdRgLnbuAwBEZG9qdWhqUePHkhMTLTYdubMGTRu3BgA0LRpU+h0OuzcuVPaX1JSgv379yMkJAQA0LlzZzg5OVnUpKam4uTJk1JN9+7dYTAYcPjwYanm0KFDMBgMUg2RTz0VPni6AwDgm5hkrD6YhFp8HQUREVWzWh2aXnnlFcTExGDu3Lk4d+4c1q5di5UrV2LChAkAyv/vf+rUqZg7dy42btyIkydPYsyYMXBzc8PIkSMBAGq1Gi+99BIiIiKwe/duHDt2DM899xyCg4PRv39/AOWjV4MHD8bYsWMRExODmJgYjB07FmFhYVZfOUf2oVcrP4zvXb4MwZwtpzH9v8dRXMpTdURE9qBWLzkAAFu3bsWsWbNw9uxZNG3aFNOmTcPYsWOl/UIIvPXWW1ixYgVycnLQtWtXfPLJJwgKCpJqiouLMWPGDKxduxZFRUUIDQ3F8uXLLSZuZ2dnY/Lkydi8eTMAYNiwYVi2bJnFmlB3wiUH7IMQAl/8loR5v8TDLIDgBmp8+twDaOjtJndrRER0D6z9/a71ocmWMDTZl6hzVzFx3TFkF5ZA4+6MZc92QkgLX7nbIiKiu1Qn1mkiqs1CWvhi88QeCGrgiezCEjz35SF8fuAC5zkREdVRDE1Ef0NDbzf87+UQPPlAQ5gF8N62eExeH4drJWVyt0ZERNWMoYnob3JxcsD7T7fH24+1g6NSgS1/XMETy6OQnFUod2tERFSNGJqIqoFCocDz3Ztg7dhu8K2nQkJaPoYujcS+xAy5WyMiomrC0ERUjR5qqsHWST3RqZEX8orL8MLqI/hk7znOcyIiqgMYmoiqmU7tgvXjumFk10YQAlj0ayJe/jYW+cWlcrdGRER/A0MTUQ1QOTpg7j+CMf+JYDg7KPHrqXQ8/slBnM8skLs1IiK6RwxNRDXomYca4fvx3aDzdMH5zEI8tuwgdpxKu/MLiYio1mFoIqphnRp5Y8uknnioqQYFxjKM+yYWi3ckwmzmPCciIlvC0ER0H/h5qPDd/3XFCz2aAAA+3nMOL605AsM1znMiIrIVDE1E94mTgxKzh7bDhyM6QOWoxN7ETAz7JBKJaflyt0ZERFZgaCK6z/7RqSF+/GcIGni5IjnrGh7/5CC2Hr8id1tERHQHDE1EMghqoMbWST3Rs4UvikpNmLj2GOZti0eZySx3a0REdAsMTUQy8XZ3xpoXH8LLvZsDAFYcuIDRqw4ju7BE5s6IiKgqDE1EMnJQKvDaI23wycgH4ObsgIPnsjB0aSROXjbI3RoREd2EoYmoFhjSvj42/qsHmvi44XJuEZ78NAobjl6Suy0iIroBQxNRLdFa54GfJvZEvzb+MJaZMe2HPzBn8ymUcp4TEVGtwNBEVIuoXZ3wxfNdMDm0JQBgddSfGPX5IWTmG2XujIiIGJqIahmlUoFpA1rh8+e7oJ7KEYf/zMbQpZE4djFH7taIiOwaQxNRLTUgUIufJvZAC/96SMsrxogVMVh3+KLcbRER2S2GJqJarLlfPWya0AOD2+lQYjJj1oYTmLXhBIxlJrlbIyKyOwxNRLVcPZUjPn3uAcwY1BoKBbDu8EU8szIGaYZiuVsjIrIrDE1ENkChUGBC3xZYNeZBqF2dcOxiLsKWRuJwUrbcrRER2Q2GJiIb0qe1P7ZM7Ik2Og9cLTBi5OcxWH0wCUIIuVsjIqrzGJqIbEwjHzds+FcIhnXQo8wsMGfLaUT89w8Ul3KeExFRTWJoIrJBbs6O+OiZjvj3kLZwUCqw4ehlPPVZFC7lXJO7NSKiOouhichGKRQK/N/DzfDNSw9B4+6Mk5fzMHRpJA6euyp3a0REdRJDE5GNC2nuiy2TeiK4gRo510oR/uUhrDxwnvOciIiqGUMTUR3QwMsV/325O57q3BBmAczdloBJ647hWkmZ3K0REdUZDE1EdYSLkwMWPdUe7zweBEelAluPp+Ifn0Thz6uFcrdGRFQnMDQR1SEKhQLh3Rpj/bhu8PNQITE9H8OWRWJvYobcrRER2TyGJqI6qEsTDbZO6okHGnkhr7gML64+gqW7z8Js5jwnIqJ7xdBEVEdpPV2wflx3jOraCEIAH+w8g5e/jUV+cancrRER2SSGJqI6zNlRiff+EYyFT7aHs4MSO06n4/FPDuJcRoHcrRER2RyGJiI7MPzBAPz35e6or3bB+cxCPP7JQfx6Kk3utoiIbApDE5Gd6BDghS2TeqJrUw0KjGUY/00s3v81EaUms9ytERHZBIXgCnjVJi8vD2q1GgaDAZ6ennK3Q1SlUpMZ87Yl4KuDSQAALzcnDArUYUj7+uje3AdODvx/KSKyL9b+fjM0VSOGJrIlP8Vdxjtb43G1wCht83ZzwuAgHYYE69GtmQaODFBEZAcYmmTA0ES2xmQWOJyUja3Hr2D7yTRkFZZI+3zcncsDVPv66NrUBw5KhYydEhHVHGt/v23qfyPnzZsHhUKBqVOnStuEEJgzZw70ej1cXV3Rp08fnDp1yuJ1RqMRkyZNgq+vL9zd3TFs2DBcunTJoiYnJwfh4eFQq9VQq9UIDw9Hbm7ufTgqIvk4KBXo3twH7/0jGIdeD8V3/9cVzz7UCN5uTsgqLMF3hy5i5OeH0HXubry56SRiLmTBxLWeiMhO2cxI05EjRzB8+HB4enqib9++WLJkCQBgwYIFeO+997B69Wq0atUK7777Lg4cOIDExER4eHgAAP75z39iy5YtWL16NXx8fBAREYHs7GzExsbCwcEBAPDII4/g0qVLWLlyJQBg3LhxaNKkCbZs2WJ1jxxporqi1GRGzIUs/Hw8FdtPpSH32l9rO/l5qPBokA5hHfTo3MgbSo5AEZGNq1On5woKCvDAAw9g+fLlePfdd9GxY0csWbIEQgjo9XpMnToVM2fOBFA+qqTVarFgwQKMHz8eBoMBfn5++OabbzBixAgAwJUrVxAQEIBt27Zh0KBBiI+PR2BgIGJiYtC1a1cAQExMDLp3746EhAS0bt3aqj4ZmqguKjWZcfDcVfx8PBW/nkpDXvFfNwHWeqrwaHB9hLWvj04BDFBEZJvq1Om5CRMmYMiQIejfv7/F9qSkJKSlpWHgwIHSNpVKhd69eyMqKgoAEBsbi9LSUosavV6PoKAgqSY6OhpqtVoKTADQrVs3qNVqqaYqRqMReXl5Fg+iusbJQYk+rf2x6OkO+P3fA7BqzIN48oGG8HBxRHqeEasO/oknP41GjwV78M7W0zh6MQc28P9iRER3zVHuBu5k/fr1OHr0KI4cOVJpX1pa+eJ8Wq3WYrtWq0VycrJU4+zsDG9v70o1Fa9PS0uDv79/pff39/eXaqoyb948vPXWW3d3QEQ2zNlRib5t/NG3jT+MZUGIPFs+ArXjdDpSDcX4MjIJX0YmoYGXK4a0r48hwfXRvqEaCgVHoIjI9tXq0JSSkoIpU6Zgx44dcHFxuWXdzf8gCyHu+I/0zTVV1d/pfWbNmoVp06ZJz/Py8hAQEHDbzyWqK1SODghtq0VoWy2KS004cCYTP59Ixa7T6bicW4SVBy5g5YELaOhdHqDCgvUIauDJAEVENqtWh6bY2FhkZGSgc+fO0jaTyYQDBw5g2bJlSExMBFA+UlS/fn2pJiMjQxp90ul0KCkpQU5OjsVoU0ZGBkJCQqSa9PT0Sp+fmZlZaRTrRiqVCiqV6u8dJFEd4OLkgIHtdBjYTofiUhP2JZYHqN3x6biUU4QV+y9gxf4LaKRxk0ag2ukZoIjIttTqOU2hoaE4ceIE4uLipEeXLl0watQoxMXFoVmzZtDpdNi5c6f0mpKSEuzfv18KRJ07d4aTk5NFTWpqKk6ePCnVdO/eHQaDAYcPH5ZqDh06BIPBINUQkXVcnBwwOEiHpc92Quy/B+DTUQ9gSPv6cHVywMXsa/h033mELY1E3/f3YdGvCTh9JY9zoIjIJtjE1XM36tOnj3T1HFC+5MC8efOwatUqtGzZEnPnzsW+ffsqLTmwdetWrF69GhqNBtOnT0dWVlalJQeuXLmCFStWAChfcqBx48ZccoComlwrKcOehAz8fDwVexIyYCz76553zfzcERZcH0Pa69FKW48jUER0X1n7+12rT89Z49VXX0VRURH+9a9/IScnB127dsWOHTukwAQAH374IRwdHTF8+HAUFRUhNDQUq1evlgITAHz33XeYPHmydJXdsGHDsGzZsvt+PER1lZuzI8La6xHWXo9CYxl2J2Tg5+NXsDcxExcyC/HxnnP4eM85tPCvhyHXlzFoqfW48xsTEd0nNjfSVJtxpIno7uUXl2JPQga2/JGKA2cyUWL6awSqlbYehgTrMaR9fbTwrydjl0RUl9WpxS1tBUMT0d+TV1yKXafT8fPxVBw4m4lS01//PLXReSCsfX08GlwfzfwYoIio+jA0yYChiaj6GIpKsfN0On4+fgW/nb2KshvueRdY31O6Cq+Jr7uMXRJRXcDQJAOGJqKakXutBDtOp2Pr8VQcPHfV4qbBQQ08MSRYj8FBOjRlgCKie8DQJAOGJqKal1NYgl9PpeHnE6mIOp9lEaCa+7mjf6AWA9pq0amRNxx4LzwisgJDkwwYmojur6wCI349lY5tJ1IRcyHL4hSext0Z/dr4o39bfzzc0g/uKpu/WJiIaghDkwwYmojkk1dcigNnMrHrdDr2JGQgr7hM2ufsqERIcx/0b6tF/7Za6NS3vi0TEdkfhiYZMDQR1Q6lJjN+/zMHu+LTsfN0Oi5mX7PYH9xAXR6gAv0RWJ+3cyGydwxNMmBoIqp9hBA4l1GAnfHp2HU6HcdScnHjv3p6tQv6B5aPQHVtpoHK0eHWb0ZEdRJDkwwYmohqv8x8I/YmZGBnfDp+O5uJ4tK/FtOsp3JE71Z+6B/oj76t/eHl5ixjp0R0vzA0yYChici2FJeacPDcVeyKT8eu+Axk5hulfQ5KBbo09saA66NQXA+KqO5iaJIBQxOR7TKbBU5cNkjzoBLS8i32t/Cvh/5ttRgQ6I+OAVzOgKguYWiSAUMTUd2Rkn0Nu6+PQN28nIFPxXIGgVo83NIXbs5czoDIljE0yYChiahuyisuxf7ETOyKT8feKpYz6NnCF6Ft/dG/rRZaTy5nQGRrGJpkwNBEVPeVmsw48mc2dp3OwM74NKRkF1nsb99QLa0H1ba+B5czILIBDE0yYGgisi9CCJzNKMDO0+nYFZ+OuJuWM2jg5Yr+bctP43Vt6gNnR6V8zRLRLTE0yYChici+3W45Aw+VI3q19sOAtlr0ae3H5QyIahGGJhkwNBFRhTstZ/BgE+/rV+Np0diHyxkQyYmhSQYMTURUFbNZ4PhlA3ZdP41383IGLf3rSauSt2+ohpMDT+MR3U8MTTJgaCIia6RkX7s+ApWOQxeyLZYzcHZQorl/PbTReaD19UdbnSe0nipOKieqIQxNMmBoIqK7ZSgqxf4zmdh1Oh37Ei2XM7iR2tUJrXUeaKPzQBudpxSo6qm4RhTR38XQJAOGJiL6O4QQuJRThMS0fCSk5SEhLR+Jafm4cLUQJnPV/1QHaFzRWuspjUy1re+BJj7ucOQpPiKrMTTJgKGJiGqCscyEcxkFSLweouLT8pGYlof0PGOV9c6OSrTwKz/F16a+B1rrykOVvwdP8RFVhaFJBgxNRHQ/5RSWXB+NykNier40MnWtxFRlvZebk8XpvTY6D7TSesCdp/jIzjE0yYChiYjkZjaXn+K78fReQloekq4W4hZn+NBI43Z9wnn5qFRrnQea+LjxFB/ZDYYmGTA0EVFtVVxafoqvYmQqIa18ZOrG9aNu5OyoRCttPWm+VPlpPg/41eMpPqp7GJpkwNBERLYmu7CkfFQq9fqoVHo+zqTlo6i06lN8GndntNaWh6g210emWmnrwc2Zp/jIdjE0yYChiYjqArNZ4GL2NYvTe4lp+fgzq+pTfAoF0Pj6Kb6KSedNfd3RwNsVni5O9/8AiO4SQ5MMGJqIqC4rKjHhbMZfE84rAtXVgpJbvsbTxRENvN3QwMsVDb1d0cDLFQ28//pvjbszT/eR7BiaZMDQRET26GqB8XqAykdCah7OpOfjYvY15FwrveNrXZ0coPdyQQNvNylI/fWnG/w9VFAqGaqoZjE0yYChiYjoL4XGMlzOLcLlnCJcqvgz55q0LeMWk9Bv5OSgQH31XyNUUqjydkVDLzfo1C5wduRVfvT3WPv7zZl7RERUI9xVjmilLV8LqirGMhNSc4txKacIl3Ov3RSuipCWV4xSU/n8qovZ16p8D4UC0Hq4SEHKMlyVnxZ0dXaoycMkO8LQREREslA5OqCJrzua+LpXub/MZEZ6vhGXr4eqS9lF5aNUN4xelZSZkZZXjLS8YvyenFPl+/i4O1uOUnm5SvOsGni7Qu3KyepkHYYmIiKqlRwdlOXBxssVgKbSfrNZ4GphRagqkv68lPPXfxcYy5BVWIKswhIcv2So8nM8XByrmKheHqr0Xq7wcXfmvCoCwDlN1YpzmoiIag8hBPKKynCp4tTfTeHqcm4RsgtvfeVfBScHBfw9XFBf7QKd2gU6z/I/66tdoVOroFO7wt9DBSeuoG6zOKeJiIjsmkKhgNrNCWo3Ndrp1VXWXCsps5hL9ddIVfmE9Yx8I0pNQgpZt/4swK+eSgpV5QHreqjydJUCl4sT51fZMoYmIiKyW27Ojmip9UDLW0xWLzWZkZlvRKqhGGmG8rlTaYYii+fp1yesZ+QbkZFvxHFUfRoQKL9pskWo8rxhBOv6w0PlyLWraimGJiIioltwclBCf31u062YzQJZhSWVQ1Xe9WBlKEaqoRhFpSbkXitF7rVSJKTl3/L93J0d/gpRN4xS/XVa0IWLgsqEoYmIiOhvUCoV8PNQwc9DhWBUfRpQCIG84rLrAarohoBVbDFqZSgqRWGJCeczC3E+s/CWn+nsoIRWrUJ9T9cbApblqJVfPRUcOc+qWjE0ERER1TCFQgG1qxPUrk5orav6VCBQPscq7YYQlXrDSFX69edXC4woMZmRkl2ElOxbz7NSKgB/Dxdo1S7QXg91vvVUUsDzraeC//U/uZaVdWp1aJo3bx42bNiAhIQEuLq6IiQkBAsWLEDr1q2lGiEE3nrrLaxcuRI5OTno2rUrPvnkE7Rr106qMRqNmD59OtatW4eioiKEhoZi+fLlaNiwoVSTk5ODyZMnY/PmzQCAYcOGYenSpfDy8rpvx0tERPbNzdkRzfzqoZlfvVvWlJSZkZFfeZTqxlGs9HwjTGYhrWF1J/VUjvCt52wRrCr/6Qzfeiq7nsxeq5ccGDx4MJ555hk8+OCDKCsrwxtvvIETJ07g9OnTcHcvXwxtwYIFeO+997B69Wq0atUK7777Lg4cOIDExER4eJSn+X/+85/YsmULVq9eDR8fH0RERCA7OxuxsbFwcCj/y3/kkUdw6dIlrFy5EgAwbtw4NGnSBFu2bLG6Xy45QEREtYHJLJBVUD6BPdVQjMz8YmQWlCAz34irBUaLP41l5rt6bw8XR8tRqxtC1Y3bfdxVNnOLmzp577nMzEz4+/tj//796NWrF4QQ0Ov1mDp1KmbOnAmgfFRJq9ViwYIFGD9+PAwGA/z8/PDNN99gxIgRAIArV64gICAA27Ztw6BBgxAfH4/AwEDExMSga9euAICYmBh0794dCQkJFiNbt8PQREREtkQIgQJj2fUQVXWo+uvPEpSY7i5gebk5lYeoeir4elT86WzxvDxgOcs6/6pOrtNkMJRfxqnRlK8Mm5SUhLS0NAwcOFCqUalU6N27N6KiojB+/HjExsaitLTUokav1yMoKAhRUVEYNGgQoqOjoVarpcAEAN26dYNarUZUVNQtQ5PRaITR+NcNJ/Py8qr1eImIiGqSQqGAh4sTPFyc0Mzv9rUVi4Vm3jJUGZFZYMTV/BJcLTCizCykqwXPZRTcoQ/A283ZMlTd4jShnCu020xoEkJg2rRp6NmzJ4KCggAAaWlpAACtVmtRq9VqkZycLNU4OzvD29u7Uk3F69PS0uDv71/pM/39/aWaqsybNw9vvfXWvR8UERGRjfhrsVAntPC/9ZwroHwZBkNR6fUQZZSCVkWounF7VoERZgFkF5Ygu7AEiem372PrpJ4IalD1VYo1zWZC08SJE3H8+HFERkZW2nfzWhVCiDuuX3FzTVX1d3qfWbNmYdq0adLzvLw8BAQE3PZziYiI6jqlUgFvd2d4uzuj1S0WDq1gMgvkXCup8nTgzSNZWYUl8PNQ3aejqMwmQtOkSZOwefNmHDhwwOKKN51OB6B8pKh+/frS9oyMDGn0SafToaSkBDk5ORajTRkZGQgJCZFq0tMrR9vMzMxKo1g3UqlUUKnk+8sjIiKydQ5KBXyvn45ro7t9bZnJDAcZb55cq6e1CyEwceJEbNiwAXv27EHTpk0t9jdt2hQ6nQ47d+6UtpWUlGD//v1SIOrcuTOcnJwsalJTU3Hy5Emppnv37jAYDDh8+LBUc+jQIRgMBqmGiIiI5OXooJR1JfRaPdI0YcIErF27Fj/99BM8PDyk+UVqtRqurq5QKBSYOnUq5s6di5YtW6Jly5aYO3cu3NzcMHLkSKn2pZdeQkREBHx8fKDRaDB9+nQEBwejf//+AIC2bdti8ODBGDt2LFasWAGgfMmBsLAwq6+cIyIiorqtVoemTz/9FADQp08fi+2rVq3CmDFjAACvvvoqioqK8K9//Uta3HLHjh3SGk0A8OGHH8LR0RHDhw+XFrdcvXq1tEYTAHz33XeYPHmydJXdsGHDsGzZspo9QCIiIrIZNrVOU23HdZqIiIhsj7W/37V6ThMRERFRbcHQRERERGQFhiYiIiIiKzA0EREREVmBoYmIiIjICgxNRERERFZgaCIiIiKyAkMTERERkRUYmoiIiIiswNBEREREZIVafe85W1NxR5q8vDyZOyEiIiJrVfxu3+nOcgxN1Sg/Px8AEBAQIHMnREREdLfy8/OhVqtvuZ837K1GZrMZV65cgYeHBxQKhdzt1Dp5eXkICAhASkoKb2hcS/DvpHbh30ftwr+P2qUm/z6EEMjPz4der4dSeeuZSxxpqkZKpRINGzaUu41az9PTk/8A1TL8O6ld+PdRu/Dvo3apqb+P240wVeBEcCIiIiIrMDQRERERWYGhie4blUqF2bNnQ6VSyd0KXce/k9qFfx+1C/8+apfa8PfBieBEREREVuBIExEREZEVGJqIiIiIrMDQRERERGQFhiYiIiIiKzA0UY2bN28eHnzwQXh4eMDf3x+PP/44EhMT5W6Lrps3bx4UCgWmTp0qdyt26/Lly3juuefg4+MDNzc3dOzYEbGxsXK3ZZfKysrw73//G02bNoWrqyuaNWuGt99+G2azWe7W7MaBAwcwdOhQ6PV6KBQKbNq0yWK/EAJz5syBXq+Hq6sr+vTpg1OnTt2X3hiaqMbt378fEyZMQExMDHbu3ImysjIMHDgQhYWFcrdm944cOYKVK1eiffv2crdit3JyctCjRw84OTnhl19+wenTp/HBBx/Ay8tL7tbs0oIFC/DZZ59h2bJliI+Px8KFC7Fo0SIsXbpU7tbsRmFhITp06IBly5ZVuX/hwoVYvHgxli1bhiNHjkCn02HAgAHS/V9rEpccoPsuMzMT/v7+2L9/P3r16iV3O3aroKAADzzwAJYvX453330XHTt2xJIlS+Ruy+689tprOHjwIH777Te5WyEAYWFh0Gq1+PLLL6VtTz75JNzc3PDNN9/I2Jl9UigU2LhxIx5//HEA5aNMer0eU6dOxcyZMwEARqMRWq0WCxYswPjx42u0H4400X1nMBgAABqNRuZO7NuECRMwZMgQ9O/fX+5W7NrmzZvRpUsXPP300/D390enTp3w+eefy92W3erZsyd2796NM2fOAAD++OMPREZG4tFHH5W5MwKApKQkpKWlYeDAgdI2lUqF3r17IyoqqsY/nzfspftKCIFp06ahZ8+eCAoKkrsdu7V+/XocPXoUR44ckbsVu3fhwgV8+umnmDZtGl5//XUcPnwYkydPhkqlwvPPPy93e3Zn5syZMBgMaNOmDRwcHGAymfDee+/h2Weflbs1ApCWlgYA0Gq1Ftu1Wi2Sk5Nr/PMZmui+mjhxIo4fP47IyEi5W7FbKSkpmDJlCnbs2AEXFxe527F7ZrMZXbp0wdy5cwEAnTp1wqlTp/Dpp58yNMng+++/x7fffou1a9eiXbt2iIuLw9SpU6HX6zF69Gi526PrFAqFxXMhRKVtNYGhie6bSZMmYfPmzThw4AAaNmwodzt2KzY2FhkZGejcubO0zWQy4cCBA1i2bBmMRiMcHBxk7NC+1K9fH4GBgRbb2rZtix9//FGmjuzbjBkz8Nprr+GZZ54BAAQHByM5ORnz5s1jaKoFdDodgPIRp/r160vbMzIyKo0+1QTOaaIaJ4TAxIkTsWHDBuzZswdNmzaVuyW7FhoaihMnTiAuLk56dOnSBaNGjUJcXBwD033Wo0ePSktwnDlzBo0bN5apI/t27do1KJWWP40ODg5ccqCWaNq0KXQ6HXbu3CltKykpwf79+xESElLjn8+RJqpxEyZMwNq1a/HTTz/Bw8NDOietVqvh6uoqc3f2x8PDo9J8Mnd3d/j4+HCemQxeeeUVhISEYO7cuRg+fDgOHz6MlStXYuXKlXK3ZpeGDh2K9957D40aNUK7du1w7NgxLF68GC+++KLcrdmNgoICnDt3TnqelJSEuLg4aDQaNGrUCFOnTsXcuXPRsmVLtGzZEnPnzoWbmxtGjhxZ880JohoGoMrHqlWr5G6Nruvdu7eYMmWK3G3YrS1btoigoCChUqlEmzZtxMqVK+VuyW7l5eWJKVOmiEaNGgkXFxfRrFkz8cYbbwij0Sh3a3Zj7969Vf5mjB49WgghhNlsFrNnzxY6nU6oVCrRq1cvceLEifvSG9dpIiIiIrIC5zQRERERWYGhiYiIiMgKDE1EREREVmBoIiIiIrICQxMRERGRFRiaiIiIiKzA0ERERERkBYYmIrIJf/75JxQKBeLi4uRuRZKQkIBu3brBxcUFHTt2vOvX18ZjIqJbY2giIquMGTMGCoUC8+fPt9i+adOm+3J38dpo9uzZcHd3R2JiInbv3i13O1i9ejW8vLzkboOozmJoIiKrubi4YMGCBcjJyZG7lWpTUlJyz689f/48evbsicaNG8PHx6cau5KXyWTiDWqJqsDQRERW69+/P3Q6HebNm3fLmjlz5lQ6VbVkyRI0adJEej5mzBg8/vjjmDt3LrRaLby8vPDWW2+hrKwMM2bMgEajQcOGDfHVV19Vev+EhASEhITAxcUF7dq1w759+yz2nz59Go8++ijq1asHrVaL8PBwXL16Vdrfp08fTJw4EdOmTYOvry8GDBhQ5XGYzWa8/fbbaNiwIVQqFTp27Ijt27dL+xUKBWJjY/H2229DoVBgzpw5t3yfBQsWoEWLFlCpVGjUqBHee++9KmurGim6eSTvjz/+QN++feHh4QFPT0907twZv//+O/bt24cXXngBBoMBCoXCoqeSkhK8+uqraNCgAdzd3dG1a1eL763ic7du3YrAwECoVCokJydj3759eOihh+Du7g4vLy/06NEDycnJVfZOZA8YmojIag4ODpg7dy6WLl2KS5cu/a332rNnD65cuYIDBw5g8eLFmDNnDsLCwuDt7Y1Dhw7h5Zdfxssvv4yUlBSL182YMQMRERE4duwYQkJCMGzYMGRlZQEAUlNT0bt3b3Ts2BG///47tm/fjvT0dAwfPtziPdasWQNHR0ccPHgQK1asqLK/jz76CB988AHef/99HD9+HIMGDcKwYcNw9uxZ6bPatWuHiIgIpKamYvr06VW+z6xZs7BgwQK8+eabOH36NNauXQutVnvP39uoUaPQsGFDHDlyBLGxsXjttdfg5OSEkJAQLFmyBJ6enkhNTbXo6YUXXsDBgwexfv16HD9+HE8//TQGDx4sHQsAXLt2DfPmzcMXX3yBU6dOQaPR4PHHH0fv3r1x/PhxREdHY9y4cXZ7KpYIAHBfbgtMRDZv9OjR4rHHHhNCCNGtWzfx4osvCiGE2Lhxo7jxn5LZs2eLDh06WLz2ww8/FI0bN7Z4r8aNGwuTySRta926tXj44Yel52VlZcLd3V2sW7dOCCFEUlKSACDmz58v1ZSWloqGDRuKBQsWCCGEePPNN8XAgQMtPjslJUUAEImJiUIIIXr37i06dux4x+PV6/Xivffes9j24IMPin/961/S8w4dOojZs2ff8j3y8vKESqUSn3/+eZX7K47p2LFjQgghVq1aJdRqtUXNzd+vh4eHWL16dZXvV9Xrz507JxQKhbh8+bLF9tDQUDFr1izpdQBEXFyctD8rK0sAEPv27bvl8RHZG440EdFdW7BgAdasWYPTp0/f83u0a9cOSuVf/wRptVoEBwdLzx0cHODj44OMjAyL13Xv3l36b0dHR3Tp0gXx8fEAgNjYWOzduxf16tWTHm3atAFQPv+oQpcuXW7bW15eHq5cuYIePXpYbO/Ro4f0WdaIj4+H0WhEaGio1a+5k2nTpuH//u//0L9/f8yfP9/iuKpy9OhRCCHQqlUri+9l//79Fq91dnZG+/btpecajQZjxozBoEGDMHToUHz00UdITU2ttuMgskUMTUR013r16oVBgwbh9ddfr7RPqVRCCGGxrbS0tFKdk5OTxXOFQlHlNmsmJFecMjKbzRg6dCji4uIsHmfPnkWvXr2kend39zu+543vW0EIcVenp1xdXa2uBaz77ubMmYNTp05hyJAh2LNnDwIDA7Fx48ZbvqfZbIaDgwNiY2MtvpP4+Hh89NFHFr3efGyrVq1CdHQ0QkJC8P3336NVq1aIiYm5q2MiqksYmojonsyfPx9btmxBVFSUxXY/Pz+kpaVZ/PhX5zpEN/5ol5WVITY2VhpNeuCBB3Dq1Ck0adIELVq0sHhYG5QAwNPTE3q9HpGRkRbbo6Ki0LZtW6vfp2XLlnB1dbV6OQI/Pz/k5+ejsLBQ2lbVd9eqVSu88sor2LFjB5544gmsWrUKQPlokclksqjt1KkTTCYTMjIyKn0nOp3ujj116tQJs2bNQlRUFIKCgrB27VqrjoWoLmJoIqJ7EhwcjFGjRmHp0qUW2/v06YPMzEwsXLgQ58+fxyeffIJffvml2j73k08+wcaNG5GQkIAJEyYgJycHL774IgBgwoQJyM7OxrPPPovDhw/jwoUL2LFjB1588cVKYeJOZsyYgQULFuD7779HYmIiXnvtNcTFxWHKlClWv4eLiwtmzpyJV199FV9//TXOnz+PmJgYfPnll1XWd+3aFW5ubnj99ddx7tw5rF27FqtXr5b2FxUVYeLEidi3bx+Sk5Nx8OBBHDlyRApyTZo0QUFBAXbv3o2rV6/i2rVraNWqFUaNGoXnn38eGzZsQFJSEo4cOYIFCxZg27Ztt+w9KSkJs2bNQnR0NJKTk7Fjxw6cOXPmrkIjUV3D0ERE9+ydd96pdDqpbdu2WL58OT755BN06NABhw8fvuWVZfdi/vz5WLBgATp06IDffvsNP/30E3x9fQEAer0eBw8ehMlkwqBBgxAUFIQpU6ZArVZbzJ+yxuTJkxEREYGIiAgEBwdj+/bt2Lx5M1q2bHlX7/Pmm28iIiIC//nPf9C2bVuMGDGi0jytChqNBt9++y22bduG4OBgrFu3zmIpAwcHB2RlZeH5559Hq1atMHz4cDzyyCN46623AAAhISF4+eWXMWLECPj5+WHhwoUAyk+zPf/884iIiEDr1q0xbNgwHDp0CAEBAbfs283NDQkJCXjyySfRqlUrjBs3DhMnTsT48ePv6viJ6hKFuPlfPCIiIiKqhCNNRERERFZgaCIiIiKyAkMTERERkRUYmoiIiIiswNBEREREZAWGJiIiIiIrMDQRERERWYGhiYiIiMgKDE1EREREVmBoIiIiIrICQxMRERGRFRiaiIiIiKzw/xOK4blMlFBoAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# K-Means Clustering\n",
    "# Elbow Method\n",
    "wcss = []\n",
    "for i in range(1, 11):\n",
    "    kmeans = KMeans(n_clusters = i, init = 'k-means++',\n",
    "                    max_iter = 300, n_init = 10, random_state = 0)\n",
    "    kmeans.fit(df['vector'].tolist())\n",
    "    wcss.append(kmeans.inertia_)\n",
    "plt.plot(range(1, 11), wcss)\n",
    "plt.title('The Elbow Method')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akhmadramadani/Documents/TF/miniconda3/lib/python3.9/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tweet</th>\n",
       "      <th>real_tweet</th>\n",
       "      <th>token</th>\n",
       "      <th>vector</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>semester studi independen magang yaa semester ...</td>\n",
       "      <td>[cm] smt 5 mending ikut studi independen dulu ...</td>\n",
       "      <td>[semester, studi, independen, magang, yaa, sem...</td>\n",
       "      <td>[-0.4777812850661576, 0.731285504065454, 0.182...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kampus mahasiswa wajib publish jurnal indeks s...</td>\n",
       "      <td>[cm] di kampus kalian mahasiswa s1 wajib publi...</td>\n",
       "      <td>[kampus, mahasiswa, wajib, publish, jurnal, in...</td>\n",
       "      <td>[-0.3680373860730065, 0.6005052402615547, 0.14...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iya tugas uas manja bantu kerjain</td>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
       "      <td>[iya, tugas, uas, manja, bantu, kerjain]</td>\n",
       "      <td>[-0.17908126286541423, 0.4448075814483066, 0.0...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dosen praktikum kasih waktu temu kelas haru ka...</td>\n",
       "      <td>[CM] Dosen praktikum aku ngasih ini waktu pert...</td>\n",
       "      <td>[dosen, praktikum, kasih, waktu, temu, kelas, ...</td>\n",
       "      <td>[-0.34827083740383386, 0.5752464842051268, 0.1...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alhamdulilah hidup kasi include kos kuliah jak...</td>\n",
       "      <td>[cm] Alhamdulilah masih bisa hidup walaupun di...</td>\n",
       "      <td>[alhamdulilah, hidup, kasi, include, kos, kuli...</td>\n",
       "      <td>[-0.25330586758043083, 0.40945335796901156, 0....</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal indeks s...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu temu kelas haru ka...   \n",
       "4  alhamdulilah hidup kasi include kos kuliah jak...   \n",
       "\n",
       "                                          real_tweet  \\\n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...   \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...   \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...   \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...   \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...   \n",
       "\n",
       "                                               token  \\\n",
       "0  [semester, studi, independen, magang, yaa, sem...   \n",
       "1  [kampus, mahasiswa, wajib, publish, jurnal, in...   \n",
       "2           [iya, tugas, uas, manja, bantu, kerjain]   \n",
       "3  [dosen, praktikum, kasih, waktu, temu, kelas, ...   \n",
       "4  [alhamdulilah, hidup, kasi, include, kos, kuli...   \n",
       "\n",
       "                                              vector  label  \n",
       "0  [-0.4777812850661576, 0.731285504065454, 0.182...      2  \n",
       "1  [-0.3680373860730065, 0.6005052402615547, 0.14...      2  \n",
       "2  [-0.17908126286541423, 0.4448075814483066, 0.0...      1  \n",
       "3  [-0.34827083740383386, 0.5752464842051268, 0.1...      2  \n",
       "4  [-0.25330586758043083, 0.40945335796901156, 0....      0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# K-Means Clustering using 3 clusters\n",
    "\n",
    "kmeans = KMeans(n_clusters=3, random_state=0).fit(df['vector'].values.tolist())\n",
    "kmeans.labels_\n",
    "\n",
    "#input label to dataframe\n",
    "df['label'] = kmeans.labels_\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hx+28vW0ATl19wjGyfQcP4hdCCNHcSTLSKHiyxLXyh7ouWYjbku1VaFTk9cCN6KLZ4EwDIwEVeiHYBqKUKmvl4ZCJub/iujWZ3H09s3b1It8RVG1C8ty4czy7pxBCiGZNkpHGIHh02b4v1Q2RWCD41MqHCuuy8Z5C2fqgbH2qb+LlUu6twwr55PSv+cvvY0kpiK58K6X48MJLiQ0N9eo9hRBCNE2SjDQCKuxKdMG7uAqYuetx0KiwSUe/M/PBvtTDq4eBtbvbV7R9/dHhH0vHuoTskZ4xWfx0zqc8vfXvfLdHY1EGF/buwx3DRnr9XkIIIZouSUYaAWVpC7GvoI/chishKe8hcQ3fqOinUbZjEgrzMDXPMTlG+LVVluJqZyo6+3awr+Po8I+Jd/ewcTGMaO47dTL3nyYl4IUQQrgnyUgjoYJPhcT5rg3ySn8HTAgajgq9AmVNrtRWO7M9u6jRARXx18rnmvnozCvALK8DcuzQkJf3sAEwWoG2B6xarBBCiMZPaa1rnonYCOTm5hIdHU1OTg5RUVGBDiegtHaiM8aDc2/tjeN/xLB1OnqufRM6+x5w7vBdgO5YuqLiv0QZYf69rxBCiIDy9PNbCj00NSWLPEtEgi+qnIiUrkJnXu7/RATAuRMK3/P/fYUQQjQJMkzTxOjSP3D9b6tlWW/ErejSFeiib8A8Ujb0E7iKp7rgQ3TIxShLbLXl5IUQQrRMkow0OR6OquU+iLYv52ihswCPxulMyDgFjUJb+0P0dAyb+1U+QgghWhYZpmliVNAQai92FgL28oqtTgKeiFSiwbEOMidglvxZe3MhhBDNniQjTU3wGDDaUHPV1mIaVwLijglHbqYJzJ8WQgjhY5KMNDFKWVGxr4GKoPL/vrLkxNq3/hcPPrMhodVDPrrkJz/fUwghRGMjyUgTpGy9UQnzXDVELJ1dtTyChqNiXobgszm6iV0d2IaCtQt+n0ZUstC/9xNCCNHoyATWJkpZEiHidlTE7ce98Bu6oB5DH0Y86EBMdJViaEII0dJJMtKEaa3BvgbsqwADgkZA0Cg8Wvp7vJIfcCUGHpaZ95bgc/17PyGEEI2OJCNNlHakuPaXcWym0v4yRlvqnIhU8EE5+FooVez3ewohhGhcZM5IE6TNI+isK8GxreyIScUeM+bBQIVVDxZ0yc+BDkIIIUSASc9IU1T4KZiZVN7kronSVXtGdMkydOF7ULoUtIagYajw61DBJ/k/PiGEED4nyUgTpIu+olkkIpgoa89KR3TBO+i8J3AtVS6bv1L6O7p0EURMQ0X8BYD0gnzeX7uaLzZtJLu4iCCLhSCLhbjQMM7q0o2rBwykXWTL3lRRCCGaCtm1twkyDw0HfSTQYXiBDdVqCcqIBkDbN6IzL6rxDBX3KbsLOnH5F5+SU1yM081fXwOwWay8df6FnJTc0ReBCyGE8IDs2tucWTtQr1oijY31hIpEBEAXfETNlWUt6IKPuP27b6pNRMDVZ1TqdHDT11+RVVTo1ZCFEEJ4nyQjTZAKvYLGX+7dA45NmDmPY2ach3l4DBR/S81Li52UFv/J5ozD1SYi5TRQ4nDy+aYN3oxYCCGED0gy0hSFTgDbibj/36cgeDxEPlK2zNcC2MA2yPXfRiUfit5zrQpy7se1p07Nip3K4z4hE83CPXsqvtdmFjr/NczMKzAzLsbM/Q/asaM+gQshhPAimcDaBCkVBHFvo/OehaJZoIvKXoiCsMmoiFtRygrhV1Y6z8w4HxxbAhCx9xwoCK5T+7WHUim02wnV69FHppY9q7LJv47N6MKPIOohVNjV3g9WCCGER2QCaxOnzQJw7AClwNoTpar/sDaLf4Ts2/wYnffl2218v78z23Limb2nB9mlIbWe8+SYkVySeDvoAqpbhaTiPkYFnejlaIUQomWTCawthDLCUUEDUbYBNSYiACr4DLANpylPfo2w2bmo43buH7iUJRM+ZEKH7TW2V0Bu9qeg86l+ObQFXfCOt0MVQgjhoXolIzNmzKBz586EhIQwZMgQFi9eXGP7jz/+mIEDBxIWFkabNm247rrryMzMrFfAov6UsqDi3oDQS6k6Qtd0EhSLoTEUWA2TZ4b/wl/7rOT8DtvpHpVVpa0GekVtp+YJv04o+d1X4QohhKhFnZORWbNmcdddd/Hggw+yevVqRo8ezdlnn01KSorb9r/99huTJ09m6tSpbNy4kc8//5w///yTG264ocHBi7pTKhQj+v9QrX5DxbyEin4OYt6iKa7OMZQrhZrWbwXPjviF78Z/zqwxX9ExIueYNooIW03Lhcs1hyJyQgjRNNU5GXn22WeZOnUqN9xwA7179+b5558nOTmZV1991W37ZcuW0alTJ+644w46d+7MySefzM0338yKFSsaHLyoP2XEoULGoULPxQg5BUIvD3RI9aKO69AZGJfOZ2O+Iim0AABTa0LDhlHzX3UDgk7wWYxCCCFqVqdkpLS0lJUrVzJ27NhKx8eOHcuSJUvcnjNq1Cj279/PvHnz0Fpz6NAhvvjiC849t/qt40tKSsjNza30JXxLRT0C4bcCtU8IbcyshiY6qIQbeq5BoTm1QwJd296Ea4lzdUNRJirsWv8FKYQQopI6JSMZGRk4nU6SkpIqHU9KSiItLc3tOaNGjeLjjz9m4sSJBAUF0bp1a2JiYnjppZeqvc/06dOJjo6u+EpOTq5LmKIelLJgRN6FarUUol4FoxVNaR7JsayG5rLOW5nacy2vnnsOFltrVMzzuBKSY4dsyv4cfiMq5Az/ByqEEAKo5wRWdVzfuNa6yrFymzZt4o477uChhx5i5cqVfP/99+zevZtbbrml2us/8MAD5OTkVHzt27evPmGKOtD27Zi5j6Nz/g72XyDi72AkBjqseouw2bmgcxEhNtd7UCFnoeL/B6GXud6XioGg0ajYdzAi7+VAbi63zpvLCa+/woDXXuKCTz9iccqegL4HIYRoKepU9CwhIQGLxVKlFyQ9Pb1Kb0m56dOnc9JJJ3HvvfcCMGDAAMLDwxk9ejSPPfYYbdq0qXJOcHAwwcF1K24l6kdrjc6bDoXv4eopMAEDij53VXnVCvShwAZZD6aGkNh/AaCdGVA0G+3YBioUFT0dgk5GKVcu/s22Ldz5/beVpvCuTz/ElK++5LzuPXnx7PMC8A6EEKLlqFPPSFBQEEOGDGH+/PmVjs+fP59Ro0a5PaewsBDDqHwbi8XVPd4E6q01f4XvliUi4NoXRlOxP4x9JRjhgYmrAUwN2wtPp2viQMyCz3Gmn4wz72kchV/jKPgMfeQGStLPRzszyCgsrJKIHOub7Vt5Z/VKv8YvhBAtTZ2Hae6++27eeust3nnnHTZv3sy0adNISUmpGHZ54IEHmDx5ckX7CRMmMHv2bF599VV27drF77//zh133MGwYcNo27at996JqDOt7eiCN2poYYJzl9/i8QanqbCbBj3jQnAWzEbnPojCLKtLorEarrTDcG4nJ20S0xf/Wuui5ldXLPd53EII0ZLVeW+aiRMnkpmZyaOPPkpqair9+vVj3rx5dOzYEYDU1NRKNUeuvfZa8vLyePnll/nb3/5GTEwMY8aM4cknn/TeuxD1Y98EZtVCYZUp14Z75gG/hNRQFkNjQUPJdxw6soiEEFWRgBzLamii2Mn6tE3UNlE3s6gQ0zSr9PAJIYTwDtmbpgXTpX+is2rbIM6AsKuh8GOaUmEwravWIDme3VRc+vNlbDwSW+v1dtw2TZIRIYSoI9mbRtTO2pXaO8dMVPBYVMyroML8EZVXHCmpfQK0ArpFHqa26rOhVqskIkII4UPyE7YFU0YchJxL5dobx7KApRMEDUOFnA5hU2kqtUdig0uorc/PojQnJKSWfVd940v79PNeYEIIIaqo85wR0byoqAfQ9rXgTKHyMIzFtQw25vmKGjLKCEWjaAr72NQ2RFNuQGwGn57+PzYcSWRlRmvmH+iEQx9NzrrFxvHwKaf7KEohhBAgc0YEoM1cdMG7UPQpmJmgQiHkIlT49Shrh6Pt7FvQmecHMFLfMbVr471DRWHctHg8KYVtuaLfQO4bNVqGaIQQop48/fyWZERUorUDsLitqKt1CfrQiUCx3+Pyu8iHMMKvCXQUQgjRpMkEVlEvSlmrLe1P8Xe0hEREaziSuzDQYQghRIshyYjwmC76jpbwV0YpcJQsZ9HePYEORQghWgSZwCo8p/NoSrVGGiLCWsqtc79i6dS/EHncPklO02TBnl3M3baFI0VFdIyJZWLf/vRv5X5/JiGEEDWTZER4ztoV7Kup2LummdIackqDKXI4mL3y70waMgll/wNtHqbEjOf2Xy38klKERSmcWrNs/z4+Wb+WSf0H8vBpZ2B4upRHCCEEIMmIqAMVNhFdNCvQYficUvDdvi4YSrPucDEcuQqNARhYtZPXhmveiB7E0+uHAa6EBODD9WtJjo5hysATmL9rJxvSD2GzGJzeqQsDk1pXPxdHCCFaOElGhMeUrR86bCoUvh3oUHzKqeH1LQMBsBjlw1ImYGIpyydu6b2GnNJg3tw6qNK5r/y5jNdX/klmUSFWw0BrzUvLlzGkTVtePfcCEsKaThVbIYTwl+Y/G1F4lYr8O0Q+FOgwvMbtwnateGnUzzi1YnTrfdWe+5feqwkyHJWO5ZSUkFVUCIDDNCt6TdakpTLlqy9wmM1rzk2R3c6sjeuZ9sM87vrhWz5ct4a8kpJAhyWEaGKkZ0TUiVIKgk9yzWVtBtyNnFgMzYmJaZzdfhdntd1TcfxgYTiHisKJDy6iQ0QeUUGlDE9MZfGh5Ernu8tvnFqzOeMwC3bv4qyu3bz7JgJk3aE0rv/fbLKKiyrmyczduoX/LlnMm+ddyPD2ybVcQQghXCQZEXWnQgIdgc9pDf8d/gtBFpMNRxJ4Yu0IlqW3q3h9UNwh/j7wD8Ksdo+vaVGKb3dsbRbJSGZhIZPnfEG+vRQA85gupkK7nevmzubHa66lfVR0oEIUQjQhkoyIujNag4oDnRXoSHxGKQixmKzLSuTKBefjMCuPaK7LSmTSr+eRFFrg8TWdWpNfUlrt61pr1qSl8vX2reQUF5McFc2lffo2yg/0WRvXk28vrZSElDO1xu508tG6Ndx/8qkBiE4I0dRIMiLqTCmFDjkDij4PdCg+99DKk7GbBqaunIyYGCitOVgY6fG1LErROTa24vvVqQf5ZMM6dh7JJCoohIN5uew4koVVGeiywZ6Xli/lzuGjuH3YiEa1GufHXdvdJiLlnFrz3Y7tkowIITwiyYioFxV5D7roS5pzEbRtObFsONKq2tddOxh7zqk1E/v2Z0dWJrfN+5ptWZlu2zl05Wf6/B9LSAoPZ2K/AXW6ny8V2x21tilx1t5GCCFAVtOIelJGrGtlTTOWku/dTRlvHzYCi2Fw0ayPq01EqvPSn8tq7Inwt/5JrbHU0FNjUYp+iVKRVgjhGUlGRL2p8OtRUf8HRmKgQ/GJ6CDvLFENt9l46sxx3DV8FM8s+Y1Cu+eTXssdzMtja2aGV+LxhmsGDKpYtuyOU2smDzzBjxEJIZoyGaYRDaLCLoPQi8G+FnQ+WiXAketA59DUh3AGxx+iVUgB6cVhUM2QjIHCdLuY96g7h4/i0j79yC0p5vud22tpXb0SR+3DHvmlpXy2cT1fbt5IRmEh7aKiuKJvfy7o2Ztgq/f+uQ9Mas2dw0fywh9LMZSq6LUp//O1A09gdIeOXrufEKJ5k2RENJhSFgga7PozoOPeRWddB/pIYANrIIuhuWfAcv6+/PRq25zfow1fbTvo9jUFBFusXNanHwAZhYX1HmqxGgadYmJqbJNekM/lX8xiX0424Kp3kllYyJq0VD7duJ4PL7yU8KCget3fnTuHj6JXQiJvrlrBqlTXM+iTkMj1Jwzlgp69GtWEWyFE4ybJiPA6ZesDiT+hCz6CgpeBug9LNBYXd9pGscPC9LUjKXJasSiNqRU2w+SvfVZya79MgizT+Gzzlko9BBalUErx8jkTiA5x1WWJCQlxJWt1jMGiFBN69CImJLTGdtN+mMeB3JxK1y/vtVl3KI3HFv/K9DPG1vHuNRvXtTvjunbH7nSigSCLxavXF0K0DJKMCJ9QRiQq8i/o0HPQ2XeBY2OgQ6q3q7pt5oKO2/nxQGdSCyNICClkXPvdRAeVggmPn2zhlE7n8f7a1Ww6nE6QxcLYrt25btBgesQnVFwnu7iYXvGJbMk87HFCYlGKdpFRPFDLEtntmZks3V996XpTa2Zv3sh9J42uNampD5skIUKIBpBkRPiUsnZEJcxB29ehS1dD3jtAaqDDqrNwm4OLOm2vctxhKnYd/IKTkl/mnO49yS0p4ZP1a5m1YR3/27qZqKBgTkruwN6cbFalefa+y3tYooKCuaJff24eMozY0JoTiJWpB2q9rt00WZ9+iNEdOnkUhxBC+IskI8IvlG0AyjYAHXoZOudeKJkf6JC8QgHbMg9wzc9v8Mo5F/GfX/9HbvFhUovCcWoLxQ4Hc7Zu9uhaHaOieeyMs+ib0IqlB/ZhVQb9WiXVmoiUx+FZvDKPQwjR+EgyIvxKGWEQdAK65CfqPnui8VFKs/FIAjklJfz12/d56sRfuOn3s93vBlzTdYDx3Xvwe0oKN339P4oc9orjZ3bpymOnn0VieHi15w/zYFO6YIuFAUmt6xaYEEL4gdQZEQFgozkkIlqDwzT4ck9PnNogqySUf6w4Da1B1/GflgbeXrWS11Yur0hEyo//snsXl34+k+ziomrP7xwTy+mdOldbiMxQiiv7DSAqOLhOcQkhhD9IMiL8Qmt72byRlWjboECH02DlPR9f7e1OVolrGCU+KJ/04vA6JyLlji8DX86pNftzc3howc8UO6pfmfT0WWfTLS4ecCUfx/53VPtk7jvplHrFJYQQvqa0bkQ1pquRm5tLdHQ0OTk5REV5t0S38C2tTSh8F13wJpjlu/zawIgH8xBNvYfE1HDf8lOIDS7hg+39cWjfriqJDApi2oiTmDLwBJRSHMrP59c9uzhcWECJ00nr8AiOFBezZF8KhwsLSI6KZmK//gxu3ZY9OUcItdronZCIxZDfQ4QQvufp57fMGRE+pXMfg6KPjjtqBzMdCAK8U3I9UAwF/x2+iHy7DYdp4YMd/X16v7zSUh5dtIBih4M92Uf4fNMGt+nc4NZteef8iwmxWnls8a/cNu/rivLtSeER/PXE4Vzdf2CthcnsTicH8/KwWgzaRkRKITMhhE9Iz4jwGW3fhM68MNBh+E2pU3Hqt1dzuIby8d5SW/E0QyniQkNRQFZRkdt9ZG4fNoJpI05ye36Jw8GMFX/w4bo1ZBcXA655KbcMHcalvftKUiKE8Iinn9/SVyt8Rhd9AbScYlhBFs2Dg5bg60QEah/cMrUmo7CQw4WF1W5o9/LyZezLyalyvNTp5Pq5s3nlzz8qEhGAPdlHuO+nH3h22e8NCV0IIaqQZET4jmM/4Ax0FH51XoddPD70V5rCXBhDKb7YvKHK8c82rmfp/n1V9tEp/+6VP/9gS8ZhP0QohGgpJBkRvmPEUHvPiPc2bmssLu+ylbig6pfhNibuekY+Wr+mxr4di1J8umGd74ISQrQ4MoFV+IwKnYAu/qqGFgbg8FM0/mNqeHjwb/SOycKpFZuz41mVmcRHPp7cWlcKRYyb6q67jhypsV/HqTU7srJqaCGEEHUjyYjwnaCTwDYU7KupOlxjAUyawnBGXRkKzu2wu+L7rlHZnJO8iyMlIXy7r3sAI6vMoU0u6NkbcE02Lin8jbGfZ+Awg6hp3ouhFJHBza9HSwgRODJMI3xGKQMV+wYEjyk/QsVfOaMVzTERccdQYCjNk8MWkhhcEOhwKukZC6u33sjN/5tBn/dz2V8YTG0TcE2tOad7T/8EKIRoEaRnRPiUMiJQsa+gHXuhZBFQCtZ+6JIFUPhOoMPzG0NBkOFkUvcNPLtheKDDqfD+0vt5Zl3fsrSw9lVAhlJ0iYllXNfG08MjhGj6pGdE+IWydkSFT0KFT0UFD8cfy18bG0PBTb3WckJ8ms/uMahVEm0jIj1srXlufR+cWuHUnv0oUMCHF11GkKXlLNkWQvieJCMiMEIvCnQEAWFRmvdOmUdSqG+Ga9YdTudgfp6HrTWlpoGuQ2IYbLGQFBFRv+CEEKIakoyIgDBsPcDoEOgw/E4pCLE4uLLrJizK+xN4j68NUmMsKIw63r9tpFRAFkJ4nyQjInDiZgLh1bzYfLe6txia8e134dSK6oerfD+5V6Pq1CsC8MklE30UjRCiJZMJrCJgDGsiZqvfIe85KP4f6AJQYRByPkTcDflPQ9HHgQ7TJ0IstdVX8c2cmuTwXC7pvJX2YXlklwYzN6U767ISPbpfz/gEEsLCfBKXEKJlk2REBJRhhEH0g66v4+ioh8DWA13wFjj3BSA633CYivUeJgDeo7mr3wpu7b0KU6uyI3Btjw38dKAjdy47kxJn9T8Ohrdtz8xLpVdECOEbkoyIRkspBWFXQuhEMA+htR2yrgVzf6BDaxCrofl4R1+/3vPqrpu4rc8qwFXz5Fint03hP0MW8fflY6qcd1L7Dnx48WV+iVEI0XJJMiIaPaUMsLRBAWbsK5B5QaBDqhetXRNYj5QEs+xwW06IT+PyzltoF55HZkkoc/d2Z2FaMqaHy2yPuzrV9bRYlMmtfVZV3L/q65oLO27nuQ0nklp4dFmw1TDolZgIgNM0SS1bpdM2MgrD3YWEEKKeJBkRTYph640ZejkUfRboUOqs/PM7NriEn86eSafIPEztqj/iMGFCh538ebg1Ny4+m3xHXcutK6pLSHrHZJIUWljrFU5vk8InO4/22DhMkz4JrXhr1QreXr2SQwX5ALSJiGTqCUO4dtBgSUqEEF4hq2lEk6OiHoHwm2nKK246Rrh6GYyyz3Jr2b/EwfGHmH7ir9WcVdsKG/eJQe2TZUFrRYjl6P5BhlLEhITwy+6dPP7bwopEBCA1P4/HFv/KvfO/R9dhKbEQQlRHekZEk6OUBRX5N3T4zeiSnyD3KdAZgQ6rTqrrULAYmnHtd9M+PJf9BUdreliU6XGV1ON7SHblxeAwFVaj+sTBYmi25sSV3UthNQxuGDSEp5f9Xu05c7ZsIruoiM6xcQxt244zu3TFasjvN0KIupOfHKLJUkYERuiFEDSE5lReXgEnJR2dpPvUsF8INo7f9dhzWSWhfLe/Cw7T/TNymop9+ZEsOdSOMKuNS3r35esrJ7Ei9SCWWoZhFuzdzbtrVnLrvLmc8t6bbD6cXu84hRAtl/SMiKbP2hVKyudMNA9hFjug6RZ1hAs6bOcff55ah7OrJhDT14xgUNwh2oQVVOohcZgKu2mhOOIpdtw+xrWCqcz2rEycHgzDlLdIz8/nqtmf8+Oka0kMq66YnRBCVCU9I6LJU2GX0ZwSEaXgb/2Xc07yTv7WfzkWAzpH5qDq+R4VmiEJaeTZgyh2Wilxuja5c5iKPUVDMGO/oGfrMyolIgARQXWbRGsCeaUlzFy/rl5xCiFarnolIzNmzKBz586EhIQwZMgQFi9eXGP7kpISHnzwQTp27EhwcDBdu3blnXdazvbxwreUpR0q8oFAh+FVQRaTF0b8TI+oLJ5YO4Jwq73e6ZZGcUPPdfSJzSLCZie4bKKq1YBu0flEhPV2e96EHr3qvFrG1Jqvt22pZ6RCiJaqzsnIrFmzuOuuu3jwwQdZvXo1o0eP5uyzzyYlJaXacy6//HJ+/vln3n77bbZu3crMmTPp1atXgwIX4lgq/FqIrFrFtakylKuvJ7MkjHe39WddVkLZK3VPSSzKZM7eHm5e0eDYAo4Nbs+7ol9/YkJC6jwbp8BeWucYhRAtW53njDz77LNMnTqVG264AYDnn3+eH374gVdffZXp06dXaf/999+zcOFCdu3aRVyca7Z+p06dGha1EO6YWYAFqP9kz8bEUDA44RDxwUWkF9d/DoapIbM4tOL7PLuNnw50IrMklDZh+ZwZtoVQW/8q58WFhvGPk0/jnvnfeXwvi1L0iEuovaEQQhyjTslIaWkpK1eu5P777690fOzYsSxZssTtOXPnzmXo0KE89dRTfPjhh4SHh3P++efzn//8h9DQULfnlJSUUFJSUvF9bm5uXcIULZWZQ3NaVVMuwlbaoGTEUNA6rACtYcamE3hl82BKTQsKjcbAumw38Bzju3XnxiEn0r9VUsW532zbgkUpjyayAji1Ji0/j7t++JYJ3XtxWqfOWGS5rxCiFnVKRjIyMnA6nSQlJVU6npSURFpamttzdu3axW+//UZISAhz5swhIyODW2+9laysrGrnjUyfPp1HHnmkLqEJgbK2R2MGOgyvspsGh4oatjLFqQ3Obb+DKxecz4qMNhXHdVni5tAGYPLt9q18t2MbL58zgXFdu1PqdLJw7+46Dwxty8pkR1Ymc7duYUBSa96/4BKiQ0Ia9B6EEM1bvX5lOX7Wvda6yrFypmmilOLjjz9m2LBhnHPOOTz77LO89957FBUVuT3ngQceICcnp+Jr377ms2Or8KGQC2hOPSNau1bCJITUXsq9hqtwaafNPLZmFCsyWlfTxrUsWuPq2bjr+2/JLi7C7nTWe9JseUq4Mf0Qd37/bT2vIoRoKeqUjCQkJGCxWKr0gqSnp1fpLSnXpk0b2rVrR3R0dMWx3r17o7Vm/373u68GBwcTFRVV6UuI2ihLIirynmpetdDUEhWlXJvYhVkdNGTpcp49iDVZSdT8/o++Vup08uXmTYTZbLSOiKj3fcGV3CxK2cPWzKZVIVcI4V91SkaCgoIYMmQI8+fPr3R8/vz5jBo1yu05J510EgcPHiQ//+jeFtu2bcMwDNq3b1+PkIWongqfiop+AiztjjlqQPBYiPhXwOKqL61hc3Y89U+kFD8c6Fqn85VSbEg/hFKKyQNOwGhgEqeAhXt2N+gaQojmrc7DNHfffTdvvfUW77zzDps3b2batGmkpKRwyy23AK4hlsmTJ1e0v+qqq4iPj+e6665j06ZNLFq0iHvvvZfrr7++2gmsQjSECr0YlfAzKn4uKu5TVOLvGLEvoCxNr4fNMODGnmv8ek9Ta35P2ct1/5vNc8t+x2xgQTkNpORkeyU2IUTzVOdkZOLEiTz//PM8+uijDBo0iEWLFjFv3jw6duwIQGpqaqWaIxEREcyfP5/s7GyGDh3K1VdfzYQJE3jxxRe99y6EOI5SBsrWCxU0GGWJLzsYWefr5OcYbF8fQnZm4FaE3N1vBVG2Yh/fRXPsUFBGUSEL9+7GbnpnQvD8XTsxZYdfIUQ1lG4Ce4Dn5uYSHR1NTk6OzB8R9aZ1MTp9FOj8WtseOWzlrcfa8OtXMbTrUsLrP29DBXCF6mc7e/C/lB78cbhd7Y2B43furY1FmZybvIO5Ke6Ko3nHRxddxqjkDj67vhCi8fH081s2yhMthlIh6PBbIf+pGttlZ1q449zuZKTaMJ2K86Zk4jRd5dMD5fKu27i0yzYOF4exMzeGmTv78MOBzpj6+KDKkxDPE5EQi513T/mWYIuTuSnd63Supwyl2JqZIcmIEMItSUZEi6DNXLCvAWdqrW0/eS6pIhEB6D24AGsj+JdiKEgKLSQhuIhRSQf55WAH/rpkLPaKAmbQOzqTa7pt4MGVp9VyNVfS0is6g5dGzseJwRW/XOCz2LXWhBz3EPNKSvhh53YOFxbQKjyCcV2713lzPiFE89AIfsQK4Ttal6Bzn4SiWYD9mOOupbPHs5cqfvg0riIRcR0zqm0fCBbDNbJ6WpsUbu29ihc2nkjbsHweHvwbY9q65mt9tLNfLatwXLVFtuQkMO77iYTb7BQ4bDW0bxilFKd36lzx/TurV/L00t8odjgqKrw+tOAn/n7SaKYMHOyTGIQQjZckI6LZ0tqJPnILlC6F4yqzVpdY5GZZKC60VDq2/Kcoep1QiLK4PydQDAU391rD8MSDDE1Mwyh7T1rDecnb2Zxd3R4x5dPEXCeYGOTZg30Yp+KS3n1pHeGaQPzRujU8tvjXitfLS80XORw8snABQRYrV/Yb4LN4hBCNj2waIZqvkl+h9HeOT0SOl5FmpXzRSGiEiVKV53R/90kcxYUGzka4/16QxWRYq8qJiFJwUafttAnNO6alxqj0HLzfAzKyfTIK12Z55V8AZ3XpxqOnnQFAicPBM0t/r/E6zyz5DXstD9vppVU+QojGQXpGRLOli77AlW9X/8H185cxPHNXB6Y9s48xFx8hLMLkxDPyWLEgsmKoJjvDxoNXd+E/H+0iLMKkMe/7Vt7jExdczNyxX3LmvInk2EMBdcxT8H4iYlGKpPAIfp1yA19s3sCB3FxiQ0M5v2dvusXGMXPDOj5ev5aUnOxalwtnFRex7MA+RnfoVOn4/twc3ly1gtmbN1Jgt5MQFsaV/QZw/aAhsveNEE1cI/6xKkQDOQ9QUyKyeWUY/72jA04nvPJgO7atDQPgyjvSUIpKPSSbVoQzeVgfvnwt0ddRe4XV0EQHlTCx69Zjjvpu0otTa77auplpP3xL6/AIgiwW9uXkMGvDOi749CP+s2gBu45keVy3JKe4cl2VLRmHOe+TD/lk/VoK7K65PxmFhbzy5x9cOOtjMgsbsn+PECLQpGdENF9GK2Ar1e3r8sVriRgGOJ2KogIL91zclVMm5DD+ykzu+u8+Xnu4LQW5VixWjWlCQZ6BYTEb1WTW2oxvv4s3tgyqcjzY4uDs9rvoHJlNocPGxzv6kO9o+LyRVWmprEpLrZiU6pomW3fJUUf3stJac8d331BgL62YX1LO1Jr9uTn8Z9ECnh9/bsOCF0IEjCQjotlSoRehSxdV+/ofP0XhPGbVjMNu8MvsWH6ZHVt2RFf899xJWVx11yHikxy+C9jLDAWhlqrxntl2N08N+5WooFLspoGB5o6+K5n48/lsyG7llXuXJw11TUQMpegSE8uApKM7DP958AA7jmTVeK9vt2/lX6ecTnxYWH3CFUIEmAzTiOYrZCxYq1+V4XTU1r2hytoZfPtBHLGJTScRAci3W/lkZ+9jjmhOTDjAKyfNJ8JWCoCpFV/v68Zdy84gIsjOoLg09xfzE4tSPDbmLNQxXU+bDqfXOsDk1JrtWZlei6PQbudAbi55JSVeu6YQonrSMyKaLaVsEPcuOvtOKP2tyuut2pWSluLZ0ET3AUUeTVx1Oly1SoJDdUCHcpYcasetv48l32ErK4jmqsq6ryCafXmRdIrK5UBBOFf8cgGpRZEoTDQGFhXYVSonJXegfVQUS/elEB4URL9WSQRZLB71sARZGr72el9ODs//sYSvt23BYZoYSnFm567cNWIUvRKaxnwhIZoiSUZEs6aMSFTcO5il6yH/FbCvAl3Kb993Ii3F5vF1Ro7LrfH17etC+fSlViz5PhrTqYhPsjPhugwuuiGdED+PHKTkR3LD4rOxm64E5NgP8sPFYUxZdB6PDF7ErUvGUWq6fgTosk5SZ5Xy8v61aO8eRr/7ZkXMbSMjubJv7TVHooOD6dcqqeL7nOJi1qSlooEBSUnEhVb+n6C1Zun+faw9lIrNsDC6YydshsEln80kv7SkYpjJ1Jqfd+9k4d49fHLxZZzQpq233qoQ4hiyUZ5ocbTW3Nj/bvZu2n/sUWpabXL7E/s4b7L7eQt//BTJI9d3RmuOqdyqUQb0GFjIs1/twOp53uMVv6W1497lp3O4ONzt6+U9Ic3FaR07884FF1Nkt/PgL/P5etuWioTCohQX9e7Dv0afTmRwMJszDvPXeXPZk52NRbmSNVNrooODySspcbv+ylCKDtHR/Dzp+kpDSEKImnn6+d18fhoJ4aGDO9OOS0SAinUf7nPzjNQg3KXtxYUG02/tiOmkUgl5UGhTsX1tGJ++5J1JoXUxKukAn54+lwhrqZtXy4dtmo/U/DzsTifjPn6Pr7ZurrTqxqk1X2zayNVzPmfPkSNc+eUs9uXkVLxmlrXNqSYRAVeysic7mz8PHvD1WxGiRZJkRLQ4RfnF1byiCA6tWoEVXCtv3P1CvPB/MRTlG2jt/sPdNBVz3krEYXf7ss8YCjpE5HJjzzVVXguzOkgMKcRmNMKSsvWUW1LCw7/+zP7c6ofTNqQf4uGFP1NQWnWJsKd21rCqRwhRfzJnRLQ4rTu1wmqz4LBX/TAuKXI/CXLXxlBW/BrBCSfnYznmX83OjSFYrLrGlTn52Vay0m20aufnjAS4qfcaXt1yAsVOG/1j07mt70pOb5OCoaDAbuXz3b2YsXkwWSWhfo/NWwyl6BQTw+wtm2ptuyQlBWe9Kp+4hNn8PN4mRAshPSOixYmICefUiaMwLHX56695/C8d2bzKNRHSYQfTBFtwzXNNygWF+H+VilJgMzTnJu/k5KR9fHbGV5zael/FPjbhNgfXdNvIl2fMIT646VYwNbWmb6skSj3YPKghiUiQxcJpHTvX3lAIUWfSMyJapBufnMTaXzeRlXYE0+FZolCQY+VvF3Zj0Mn5nHp+NmGRJgU5Ro29IsrQdO9fREx8YIZEtIax7XYzKD4di9IViUg5q6FpE5bPvQP+4P4/T/dbXK3DI4gOCcFuOhnYqjUHcnNYkZZaMX/DUwqICw3lrVUrPGpvUQpT63qlJNcNGix74AjhI7KaRrRYWWlH+ODhz/jhg19xlNRW0ExjWI6fpOrSvmsxB/cEu30N4OF3djNqfM1Lg31pa3YsPWOO1NjGbhoM+99k8uwNLwnviWCLhY1/uQOjrHjL6tSDTPxyFo467MYbGRSExTDILS6uZV/mo4a0acuq1IM1JiMKUEphKIUum+B6Tf+BPHTqGCyNeZdEIRohWU0jRC3iWsdy1+s38+rKpzxorcqSjaofY8GhJl37FgFgsWoMQ6OU6+uqu9ICmohoDV0is92uBDqWzTDpEF4eZ/WrirylxOnkvbWrKyqcntCmLe9ecDFxIZ7NXTGUQilFdh0SEYtSvHL2BPq1SsJSzfLcq/sPZMn1N3PvqJO5qt8A7hg+kgVTpvLI6WdKIiKED0nPiGjxCvOKuCjuWkynZx9rytBo8+iHmVKaEWNzOfvqTH78NI4Nf4aTffjoRMdu/QuZ+mAqg0/J93rs3pRXaiXc5qDUaeGrvd15d9sAdubF1n5iA/VNbMXNQ06kVXgEk7/6AruzITM73DOU4rVzz+fMLt3ILy3lv0sW8/mmDRQ7XD1iCWFh3DT4RK4/YQiG1BERwms8/fyWZEQI4LErnmXxl394lJC071pM+oEgSouNiu+vnnYIi0Xz5O0dMU2qJCsoePjtPW4ruWrtmgz78gPtmHBtBp16lmA0vLJ5xbVr+2wt/wlwbDuHqXBogxsXj2dpenvvBFMLm2HgME2vJyKndujE/aNPpWd8QqXj+aWl7DqShc0w6B6fgFV6PoTwOhmmEaIOpjwykeCwII9W2KQfCOLNX7cyY/5W3l2ymbcWbWXMxdl8NqMVTkflRARw1SDR8MLf2+N0MzVFKbBYICPVxldvJ6K8+K/SoRVmLZ/uSlVNWKyGxqacvDJqPiEW/yxJtvsgEQEIC7IRFVR1LkxEUFDF7sBP/r6Ip5csJrOwwAcRCCFqIz0jQpTZtW4vz974Glv/3FFr2279i5j+6U4iop0oBal7g7huVO9az3vk/V2MOCuvynGHHT59KYnPXmnFjB+30rZTqVd6RwrsVsKsjnpv2qc1ZBSHohTsyo3m4519+W5/F8wA72FTFwoItdl494KLObHt0V6erRkZXP75TPLslavUdoyO4YdrrvXKxntCtHTSMyJEHXUZ0JGX/5jOG2uf5tybzgLAsBz9FLdYXf9cbn56Mrk5ydxwSk/efqwNO9YHs2ND7Us+ldKk7XW/WkUpcNgVJUUG917ajZSdQQC1TjytTbit/olIucTQIhJCihiccIgXRv7Mayf9gFU58fUkV2/RQKHdzuQ5X5JR4Or52JeTwzmfvF8lEQHYm5PNkDdewelB3RIhhHdIMiLEcTr378hdr93Ey39M57SJJxGdGEV0QiSjLhjGc4se5dK7JxDXOoacTBtfvNaK28b35P9uqr0YltaKyFj3S4gtVlj2YyQAJUUGia0dmGbt8z187dj7Ww1X8nFamxRu6b2GhEZQKC2oDvM8SpwOzv3kAzIKC7nh69k1plIFdjtPL/u94QEKITwiRc+EqEbPE7vxwEd3un1t7JTT2PLnjrLOAc8yhqBgkxFnVZ3AmpNl8NTtHdm92VXd9czLsgiNMGms8ykNBZO7beC1zYMIsdhpFVLIvoLIgOwCfFrnzvy4c6fH7TOKCrnnx+/YnlX7HjMfr1/LfSed0pDwhBAeaqQ/7oRo3M6cfCrJPdpiWD3/JzRgVD7hUWbFpnlaQ0mR4p6LurNiQWRFu0EnNe4lwABxIcV0iMil2Gljf2Ek7cPzsKnaCsd5X0JoOPeOOtnjPYg1sChlj0dtC+3+30tIiJZKkhEh6iE0PIRnfn2EQaf386C1q4jYxj/D+Ou47vwwM55NK8LYsiqU7z+NI2V7MMf2rihDe/zh6guezlMxy3YqNrXBgYJI7Nr/Ha1fbt7I1f0H8dt1N9E2MrL2E4QQjZIM0whRT7FJMTz5w79I2XKADYs3U5hbxPwPf2XXupSyFuWb6Lk+tIvyrexYb+HF+9tXvN6+awlKVU4ANv0ZwfAz8vyWkBxfi8STuiTpxWGkFBydGW9icPT9+k+J08mfB/YzpnMXLuvTnxf+WOK1a1sDPWFHiBZEekaEaKAOvdoxfuoYfpn5G3s27T/mFXcfZqrSn/fvDHbVITnGDzPjsJcqTD8t5nD3mVtb78jbWwe6Wd4bmA/v9MJ8Jsz8kBf+WOLVCEKtttobCSG8QpIRIbxgxQ9r2b5ql8c7AB9V9eMzJ8vKozd0wulUDV7aW1/lCcqx93eUFXP7ck8P3t3WPwBRuff6yj/ZmpkBeHexcavwcC9eTQhRE0lGhPCCxV8uq6hD4g0rFkRx0+k9KSkK7FBBeVJS7LTww/7OTPr1XO7/8zSqzmpxzYtRHm9b54XYgD4JiaTk5OD0QdaWUVSEu5qQptZsy8xg3aE0css2+hNCNIzMGRHCC4oLS9C11V2vo4O7g9m7LYTu/YuqrcbqdMDWNWEcPmhj9Hk5PlsOXOK0cNeyM90kIeVcOxpblcau/TN3RANbMg7jfi/lhjtSXMTmjMP0SWzlup/WfLZxPS//+QcH8lxLtIMsFi7s2Zv7TjqF2FDPdhwWQlQlPSNCeEGHXu18UqHsu4/ia9yrxmKFL19L5PFbOvHEXzvgq6KhazKTakhEyinGtN1Dj6jaa3h4i4lv68Duy82p+POLy5fywC/zKxIRgFKnky83b+Syz2eSW1Lsw0iEaN4kGRHCC8ZfP6bhtdvd+OnLWLatCXW7wZ7phJULI1jyfTQAC+fGMPfdhKoNvcDTdxZhs3NW+z31OLNxig1x9Xak5GTz4h9L3bZxas2enGzeXLXCn6EJ0axIMiKEFyS2j+fWF64HQBne6yGxlxjcP7ErP30ei7306HVLixVz3k7gX5M6Y5bvEqzho2eT8EWtrv6xh7Go2ueDnNdhJ+uzEo850nSXxyaEhTGkTVsAPt+0AaOGni9Taz5Zv9btHBMhRO1k114hvGjJ3D+Z+fhstix37fwbFR+BUgY5GVXLwNdVVJyD7v0L0Sg2rQijuMD9RJJbH9vPBddnNvh+x3tizXDe3d4fp656X4syCTZcm/IVOGw0hSQkKiiY3NLqJ6CGWq0YStGvVRI7sjLJLCqq9Zob/nIHYTZZEixEOU8/vyUZEcIHcjPzKC0uJTYphszUI9w+/AGy0rL9cm+lNM/M2UHfYd7fyG5XbjTT147g19SOaBSqbBgmMaSQ9OJQmlJna8/YeIa2b8/MDeswj/sxaChV5VhtbIbBplvvxNJYNxUSIgA8/fyWfzVC+EBUfCQJ7eKxWC20Sk7glT+f8OrwTU20hs9mtPLJtTtH5vDm6B/4a++VxAcX0iM6iwcHLeHExIM0tR8nW49ksunwIb6/+lqePms8twwZhqK8xH3dEhGLUpzXo5ckIkLUk/zLEcIPEtrFM/660/2UkCiW/xSF6YOSH+XTJu7qv5I/LviQ/531JSe33s/8A529fzM/WJ2WxviP3yM1P78sDal7R7ECrIaFm4ec6OXohGg5pM6IEH7y1xevJ+PgEf78brXP72WarnLy7n5RL8gzKMi1EN/ajqWa+iWe2pAVzy1LzqbUbLo/SkyteWbpb0QHB9dr7U+I1cr7F15Kj3jfrGQSoiWQnhEh/CQ4NJhH/3cfSZ18M4RSidLkZVf9552WEsTNp/fkkes64ShVVZYM13UG2fojSWQUh9XQQlN9b0Pjmq6WU89qqhf37svQtu28HI0QLYskI0L40fJ5qzi0J933N9Lw2kPtKyUbWsPD13Ui85CNHRvCuOfibuzcWLlqaHGh58NIWsMprVNqaaWwKSeG2zLxjX/FjScu7tUn0CEI0eQ13b5VIZqgH95dgM/ql1ei+PV/MZx8XjYnn52LMmD9snD2bD6afGxbG8btZ/egU68i2nQsJS/bQureID5cvhmLBz8ZlIJWoYW43kzVxMKiTAbGHeL9U7/l78tP47v93bz39hqJ1uERnFBWi0QIUX+SjAjhR4f3ZfpxdELx2I2dSUouofeQQnZvCsFd4rBnSyh7thxNUg7sDibzkJVBJxXUWuE+xOLk0s5b+GJ3byzKxKkNDExMDDpHZPPKqPmEWp28NOpnUguXkloYyeqMRKavO8n7b9fPQixWPrvsikCHIUSzIMmIEH6U0D6OnWt3Yzr9N1/i0L5gDu0L9rj9b/Oi+OC/bXjwtb2cfG5OjQmJUjB96CIu6bSNT3f1ZndeNDFBJUzosINzkncSbDk6PNMmrJA2YYW0Dstn+rpRNOVhms4xMYTagjhv5ofYDAt9EhO5ecgwRrZPRvlgjyIhmjspeiaEH/025w8eueTpQIdRAw0KLvvLYW74ZypaN3z/P6cJ3+/vyqxdvdhXEEVCSCFFDgtbchJoygmJOwNaJfHW+ReTEFbTpF4hWg4peiZEIzRywlD6j+6NYWms//QUUTFObvhnquu7BuYKWsP23FjuXHYGy9Lbsq8gijWZSWzJScCmyrcYbvS/D3lsXfohpnz1BQ5fFHkRohlrrD8RhWiWLFYL//ftA5xx9WgM6zH//BR07t+B0688OXDBlQmPduJ01t7OE0pBr5gj/DB+FmPLdvPVKEBhYtAtMoukkHzv3KyR2JxxmF/37Ap0GEI0KTJnRAg/C40I5e/v3cYNT1zN+kWbcTqc9B7RgzZdkgA4tPcwm5ZsDVB0mm79ihpcDO14XSJzeHnUfB5cMZpZu1xLYZ3aYEdeHM2pZwRcv+F9u30bZ3ZpfKuHtmdmkl6YT2JYON3j4mV+i2g06tUzMmPGDDp37kxISAhDhgxh8eLFHp33+++/Y7VaGTRoUH1uK0SzEtc6llMvH8WYq0ZXJCIA9394ewA/JBRnX+39HX/L384jg38jwnZ8cbHm9YFoAvk17AYcCH/s38eEmR8y7uP3mDTnC8Z//D7nzfyQJftqqxMjhH/UORmZNWsWd911Fw8++CCrV69m9OjRnH322aSk1PyXOicnh8mTJ3PGGWfUO1ghWoI2nZP42zu3BujumqTkUp/sawNgNTTvjJ7HZ2PmcEOPNRiq+c2tsChFl9i4QIdR4fd9e7lmzudsPly52N6WjMNM/uoLFu/dE5jAhDhGnZORZ599lqlTp3LDDTfQu3dvnn/+eZKTk3n11VdrPO/mm2/mqquuYuTIkfUOVoiW4qxJp9Cpb7Kf7+qqQfLVW4k+66vQGgYnpDMw7jCrMpMwdfObtubUmol9+wc6DAC01jy04GdMTZUauLrs9X8u+IkmsKhSNHN1+klQWlrKypUrGTt2bKXjY8eOZcmSJdWe9+6777Jz504efvjh+kUpRAuz+uf17Nm4z893daUg334Qz6JvogG8NpG14g5lWY7F0FiMmvatabruGj6q0fSMrElLZXf2EXQ1z1kD+3JzWJF6wL+BCXGcOk1gzcjIwOl0kpSUVOl4UlISaWlpbs/Zvn07999/P4sXL8Zq9ex2JSUllByzaVVubm5dwhSiyZv/4UIMi4Hp9P8whmkqpv+lI0t/yOaC6zPo0qeQ4NDaz6ur0Un7WXm4NWYzmTPSJTaWvw4dwXk9emJqjdEIJoceyPPsZ+eB3DxOlKr2IoDqtZrm+Ml1Wmu3E+6cTidXXXUVjzzyCD169PD4+tOnT+eRRx6pT2hCNAtH0nICkoiU01qxYE4sC+bEYgs2+XTNRiKivRvPxC6beW3zCRQ5QTfxKgNzr7ialQcP8sqfy/jb/O8wgBHtk7m8b3/Gd+tBUNnyJLvTyU+7d7JkXwqm1gxu05Zzu/cgxGrzSVyxoZ5lkXEethPCV+pUgbW0tJSwsDA+//xzLrrooorjd955J2vWrGHhwoWV2mdnZxMbG4vlmHWCpmmitcZisfDjjz8yZsyYKvdx1zOSnJwsFVhFi/HM1BnM/3AhTkfjmOB5+V/Tmfpgar3OPXzQxpw3E5j/eRwFORYS2pRy7qQsJlyXwbrCJG5afDbFTktZD0ngexPqKshiIdRqI6ek2O3rYTYbU08Ywrgu3bn52/9xIC8Xq+FKvhymSUxICG9OuJAhbdp5PTaHaTLqndfJKCystk1sSCjLpt6MzdvruYXA8wqsdS4HP3z4cIYMGcKMGTMqjvXp04cLLriA6dOnV2prmiabNm2qdGzGjBn88ssvfPHFF3Tu3Jnw8HCvvRkhmosNv29h2uh/BTqMY2im/jOVy245jGmCUfa5VdtIxN5twfztom4U5FowneWNNcqAjj2KeXr2TkpCgvhsVy8WpiazOz+arJLmWUrdZhiYWuM87keuoRQhFivfXzOF9lHRXr/vl5s3cu/876t9ffoZYxvNhFvR/PisHPzdd9/NW2+9xTvvvMPmzZuZNm0aKSkp3HLLLQA88MADTJ482XVxw6Bfv36Vvlq1akVISAj9+vXzKBERoiXqO6onp00c1YiKUinefqwtU0b24uPnk1gwJ4bsDFdGYpq4XQqsNfzfzR2PS0Rc19KmImV7CG880paEkCJu7bOaGSf9SLHTN8MVjYHdNKskIgCm1pQ4HXy4bk2152qtyS8trVeZ+Ut692X6mLOIDAoCqJjLEhEUxH9OP1MSEdEo1HnOyMSJE8nMzOTRRx8lNTWVfv36MW/ePDp27AhAampqrTVHhBA1U0px3we307pzEl+9NI/igsZRROvQvmA+frZ12XeaLn2KGHPJETr1LGHIqXkVPSYAm/4MY+/W6ucimE7FL7NjuPFfB1EGTFlyNoWO5puM1MSpNd9u38oDJ59a6XhOcTFvrPqTT9avI6ekGJthcE73nvxl6DB6xCd4fP2J/QZwQa/e/LJ7F4cKCmgVFs6Yzl0ItbXM5y0aH9m1V4hGrii/iE1Lt+EodbD8u9XMnfFDoEM6jiahrZ0Pl2/GOKavdfabCbzxSFu0WXPvzoizcli7NJx9Z3UlZ3RrMBpLb5B/xYaEsvKmo8XusooKufTzmezLyanUo2JRCqth4cOLLmVoW+/PMxHCm2TXXiGaidCIUIacNZDh5w7h+sevCnQ4bigyDgbxw8y4SsM1Npv2qIzIsvnRFOVbiV6Yhio1XeM7LYyhFD0TKvd0PPHboiqJCLh6Ueymkzu++wan7A4smglJRoRoQsKjwhh5wYmBDsOtlx5ox4I5rgmYWsOQ0/LQ2vNeDltWCW1e24QqLq+01nKSElNrJg0YVPF9bkkx/9u62e0ck/L2aQX5/Lp3t58iFMK3JBkRool56LO7SeqUGOgwqnA6DJ66vRN3nteN4gJF206lnHR2NoaluqRCY1gq/2YfuqeAgV+t5S+9VtAUl/nW14U9ezOua/eK73dnZ2OvpdfDohRbMjJ8HZoQfiHJiBBNjNVm5bGv7w90GNXasiqchd/EoE342/P76D2kAKAiKbGU/Xf8VVlERFeuN3/CKbl88MkqcuyhWGrcRK/59Jr8fdRonh57dqWKrSEeVKvWQLDUBhHNRL0qsAohAmvdws2BDqFGh1KCMU0IjzR5evZOVv4ayS+zY8nOtNC6QynjrsiiU89i5n92dA+XcVdmMu3p/SgFG48k4KxxEz1FqMWOUjT5FTindurMjqxMZm5Yx9aMDMKDbIzr2p02ERGk5udXe56pNWd06Uqp08n8nTv4dvtWckqK6RIbxxV9+9O3VVK15wrR2EgyIkQT5Ch1uEYxGmkHwU+fxzDpHtd+VYYBJ47J48QxeRWvOx3w7YfxOB2u3oDYRDt3PLm/4vVQiwOFRtcwVBNhK+W/wxZw7aJzacpDOnO3bOb1VX9iUQqndr3jn3fvIjak+mXRhlKM7dKNiKAgzv/0I7ZlZmAohak1yw/s5+P1a7lx8FDuP+mURlSrRojqyTCNEE1QjxO7NtpEBDSlJYqPn3P9Zn78HEynA7IzrMx8Iami/bgrssg7YuHzGYn885rOWF/YS/RPBzHy7W7vYFEmZ7ffxcmtDzAkwf0mnU1B6/BwXl/1J0DFZNXyx5VbUkyEraxQGa4EpLyM/EnJHXjqzHH85Zu57MzKBFw9Jcde581VK5i5YZ2f3okQDSM9I0I0QX1H9aRj32RSNu9Hm40rK2nbqYT4NnY+eiaJ7AwrV915iPjWDg4ftLFncwjb14fy7YfxZKWXDa8oiEmwM2VEb0pLDHTZVJE4tY/YHw6QNrUHRb1iKq6vMLEqk8ndN2A3Fed32MHKjDb+f6NecKS4uNoOLqfW5NtLoez19pGRnJTckUv69OWEpDb8tHsXq9IOVnttBby2YjlX9BvQKHYQFqImUvRMiCZq76Z9TDvlIfKzCxpZQqI5b0om37wfDyiUoQmLcFKQa8HdcIphMbFYwGFXVZYCawXaojj4jwE44oNxakW41c5rJ/3AyKSDOE3FvP1dmLbsTP+8tQAylGJAqyQmDzyBV1csZ3tZj0htfpl8PZ1iYn0cnRDuefr5LT0jQjRRHfsk88bap5nzwjxmvzjPNY+kUVCsW1a+75RGm4qC3Op/1JimQmvc1iRRGgzTZPCGLbSeFMYJ8Yc4v+MOImz2sqvDvvyosj8179/+Ta1ZcyiNNT9+V6d3Wp/9bITwN5kzIkQTltAunhufmkTfUT0DHUolKRV70njwsampsWS8NhXB63L47/Bfuarb5opEBMBQmtl7evD4kAWuCzXeiTRe5em7jAwKItkHOwEL4W2SjAjRDMS0isawNLZ/zp78/q6xBdX+0WovPW74puyUDVkJTOv/J5d12c7csz4n1OJ+wmtLZCjFVf0HEuxBzRIhAq2x/fQSQtTDGVePxnQ2ze5419zK6hMSi0XTZ2hBlXO0hi5R2ZybvAuA3jFHmHPmHM7vsM2H0TZ+quxrcOu23Dl8ZKDDEcIjkowI0QwMO+cE+ozq2Qh7R2qjKC0xqKkXxelUnH9d1cmaSkGEzVHxZ6WgW3Q2Tw37lQ9P+5oO4Tm+CrpR6xIbx8OnjuHDiy4lxNq0C8KJlqOp/eQSQrhhsVh4/NsHGHn+0OYzj1O5ekuueyCV3kMKPT7NamhGtjrIL+d+yimtU3wVXaMUFRTE91dPYfLAE2R4RjQpkowI0UyER4fz7y/v5YPtL3Pby1Pp0Ktdk66+GRxs8vgnO7ni9vR6ne8wFdf3aFlFv3JLS1mUsifQYQhRZ5KMCNHMtOmSxAW3jufV1f/lsr9NIDQiJNAh1UtJscH3M+PYsDysXudbDc3wVqm0lBU25b7dvjXQIQhRZ9KPJ0QzFRRs48anJnHNw5exa+1eDu5I5b9TZ6CdTeXDWbHo61gcdoN+w/bU8wpN5b16z/+2bCa3pIRih51dWUcItdkY37U7bSMj+Xn3LjKKCkiOiubyPv05uUPHJt17JpoPSUaEaOZCw0PoO6onfUf1pCC3kFfueDfQIdXJ+j/CsZeqGpcAOxzwzuOtGX5mHgNGFqCUa5hmRUZrms8kGs84teanXTsrHXtlxR/A0b0VN6an8+32bYzt0o0Xzz6PIIvF/4EKcQwZphGiBTn5ouFN7jfhvCNWvvskDtPp/nXTCd9/HMdXb7Xi75d24++XdqUgz8BqaN7bNsC/wTZy5elc+WZ683ft4LllvwcuICHKSDIiRAuS0C6e4ecNbnJLgN94pC0rFkQCrl6QY/+7YkEkr/27HU6HK8nasDycJ/7agZc3Deang53KrlBdddaWN4xzLA18uG4NhXYpFicCSzbKE6KFObT3MLePeICczDxMR1MqlKYZdHI+Z11+hMS2pWSk2pj/WRyrF0fgbigm5f4B2FuHVZzb0oZr6uKTiy9nRPvkQIchmiHZKE8I4VZSx0Re+fNJPv7PF3z/7gKcjmrGPxodxZrfIlnzW2StLbWC8A1HyK5IRiQRqYlspicCrWn11QohvCKxfTx3vX4z5986NtCh+IZSKLt8wHrCahj0TWwV6DBECyfJiBAtmNVmbXLzRzyhTE1p23DXN6Y+urOeqOLk5A7EhobW3lAIH2p+P4WEEB4beFrfJrvBXnW0AkekjYJ+Ma4DhudDNBbVVIasvOec7j0DHYIQkowI0ZINHT+INl2Smk3viDZAWxSHru1ecSxqcRoRyw/X0jui6RCew+wzZxNhLfV9oI3IxvR0SsqXJjVQscPOgj27+GbbFjamH/LKNUXLIBNYhWjBLBYL//n6fu45/d/kZOSizaY7nKGBkrZhZJ3XAXtiCGFrs4j/fj+29GKwKDAU+ScmgrNs2MaiwNQou0lUpIM3R39Pp4gcbui5huc3Dgv02/Gb99etZkvmYd674BKPN9fbdSSLdYcOYTUUI9p3ID40lLdWr+Dl5cvIKz2azPVJTOSJM8bRr1WSr8IXzYQs7RVCkJuVx/dv/8KCWb+Tl5mPaZoc3p/Z7MpwlLYKIX9QPGaoBVtGCeGrMwiPNnn52620TizhibUjmLmrD3azZVUkVcC/TjmdawcNrrHdwbxc7p3/PUv376s4ZlGKnvEJbMo4XKW9oRTBFguzJ15Nz/gEb4ctmgBPP78lGRFCuLVn4z7ee+hTfp+zPNCh+JRh0Vx0w2HWntmPHw90RrfQZcCdYmL4ZfLUal8/UlTEeTM/JL0gv6KCqycsSjGmcxdeP+9CL0QpmhpPP7+bx0CxEMLrOvVN5t9f3kvbrq0DHYpPmU7FNx8l8ENKy01EAPZmZ1PT76YfrFvNoTomInB0r5zs4qKGhiiaMUlGhBA1enjOPYEOwedKCgxsRS1r4urxgq3WGvct+mzjesx6dqRrIKtIkhFRPZnAKoSoUX5WQaBD8IvYr/ZhhlrIHxBHcbcoaGIbCjbUOd161Ph6ZgOSCQXESS0TUQNJRoQQNZr//q9YrAbOJrWPTV249q2JWJUBKKIXH6K4UwSpN/bEDLcFOji/uah3nxpfTwwL50Bebr2ufWaXrsSESDIiqifDNEKIGmWmHmnGichRynRVbgUITsmn9VtbW1Tl1uJaao1c0a8/Rj16ixRw98iT6xmVaCkkGRFC1CiudSwWa/P6UVE5xaj6AatMCN2dT8jufFc5+Rbgzu++4bT33+Kfv8wnr6S4yuuTBgyifVQUljomJFf2GyDLekWtmtdPGCGE15015dQW0TNyPG0owtdnYclrGRNbCx0OUnJy+GTDOga9/gpvrlpR6fWo4BA+v/RKTuvU2aM1RwqIsNm4c/gon8QrmheZMyKEqNGAU/ow6oITWfr1iiZdofVY2qpQjlrei6mJ+COdrDNaux+uacYTXDUw/beFtI+K4uxjJrYmhofz5oSLOJCby4bDh7AaBkGGhTu+/5ackuKKXhOn1sSEhPDO+ReTGB4eoHchmhIpeiaEqFVpiZ037vmAb9/8CUepd/YxCSRHpBVl11iKa98YTyvIHxhH/tAESpLCwKYwlUJHB/kh0sBKjopm4bU31Nqu0G7n662bWX7wAAoY3j6Z87r3JNTWciYAC/ekAqsQwutyM/N49e73+OnDRYEOpcG0ArS7GSPVtOdoW60gv18smZd0whkT7JsAG4kdt01jf14uH65bww87tlPidNK/VRKTB57A6A4da6xNIoSnn98yTCOE8FhkXATrFm4KdBheoer4a9ixH7lKQ8TGI4TtyCXlwUGYEc23B2Dh3t3cOu8bHKazovrqwr27+WXPLq4bNJh/jj5NEhLRYDKBVQjhsdLiUtJTMgIdRqOgTDBKnCR8uSfQofiMAm7/7htKnI5KZeDL//zumlV8s31rgKITzYkkI0IIj1mDrBiG/BZcTpkQsToTVWgPdCg+EWSxUlhD/RFDKd5evdKPEYnmSpIRIYTHLBYLIyYMbXZ1RxqipGMEOqR5jniXOGuerGxqzbpDaTjMlrf0W3iX/EQRQtTJxPsuRGtkngCuSa3pV3f1fBZsM6Ro0W9feIkkI0KIOukzogf//HQathAbSikMo+X+GCnuEom9VWizrjlSGw1c/NknLNizK9ChiCas5f4UEULU2+hLRjDrwBvc+sJ19BzWLdDhBIy9lWz+BrDxcDo3zJ3D7M0bAx2KaKIkGRFC1EtETDgX3nY2F995TqBDCRhVWnvRtJbA1BoN/HPBT+SXtozy+cK7JBkRQjTI8HMHExLevAt/VSdsc3aL2tm3NsUOB99s2xLoMEQTJMmIEKJBQiNCmfLIxECHERCWIieqrKS87VAR8V/tpfUbW2j1/nbC1mUGOLrAWLIvJdAhiCaoea5HE0L41SXTzgPg/YdnUVxQgjIU2tQEhdgoLW6eNTjKRazKxJpnJ+77/WjDVXtEK1f9kd1PxaCDLIEOsV6sysChTaxlE5SdpoknfUDrDqX5NjDRLMneNEIIrynKL2Lp1yvJOZxLqw4JtOqYwK1D7gt0WD7liLJhzXWfcB2+tBO5I5PA0rRW2zw+5iwu6tWHZfv38cueXZQ6nfRJbMUrfy4jvaCg1vOv7DeAlakHUcBJyR25ZsBAOsXE+j5w0eh4+vldr2GaGTNm0LlzZ0JCQhgyZAiLFy+utu3s2bM566yzSExMJCoqipEjR/LDDz/U57ZCiEYuNCKUMVeezEV3nMNJFw6j+wlduOSucwMdlk9Zc+3V9hjE/HQQo9gBzqZTFOzKvv25ot8Agq1WTu3UmUdOO4PpZ4xl0oBB9IpP8OgaszasY1tmBlszM3h/7SrO+vBd5knZeFGDOicjs2bN4q677uLBBx9k9erVjB49mrPPPpuUFPfjhIsWLeKss85i3rx5rFy5ktNPP50JEyawevXqBgcvhGj8bn5mCvFt4wIdhk9V1+9hyy6l3YubCEotqvxCLR3S8aGhnN2tu3eCq6PPNm3glT+X4XA6ySspwVlWXXVP9hFWp6V6dI1jUy+n1phac+f337LrSJYPIhbNQZ2HaYYPH87gwYN59dVXK4717t2bCy+8kOnTp3t0jb59+zJx4kQeeughj9rLMI0QTdtPHy3iyckvBTqMgNGAGWaluGMEzjALQenFHJjWD6rZ5+eu4aN4cflSzACOoluVwqE1wRYrF/XqzdL9KezPza20YV5dWJRi0sATeOiU070cqWjMfDJMU1paysqVKxk7dmyl42PHjmXJkiUeXcM0TfLy8oiLq/43pZKSEnJzcyt9CSGartOvOIlh5w5usSXkFWApdBC+OZuolZmE7CsgeL/7uRcK+Gj9Gn+G55ajLOkocTr4bON69ubk1DsRAVcPyW9793gpOtHc1CkZycjIwOl0kpSUVOl4UlISaWmezaB+5plnKCgo4PLLL6+2zfTp04mOjq74Sk5OrkuYQohGxmK18O8v72HSw5cRnRAZ6HAaBaPIfcE0DeQUF2NpRImbt2a8NJ2ZM8Lf6jWB9fjfbrTWHv3GM3PmTP79738za9YsWrVqVW27Bx54gJycnIqvffv21SdMIUQjYguyMemhy/j0wBtMebRl1iU5liOi+soKYbagBvVCBEpNHygWpRjVXn6xFO7VKRlJSEjAYrFU6QVJT0+v0ltyvFmzZjF16lQ+++wzzjzzzBrbBgcHExUVVelLCNE8WG1WLvvbBKy2pll/w1tsOe7LphtKcVmfvjSBqguVxIaE1NjzoYFrBgzyUzSiqalTMhIUFMSQIUOYP39+pePz589n1KhR1Z43c+ZMrr32Wj755BPOPbd5L/MTQtQuODSYO1+7KdBhBJbdTbLh1MTagvnL0OFcO2iw/2OqJwXcOPhEHh9zlmt+jDr60WJRCgU8ccZYeni4NFi0PHWuwHr33XczadIkhg4dysiRI3njjTdISUnhlltuAVxDLAcOHOCDDz4AXInI5MmTeeGFFxgxYkRFr0poaCjR0dFefCtCiKZk/HVjiIgNZ/pVLzT7Kq3H04Al3155ia9SBKfkM61/P2JDQzmhdVveZVXAYqyLs7v34MbBQ7EYBv1bJfH+2tUs2Z9SUfRsysAT6J1Y/dC8OznFxczauJ6vtm4mt7iYzrGxXNlvIOO6dsNiyE4mzU29KrDOmDGDp556itTUVPr168dzzz3HKaecAsC1117Lnj17+PXXXwE47bTTWLhwYZVrTJkyhffee8+j+8nSXiGar9ysPC5LugGzCRUGq45hKEzT8x+p9vhgirpHoQ1FyN58gg8Uct8Ht3PmNafwl2/nMn/XjoAu7/XEoNat+eKyqzC8OOF2b3Y2V3z5KekFBRUF5QylMLXmzM5deeWcCdgsLXuYr6nw9PNbysELIQJu7cKN3D/uMRyljkCHUm8Wq4VTLh/Jul83kpl6BHdlWTXVF0gr90nKayS2j+eq2Z+xbL//Ju8rYGLf/gB8unF9nc79/uopXhuC0Vpz9icfsDMr0+0kXgXcPmwkd42ofmqAaDx8Wg5eCCG8aeCpfXl3ywu1f1I3QrZgK0/88E8+PfA6//joTv7+/u1YLBaUm4JmhqFq3Wxuw29bAOgcE+vX5b0amL9rJx1jYogICvL4PAW8vvJPr024XX5gP9syM6pdTaSBD9atptTpfmk0uBKaFQcP8OAv87np66/454KfWJuW2uQmBbckkowIIRqF1p1aMebKkwMdRp3ZSxwU5hUTk+iaAzf4jP48u/AR+o/uXdFGKcXI84dy1qRTa8y3lII5L80D4Ip+A/y+vDezqJAnf19Mfqn7lT7uaGDOlk2c88kHHPBCgcrlB/fXmoRlFxdXW1q+xOHg1nlzufyLT/ls43p+2r2TWRvWcdFnnzDth3nYa0hiROBIMiKEaDTufPVGImLCAx1GnX353NeVvu8zsifPLHiEmfte47XV/+WztDd59Kv7OLw/s8braA07V+8GoH+rpCa1omZHViZXzf6MInvDJiMrD7vHqqtt9fhvC5m/cydARTJX/t+vt23hmWW/Nyg+4RuSjAghGo2wyDA+2PkysUmBXWmnlKJVB8/nQGxeus3tEEBCu3i6DuxU0WsSFGKrtUCkMgy+fO4b5rw4j8kJ3fj3qWMIt9nq9gYCwKk1+3JzmLt1c4OuM7x9+1p7hGJDQukSE1vl+JGiImZuWIdZzWCYBj5Yu7pOPT/CPyQZEUI0KpGxEdz12s2BC6AsVzjpwmF1OMez3+aHnzsEXcuskZLCEl6/9wNenfYeN/a7m833f8uvF0/ikt59PY8ngL7ZvrVB5w9t044+CYnVDtUo4LpBg92uplmyLwWHWfOqrGKHg+UH9jcoRuF9kowIIRqd4ecOJjI2MMM10fGR/Ovzv3HmpFM8PmfgaX092hLjzGtGE9sqGsNS849ebeqKnpZVP63nwfH/x+OnnsHUE4Z4HJM7wRYLSeERKI7OFfZ0Sa6nU2lzS0rqE9rR+yjFa+ddQFJE5TjLk5NxXbtzy1D3iaLd9Gw+SE2TX0VgSDIihGh0LFYLz//+WEDufeNTkxh98XB6DOlKVHyER+dM+MvY2hsBO9fupesJnY8O6aiyTpUaPulNp8nOtXv5bfZyLu/T36P7VKfE6aTQXkq/VkkVSYmptUcfBBpoFV57ghgfGtagGAHaR0Xz3VVTeOjU0xmQ1JpO0TGc0rETb553IS+fMwFrNUXP+nhQWE0BfRITGxyj8C5JRoQQjVKHXu25992/+v2+T18/g/ce+hStNUPHD/LonG6DOtfa5tMn5jBt9L9Y/dM69DGF0TQQEh5S47mGofjpo4V0j4/nlA6dGrTkN6+0lPXph0gryK8YMKqt3JxFKfoktPKooJvhZklzfUQGBzNl4GDmTLyaX6ZM5e3zL+aMLl1r7MnpEZ/A0Dbtqn0+FqUY3aETHaJjvBKj8B5JRoQQjdbYKacx+ZHL/H7fjx/7kik9bmfZ3BW1tjUsBpFx4dhLq19Fsurn9bz9j08AcDqO+egv+2wvzi+u8R6mqck57Fo2+9y4c+jth9/sXXvMuD7UO0bHkFFYQEZRYa3nZRbW3saXnh47ntjQ0CoJiUUpEsPDmX6GZ71Ywr8kGRFCNGqT/nU5/U7q6ff7pu48RGFezUkCQFLHRK7q+BfOCbmKixOu4+0HPiYno3K9jTkvfIthrebHrQelRAyrQZuurQGIDQ1l9uVXc1mffrWf2AAntmvPRb37MOOcCRTYS8n0IBFRUKeCab7QITqGb66cxNQThhAT7OpxigsN5aYhJ/L1FZNoExkZ0PiEe3XeKE8IIfxt/NQz2PB7w1Zp+EranvSKYZe8rHw+e3ouv8z8jReW/B8JbeMAWL94M6aj/nvvmA6Ts6eeUfG91TB4cPSpfL1tC8UO35TQnzzgBM7p3oMvN2/kUEGBR+do4OxuPXwST120Co/g/pNP5f6TT3XNifFjJVtRP9IzIoRo9Pqd3CvQIVRLHzePwnSaZBzM4sVb3zx60IPPwtDIELerbJSC0yaO4oQxlXtCooJDuLWaVSXekFy2q/qC3bs8+jC3KEXriAgu6Nm71rb+JIlI0yDJiBCi0WvXrQ0nnn1CoMPwmOkwWfb1yoqKq4PPGIClumEaQBmKi+88l3NvOougkKMFziJiwrnmX5dx/4d3oJSiqKCYzNQjFfNTfDER01CKnvEJ9CtbmVLqdHq0c3BydDSfXHw54QEephFNkwzTCCGahL+/91f+dtrDpGw+EOhQPKK1Zvf6FBLbx3PxXeey+Mtl1ba1BVm54K/jiU2KYer0q9i9PgWL1ULXgR0JCglix+rdfPjo5yz7egWmqQkOC2bslNPod6N3d641lMKiFP85/Uw2HU5n6f59FDscKGqe2tI+Kor511yHpZolt0LURukmsI2hp1sQCyGat6KCYn76YCFvPfAxhblFgQ6nVv/5+n62r9zFN6//SFZqdrXtIuMimPLIRM6/dVyV4mlrf93I/eMfw3SamM6j804sVoPY1rEcnNaP/ZYST+bB1urEtu24eciJvLZiOStSD1YMcdTWM/LkmeN8PqFWNE2efn5LMiKEaHL2bT3ALYP/TmlR491jJCwylIT28ezfdrBSElGTq/5xMdc9dmXF906Hk6s7/YWstOwqc1PAlZD0HNefb8eGeTSUUp0gi4UPL7yE7vEJXPzZJ+zLyal1f5jy3pLzuvfk+fHnytwM4ZYkI0KIZm33hhRuH/EPSgobVn7cV7oP6cLONXs8TkQAUPDB9pdp0yUJgGXfrORf5z9R4ymG1WDasn/xwJ+/klOPUuzlBWBNXJNQPU1CesYncO2gwVzWp1+dExG708k327Yyc8Na9uXmEhcaysW9+3JZn35EBQfX+T2IxsvTz28Z4BNCNEmd+3XgzfXPEBpZc/XSQNm1bm/dEhHAMAx+eHdBxfd7Nu6rdR8b02GSXGwjzOZ+4qhR6CB00xEwNbhJNDRHK7DWlogAFcNIe7KzWZuWSkpOdq3nHKvIbmfSnC/42/zvWJWWyqGCfLZkHObxxb8yYeaHpObl1el6onmQZEQI0WS16ZzEh7tm0K57m0CHUoXTXr/N2NL2plf8OSQ8GE86r0MjQtxuEhe9MJWOD62kzRtbaffCRsLXZbmSEly9IPUZWCkfDipxOvh80wYmzPyIjemHPD7/qSWLWZF6oNK1dNnXwbxc7vj+m3pEJZo6SUaEEE1adHwkb65/hpHnDw10KA2mlCIq7miF0FHnD615GYuCpE6JdOqXTP9WSZVKoEcuTSdhzl4Mh0YBIXvzaf3udrrcu5y2L27AqXWDJ706tabYYeeO77/1KGnKKynh0w3rq53f4tSalakH2Xw43e3rovmSZEQI0eTZgmxccf9FfrtfUIit2kJmhsVg6PhBtOqQUOfrOh1Ozrh6dMX3rTokctaUU1HVbT6nYdJDl2EYBlMGDj46zOLUxM3b5zbZUE6NI857Q1tOrdmdfYTlB/bX2nZzxmFKnDVXjFXAnwebxvJt4T2SjAghmoXYpGi/3au0xI5hGFXmc1isBhEx4dz+0lSu+sfFdbqmMhSjLjiRnid2q3T8zhk3csqlIyqub7FZMAyFYTG44YlrGHft6QCM7tCRqYOGABC2Jw9rnr3aYRhVWr8hpOoYSrE547BXrylaFil6JoRoFtp0TqLXiO5sWbbd9zfTrtogvYZ1Y/m81WitsQZZGXPlyUx6+DJad2pFmy5JpKdk8Mnjs7FYDUynxrAonA4TW7ANe1lCo7UJKM685hTufPXGKrcKCgnin5/ezdUP7uWXT34jLyuf1p1bcdaU04hvE1vRTinFP0afyglt2vD6m99Q07Z2YVtzUaUmOsg7v49qrQm21v5x0jexFaFWG0WO6nc41sCI9sleiUs0HbK0VwjRbGz4bTN3n/aw25ocvvBJymuERoSQl5VPTFI0oeFVhz/2bzvId2/9TOruQ4RHh3P6lScz8LQ+rF2wkV3r9hIcGsTw84bQKrnuwzrV2bNxHzf2v7vGNpnnJJM9tl2t1zKUwgAcNXxUGEqx+NobPdoRd/pvC3l71UpMN4NIFqUY3j6Zjy66rNbriKZB6owIIVqkVT+v5z8TnyE/y7OdZhvi0wNvVOqdaExuG/4A21ftcru8WBmKpM6JdHz9Qj7ZsK5B9zGU4vwevXh23DketS9xOLjl2/+xcO+eirom5bVLusbG8ckll5MYFt6gmETjIXVGhBAt0uAz+vNl+js+vYdS0L5nW+Jax/j0Pg1x56s3YguyVpnXYhgKwzC4581beWzMWdw1fKRH1xvbxTWXxaIUhlJYleu6p3ToxP+NOcvjuGwWC7cPG8ltJw5ncJu2dImJZWjbdkw/YyyzL7+KrRkZzNm8icUpe3CYdavTIpoumTMihGh2DMPgoS/u5tFLn/XJ9bWGifdeUGUfmcak++AuvLj0cd66/yP+/GFNxRLhfqN7M/Xxq+gzsicAHWM869mZOngI9446mc83beBAXi4xIaFc2Ks3g1u39fg5zNu+lSd+X8T+3NyKYwOTWvOP0adxIDeHsz58l/TCoz1aiWFh/OuU0zmvRy/P3rRosmSYRgjRLDmdTsbbrvDKtZRSaK2xWC04HU4uvXsCN/13Uo0fwqZpopRqFAlLVtoRMg8eIaZVNInt4yu9llNczPC3X6PUWf0Km/jQMJZOvRlrA3blnb15I/fM/77KcaOsp6WmXpAXx5/bbBOSvJIS5mzZxNxtW8grKaFbXDxX9R/AqPYdGsXfnYby9PNbekaEEM2SxWIhtnUMR9KyG3Sdzv07EJMYRUFuEZ37JXPuzWPpPby727ZaaxZ9vpQvnvuaLct3oIB2Pdpy9YOXcOY1pzQojoaIax1LXGv3PSDRISFcN2gwr6/8s9rzbx82okGJSLHDziMLf3H7mql1rZv8Pbb4V87u1gNLA2JojFJysrniy1kcys8HXJ1Xu45k8d2ObVzWpx/TzxjbYjYgbF7/Z4UQ4hhX3n9hg6+xf3sqoy8dyUvLHueed/5aYyLy6t3v8dgVz7Hljx1QthXM/q0HeXLyS9w+4gHspdUvaQ2ke0aezKQBg1C4eipshlHRY3H3iJOYNGBQg67/865d5JXWf4fl9IIC/vCgqFpTYmrNDXPncLigoKIcPhzdH+jzTRt4f+3qgMXnb5KMCCGarbNvOJOkjokNuoa92M6Lt77Jf697pcaS5yt+WMOcF+ZV+/qW5Tv498VPNygWX7EYBo+cdga/TrmBaSNGcXX/gdx30miWXn8ztw0b0eDhgoP5uQ3+DT+9wPero/xpyb4UdhzJqnFzwrdWrai116i5kGRECNFshYQF8/Ly6YTHhDX4Wj99uIjf5iyv9vWvXv6u1mssn7eKvZv2NTgWX0mOjuavJ47goVPHcOPgE0kM984S27jQsAZ/qLaOiPBKLI3F0v0ptQ59pebnsT83x08RBZYkI0KIZi0mMZp3Nj1Pt8FdGnytRy97mjfv+5DczKrb3G/5Y4dH11gw8/cGx9HUnNWlG8GW+k1RVECbiEiGtWvv3aACzNPcrIV0jEgyIoRo/uJaxzLjzyd44ffHCA4Lqv+FNHz237ncPOgeDu2tvBeLxWap/XyF20SmuYsKDuaO4SPcvqY4uufg8QM55d8/dOrpzW4i59C27Wqto5IQFka7FrKCVJIRIUSLoJSiz8ie2IJtDb5WZuoRpl/9QqVjJ11wYu0xAK07t2rw/ZuiW4YM4/6TTiHUWvn5J4SF8fp5F/DGeRfQLrLyB2+7yCheO/cCxnV1P2m4KTu1YyfaR0VhqSbJUsC1Awc3aBVTUyJ1RoQQLcqUHrdzcEeaV671+pqn6TKgI+Dag+b6PnfVuC+OYTGYue+1apfZtgQFpaX8umc3R4qLaB8VzckdOlZ84Jpasyr1IOkF+SSGhzOkTbtm1yNyrK2ZGVz15SxySkoq5tQYSmFqzdiu3Xj57AlNPhmROiNCCOHGmCtP5qP/fOGVa21etq0iGWnfoy33vX8bT0x6qdr2Ux+/qkUnIgDhQUGc26On29cMpRjatvLmff/46Ue+3LIRu2migC6xcbw74SLax8T4Plgf6xmfwPfXXMvM9euYu3UzeaWldI2L45r+AxnXtXuzq6tSE+kZEUK0KBkHs5jU5a84Sh0Nvtbf3voL468fU+nYrvV7eeKaF9m9PqXiWGJyPFMemci4a09v8D1bCofDwcA3XqHI4f7/0zNnjueiPn39HJWoK9m1VwghqrF24UYeGPcY9gYmJG9ueJZOfZLdvnbkUDYHtqcSEhFClwEdMVrQb7necMq7b7I/L7fGNttuvROrVTr4GzPZtVcIIaox8NS+fHrwDa556FLadmtd7+t8+/r8al+LTYqh38m96TaosyQidVTscNSaiADc+UPttV1E0yD/QoQQLVJUXCRT/j2R97e9xJmT6rdvzFcvfccf81Z6OTLx2Yb1HrVblLLbx5EIf5FkRAjR4t324vVVi1x46LmbXvduMIJih2d7+DT+SQbCU5KMCCFavPDocE66cFi9zs08eIQD21O9HFHLdnHvPh616x4f7+NImj+nabImLZXfUvZyILf2oTFfkZk/QggB/O2tW/i9hr1nanLrifdx4e3n0KF3OwafOYDYVtFejq5lSQiPIMIWRL695p1+373gEj9F1Dx9tnE9zy1bwqGC/IpjJyd34N+nnUGX2Di/xiI9I0IIAUTGRjLgFM9+Iz9eYW4Rn/zflzxxzYtc0e5Gnr3xVUqKSrwcYcvywzVTanz9ij79iQkJ8VM0zc9bq1Zw/88/VkpEAJbu38fFn33C3uxsv8YjyYgQQpSZ/v2DtOqQ0KBrmE7N9+8u4JFLn6EJVE5otNpERrF86s0kH7ccNMRq5T+nncHjZ44NUGRNX1ZRIU8tWez2NafWFJSW8uwy/27oKHVGhBDiGKZp8ubfP+KLZ79u8LWe+ukhThjT3wtRCeE9761ZxX8WLaCmD3+LUqy66a9EBgc36F5SZ0QIIerBMAxufOoaYpIa9ouPYVHM/3Chl6ISwnsO5uXWuueNU2sOFxb4KSJJRoQQogrDMLj8bxc06BqmU5N54IiXIhLCe2JDQ3HWMiiigNiQUP8EhCQjQgjh1mX3nE9Sp8QGXWPrnzvIyQjcckkh3Dm/R+8a5zNZlGJ0h07EhkoyIoQQAfefufdjDa5/BYSCnEImdf0raXvSvRiVEA3TLiqKCdXsnKxw7Z48beRJfo1JkhEhhKhG534deHXlU4SE138SX1FeMf88b7qsrBGNxqrUg/ywc4fbosOGUrww7lwGJtV/z6b6qFcyMmPGDDp37kxISAhDhgxh8WL3S4TKLVy4kCFDhhASEkKXLl147bXX6hWsEEL4W6c+yTz100MNusbeTftZt3CTlyISov6cpskd33+D3TSrXU3z/a7tfo0J6pGMzJo1i7vuuosHH3yQ1atXM3r0aM4++2xSUlLctt+9ezfnnHMOo0ePZvXq1fzjH//gjjvu4Msvv2xw8EII4Q+9h/fAYrXU+3xlKFb/4tnmb0L40m8pezmYl4dZTU+dU2vmbd9GVlGhX+OqczLy7LPPMnXqVG644QZ69+7N888/T3JyMq+++qrb9q+99hodOnTg+eefp3fv3txwww1cf/31PP300w0OXggh/CUiLrze52pTk7Zb5o2IwNuamYFF1bwrpMM02Z3t35VgdUpGSktLWblyJWPHVq58N3bsWJYsWeL2nKVLl1ZpP27cOFasWIHd7n5nxpKSEnJzcyt9CSFEIPUa1q1B529Z7v+ubyGOF2K1VtsrUqmdxb9b19UpGcnIyMDpdJKUlFTpeFJSEmlpaW7PSUtLc9ve4XCQkZHh9pzp06cTHR1d8ZWcnFyXMIUQwuvGXXt6g84/sD2N/dsOeikaIernjM5da23TOiKCXgkNW9ZeV/WawKqO6+LRWlc5Vlt7d8fLPfDAA+Tk5FR87du3rz5hCiGE13QZ0LHB18hMlSJoIrDaRUVxfs/eGDV8Zt924ggstVRo9bY63S0hIQGLxVKlFyQ9Pb1K70e51q1bu21vtVqJj493e05wcDBRUVGVvoQQIpDadWvT4KGahHb+3ZZdCHemn3EWp3fqDIBVGViUwqIUCrhj2Eiu7DfA7zHVaVAoKCiIIUOGMH/+fC666KKK4/Pnz+eCC9yXTh45ciRff115w6kff/yRoUOHYrPZ6hGyEEIExo1PTeLvZz6C02HW6TzDUPQc3p123dr4KDIhPBditfHGeRey7lAac7dtIbu4mPZRUVzWpx/to6IDElOdZ6jcfffdTJo0iaFDhzJy5EjeeOMNUlJSuOWWWwDXEMuBAwf44IMPALjlllt4+eWXufvuu7nxxhtZunQpb7/9NjNnzvTuOxFCCB8bcEofHv/unzx342seV1VVhsKwGNz838k+jk4IzymlGNi6DQNbN44Euc7JyMSJE8nMzOTRRx8lNTWVfv36MW/ePDp2dI2npqamVqo50rlzZ+bNm8e0adN45ZVXaNu2LS+++CKXXHKJ996FEEL4yeAz+vP+jpfY8NsWNi3dyrKvV3JgZxo5h12r/ixWC45SR0X7Dr3bc9erN9J3lPvy20IIULoJ1CjOzc0lOjqanJwcmT8ihGiUyn+Umk6TDb9tITczj6ROiXQf3KXGCf5CNGeefn77dyGxEEI0U+UJh8VqYeBpfQMcjRBNi2yUJ4QQQoiAkmRECCGEEAElyYgQQgghAkqSESGEEEIElCQjQgghhAgoSUaEEEIIEVCSjAghhBAioCQZEUIIIURASTIihBBCiIBqEhVYy8ss5+bmBjgSIYQQQniq/HO7tp1nmkQykpeXB0BycnKAIxFCCCFEXeXl5REdHV3t601iozzTNDl48CCRkZEopcjNzSU5OZl9+/bJxnk1kOdUO3lGnpHnVDt5RrWTZ+SZ5vSctNbk5eXRtm1bDKP6mSFNomfEMAzat29f5XhUVFST/x/lD/KcaifPyDPynGonz6h28ow801yeU009IuVkAqsQQgghAkqSESGEEEIEVJNMRoKDg3n44YcJDg4OdCiNmjyn2skz8ow8p9rJM6qdPCPPtMTn1CQmsAohhBCi+WqSPSNCCCGEaD4kGRFCCCFEQEkyIoQQQoiAkmRECCGEEAHVZJKRI0eOMGnSJKKjo4mOjmbSpElkZ2fXeE5+fj633XYb7du3JzQ0lN69e/Pqq6/6J+AAqM8zAti8eTPnn38+0dHRREZGMmLECFJSUnwfcIDU9zmVu/nmm1FK8fzzz/ssxkCr6zOy2+3cd9999O/fn/DwcNq2bcvkyZM5ePCg/4L2gxkzZtC5c2dCQkIYMmQIixcvrrH9woULGTJkCCEhIXTp0oXXXnvNT5EGTl2e0ezZsznrrLNITEwkKiqKkSNH8sMPP/gx2sCp69+lcr///jtWq5VBgwb5NkB/003E+PHjdb9+/fSSJUv0kiVLdL9+/fR5551X4zk33HCD7tq1q16wYIHevXu3fv3117XFYtFfffWVn6L2r/o8ox07dui4uDh977336lWrVumdO3fqb775Rh86dMhPUftffZ5TuTlz5uiBAwfqtm3b6ueee863gQZQXZ9Rdna2PvPMM/WsWbP0li1b9NKlS/Xw4cP1kCFD/Bi1b3366afaZrPpN998U2/atEnfeeedOjw8XO/du9dt+127dumwsDB955136k2bNuk333xT22w2/cUXX/g5cv+p6zO688479ZNPPqmXL1+ut23bph944AFts9n0qlWr/By5f9X1OZXLzs7WXbp00WPHjtUDBw70T7B+0iSSkU2bNmlAL1u2rOLY0qVLNaC3bNlS7Xl9+/bVjz76aKVjgwcP1v/85z99Fmug1PcZTZw4UV9zzTX+CLFRqO9z0lrr/fv363bt2ukNGzbojh07NttkpCHP6FjLly/XQK0/YJuKYcOG6VtuuaXSsV69eun777/fbfu///3vulevXpWO3XzzzXrEiBE+izHQ6vqM3OnTp49+5JFHvB1ao1Lf5zRx4kT9z3/+Uz/88MPNLhlpEsM0S5cuJTo6muHDh1ccGzFiBNHR0SxZsqTa804++WTmzp3LgQMH0FqzYMECtm3bxrhx4/wRtl/V5xmZpsm3335Ljx49GDduHK1atWL48OF89dVXfora/+r7d8k0TSZNmsS9995L3759/RFqwNT3GR0vJycHpRQxMTE+iNK/SktLWblyJWPHjq10fOzYsdU+k6VLl1ZpP27cOFasWIHdbvdZrIFSn2d0PNM0ycvLIy4uzhchNgr1fU7vvvsuO3fu5OGHH/Z1iAHRJJKRtLQ0WrVqVeV4q1atSEtLq/a8F198kT59+tC+fXuCgoIYP348M2bM4OSTT/ZluAFRn2eUnp5Ofn4+TzzxBOPHj+fHH3/koosu4uKLL2bhwoW+Djkg6vt36cknn8RqtXLHHXf4MrxGob7P6FjFxcXcf//9XHXVVc1io6+MjAycTidJSUmVjiclJVX7TNLS0ty2dzgcZGRk+CzWQKnPMzreM888Q0FBAZdffrkvQmwU6vOctm/fzv3338/HH3+M1dok9rets4AmI//+979RStX4tWLFCgCUUlXO11q7PV7uxRdfZNmyZcydO5eVK1fyzDPPcOutt/LTTz/57D15my+fkWmaAFxwwQVMmzaNQYMGcf/993Peeec1uYl2vnxOK1eu5IUXXuC9996r8e9bY+frf2/l7HY7V1xxBaZpMmPGDK+/j0A6/v3X9kzctXd3vDmp6zMqN3PmTP79738za9Yst8lwc+Ppc3I6nVx11VU88sgj9OjRw1/h+V1AU6zbbruNK664osY2nTp1Yt26dRw6dKjKa4cPH66SXZYrKiriH//4B3PmzOHcc88FYMCAAaxZs4ann36aM888s+FvwA98+YwSEhKwWq306dOn0vHevXvz22+/1T/oAPDlc1q8eDHp6el06NCh4pjT6eRvf/sbzz//PHv27GlQ7P7iy2dUzm63c/nll7N7925++eWXZtErAq5/KxaLpcpvrunp6dU+k9atW7ttb7VaiY+P91msgVKfZ1Ru1qxZTJ06lc8//7zJ/Gyur7o+p7y8PFasWMHq1au57bbbANcvklprrFYrP/74I2PGjPFL7L4U0GQkISGBhISEWtuNHDmSnJwcli9fzrBhwwD4448/yMnJYdSoUW7Psdvt2O12DKNy54/FYqnoEWgKfPmMgoKCOPHEE9m6dWul49u2baNjx44ND96PfPmcJk2aVOUH5Lhx45g0aRLXXXddw4P3E18+IziaiGzfvp0FCxY0qw/coKAghgwZwvz587nooosqjs+fP58LLrjA7TkjR47k66+/rnTsxx9/ZOjQodhsNp/GGwj1eUbg6hG5/vrrmTlzZsUvjs1ZXZ9TVFQU69evr3RsxowZ/PLLL3zxxRd07tzZ5zH7RaBmztbV+PHj9YABA/TSpUv10qVLdf/+/assNezZs6eePXt2xfennnqq7tu3r16wYIHetWuXfvfdd3VISIieMWOGv8P3i/o8o9mzZ2ubzabfeOMNvX37dv3SSy9pi8WiFy9e7O/w/aY+z+l4zXk1jdZ1f0Z2u12ff/75un379nrNmjU6NTW14qukpCQQb8Hrypdjvv3223rTpk36rrvu0uHh4XrPnj1aa63vv/9+PWnSpIr25Ut7p02bpjdt2qTffvvtFrO019Nn9Mknn2ir1apfeeWVSn9nsrOzA/UW/KKuz+l4zXE1TZNJRjIzM/XVV1+tIyMjdWRkpL766qv1kSNHKrUB9LvvvlvxfWpqqr722mt127ZtdUhIiO7Zs6d+5plntGma/g3eT+rzjLTW+u2339bdunXTISEheuDAgc22Dku5+j6nYzX3ZKSuz2j37t0acPu1YMECv8fvK6+88oru2LGjDgoK0oMHD9YLFy6seG3KlCn61FNPrdT+119/1SeccIIOCgrSnTp10q+++qqfI/a/ujyjU0891e3fmSlTpvg/cD+r69+lYzXHZERpXTajSgghhBAiAJrE0l4hhBBCNF+SjAghhBAioCQZEUIIIURASTIihBBCiICSZEQIIYQQASXJiBBCCCECSpIRIYQQQgSUJCNCCCGECChJRoQQQggRUJKMCCGEECKgJBkRQgghREBJMiKEEEKIgPp/i4bLoNM3OowAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot vector to 2d plot\n",
    "plt.scatter(df['vector'].apply(lambda x: x[0]), df['vector'].apply(lambda x: x[1]), c=kmeans.labels_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.40004946079085346"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# evaluate the model\n",
    "from sklearn.metrics import silhouette_score\n",
    "silhouette_score(df['vector'].values.tolist(), kmeans.labels_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>tweet</th>\n",
       "      <th>real_tweet</th>\n",
       "      <th>token</th>\n",
       "      <th>vector</th>\n",
       "      <th>label</th>\n",
       "      <th>label_text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>semester studi independen magang yaa semester ...</td>\n",
       "      <td>[cm] smt 5 mending ikut studi independen dulu ...</td>\n",
       "      <td>[semester, studi, independen, magang, yaa, sem...</td>\n",
       "      <td>[-0.4777812850661576, 0.731285504065454, 0.182...</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>kampus mahasiswa wajib publish jurnal indeks s...</td>\n",
       "      <td>[cm] di kampus kalian mahasiswa s1 wajib publi...</td>\n",
       "      <td>[kampus, mahasiswa, wajib, publish, jurnal, in...</td>\n",
       "      <td>[-0.3680373860730065, 0.6005052402615547, 0.14...</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>iya tugas uas manja bantu kerjain</td>\n",
       "      <td>[cm] Bisa gk yh tugas UAS ini ngerjain dirinya...</td>\n",
       "      <td>[iya, tugas, uas, manja, bantu, kerjain]</td>\n",
       "      <td>[-0.17908126286541423, 0.4448075814483066, 0.0...</td>\n",
       "      <td>1</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dosen praktikum kasih waktu temu kelas haru ka...</td>\n",
       "      <td>[CM] Dosen praktikum aku ngasih ini waktu pert...</td>\n",
       "      <td>[dosen, praktikum, kasih, waktu, temu, kelas, ...</td>\n",
       "      <td>[-0.34827083740383386, 0.5752464842051268, 0.1...</td>\n",
       "      <td>2</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>alhamdulilah hidup kasi include kos kuliah jak...</td>\n",
       "      <td>[cm] Alhamdulilah masih bisa hidup walaupun di...</td>\n",
       "      <td>[alhamdulilah, hidup, kasi, include, kos, kuli...</td>\n",
       "      <td>[-0.25330586758043083, 0.40945335796901156, 0....</td>\n",
       "      <td>0</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  \\\n",
       "0  semester studi independen magang yaa semester ...   \n",
       "1  kampus mahasiswa wajib publish jurnal indeks s...   \n",
       "2                  iya tugas uas manja bantu kerjain   \n",
       "3  dosen praktikum kasih waktu temu kelas haru ka...   \n",
       "4  alhamdulilah hidup kasi include kos kuliah jak...   \n",
       "\n",
       "                                          real_tweet  \\\n",
       "0  [cm] smt 5 mending ikut studi independen dulu ...   \n",
       "1  [cm] di kampus kalian mahasiswa s1 wajib publi...   \n",
       "2  [cm] Bisa gk yh tugas UAS ini ngerjain dirinya...   \n",
       "3  [CM] Dosen praktikum aku ngasih ini waktu pert...   \n",
       "4  [cm] Alhamdulilah masih bisa hidup walaupun di...   \n",
       "\n",
       "                                               token  \\\n",
       "0  [semester, studi, independen, magang, yaa, sem...   \n",
       "1  [kampus, mahasiswa, wajib, publish, jurnal, in...   \n",
       "2           [iya, tugas, uas, manja, bantu, kerjain]   \n",
       "3  [dosen, praktikum, kasih, waktu, temu, kelas, ...   \n",
       "4  [alhamdulilah, hidup, kasi, include, kos, kuli...   \n",
       "\n",
       "                                              vector  label label_text  \n",
       "0  [-0.4777812850661576, 0.731285504065454, 0.182...      2   positive  \n",
       "1  [-0.3680373860730065, 0.6005052402615547, 0.14...      2   positive  \n",
       "2  [-0.17908126286541423, 0.4448075814483066, 0.0...      1    neutral  \n",
       "3  [-0.34827083740383386, 0.5752464842051268, 0.1...      2   positive  \n",
       "4  [-0.25330586758043083, 0.40945335796901156, 0....      0   negative  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# label rename\n",
    "# 0 = negative\n",
    "# 1 = neutral\n",
    "# 2 = positive\n",
    "df['label_text'] = df['label'].apply(lambda x: 'negative' if x == 0 else ('neutral' if x == 1 else 'positive'))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save word2vec model\n",
    "model.save('models/word2vec.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # save result to csv\n",
    "# df.to_csv('data/collegemenfess_cluster.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "import pickle\n",
    "pickle.dump(kmeans, open('models/kmeans.sav', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model\n",
    "kmeans = pickle.load(open('models/kmeans.sav', 'rb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    negative       0.84      0.68      0.75       812\n",
      "     neutral       0.89      0.78      0.83       249\n",
      "    positive       0.73      0.88      0.80       906\n",
      "\n",
      "    accuracy                           0.78      1967\n",
      "   macro avg       0.82      0.78      0.79      1967\n",
      "weighted avg       0.80      0.78      0.78      1967\n",
      "\n",
      "[[549  13 250]\n",
      " [  9 195  45]\n",
      " [ 96  11 799]]\n",
      "0.784443314692425\n"
     ]
    }
   ],
   "source": [
    "# split the data\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df['real_tweet'], df['label_text'], test_size=0.2, random_state=42)\n",
    "\n",
    "# model training\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
    "\n",
    "text_clf = Pipeline([('tfidf', TfidfVectorizer()),\n",
    "                        ('clf', SVC(kernel='linear')),\n",
    "])\n",
    "\n",
    "text_clf.fit(X_train, y_train)\n",
    "\n",
    "# model evaluation\n",
    "predictions = text_clf.predict(X_test)\n",
    "print(classification_report(y_test, predictions))\n",
    "print(confusion_matrix(y_test, predictions))\n",
    "print(accuracy_score(y_test, predictions))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the confusion matrix\n",
    "import seaborn as sns\n",
    "cm = confusion_matrix(y_test, predictions)\n",
    "sns.heatmap(cm, annot=True, fmt='d')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the model\n",
    "\n",
    "pickle.dump(text_clf, open('models/svm.sav', 'wb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load the model\n",
    "text_clf = pickle.load(open('models/svm.sav', 'rb'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import twint\n",
    "# import nest_asyncio\n",
    "\n",
    "# nest_asyncio.apply()\n",
    "# #\n",
    "# # from collegemenfess get 500 latest tweet\n",
    "# c = twint.Config()\n",
    "# c.Username = \"collegemenfess\"\n",
    "# c.Limit = 500\n",
    "# c.Store_csv = True\n",
    "# c.Output = \"test.csv\"\n",
    "# twint.run.Search(c)\n"
   ]
  },
  {
   "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>id</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>timezone</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>...</th>\n",
       "      <th>geo</th>\n",
       "      <th>source</th>\n",
       "      <th>user_rt_id</th>\n",
       "      <th>user_rt</th>\n",
       "      <th>retweet_id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>retweet_date</th>\n",
       "      <th>translate</th>\n",
       "      <th>trans_src</th>\n",
       "      <th>trans_dest</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1608574820035465218</td>\n",
       "      <td>1608574820035465218</td>\n",
       "      <td>2022-12-30 04:24:57 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>04:24:57</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1608570538049536001</td>\n",
       "      <td>1608570538049536001</td>\n",
       "      <td>2022-12-30 04:07:56 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>04:07:56</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1608562874859020288</td>\n",
       "      <td>1608562874859020288</td>\n",
       "      <td>2022-12-30 03:37:29 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>03:37:29</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1608561019739336704</td>\n",
       "      <td>1608561019739336704</td>\n",
       "      <td>2022-12-30 03:30:07 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>03:30:07</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1608550643492618243</td>\n",
       "      <td>1608550643492618243</td>\n",
       "      <td>2022-12-30 02:48:53 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>02:48:53</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id      conversation_id               created_at  \\\n",
       "0  1608574820035465218  1608574820035465218  2022-12-30 04:24:57 WIB   \n",
       "1  1608570538049536001  1608570538049536001  2022-12-30 04:07:56 WIB   \n",
       "2  1608562874859020288  1608562874859020288  2022-12-30 03:37:29 WIB   \n",
       "3  1608561019739336704  1608561019739336704  2022-12-30 03:30:07 WIB   \n",
       "4  1608550643492618243  1608550643492618243  2022-12-30 02:48:53 WIB   \n",
       "\n",
       "         date      time  timezone              user_id        username  \\\n",
       "0  2022-12-30  04:24:57       700  1046084008742801408  collegemenfess   \n",
       "1  2022-12-30  04:07:56       700  1046084008742801408  collegemenfess   \n",
       "2  2022-12-30  03:37:29       700  1046084008742801408  collegemenfess   \n",
       "3  2022-12-30  03:30:07       700  1046084008742801408  collegemenfess   \n",
       "4  2022-12-30  02:48:53       700  1046084008742801408  collegemenfess   \n",
       "\n",
       "                    name  place  ... geo source user_rt_id user_rt retweet_id  \\\n",
       "0  COLLE | CEK PINNED ‼️    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "1  COLLE | CEK PINNED ‼️    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "2  COLLE | CEK PINNED ‼️    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "3  COLLE | CEK PINNED ‼️    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "4  COLLE | CEK PINNED ‼️    NaN  ... NaN    NaN        NaN     NaN        NaN   \n",
       "\n",
       "   reply_to  retweet_date  translate trans_src trans_dest  \n",
       "0        []           NaN        NaN       NaN        NaN  \n",
       "1        []           NaN        NaN       NaN        NaN  \n",
       "2        []           NaN        NaN       NaN        NaN  \n",
       "3        []           NaN        NaN       NaN        NaN  \n",
       "4        []           NaN        NaN       NaN        NaN  \n",
       "\n",
       "[5 rows x 36 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the data\n",
    "test_data = pd.read_csv('test.csv')\n",
    "test_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the test_data using kmeans\n",
    "def predict_label(tweet):\n",
    "    tweet = func.cleanTxt(tweet)\n",
    "    tweet = func.slang_to_formal(tweet)\n",
    "    tweet = func.acronym_to_formal(tweet)\n",
    "    tweet = func.removeStopWords(tweet)\n",
    "    tweet = func.stemSentence(tweet)\n",
    "    token = func.word_tokenize(tweet)\n",
    "    vector = get_vector(token)\n",
    "    label = kmeans.predict([vector])\n",
    "    return {\n",
    "        'tweet': tweet,\n",
    "        'label': label[0]\n",
    "        \n",
    "    }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# predict the test_data using svm\n",
    "def predict_label_svm(tweet):\n",
    "    return text_clf.predict([tweet])[0]\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>conversation_id</th>\n",
       "      <th>created_at</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>timezone</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>name</th>\n",
       "      <th>place</th>\n",
       "      <th>...</th>\n",
       "      <th>source</th>\n",
       "      <th>user_rt_id</th>\n",
       "      <th>user_rt</th>\n",
       "      <th>retweet_id</th>\n",
       "      <th>reply_to</th>\n",
       "      <th>retweet_date</th>\n",
       "      <th>translate</th>\n",
       "      <th>trans_src</th>\n",
       "      <th>trans_dest</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1608574820035465218</td>\n",
       "      <td>1608574820035465218</td>\n",
       "      <td>2022-12-30 04:24:57 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>04:24:57</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1608570538049536001</td>\n",
       "      <td>1608570538049536001</td>\n",
       "      <td>2022-12-30 04:07:56 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>04:07:56</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1608562874859020288</td>\n",
       "      <td>1608562874859020288</td>\n",
       "      <td>2022-12-30 03:37:29 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>03:37:29</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1608561019739336704</td>\n",
       "      <td>1608561019739336704</td>\n",
       "      <td>2022-12-30 03:30:07 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>03:30:07</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>positive</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1608550643492618243</td>\n",
       "      <td>1608550643492618243</td>\n",
       "      <td>2022-12-30 02:48:53 WIB</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>02:48:53</td>\n",
       "      <td>700</td>\n",
       "      <td>1046084008742801408</td>\n",
       "      <td>collegemenfess</td>\n",
       "      <td>COLLE | CEK PINNED ‼️</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>neutral</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    id      conversation_id               created_at  \\\n",
       "0  1608574820035465218  1608574820035465218  2022-12-30 04:24:57 WIB   \n",
       "1  1608570538049536001  1608570538049536001  2022-12-30 04:07:56 WIB   \n",
       "2  1608562874859020288  1608562874859020288  2022-12-30 03:37:29 WIB   \n",
       "3  1608561019739336704  1608561019739336704  2022-12-30 03:30:07 WIB   \n",
       "4  1608550643492618243  1608550643492618243  2022-12-30 02:48:53 WIB   \n",
       "\n",
       "         date      time  timezone              user_id        username  \\\n",
       "0  2022-12-30  04:24:57       700  1046084008742801408  collegemenfess   \n",
       "1  2022-12-30  04:07:56       700  1046084008742801408  collegemenfess   \n",
       "2  2022-12-30  03:37:29       700  1046084008742801408  collegemenfess   \n",
       "3  2022-12-30  03:30:07       700  1046084008742801408  collegemenfess   \n",
       "4  2022-12-30  02:48:53       700  1046084008742801408  collegemenfess   \n",
       "\n",
       "                    name  place  ... source user_rt_id user_rt retweet_id  \\\n",
       "0  COLLE | CEK PINNED ‼️    NaN  ...    NaN        NaN     NaN        NaN   \n",
       "1  COLLE | CEK PINNED ‼️    NaN  ...    NaN        NaN     NaN        NaN   \n",
       "2  COLLE | CEK PINNED ‼️    NaN  ...    NaN        NaN     NaN        NaN   \n",
       "3  COLLE | CEK PINNED ‼️    NaN  ...    NaN        NaN     NaN        NaN   \n",
       "4  COLLE | CEK PINNED ‼️    NaN  ...    NaN        NaN     NaN        NaN   \n",
       "\n",
       "  reply_to  retweet_date  translate  trans_src trans_dest     label  \n",
       "0       []           NaN        NaN        NaN        NaN  positive  \n",
       "1       []           NaN        NaN        NaN        NaN  positive  \n",
       "2       []           NaN        NaN        NaN        NaN  negative  \n",
       "3       []           NaN        NaN        NaN        NaN  positive  \n",
       "4       []           NaN        NaN        NaN        NaN   neutral  \n",
       "\n",
       "[5 rows x 37 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict the test_data using kmeans\n",
    "test_data['label'] = test_data['tweet'].apply(predict_label_svm)\n",
    "test_data.head()\n"
   ]
  }
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