nlp_with_disaster_tweets.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Natural Language Processing with Disaster Tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Overview**\n",
    "\n",
    "\"Getting Started\" kaggle competitions\n",
    "\n",
    "https://www.kaggle.com/competitions/nlp-getting-started/overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Library Import**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "import gc\n",
    "import re\n",
    "import string\n",
    "import operator\n",
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('data/train.csv')\n",
    "df_test = pd.read_csv('data/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
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       "      <th>keyword</th>\n",
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       "      <th>text</th>\n",
       "      <th>target</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>144</td>\n",
       "      <td>accident</td>\n",
       "      <td>UK</td>\n",
       "      <td>.@NorwayMFA #Bahrain police had previously die...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>3850</td>\n",
       "      <td>detonation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ignition Knock (Detonation) Sensor-Senso Stand...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5382</th>\n",
       "      <td>7680</td>\n",
       "      <td>panic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@panic awesome thanks.</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>8236</td>\n",
       "      <td>riot</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Southeast Dirt Riot Series Crowns Champions:  ...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>5101</th>\n",
       "      <td>7277</td>\n",
       "      <td>nuclear%20disaster</td>\n",
       "      <td>#goingdownthetoilet Illinois</td>\n",
       "      <td>CLOSING THEIR EYES TO DISASTER!  State Departm...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>420</td>\n",
       "      <td>apocalypse</td>\n",
       "      <td>Buenos Aires, Argentina</td>\n",
       "      <td>candylit: Imagine sarumi in a zombie apocalyps...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>6350</th>\n",
       "      <td>9080</td>\n",
       "      <td>structural%20failure</td>\n",
       "      <td>Warm Heart Of Africa</td>\n",
       "      <td>@sabcnewsroom sabotage!I rule out structural f...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>260</td>\n",
       "      <td>ambulance</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>If I get run over by an ambulance am I lucky? ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5693</th>\n",
       "      <td>8125</td>\n",
       "      <td>rescued</td>\n",
       "      <td>The green and pleasant land.</td>\n",
       "      <td>Why weren't they taken back to Africa once res...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>184</td>\n",
       "      <td>aftershock</td>\n",
       "      <td>304</td>\n",
       "      <td>'Remembering that you are going to die is the ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5980</th>\n",
       "      <td>8540</td>\n",
       "      <td>screaming</td>\n",
       "      <td>nap queen</td>\n",
       "      <td>like little boy you better sit your ass down s...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2568</th>\n",
       "      <td>3683</td>\n",
       "      <td>destroy</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Industry Tryna Destroy @MeekMill cause he Expo...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2188</th>\n",
       "      <td>3136</td>\n",
       "      <td>debris</td>\n",
       "      <td>Berlin, Germany</td>\n",
       "      <td>Experts leave lab as Malaysia confirms debris ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6063</th>\n",
       "      <td>8662</td>\n",
       "      <td>sinkhole</td>\n",
       "      <td>Alger-New York-San Francisco</td>\n",
       "      <td>#SanDiego #News Sinkhole Disrupts Downtown Tro...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6115</th>\n",
       "      <td>8729</td>\n",
       "      <td>sinking</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sinking carb consultative assembly plans could...</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "        id               keyword                      location  \\\n",
       "100    144              accident                            UK   \n",
       "2684  3850            detonation                           NaN   \n",
       "5382  7680                 panic                           NaN   \n",
       "5769  8236                  riot                       Seattle   \n",
       "5101  7277    nuclear%20disaster  #goingdownthetoilet Illinois   \n",
       "287    420            apocalypse       Buenos Aires, Argentina   \n",
       "6350  9080  structural%20failure          Warm Heart Of Africa   \n",
       "181    260             ambulance                        Mumbai   \n",
       "5693  8125               rescued  The green and pleasant land.   \n",
       "128    184            aftershock                           304   \n",
       "5980  8540             screaming                     nap queen   \n",
       "2568  3683               destroy                       Florida   \n",
       "2188  3136                debris               Berlin, Germany   \n",
       "6063  8662              sinkhole  Alger-New York-San Francisco   \n",
       "6115  8729               sinking                           NaN   \n",
       "\n",
       "                                                   text  target  \n",
       "100   .@NorwayMFA #Bahrain police had previously die...       1  \n",
       "2684  Ignition Knock (Detonation) Sensor-Senso Stand...       0  \n",
       "5382                             @panic awesome thanks.       0  \n",
       "5769  Southeast Dirt Riot Series Crowns Champions:  ...       0  \n",
       "5101  CLOSING THEIR EYES TO DISASTER!  State Departm...       1  \n",
       "287   candylit: Imagine sarumi in a zombie apocalyps...       0  \n",
       "6350  @sabcnewsroom sabotage!I rule out structural f...       0  \n",
       "181   If I get run over by an ambulance am I lucky? ...       0  \n",
       "5693  Why weren't they taken back to Africa once res...       1  \n",
       "128   'Remembering that you are going to die is the ...       0  \n",
       "5980  like little boy you better sit your ass down s...       0  \n",
       "2568  Industry Tryna Destroy @MeekMill cause he Expo...       0  \n",
       "2188  Experts leave lab as Malaysia confirms debris ...       1  \n",
       "6063  #SanDiego #News Sinkhole Disrupts Downtown Tro...       1  \n",
       "6115  Sinking carb consultative assembly plans could...       0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1274</td>\n",
       "      <td>blood</td>\n",
       "      <td>Puerto Rico</td>\n",
       "      <td>Name: Chizu\\nGender: Male\\nAge: 10\\nHair: Red\\...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>698</th>\n",
       "      <td>2269</td>\n",
       "      <td>cliff%20fall</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@Spencer_VH I hope you fall off a cliff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3074</th>\n",
       "      <td>10180</td>\n",
       "      <td>violent%20storm</td>\n",
       "      <td>Worldwide</td>\n",
       "      <td>Violent Forces Radio: Now Playing Torture - St...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1953</th>\n",
       "      <td>6589</td>\n",
       "      <td>inundated</td>\n",
       "      <td>Searching for Rivendell</td>\n",
       "      <td>@eliistender10 @Splats although originally an ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>754</th>\n",
       "      <td>2450</td>\n",
       "      <td>collide</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Now there's only 2 days for collide!!! get sig...</td>\n",
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       "    <tr>\n",
       "      <th>2084</th>\n",
       "      <td>7000</td>\n",
       "      <td>mayhem</td>\n",
       "      <td>worldwide</td>\n",
       "      <td>@RaynbowAffair Editor In Chief @DiamondKesawn ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2566</th>\n",
       "      <td>8564</td>\n",
       "      <td>screams</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@sugayiffer SCREAMS UR SUCH A CUTE HANAYO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3155</th>\n",
       "      <td>10474</td>\n",
       "      <td>wild%20fires</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WILD FIRES! http://t.co/EgrMdkXpOi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>5100</td>\n",
       "      <td>famine</td>\n",
       "      <td>Dublin, Ireland.</td>\n",
       "      <td>Miners strike; Botha Apartheid SA Tutu Nobel p...</td>\n",
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       "    <tr>\n",
       "      <th>3017</th>\n",
       "      <td>9964</td>\n",
       "      <td>tsunami</td>\n",
       "      <td>NaN</td>\n",
       "      <td>omfg Johnny tsunami commercial</td>\n",
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      "text/plain": [
       "         id          keyword                 location  \\\n",
       "394    1274            blood              Puerto Rico   \n",
       "698    2269     cliff%20fall                      NaN   \n",
       "3074  10180  violent%20storm                Worldwide   \n",
       "1953   6589        inundated  Searching for Rivendell   \n",
       "754    2450          collide                      NaN   \n",
       "2084   7000           mayhem                worldwide   \n",
       "2566   8564          screams                      NaN   \n",
       "3155  10474     wild%20fires                      NaN   \n",
       "1531   5100           famine        Dublin, Ireland.    \n",
       "3017   9964          tsunami                      NaN   \n",
       "\n",
       "                                                   text  \n",
       "394   Name: Chizu\\nGender: Male\\nAge: 10\\nHair: Red\\...  \n",
       "698             @Spencer_VH I hope you fall off a cliff  \n",
       "3074  Violent Forces Radio: Now Playing Torture - St...  \n",
       "1953  @eliistender10 @Splats although originally an ...  \n",
       "754   Now there's only 2 days for collide!!! get sig...  \n",
       "2084  @RaynbowAffair Editor In Chief @DiamondKesawn ...  \n",
       "2566          @sugayiffer SCREAMS UR SUCH A CUTE HANAYO  \n",
       "3155                 WILD FIRES! http://t.co/EgrMdkXpOi  \n",
       "1531  Miners strike; Botha Apartheid SA Tutu Nobel p...  \n",
       "3017                     omfg Johnny tsunami commercial  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 7613 entries, 0 to 7612\n",
      "Data columns (total 5 columns):\n",
      " #   Column    Non-Null Count  Dtype \n",
      "---  ------    --------------  ----- \n",
      " 0   id        7613 non-null   int64 \n",
      " 1   keyword   7552 non-null   object\n",
      " 2   location  5080 non-null   object\n",
      " 3   text      7613 non-null   object\n",
      " 4   target    7613 non-null   int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 297.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Summer is lovely'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[df_train[\"target\"] == 0][\"text\"].values[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"What's up man?\", 'I love fruits', 'Summer is lovely', ...,\n",
       "       'These boxes are ready to explode! Exploding Kittens finally arrived! gameofkittens #explodingkittens\\x89Û_ https://t.co/TFGrAyuDC5',\n",
       "       'Sirens everywhere!',\n",
       "       'I just heard a really loud bang and everyone is asleep great'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[df_train[\"target\"] == 0][\"text\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id             0\n",
       "keyword       61\n",
       "location    2533\n",
       "text           0\n",
       "target         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id             0\n",
       "keyword       26\n",
       "location    1105\n",
       "text           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_with_null = ['keyword','location']\n",
    "for col in cols_with_null:\n",
    "    df_train[col] = df_train[col].fillna('no_data')\n",
    "    df_train[col] = df_train[col].fillna('no_data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>no_data</td>\n",
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       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "   id  keyword location                                               text  \\\n",
       "0   1  no_data  no_data  Our Deeds are the Reason of this #earthquake M...   \n",
       "1   4  no_data  no_data             Forest fire near La Ronge Sask. Canada   \n",
       "2   5  no_data  no_data  All residents asked to 'shelter in place' are ...   \n",
       "3   6  no_data  no_data  13,000 people receive #wildfires evacuation or...   \n",
       "4   7  no_data  no_data  Just got sent this photo from Ruby #Alaska as ...   \n",
       "\n",
       "   target  \n",
       "0       1  \n",
       "1       1  \n",
       "2       1  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id          0\n",
       "keyword     0\n",
       "location    0\n",
       "text        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "222 221\n"
     ]
    }
   ],
   "source": [
    "print (df_train.keyword.nunique(), df_test.keyword.nunique())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Target Class Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [8,4]\n",
    "plt.rcParams['figure.dpi'] = 80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Real or Not Real Disaster Tweet')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_train, x='target')\n",
    "plt.title('Real or Not Real Disaster Tweet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['location_str'] = df_train['location'].apply(lambda x: str(x).lower().replace('$$', ' ').replace('_', ' ').replace('.', ' '))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Most common words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "sns.countplot(y=df_train.keyword, order = df_train.keyword.value_counts().iloc[:15].index)\n",
    "plt.title('Top 15 keywords')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 words in disaster and non-disaster tweets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keyword\n",
      "body%20bags    40\n",
      "harm           37\n",
      "armageddon     37\n",
      "wrecked        36\n",
      "deluge         36\n",
      "ruin           36\n",
      "fear           35\n",
      "explode        35\n",
      "siren          35\n",
      "twister        35\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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Nxm3bti20Wi2uXLny0PWo4IqX4ipqfkD+uqPVajF69Gi4ubkpITMwMBDr16/H33//bbCutWjRAg4ODjh37txDaywI7k/ay6NqK2BpaYnWrVtjxIgR+Oeff3Dx4sWnWg7078U9KPSvM3r0aERFReG1117D8OHDodFoMGXKFNy9exdjxowxGNfBwQHDhg1DamoqfHx8sGLFCvz2229YuHDhE/8l6uDggG3btqF9+/bKOSG1atXCt99+C39/f7zxxhv4+OOP4enpidTUVBw9ehQajQYTJkxQpmFjY4Nu3bph2rRpaNiwIZo1a1ZoPv7+/tBqtfj9998NLoUODAzErFmzUKlSJYMvq3bt2iEgIAC9e/dGSkoK6tSpgy1btmDu3LkIDw9/7F+rzs7OGDRoECZMmAAHBwe8/vrrOHz4MObPn28wXlRUFObMmYN33nkHVatWxT///IPp06fDwcHhsSHoQVqtFuPHj0ePHj3QpUsXdOnSBdeuXcOoUaNQs2bNQofSnlTbtm3x6quvolevXsp7vWrVKiUcFOX48eP49NNP0bFjR3h7eyMvLw8LFy6Eubm5cu+W9u3bo1evXoiIiIC/vz+OHTuGSZMmGQTc9PR0tGnTBqGhofD19YWFhQU2btyI27dv4/XXXweQH1rMzc2xYMEClC1bFlZWVqhZsyaqV6+O4cOHo3///jh9+jQCAgKUy4e3b9+O3r17o2XLlsVeJkXNz8HBAc7OzmjYsCF+//13BAcHK5+FwMBA5fDW/QHF0dERU6dORb9+/ZCSkoL27dvDyckJ165dw44dO9CmTRt07NjxiXvx8PCAi4sLVq5cqQTlqlWrYs2aNdi1axf+85//oGLFikhNTcWkSZNQvnz5Yu2pIxNRyifpEj3Wg1fxiIgcOnRIXnvtNbGzsxM7Ozt57bXX5NChQwbjdOvWTSpUqCB79+6Vpk2bipWVlVSqVOmRV1oUeNgVEJmZmeLv7y8eHh7KJbMnT56Ujh07ipubm1haWkqFChXkrbfekq1btxaa5t69ewWAclXOwzRp0kQASHx8vDJs9erVAkC6detWaPyMjAzp37+/lCtXTiwsLKRGjRoGV5YU1UsBnU4no0aNEg8PD7G2tpaAgACJi4szuIrn1KlTEhISIlWqVBErKytxdXWV9u3by4EDBx61CB96FU+BpUuXSv369cXS0lJcXFzkww8/lOvXrxuM87D3/VGSk5Plgw8+EHt7e3FycpIPP/xQfv7550dexZOUlCRdu3YVHx8fsbGxkTJlyoi/v79ER0cr4+v1ehkzZox4eXmJtbW1NGvWTGJiYiQgIEB5T7Kzs+WTTz6R2rVri52dnTg4OEjTpk1l2bJlBjXOnj1bqlatKmZmZgJAYmJilOcWL14szZs3F1tbW7GzsxNfX1/p37+/XLt2TRkHgIwaNeqJl8mj5jdkyBABILNmzVKGJSUliUajkcqVKz90eps3b5ZWrVqJg4ODWFtbS/Xq1SUsLMxgfX3SXjZs2CC1atUSc3NzZX3bt2+fvP322+Ll5SWWlpZSrlw5ef/99+XUqVNP3DOZDo3IE55qTvQv89FHH+G33357pqtOStLnn3+OmTNnIjExEfb29qVdDhGRqvEQD9FzFhsbi9OnTyMyMhJ9+vRhOCEiegIMKETP2VtvvYXk5GS0a9eu0DkyRET0cDzEQ0RERKrDy4yJiIhIdRhQiIiISHUYUIiIiEh1TOIk2Rs3bjzxD3MRERFR6Sq4U/ajmERAEREGFCIiIhPCQzxERESkOgwoREREpDoMKERERKQ6DChERESkOgwoREREpDqlFlDWrFmDkJAQXLlyBQCQnp6OCRMmYODAgRgyZAhOnTpVWqURERFRKSuVgHLhwgWcPXsWrq6uyrBly5bBx8cH06dPR58+fTB9+nTk5eWVRnlERERUyoweUHJzczF//nz06NEDGo1GGb5//360a9cOAODt7Q0nJyfuRSEiInpBGT2grFq1Ci1btoS7u7syLDMzEyICR0dHZZibmxtSU1ONXR4RERGpgFHvJHvmzBmcP38eoaGhhZ67f2/Ko0RFRSEqKgoAYG1tjWnTphmEHQDwWrPm2Yt9jhKCg0u7BCIiIlUzakA5efIkEhMT0b9/fwDAzZs3MWHCBPTu3RsAkJGRoexFSUlJMThHpUBQUBCCgoIMhiUnJ/+rbnWflJRU2iUQERGVGo1GA09Pz0eOY9SA8s477+Cdd95RHvfr1w/Dhw9HpUqV4Ofnh+joaISEhODcuXNIS0uDr6+vMcsjIiIilVDNjwWGhoYiMjISAwcOhLm5OQYMGAAzM7PSLouIiIhKgUb+TcdGinD9+nWDQzxNdu0qxWoe76i/f2mXQEREVGqe5BAP7yRLREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqmNe2gVQ0UzlV5nV3Ad/WZqISJ24B4WIiIhUhwGFiIiIVIcBhYiIiFSHAYWIiIhUhwGFiIiIVIdX8RA9ATVfiQSYxhVVwJP1YQo9ENHjcQ8KERERqY7R96CMHz8eaWlp0Gg0sLGxQffu3VGlShVEREQgNTUVNjY2AICAgAAEBQUZuzwiIiJSAaMHlEGDBsHOzg4AcOjQIcyaNQtTpkwBAISFhaFJkybGLomIiIhUxuiHeArCCQBkZWVBo9EYuwQiIiJSuVI5STYyMhJxcXEAgJEjRyrDly5diuXLl8PLywudO3eGh4dHoddGRUUhKioKAGBtbY1p06bB3d3dOIWXkIf19W9kCn2YQg8A+1CTJ+3Ba82a51zJs0kIDn6i8dTchyn0ALx4fRQolYDSv39/AMCOHTuwdOlSfP755+jfvz9cXV0hIti2bRsmT56M7777rtBrg4KCCp2bkpycDBExSu0lISkpqbRLKBGm0Icp9ACwDzUxhR4A0+jDFHoATLMPjUYDT0/PR45fqlfxtGrVCidOnEBmZiZcXV0B5Bfdrl07JCcnIzMzszTLIyIiolJi1ICSlZWFW7duKY8PHToEBwcH2NjYIC0tTRl+4MABODk5wcHBwZjlERERkUoY9RBPVlYWvvnmG+Tk5ECr1cLR0REjRoyATqfD5MmTkZubC61WCwcHBwwbNsyYpREREZGKGDWguLq6YtKkSQ99bvLkycYshYiIiFSMd5IlIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItUxN/YMx48fj7S0NGg0GtjY2KB79+6oUqUK0tPTERkZiaSkJFhYWKBnz57w9fU1dnlERESkAkYPKIMGDYKdnR0A4NChQ5g1axamTJmCZcuWwcfHB6NGjcK5c+fw7bffYsaMGTAzMzN2iURERFTKjH6IpyCcAEBWVhY0Gg0AYP/+/WjXrh0AwNvbG05OTjh16pSxyyMiIiIVMPoeFACIjIxEXFwcAGDkyJHIzMyEiMDR0VEZx83NDampqaVRHhEREZWyUgko/fv3BwDs2LEDS5cuRf/+/ZU9KY8TFRWFqKgoAIC1tTWmTZsGd3f351br8+Dh4VHaJZQIU+jDFHoA2IeamEIPgGn0YQo9AC9uH6USUAq0atUKc+fOVR5nZGQoe1FSUlLg6upa6DVBQUEICgoyGJacnAwReb7FlqCkpKTSLqFEmEIfptADwD7UxBR6AEyjD1PoATDNPjQaDTw9PR85vlHPQcnKysKtW7eUx4cOHYKDgwPs7e3h5+eH6OhoAMC5c+eQlpbGq3iIiIheUEbdg5KVlYVvvvkGOTk50Gq1cHR0xIgRI6DRaBAaGorIyEgMHDgQ5ubmGDBgAK/gISIiekEZNaC4urpi0qRJD33O2dkZ4eHhxiyHiIiIVIp3kiUiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1TE35sxycnIwbdo0XLt2DZaWlnB2dkbPnj3h7u6OiIgIpKamwsbGBgAQEBCAoKAgY5ZHREREKmHUgAIAbdq0QaNGjaDRaBAdHY05c+YgPDwcABAWFoYmTZoYuyQiIiJSGaMe4rG0tETjxo2h0WgAAD4+PkhKSjJmCURERPQvYPQ9KPfbunWrwR6TpUuXYvny5fDy8kLnzp3h4eFRitURERFRaSm1gLJ+/Xpcv34do0ePBgD0798frq6uEBFs27YNkydPxnfffVfodVFRUYiKigIAWFtbY9q0aXB3dzdq7c/KVIKXKfRhCj0A7ENNTKEHwDT6MIUegBe3j1IJKJs2bcKhQ4fwxRdfwMrKCgDg6uoKANBoNGjXrh2WLFmCzMxMODg4GLw2KCio0MmzycnJEBHjFF8CTOWwlin0YQo9AOxDTUyhB8A0+jCFHgDT7EOj0cDT0/OR4xv9MuOoqCjs3bsX4eHhsLOzAwDk5eUhLS1NGefAgQNwcnIqFE6IiIjoxWDUPSg3b97E4sWL4eHhgbFjxwIALCwsMHr0aEyePBm5ubnQarVwcHDAsGHDjFkaERERqYhRA4qLiwtWr1790OcmT55szFKIiIhIxXgnWSIiIlIdBhQiIiJSnWIFlJ07dyI3N7fQcJ1Oh507d5ZYUURERPRiK1ZA+eGHH5CVlVVo+N27d/HDDz+UWFFERET0Yiv2IZ6C29QXEBGcOXMG9vb2JVYUERERvdie6Cqejh07Kv/v2bPnQ8fp0KFDyVREREREL7wnCijh4eEQEUyYMAGDBg1SbrAGAGZmZnBzc4Obm9tzK5KIiIheLE8UUOrVqwcAiIyMhIuLC7RaXvxDREREz0+xbtTm5uaGjIwMnDt3DhkZGdDr9QbPt27dukSLIyIiohdTsQLKvn378MMPP0Cj0cDR0dHgOY1Gw4BCREREJaJYAWXZsmXo0KED3nvvPR7mISIiouemWCnjzp078Pf3ZzghIiKi56pYSeOVV17B0aNHn1ctRERERACKeYjH1tYWa9aswfHjx1GpUiWYmZkZPH///VKIiIiInlaxAsr58+dRpUoV3Lt3D2fPnn1eNREREdELrlgBZcyYMc+rDiIiIiIFz3YlIiIi1SnWHpTRo0cX+rHA+40dO/aZCyIiIiIqVkApuOV9gby8PFy5cgXx8fF4/fXXS7QwIiIienEVK6AEBwc/dPiWLVtw5cqVEimIiIiIqETOQWncuDH2799fEpMiIiIiKt4elIfR6/XYuXNnod/meZicnBxMmzYN165dg6WlJZydndGzZ0+4u7sjPT0dkZGRSEpKgoWFBXr27AlfX99nLY+IiIj+hYoVUPr06VNoWGZmJjQaDfr27ftE02jTpg0aNWoEjUaD6OhozJkzB+Hh4Vi2bBl8fHwwatQonDt3Dt9++y1mzJhR6GZwREREZPqKFVAevFOsVquFo6MjqlevDgcHh8e+3tLSEo0bN1Ye+/j4YPPmzQCA/fv3Y+bMmQAAb29vODk54dSpU6hTp05xSiQiIiITUKyA0qpVqxKd+datW9GkSRNkZmZCRAwOE7m5uSE1NbXQa6KiohAVFQUAsLa2xrRp0+Du7l6idT1vHh4epV1CiTCFPkyhB4B9qIkp9ACYRh+m0APw4vZR7HNQUlNTER0djcTERABAhQoV8MYbb8DV1bVY01m/fj2uX7+O0aNHIycn55H3V7lfUFAQgoKCDIYlJydDRIo1/9KUlJRU2iWUCFPowxR6ANiHmphCD4Bp9GEKPQCm2YdGo4Gnp+cjxy/WVTx///03Pv30U8THx8PDwwMeHh44efIkPv30Uxw7duyJp7Np0yYcOnQII0eOhJWVlXJ4KCMjQxknJSWl2KGHiIiITEOx9qAsW7YMHTp0QEhIiMHwVatWYenSpWjQoMFjpxEVFYW9e/fiiy++gJ2dnTLcz88P0dHRCAkJwblz55CWlsareIiIiF5QxQooiYmJaNmyZaHh/v7+2LRp02Nff/PmTSxevBgeHh7KbfEtLCwwceJEhIaGIjIyEgMHDoS5uTkGDBjAK3iIiIheUMUKKC4uLjh27Fih40bHjh2Di4vLE71+9erVD33O2dkZ4eHhxSmHiIiITFSxAsp7772H2bNn49SpU/Dx8QEAnD17FgcPHnzoPVKIiIiInkaxAkpAQAA8PT2xZcsW7Nq1CyKC8uXLY+zYsahRo8bzqpGIiIheMMUKKH/++SfMzMzwv//9z2D433//jb/++guNGjUqydqIiIjoBVWsy4yXLl360PuNaLVaLF26tMSKIiIiohdbsQJKUlISypcvX2h4uXLlTOZGMkRERFT6ihVQnJyccPny5ULDL126BHt7+xIrioiIiF5sxT5Jdt68eRAR1K5dGwAQFxeHn376qcR/p4eIiIheXMUKKMHBwdDr9fj++++h0+kA5N9oLSgoqNDdZYmIiIieVrECilarRadOnfDee+/hxo0bAPLPP7G0tHwuxREREdGLqdi/ZgwAlpaWqFSpUknXQkRERASgmCfJEhERERkDAwoRERGpDgMKERERqQ4DChEREakOAwoRERGpDgMKERERqQ4DChEREakOAwoRERGpDgMKERERqQ4DChEREanOU93q/lksWLAAR48eRUpKCr7++mvllvkRERFITU2FjY0NgPxfTg4KCjJ2eURERKQCRg8ofn5+6NChA0aPHl3oubCwMDRp0sTYJREREZHKGD2g1K5d29izJCIion8ZoweUR1m6dCmWL18OLy8vdO7cGR4eHqVdEhEREZUC1QSU/v37w9XVFSKCbdu2YfLkyfjuu+8KjRcVFYWoqCgAgLW1NaZNmwZ3d3djl/tMTCV4mUIfptADwD7UxBR6AEyjD1PoAXhx+1BNQHF1dQUAaDQatGvXDkuWLEFmZiYcHBwMxgsKCip08mxycjJExGi1PqukpKTSLqFEmEIfptADwD7UxBR6AEyjD1PoATDNPjQaDTw9PR85viouM87Ly0NaWpry+MCBA3BycioUToiIiOjFYPQ9KPPmzcORI0eQlpaGcePGwdraGlOnTsXkyZORm5sLrVYLBwcHDBs2zNilERERkUoYPaD06NEDPXr0KDR88uTJxi6FiIiIVEoVh3iIiIiI7seAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKpjbuwZLliwAEePHkVKSgq+/vprVKpUCQCQnp6OyMhIJCUlwcLCAj179oSvr6+xyyMiIiIVMPoeFD8/P3z55Zdwc3MzGL5s2TL4+Phg+vTp6NOnD6ZPn468vDxjl0dEREQqYPSAUrt2bbi4uBQavn//frRr1w4A4O3tDScnJ5w6dcrY5REREZEKqOIclMzMTIgIHB0dlWFubm5ITU0txaqIiIiotBj9HJSiaDSaJxovKioKUVFRAABra2tMmzYN7u7uz7O0Eufh4VHaJZQIU+jDFHoA2IeamEIPgGn0YQo9AC9uH6oIKA4ODgCAjIwMZS9KSkoKXF1dC40bFBSEoKAgg2HJyckQkedfaAlJSkoq7RJKhCn0YQo9AOxDTUyhB8A0+jCFHgDT7EOj0cDT0/OR46viEA+Qf/JsdHQ0AODcuXNIS0vjVTxEREQvKKPvQZk3bx6OHDmCtLQ0jBs3DtbW1pgxYwZCQ0MRGRmJgQMHwtzcHAMGDICZmZmxyyMiIiIVMHpA6dGjB3r06FFouLOzM8LDw41dDhEREamQag7xEBERERVgQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1WFAISIiItVhQCEiIiLVYUAhIiIi1TEv7QLu169fP1hYWMDCwgIA8O6776JFixalXBUREREZm6oCCgAMHjwYlSpVKu0yiIiIqBTxEA8RERGpjur2oMyYMQN6vR4+Pj7o3LkzHB0dDZ6PiopCVFQUAMDa2hrTpk2Du7t7aZT61Dw8PEq7hBJhCn2YQg8A+1ATU+gBMI0+TKEH4MXtQ1UBZezYsXB1dYVOp8PKlSsxc+ZMfP755wbjBAUFISgoyGBYcnIyRMSYpT6TpKSk0i6hRJhCH6bQA8A+1MQUegBMow9T6AEwzT40Gg08PT0fOb6qDvG4uroCAMzNzfHmm28iPj6+lCsiIiKi0qCagJKdnY1//vlHebx3715UrVq1FCsiIiKi0qKaQzzp6en45ptvoNfrISLw8PBAv379SrssIiIiKgWqCSgeHh746quvSrsMIiIiUgHVHOIhIiIiKsCAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKrDgEJERESqw4BCREREqsOAQkRERKpjXtoF3O/69euYOXMmMjMzYWtri379+sHLy6u0yyIiIiIjU9UelDlz5qBNmzb4/vvv0aFDB8yaNau0SyIiIqJSoJqAkp6ejosXL6Jly5YAgObNmyM5ORnJycmlXBkREREZm2oO8dy8eRNlypSBmZkZAECj0cDV1RWpqalwd3d/5Gs1Go3BY3tz1bT1UA/WWxT28fyZQg/Ai9WHKfQAsA9jMIUeANPs40l60oiIPM+CntSFCxcQGRmJb7/9Vhn2+eef48MPP0Tt2rWVYVFRUYiKigIAODk5YcqUKUavlYiIiJ4v1RzicXFxwc2bN5GXlwcAEBGkpqbC1dXVYLygoCDMnj0bs2fPNlo4+d///meU+TxvptCHKfQAsA81MYUeANPowxR6ANhHSVFNQHFyckLVqlWxe/duAMDBgwfh7u7+2MM7xpCdnV3aJZQIU+jDFHoA2IeamEIPgGn0YQo9AOyjpKjqgNUnn3yCmTNnYsOGDbCxsUG/fv1KuyQiIiIqBaoKKOXLl8eECRNKu4xCgoKCSruEEmEKfZhCDwD7UBNT6AEwjT5MoQeAfZQU1ZwkS0RERFRANeegEBERERVgQCEiIiLVUVVACQkJeeazhuPi4jBixIjHjvfHH39g0KBBGDZsGC5cuKAMX7VqFfbs2aM8zsnJwVdffYVPP/0UQ4cOxYQJEwzubpueno4JEyZg4MCBGDJkCE6dOqU8FxERgaNHjz5TPw9TEsvpRbF69WosXry42M8ZU0nU8azTeNw6lZycjI8//vipp69258+fx/Tp00u7jIc6fPgwBg0ahKFDh+LKlSulXc5z87Tby379+hl9uah5fQHytwc6ne6Jx//111+V+4sV5dChQzh37tyzllYsqgooxrRu3TpMmjQJH330ETZs2AAAuHr1Ki5fvoxXX33VYNw2bdpg2rRpmDp1Kpo0aYI5c+Yozy1btgw+Pj6YPn06+vTpg+nTpyv3clGz51Xjv6H35+VF7l3tHvfeVK9eHQMHDjRSNcWzfft2hISEYOrUqahUqdIzT0+v15dAVS+2R60vatgOrF27tlgB5fXXX3/sCbGHDx9+6oDytOucqq7iAYBNmzYhNjYWGRkZCA4OVsLC33//jRUrViAvLw92dnbo2bOn8kvHK1euxN69e1G2bFlUr15dmdakSZPQsmVLg2msXr0aEydOhFarRW5uLu7duwdzc3Po9XosWrQIvXr1MqjH0tISjRs3Vh77+Phg8+bNyuP9+/dj5syZAABvb284OTnh1KlTqFOnDgAgNjYWv/zyC27duoWXXnoJXbp0gUajQVRUFPbu3Yu8vDyYmZmhe/fu8PHxAQAcOHAAK1euhKWlJZo3b45Vq1Zh8eLF0Gq1yrwGDhyIe/fuwd7eHu+//z4CAwMBAEuWLMHJkyeh0+lga2uL3r17w9PTE8nJyfj888/Rvn17HD9+HM2aNcORI0dQvXp1nDt3DsnJyWjfvj1cXFywdetW3Lp1C6GhoXjllVeUXqdPn47ExETodDq4urqiT58+SEhIwMKFC1GrVi2cP38eQUFBWLp0Kfz9/REbG4ubN2+iU6dOuHPnDnbv3o07d+6gd+/eqFOnDvLy8jB58mRkZmYiJycHVapUQa9evWBlZQWdTof58+fj5MmTcHR0ROXKlZGeno4hQ4Yo68n+/fuh1+vh7OyMnj17wtXVFVlZWZg1axYSEhLg6uoKBwcHODs7A8Ajn8vOzsaCBQtw/vx5AICfnx+cnZ1x8eJFJCYmws3NDTt37oSzszOaNm0KR0dHmJubIyYmBq+99hpiY2NRpkwZ9O3bF6tWrcKJEyeg0+lQoUIF9OzZE3Z2dsjKysLixYtx9uxZaLVaVK1aFX379jVY3xISEvDdd9+hS5cuaNSo0VP1+SQOHjyIFStWwM7ODo0aNVKGnzt3DsuWLcPdu3chInj33Xfh5+dX6PUhISFYvHgxrK2tAQAff/wxJk2aBHd3d8THx2PevHnQaDSoXbs2jhw5ghEjRqBSpUq4fv06Fi5ciIyMDOh0OrRp0wZvvPHGE9ddHCEhIfjwww9x9OhRVK9eHRUrVsTRo0eVdejo0aP45ZdfEBERgbi4OCxZsgSTJ09WPiuvv/46/vzzT2RlZSEsLMxgO2AsCxYsQHx8PBITE7F582Z89NFHD31/HvVZ2rFjB/bu3QsnJyckJCSge/fuqFGjRonU97D1JTExEWfPnsWwYcOg0+kQHh6ODh06oEWLFoiIiECVKlVw6dKlQtvE+6WlpWHu3Lm4ceMGAKB9+/Zo06YNACjrl6WlJby9vXH/dR7PY/3KycnBzJkzceXKFZibm8PJyQnvvvtuofXl/m3rq6++igULFiAlJQW5ubl46aWX0LFjRwD5e3xatWqFY8eO4fbt22jdujXee++9Z6rxfgV/QIeHh+PevXtITk7GsmXLYG5ujuHDh6NChQoYOHAgkpKSMH78eMyYMQOrV69GdnY2unbtijNnzmD+/PnQ6/XQ6/V444034OrqiiNHjiA2Nha///472rVrh9deew27du1CdHQ08vLyYG1tjY8//hiVKlUquXVOVCQ4OFhWr14tIiI3btyQ7t27S0pKiqSlpUn37t3l8uXLIiKya9cuGTx4sIiIHD58WIYMGSJ3796VvLw8mTx5sgwfPlxERI4dOybh4eHK9CdNmiQ7d+4UEZEDBw7IsGHDZPTo0ZKQkCBbtmyRzZs3P7bGGTNmyE8//SQiIhkZGRIaGmrw/DfffCM7duwQEZExY8bIuHHjJDc3V7Kzs2X48OGyd+9eERFJT09XXnP69Gmln7S0NAkLC5PExEQREYmKipLg4GC5e/euHDx4UMaNGyfBwcGyefNmyczMlKtXr8qHH34oOp2u0HT37NkjkyZNEhGRpKQkCQ4Olt27dyvPjxkzRr799lvJy8uTmzdvSufOnWXFihUiInL27Fn55JNPDHq7f9obNmyQefPmyYkTJyQkJETi4+OV5/r27SuLFy9WphMaGirR0dEiIrJ3714ZNWqUiIjo9XrJyMhQ/j9nzhzZuHGjiIhs2bJFxo8fLzqdTu7duycjR46Ur7/+WkREdu/eLbNnz5a8vDwREdm5c6dMmTJFREQWLVokM2fOVOrt3bu3LFq06LHPLVmyRL7//nvJy8uTu3fvytChQ2XLli3Sr18/GTNmjAwZMkRGjhwpixYtkr59+8rgwYPl9OnT0rdvX/nxxx9Fr9eLiMi6detk7dq1yrJYs2aNLFiwQEREZs6cKfPnz1fqLlieq1atkkWLFklsbKwMGjRILl68+Ex9Pk7BOnbt2jUREfn5558lODhYUlNTZdiwYXLr1i1lun369JHbt29LUlKSdO/eXZlGwTpZoHv37pKUlCQ5OTnSq1cvOXnypIiIHDx4UIKDg+Xy5cuSl5cnI0aMkISEBBERyc7OliFDhsj58+efqO7iCg4OlnXr1imPY2JilHVIROTIkSMyZswYERE5ceKEst0o+KwcOnRIRET++usvGThw4HOp8UmMGTNGjhw5Infu3Cny/XnUZykmJka6dOmibFNKSlH13Lp1SyZMmCAbN26UuXPnyo8//mjQS1HbxII+RUS+/fZbWbZsmYjkr6+9e/eWs2fPSk5OjnzyySdy4sQJEcnfnjzv9atgu1sgMzPzoevL/dvW8ePHS1xcnIiI6HQ6GT9+vBw8eFBE8rePBd8h6enp0rVrV7l58+Yz1fig+z+fX3zxhcTFxUlGRoZ89tlnyrr866+/Ku9NwTZIRGTKlCkGvWRmZoqISGRkpGzdulUZHh8fLxMnTpScnBwRETl58qR89tlnIlJy65zq9qC89tprAAAPDw/UrFkTp06dgrW1NapUqaLs3mzZsiXmz5+P27dvIy4uDi1atFD+kgsMDMT69esBAPXr18fChQtx6dIl2Nra4sKFC8pfT82bN0fz5s0BAKmpqThy5AhGjRqF5cuXIykpCeXKlUOnTp0Malu/fj2uX7+O0aNHK8Me94NHAQEBMDc3h7m5OVq2bInY2Fi0aNECFy9exPr163Hnzh1otVokJCRAp9Ph7NmzqFq1Kjw9PZV+Fi1aBACoXLkyrl27BgCwsrKCmZkZvLy8YGZmhrS0NLi4uOD48ePYunUrsrOzodfrcffuXaUWCwsLgz0iAPDyyy9Dq9WibNmycHR0xEsvvQQAqFatGm7fvo2cnBxYWloCAPbs2YNdu3YhNzcXOTk5cHZ2hp+fHzw9PeHr62sw3RYtWijTycnJwcsvv6w8TkpKApD/cwabN2/Gn3/+iby8PGRlZaFWrVoA8s8l8vf3h5mZGczMzPDKK68gPj4eQP6x0AsXLmD48OEA8ncfarVa5XVhYWEAAEdHR+U9ftxzsbGxCAsLg1arhbW1Nfz9/XH16lUA+X9BmZmZITQ0FEuWLEHFihURFxen7K1r1aqVsh4cPnwYd+/exYEDBwAAOp0OHh4eAPL/Yp8yZYpSq6Ojo8H8//rrL4waNUr5eYen7fNxCtax8uXLA8g/hLls2TJcvHgRSUlJmDhxojKuiCAxMbHQT04UJTExEZaWlsr72KxZM9jZ2SnPXb16FdOmTVPGv3v3LhISElCtWrUnrr84Wrdu/VSvs7KyUj4LNWrUUNbZ0nT69Oki3x9HR8ciP0sA4Ovrq2xTnnc9169fx4ABAzBs2DDY29sXurdVUdvE+8XGxqJr164A8u8y3qxZM8TGxsLCwgJWVlbKHuoWLVooewye1/pVsN2dN28eateubbDHscD929bs7GycOHECaWlpyvPZ2dnKthvI/w4D8j+7Hh4eSE5ORtmyZZ+6xkepV68eYmNjkZ6ejgYNGiAhIQFXrlxBbGyssl2+X506dbBu3TrcuHEDdevWLbRtL3DkyBFcvnwZI0eOVIYV7LkCSmadU11AKQ6NRmOwe+9h2rdvj23btsHW1haBgYGwsLAoNM7ChQvRrVs3xMXF4fbt2xg0aBAiIyNx4sQJ1K1bF0D+IYVDhw7hiy++gJWVFQDAwcEBQP6bUvBlk5KS8siNuUajgU6nwzfffIOIiAhUq1YNWVlZ+Oijj6DT6SAiRYYeDw8PfPfdd+jatSvOnz+PdevW4auvvoJWq0VeXh5SU1Px008/YeLEifDw8MDly5fx5ZdfKq+3trYuNO37l4dWq1UeF3wRFhw7PHXqFKKjozF+/Hg4OjriyJEjWLt2rTLdBz04nYKQo9VqlWnu2bMHJ0+exNixY2FjY4MtW7YoIeRRywEA/vvf/z70y+dR68Pj1pWHqVu3Lk6ePImcnBzUrl0bIoK0tDR4enoqv7z9YP89evRQ1psnVa5cOSQkJODcuXMG68/T9Pk4Rb1WRFC5cmWMHTu20HP3nxgOGL6PQH6IK5hGUe+biMDBwQFTp0592tKL7f735sGac3Nzi3xdwfr6sNeVpqLen127dhX5WQIe/hl9nvVcuHABIoKsrCzcu3fPYHk+qKj15cHhj9veP6/1q2C7e+LECRw/fhxLly7FRx99ZDDO/dvWghonTZoE8yJ+XfjB7e7zPG+lXr16WLp0KdLT09G8eXOULVsWx48fx8mTJ9GzZ89C47/55pto2rQpYmNjsWLFClSsWBE9evQoNJ6IIDAwUDl09aCSWOdUd5LsH3/8ASB/g3j69Gn4+vqiRo0auHTpEhISEgAAe/fuhYuLC5ydnVGvXj3s379f2WOwY8cOg+n5+/vj77//xs6dO9G2bdtC89u3bx+8vLxQqVIl3Lt3T1nJtFqtclVDwfki4eHhyl+DBfz8/BAdHQ0g/3hsWlqaQeLctWsX8vLykJOTgz179qBevXrIyclBXl4eXFxcAEB5PZB/jsuFCxeUY6/393Pz5k3l/507d4aIGAzLysqCubk5nJ2dISIG031Wd+7cgY2NDezt7aHT6bB9+/ZnnuY///wDe3t72NjY4O7du9i5c6fyXJ06dbB7925l2e3bt095rmnTpvj1119x584dAPl7KS5evAggP1AULLM7d+7g0KFDyuse9Vy9evXw+++/Q0SQnZ2N3bt3o379+qhfvz5u3ryp/CVQp04dJCQkKOc/PahJkyaIiorCvXv3AAD37t1T9sQ0bdoUmzZtUr7sMjIylNe5ubnhiy++wNq1a5Xl8LR9Pk7B5ykxMREA8PvvvwMAqlatiuvXr+PEiRPKuJcuXXroyXYeHh44e/YsgPzzWQr6rVChArKzs5Wr2Q4fPox//vkHQP6doq2srAze5xs3bij9PW/lypXD5cuXlc/f/Vfr/RvUqFGjyPfnUZ8lY9eTlZWF77//Hv3790fbtm0RGRlpECwetk18UL169fDbb78ByP+cHDp0CHXr1kWFChWQk5ODkydPAsg/Xy8rKwvA81u/CraxTZs2RdeuXZUfsi2KjY0NatWqhZ9//lkZduvWLYNt9fNmY2OjLBcfHx9cu3YNsbGxqFWrFurVq4etW7fCxcVF+SP7fomJifDw8ECbNm3w7rvvKp/z+6cJ5G/rdu3apSwLvV6vnMNXUlS3B8XCwgJffPEFMjIyEBYWpvw1OWDAAMyYMQN6vR62trYYNGgQgPyFdObMGQwdOhRly5ZF7dq1cevWLWV6Bbtqb926VWjPxp07d7Bt2zaMGjUKANCwYUP8/vvv+Oyzz+Du7o6GDRvi5s2bWLx4MTw8PJS/FCwsLJTdmqGhoYiMjMTAgQNhbm6OAQMGKH9ZA/kb/XHjxiknhPn5+UGj0SAkJAQjR46Eq6srmjRpooxfcCLkpEmT4ODggCZNmsDMzAyWlpaIj4/H8uXLAQBffPEF/P39UblyZeW1lSpVgp+fHwYPHgxXV1fUr1+/xN6XRo0aYffu3fjf//4HFxcX1KhRA8eOHXumaQYEBODIkSMYNGgQypYtC19fX+W9e/3113H58mUMHjwYLi4uqFq1qvJXur+/PzIzMxEREQGNRoO8vDy0bt0aVatWxfvvv48ffvgBgwYNgpubm8EyeNxzCxYswGeffQYgP3j6+fkhIyMDubm5qFKlCoD8w4a//PJLkQHlnXfewdq1azFy5Egl7Hbo0AEVK1ZEt27dsGjRIgwZMgTm5uaoXr06evfurby2bNmyGD16NCZOnIjs7Gy88cYbT9Xn4zg5OeGTTz7BlClT4ODgoJwEa29vj+HDh2Pp0qVYtGiRcjL00KFDC03jo48+wvz58+Hk5IQ6deooGzoLCwt8+umnmDt3LiwtLVGnTh04OTnB1tYWZmZmGD58OBYtWoRffvkFer0ejo6ORrt6pkaNGmjQoAGGDBkCd3d3VKlSxWjhqCQ86v151GfJ2PXY2Njg1VdfRd26dVGnTh3Ex8dj48aNeOeddwA8fJv4oLCwMMydOxefffYZRAT//e9/4e3tDQD49NNPlZNk69Spo2zXn9f6deXKFSxfvhwiAhEptN19mIEDByqfdSB/b0LPnj2VP0qft6CgIIwdOxaWlpYIDw+Hr68vsrOzYWlpiYoVK0Kn0z00GALA1q1bERcXB3Nzc2i1Wnz44YcA8re7P/zwA/bv36+cJNupUydMnToVer0eOp0OjRs3NrhQ5VmZ/K3u9Xo9hg8fju7duxsck1Wzu3fvwsbGBgAQExODP/74A+PGjSvlqoyvYDnk5ubiq6++gp+fn3KOEqnX/evviRMnMHPmTMycOVM53EcvroiICLz11lsGf5QRFUV1e1BK0pEjR7BgwQI0atToXxNOgPwEW3Bpqb29faFLn18U48aNQ25uLnJzc1GvXj20atWqtEuiJ3Dw4EFs3rwZer1e2aPCcEJExWXye1CIiIjo34d/1hAREZHqMKAQERGR6jCgEBERkeowoBBRsUVERGDlypWlXUaJuHHjBkJCQgrdjI6IShcDChEREakOAwoRvRAKfkqCiP4dGFCI6Jlt3boVPXr0wKVLl3D8+HGMGDECoaGh+PTTTw1+cmHQoEHYtm2bwWv/+OMP9OvXDyKCzz77DDExMcpz/fr1Q3h4uPL4559/NvjxuZ07d2LAgAHo3LkzhgwZgr/++kt5Li4uDiEhIfj7778xePBgdOnSBZmZmUhNTcWXX36J0NBQDB06FBcuXHgei4SInpFJ36iNiJ6/jRs3YvPmzRgzZgzMzMzwzTffoFu3bsrvFs2aNQuOjo5o0aIFAgICsGvXLrzxxhvK63ft2gV/f39oNBr4+voiPj4egYGBuHnzJv755x+kp6crv6p96tQp5aaLp0+fxqxZs9C1a1c0bNgQu3fvxtSpUzFt2jS4u7sr01+zZg169eoFOzs72Nra4rvvvoNOp8OECROQnp6O+fPnG32ZEdHjcQ8KET21tWvXYuvWrYiIiEDFihXx888/47XXXkPr1q3h4eGBJk2a4D//+Y/yg4T+/v44f/48rl+/DiD/17/j4+MREBAAAKhVq5byQ4Px8fGoVasWKlasiDNnzkCv1ys/IArk77Vp1qwZ/vOf/6B8+fLo2LEjqlatWmgPTWhoKGrWrAkvLy/cuHEDcXFx6N27N6pUqYIGDRogKCjIWIuLiIqBe1CI6Kns2LED9+7dw+TJk+Hh4QEAuHr1Kq5cuWLwa9d6vR5lypQBkP+DiPXq1cOuXbvQsWNH7N69Gz4+PihXrhyA/IBy48YNpKWlKQElLS0Np06dgr29Pe7du6f8aFxiYiL8/f0NavLx8VF+pblAwQ89FrzGxsYGFSpUUIYVTI+I1IUBhYieSvXq1REfH4+DBw/i7bffBgBkZ2fjzTffROvWrQ3Gvf+3eFq1aoUVK1YgJCQEu3btwptvvqk8V7ZsWbi7uyM+Ph7x8fFo1aoVbt++jW3btsHOzg7Vq1eHpaUlADzxCa9WVlbP2ioRlQIe4iGip1KxYkWMHDkS69atw6+//goAqFy5MhITE1GuXDmDf/efE/LSSy/hn3/+wS+//IKUlBS0aNHCYLq+vr44fPgwUlNTUa1aNdSqVQtnz57FiRMnDH70s0KFCjhz5ozBa8+ePWuwd+RB5cuXx927dw32spw/f/6ZlgMRPR8MKET01Ly9vTF8+HAsWbIEu3btwttvv40///wTK1euREJCAq5evYodO3YYnBdiaWmJFi1aYMWKFWjSpAns7OwMplmrVi3s27cPPj4+MDMzg4ODA1xdXXH06FHl/BMAaN++PQ4dOoTo6GgkJiZi1apVuHjxIl5//fUi6/Xy8kKtWrXw448/KlccRUVFlfyCIaJnxoBCRM+kdu3aGDJkCObOnYuUlBSEh4fj5MmTGD58OEaPHo2YmBi4ubkZvCYgIAB5eXmFziEB8gOKXq832FtSq1YtiAhq1qypDKtZsyb69OmDzZs3Y8iQITh06BCGDh1qsLfmYQYMGACtVouRI0di0aJFCAkJecYlQETPg0Z45yIiMrKDBw9i3rx5mD17NszMzEq7HCJSIZ4kS0RGk5ubi5SUFPz8888IDAxkOCGiIvEQDxEZzd69ezF48GBYWlrinXfeKe1yiEjFeIiHiIiIVId7UIiIiEh1GFCIiIhIdRhQiIiISHUYUIiIiEh1GFCIiIhIdRhQiIiISHUYUIiIiEh1/g8s2LTOxZL1fAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "keyword\n",
      "no_data              42\n",
      "outbreak             39\n",
      "wreckage             39\n",
      "derailment           39\n",
      "debris               37\n",
      "typhoon              37\n",
      "oil%20spill          37\n",
      "evacuated            32\n",
      "suicide%20bombing    32\n",
      "rescuers             32\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kw_nd = df_train[df_train.target==0].keyword.value_counts().head(10)\n",
    "print(kw_nd)\n",
    "\n",
    "sns.barplot(kw_nd, color='c')\n",
    "plt.title('Top keywords for disaster tweets')\n",
    "plt.show()\n",
    "\n",
    "kw_d = df_train[df_train.target==1].keyword.value_counts().head(10)\n",
    "print(kw_d)\n",
    "\n",
    "sns.barplot(kw_d, color='y')\n",
    "plt.title('Top keywords for non-disaster tweets')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Characters Distribution in Tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='char_count', ylabel='Density'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['char_count'] = df_train['text'].apply(lambda x: len(str(x)))\n",
    "sns.kdeplot(df_train['char_count'], fill=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='char_count', ylabel='Density'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(df_train[df_train['target'] == 1]['char_count'], fill=True, color='red')\n",
    "sns.kdeplot(df_train[df_train['target'] == 0]['char_count'], fill=True, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of Characters Distribution in Tweets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='words_count', ylabel='Density'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['words_count'] = df_train['text'].apply(lambda x: len(str(x).split()))\n",
    "sns.kdeplot(df_train['words_count'], fill=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='words_count', ylabel='Density'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.kdeplot(df_train[df_train['target'] == 1]['words_count'], fill=True, color='red')\n",
    "sns.kdeplot(df_train[df_train['target'] == 0]['words_count'], fill=True, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unique words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='unique_word_count', ylabel='Density'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['unique_word_count'] = df_train['text'].apply(lambda x: len(set(str(x).split())))\n",
    "sns.kdeplot(df_train[df_train['target'] == 1]['unique_word_count'], fill=True, color='red')\n",
    "sns.kdeplot(df_train[df_train['target'] == 0]['unique_word_count'], fill=True, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stop words\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='stop_word_count', ylabel='Density'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['stop_word_count'] = df_train['text'].apply(lambda x: len(set(str(x).split())))\n",
    "sns.kdeplot(df_train[df_train['target'] == 1]['stop_word_count'], fill=True, color='red')\n",
    "sns.kdeplot(df_train[df_train['target'] == 0]['stop_word_count'], fill=True, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hashtag count\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='hashtag_count', ylabel='Density'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_train['hashtag_count'] = df_train['text'].apply(lambda x: len([hashtag for hashtag in str(x) if hashtag == '#']))\n",
    "sns.kdeplot(df_train[df_train['target'] == 1]['hashtag_count'], fill=True, color='red')\n",
    "sns.kdeplot(df_train[df_train['target'] == 0]['hashtag_count'], fill=True, color='green')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing and Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['text'] = df_train['text'].apply(lambda x: str(x).lower())\n",
    "df_test['text'] = df_train['text'].apply(lambda x: str(x).lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       our deeds are the reason of this #earthquake m...\n",
       "1                  forest fire near la ronge sask. canada\n",
       "2       all residents asked to 'shelter in place' are ...\n",
       "3       13,000 people receive #wildfires evacuation or...\n",
       "4       just got sent this photo from ruby #alaska as ...\n",
       "                              ...                        \n",
       "7608    two giant cranes holding a bridge collapse int...\n",
       "7609    @aria_ahrary @thetawniest the out of control w...\n",
       "7610    m1.94 [01:04 utc]?5km s of volcano hawaii. htt...\n",
       "7611    police investigating after an e-bike collided ...\n",
       "7612    the latest: more homes razed by northern calif...\n",
       "Name: text, Length: 7613, dtype: object"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       our deeds are the reason of this #earthquake m...\n",
       "1                  forest fire near la ronge sask. canada\n",
       "2       all residents asked to 'shelter in place' are ...\n",
       "3       13,000 people receive #wildfires evacuation or...\n",
       "4       just got sent this photo from ruby #alaska as ...\n",
       "                              ...                        \n",
       "3258    do you feel engulfed with low self-image? take...\n",
       "3259    man is equally incapable of seeing the nothing...\n",
       "3260    fully engulfed garage fire: propane tanks insi...\n",
       "3261    he came to a land which was engulfed in tribal...\n",
       "3262    @suelinflower there is no words to describe th...\n",
       "Name: text, Length: 3263, dtype: object"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test['text']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load GloVe Vector "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "glove_vectors = dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "file = open('glove/glove.6B.100d.txt', encoding='utf-8')\n",
    "\n",
    "for line in file:\n",
    "    values = line.split()\n",
    "    \n",
    "    word  = values[0]\n",
    "    vectors = np.asarray(values[1:])\n",
    "    glove_vectors[word] = vectors\n",
    "    \n",
    "file.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# glove_vectors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text to GloVe Vectors "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "vec_shape = 100\n",
    "\n",
    "def get_vec(x):\n",
    "    arr = np.zeros(vec_shape)\n",
    "    text = str(x).split()\n",
    "    \n",
    "    for t in text:\n",
    "        try:\n",
    "            vec = glove_vectors.get(t).astype(float)\n",
    "            arr = arr + vec\n",
    "        except:\n",
    "            pass\n",
    "        \n",
    "    arr = arr.reshape(1, -1)[0]\n",
    "    return arr/len(text)\n",
    "\n",
    "x = 'gafa'\n",
    "\n",
    "def build_vocab(X):\n",
    "    tweets = X.apply(lambda s: s.split()).values      \n",
    "    vocab = {}\n",
    "    \n",
    "    for tweet in tweets:\n",
    "        for word in tweet:\n",
    "            try:\n",
    "                vocab[word] += 1\n",
    "            except KeyError:\n",
    "                vocab[word] = 1                \n",
    "    return vocab\n",
    "\n",
    "\n",
    "def check_embeddings_coverage(X, embeddings):\n",
    "    \n",
    "    vocab = build_vocab(X)    \n",
    "    \n",
    "    covered = {}\n",
    "    oov = {}    \n",
    "    n_covered = 0\n",
    "    n_oov = 0\n",
    "    \n",
    "    for word in vocab:\n",
    "        try:\n",
    "            covered[word] = embeddings[word]\n",
    "            n_covered += vocab[word]\n",
    "        except:\n",
    "            oov[word] = vocab[word]\n",
    "            n_oov += vocab[word]\n",
    "            \n",
    "    vocab_coverage = len(covered) / len(vocab)\n",
    "    text_coverage = (n_covered / (n_covered + n_oov))\n",
    "    \n",
    "    # sorted_oov = sorted(oov.items(), key=operator.itemgetter(1))[::-1]\n",
    "    return vocab_coverage, text_coverage\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GloVe Embeddings cover 39.80% of vocabulary and 78.64% of text in Training Set\n",
      "GloVe Embeddings cover 46.94% of vocabulary and 78.89% of text in Test Set\n"
     ]
    }
   ],
   "source": [
    "train_glove_vocab_coverage, train_glove_text_coverage = check_embeddings_coverage(df_train['text'], glove_vectors)\n",
    "test_glove_vocab_coverage, test_glove_text_coverage = check_embeddings_coverage(df_test['text'], glove_vectors)\n",
    "print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_glove_vocab_coverage, train_glove_text_coverage))\n",
    "print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_glove_vocab_coverage, test_glove_text_coverage))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Improve Text to GloVe Vectors After Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Reference: https://github.com/MahalavanyaSriram/Natural-Language-Processing-with-Disaster-Tweets/blob/master/Jupyter%20Notebooks/disaster-tweets-eda-topic-modeling-lstm-bert.ipynb\n",
    "def clean(tweet): \n",
    "            \n",
    "    # Special characters\n",
    "    tweet = re.sub(r\"\\x89Û_\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÒ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÓ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÏWhen\", \"When\", tweet)\n",
    "    tweet = re.sub(r\"\\x89ÛÏ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"China\\x89Ûªs\", \"China's\", tweet)\n",
    "    tweet = re.sub(r\"let\\x89Ûªs\", \"let's\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û÷\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Ûª\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û\\x9d\", \"\", tweet)\n",
    "    tweet = re.sub(r\"å_\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û¢\", \"\", tweet)\n",
    "    tweet = re.sub(r\"\\x89Û¢åÊ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"fromåÊwounds\", \"from wounds\", tweet)\n",
    "    tweet = re.sub(r\"åÊ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"åÈ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"JapÌ_n\", \"Japan\", tweet)    \n",
    "    tweet = re.sub(r\"Ì©\", \"e\", tweet)\n",
    "    tweet = re.sub(r\"å¨\", \"\", tweet)\n",
    "    tweet = re.sub(r\"SuruÌ¤\", \"Suruc\", tweet)\n",
    "    tweet = re.sub(r\"åÇ\", \"\", tweet)\n",
    "    tweet = re.sub(r\"å£3million\", \"3 million\", tweet)\n",
    "    tweet = re.sub(r\"åÀ\", \"\", tweet)\n",
    "    \n",
    "    # Contractions\n",
    "    tweet = re.sub(r\"he's\", \"he is\", tweet)\n",
    "    tweet = re.sub(r\"there's\", \"there is\", tweet)\n",
    "    tweet = re.sub(r\"We're\", \"We are\", tweet)\n",
    "    tweet = re.sub(r\"That's\", \"That is\", tweet)\n",
    "    tweet = re.sub(r\"won't\", \"will not\", tweet)\n",
    "    tweet = re.sub(r\"they're\", \"they are\", tweet)\n",
    "    tweet = re.sub(r\"Can't\", \"Cannot\", tweet)\n",
    "    tweet = re.sub(r\"wasn't\", \"was not\", tweet)\n",
    "    tweet = re.sub(r\"don\\x89Ûªt\", \"do not\", tweet)\n",
    "    tweet = re.sub(r\"aren't\", \"are not\", tweet)\n",
    "    tweet = re.sub(r\"isn't\", \"is not\", tweet)\n",
    "    tweet = re.sub(r\"What's\", \"What is\", tweet)\n",
    "    tweet = re.sub(r\"haven't\", \"have not\", tweet)\n",
    "    tweet = re.sub(r\"hasn't\", \"has not\", tweet)\n",
    "    tweet = re.sub(r\"There's\", \"There is\", tweet)\n",
    "    tweet = re.sub(r\"He's\", \"He is\", tweet)\n",
    "    tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
    "    tweet = re.sub(r\"You're\", \"You are\", tweet)\n",
    "    tweet = re.sub(r\"I'M\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"shouldn't\", \"should not\", tweet)\n",
    "    tweet = re.sub(r\"wouldn't\", \"would not\", tweet)\n",
    "    tweet = re.sub(r\"i'm\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"I\\x89Ûªm\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"I'm\", \"I am\", tweet)\n",
    "    tweet = re.sub(r\"Isn't\", \"is not\", tweet)\n",
    "    tweet = re.sub(r\"Here's\", \"Here is\", tweet)\n",
    "    tweet = re.sub(r\"you've\", \"you have\", tweet)\n",
    "    tweet = re.sub(r\"you\\x89Ûªve\", \"you have\", tweet)\n",
    "    tweet = re.sub(r\"we're\", \"we are\", tweet)\n",
    "    tweet = re.sub(r\"what's\", \"what is\", tweet)\n",
    "    tweet = re.sub(r\"couldn't\", \"could not\", tweet)\n",
    "    tweet = re.sub(r\"we've\", \"we have\", tweet)\n",
    "    tweet = re.sub(r\"it\\x89Ûªs\", \"it is\", tweet)\n",
    "    tweet = re.sub(r\"doesn\\x89Ûªt\", \"does not\", tweet)\n",
    "    tweet = re.sub(r\"It\\x89Ûªs\", \"It is\", tweet)\n",
    "    tweet = re.sub(r\"Here\\x89Ûªs\", \"Here is\", tweet)\n",
    "    tweet = re.sub(r\"who's\", \"who is\", tweet)\n",
    "    tweet = re.sub(r\"I\\x89Ûªve\", \"I have\", tweet)\n",
    "    tweet = re.sub(r\"y'all\", \"you all\", tweet)\n",
    "    tweet = re.sub(r\"can\\x89Ûªt\", \"cannot\", tweet)\n",
    "    tweet = re.sub(r\"would've\", \"would have\", tweet)\n",
    "    tweet = re.sub(r\"it'll\", \"it will\", tweet)\n",
    "    tweet = re.sub(r\"we'll\", \"we will\", tweet)\n",
    "    tweet = re.sub(r\"wouldn\\x89Ûªt\", \"would not\", tweet)\n",
    "    tweet = re.sub(r\"We've\", \"We have\", tweet)\n",
    "    tweet = re.sub(r\"he'll\", \"he will\", tweet)\n",
    "    tweet = re.sub(r\"Y'all\", \"You all\", tweet)\n",
    "    tweet = re.sub(r\"Weren't\", \"Were not\", tweet)\n",
    "    tweet = re.sub(r\"Didn't\", \"Did not\", tweet)\n",
    "    tweet = re.sub(r\"they'll\", \"they will\", tweet)\n",
    "    tweet = re.sub(r\"they'd\", \"they would\", tweet)\n",
    "    tweet = re.sub(r\"DON'T\", \"DO NOT\", tweet)\n",
    "    tweet = re.sub(r\"That\\x89Ûªs\", \"That is\", tweet)\n",
    "    tweet = re.sub(r\"they've\", \"they have\", tweet)\n",
    "    tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"should've\", \"should have\", tweet)\n",
    "    tweet = re.sub(r\"You\\x89Ûªre\", \"You are\", tweet)\n",
    "    tweet = re.sub(r\"where's\", \"where is\", tweet)\n",
    "    tweet = re.sub(r\"Don\\x89Ûªt\", \"Do not\", tweet)\n",
    "    tweet = re.sub(r\"we'd\", \"we would\", tweet)\n",
    "    tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
    "    tweet = re.sub(r\"weren't\", \"were not\", tweet)\n",
    "    tweet = re.sub(r\"They're\", \"They are\", tweet)\n",
    "    tweet = re.sub(r\"Can\\x89Ûªt\", \"Cannot\", tweet)\n",
    "    tweet = re.sub(r\"you\\x89Ûªll\", \"you will\", tweet)\n",
    "    tweet = re.sub(r\"I\\x89Ûªd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"let's\", \"let us\", tweet)\n",
    "    tweet = re.sub(r\"it's\", \"it is\", tweet)\n",
    "    tweet = re.sub(r\"can't\", \"cannot\", tweet)\n",
    "    tweet = re.sub(r\"don't\", \"do not\", tweet)\n",
    "    tweet = re.sub(r\"you're\", \"you are\", tweet)\n",
    "    tweet = re.sub(r\"i've\", \"I have\", tweet)\n",
    "    tweet = re.sub(r\"that's\", \"that is\", tweet)\n",
    "    tweet = re.sub(r\"i'll\", \"I will\", tweet)\n",
    "    tweet = re.sub(r\"doesn't\", \"does not\", tweet)\n",
    "    tweet = re.sub(r\"i'd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"didn't\", \"did not\", tweet)\n",
    "    tweet = re.sub(r\"ain't\", \"am not\", tweet)\n",
    "    tweet = re.sub(r\"you'll\", \"you will\", tweet)\n",
    "    tweet = re.sub(r\"I've\", \"I have\", tweet)\n",
    "    tweet = re.sub(r\"Don't\", \"do not\", tweet)\n",
    "    tweet = re.sub(r\"I'll\", \"I will\", tweet)\n",
    "    tweet = re.sub(r\"I'd\", \"I would\", tweet)\n",
    "    tweet = re.sub(r\"Let's\", \"Let us\", tweet)\n",
    "    tweet = re.sub(r\"you'd\", \"You would\", tweet)\n",
    "    tweet = re.sub(r\"It's\", \"It is\", tweet)\n",
    "    tweet = re.sub(r\"Ain't\", \"am not\", tweet)\n",
    "    tweet = re.sub(r\"Haven't\", \"Have not\", tweet)\n",
    "    tweet = re.sub(r\"Could've\", \"Could have\", tweet)\n",
    "    tweet = re.sub(r\"youve\", \"you have\", tweet)  \n",
    "    tweet = re.sub(r\"donå«t\", \"do not\", tweet)   \n",
    "            \n",
    "    # Character entity references\n",
    "    tweet = re.sub(r\"&gt;\", \">\", tweet)\n",
    "    tweet = re.sub(r\"&lt;\", \"<\", tweet)\n",
    "    tweet = re.sub(r\"&amp;\", \"&\", tweet)\n",
    "    \n",
    "    # Typos, slang and informal abbreviations\n",
    "    tweet = re.sub(r\"w/e\", \"whatever\", tweet)\n",
    "    tweet = re.sub(r\"w/\", \"with\", tweet)\n",
    "    tweet = re.sub(r\"USAgov\", \"USA government\", tweet)\n",
    "    tweet = re.sub(r\"recentlu\", \"recently\", tweet)\n",
    "    tweet = re.sub(r\"Ph0tos\", \"Photos\", tweet)\n",
    "    tweet = re.sub(r\"amirite\", \"am I right\", tweet)\n",
    "    tweet = re.sub(r\"exp0sed\", \"exposed\", tweet)\n",
    "    tweet = re.sub(r\"<3\", \"love\", tweet)\n",
    "    tweet = re.sub(r\"amageddon\", \"armageddon\", tweet)\n",
    "    tweet = re.sub(r\"Trfc\", \"Traffic\", tweet)\n",
    "    tweet = re.sub(r\"8/5/2015\", \"2015-08-05\", tweet)\n",
    "    tweet = re.sub(r\"WindStorm\", \"Wind Storm\", tweet)\n",
    "    tweet = re.sub(r\"8/6/2015\", \"2015-08-06\", tweet)\n",
    "    tweet = re.sub(r\"10:38PM\", \"10:38 PM\", tweet)\n",
    "    tweet = re.sub(r\"10:30pm\", \"10:30 PM\", tweet)\n",
    "    tweet = re.sub(r\"16yr\", \"16 year\", tweet)\n",
    "    tweet = re.sub(r\"lmao\", \"laughing my ass off\", tweet)   \n",
    "    tweet = re.sub(r\"TRAUMATISED\", \"traumatized\", tweet)\n",
    "    \n",
    "    # Hashtags and usernames\n",
    "    tweet = re.sub(r\"IranDeal\", \"Iran Deal\", tweet)\n",
    "    tweet = re.sub(r\"ArianaGrande\", \"Ariana Grande\", tweet)\n",
    "    tweet = re.sub(r\"camilacabello97\", \"camila cabello\", tweet) \n",
    "    tweet = re.sub(r\"RondaRousey\", \"Ronda Rousey\", tweet)     \n",
    "    tweet = re.sub(r\"MTVHottest\", \"MTV Hottest\", tweet)\n",
    "    tweet = re.sub(r\"TrapMusic\", \"Trap Music\", tweet)\n",
    "    tweet = re.sub(r\"ProphetMuhammad\", \"Prophet Muhammad\", tweet)\n",
    "    tweet = re.sub(r\"PantherAttack\", \"Panther Attack\", tweet)\n",
    "    tweet = re.sub(r\"StrategicPatience\", \"Strategic Patience\", tweet)\n",
    "    tweet = re.sub(r\"socialnews\", \"social news\", tweet)\n",
    "    tweet = re.sub(r\"NASAHurricane\", \"NASA Hurricane\", tweet)\n",
    "    tweet = re.sub(r\"onlinecommunities\", \"online communities\", tweet)\n",
    "    tweet = re.sub(r\"humanconsumption\", \"human consumption\", tweet)\n",
    "    tweet = re.sub(r\"Typhoon-Devastated\", \"Typhoon Devastated\", tweet)\n",
    "    tweet = re.sub(r\"Meat-Loving\", \"Meat Loving\", tweet)\n",
    "    tweet = re.sub(r\"facialabuse\", \"facial abuse\", tweet)\n",
    "    tweet = re.sub(r\"LakeCounty\", \"Lake County\", tweet)\n",
    "    tweet = re.sub(r\"BeingAuthor\", \"Being Author\", tweet)\n",
    "    tweet = re.sub(r\"withheavenly\", \"with heavenly\", tweet)\n",
    "    tweet = re.sub(r\"thankU\", \"thank you\", tweet)\n",
    "    tweet = re.sub(r\"iTunesMusic\", \"iTunes Music\", tweet)\n",
    "    tweet = re.sub(r\"OffensiveContent\", \"Offensive Content\", tweet)\n",
    "    tweet = re.sub(r\"WorstSummerJob\", \"Worst Summer Job\", tweet)\n",
    "    tweet = re.sub(r\"HarryBeCareful\", \"Harry Be Careful\", tweet)\n",
    "    tweet = re.sub(r\"NASASolarSystem\", \"NASA Solar System\", tweet)\n",
    "    tweet = re.sub(r\"animalrescue\", \"animal rescue\", tweet)\n",
    "    tweet = re.sub(r\"KurtSchlichter\", \"Kurt Schlichter\", tweet)\n",
    "    tweet = re.sub(r\"aRmageddon\", \"armageddon\", tweet)\n",
    "    tweet = re.sub(r\"Throwingknifes\", \"Throwing knives\", tweet)\n",
    "    tweet = re.sub(r\"GodsLove\", \"God's Love\", tweet)\n",
    "    tweet = re.sub(r\"bookboost\", \"book boost\", tweet)\n",
    "    tweet = re.sub(r\"ibooklove\", \"I book love\", tweet)\n",
    "    tweet = re.sub(r\"NestleIndia\", \"Nestle India\", tweet)\n",
    "    tweet = re.sub(r\"realDonaldTrump\", \"Donald Trump\", tweet)\n",
    "    tweet = re.sub(r\"DavidVonderhaar\", \"David Vonderhaar\", tweet)\n",
    "    tweet = re.sub(r\"CecilTheLion\", \"Cecil The Lion\", tweet)\n",
    "    tweet = re.sub(r\"weathernetwork\", \"weather network\", tweet)\n",
    "    tweet = re.sub(r\"withBioterrorism&use\", \"with Bioterrorism & use\", tweet)\n",
    "    tweet = re.sub(r\"Hostage&2\", \"Hostage & 2\", tweet)\n",
    "    tweet = re.sub(r\"GOPDebate\", \"GOP Debate\", tweet)\n",
    "    tweet = re.sub(r\"RickPerry\", \"Rick Perry\", tweet)\n",
    "    tweet = re.sub(r\"frontpage\", \"front page\", tweet)\n",
    "    tweet = re.sub(r\"NewsInTweets\", \"News In Tweets\", tweet)\n",
    "    tweet = re.sub(r\"ViralSpell\", \"Viral Spell\", tweet)\n",
    "    tweet = re.sub(r\"til_now\", \"until now\", tweet)\n",
    "    tweet = re.sub(r\"volcanoinRussia\", \"volcano in Russia\", tweet)\n",
    "    tweet = re.sub(r\"ZippedNews\", \"Zipped News\", tweet)\n",
    "    tweet = re.sub(r\"MicheleBachman\", \"Michele Bachman\", tweet)\n",
    "    tweet = re.sub(r\"53inch\", \"53 inch\", tweet)\n",
    "    tweet = re.sub(r\"KerrickTrial\", \"Kerrick Trial\", tweet)\n",
    "    tweet = re.sub(r\"abstorm\", \"Alberta Storm\", tweet)\n",
    "    tweet = re.sub(r\"Beyhive\", \"Beyonce hive\", tweet)\n",
    "    tweet = re.sub(r\"IDFire\", \"Idaho Fire\", tweet)\n",
    "    tweet = re.sub(r\"DETECTADO\", \"Detected\", tweet)\n",
    "    tweet = re.sub(r\"RockyFire\", \"Rocky Fire\", tweet)\n",
    "    tweet = re.sub(r\"Listen/Buy\", \"Listen / Buy\", tweet)\n",
    "    tweet = re.sub(r\"NickCannon\", \"Nick Cannon\", tweet)\n",
    "    tweet = re.sub(r\"FaroeIslands\", \"Faroe Islands\", tweet)\n",
    "    tweet = re.sub(r\"yycstorm\", \"Calgary Storm\", tweet)\n",
    "    tweet = re.sub(r\"IDPs:\", \"Internally Displaced People :\", tweet)\n",
    "    tweet = re.sub(r\"ArtistsUnited\", \"Artists United\", tweet)\n",
    "    tweet = re.sub(r\"ClaytonBryant\", \"Clayton Bryant\", tweet)\n",
    "    tweet = re.sub(r\"jimmyfallon\", \"jimmy fallon\", tweet)\n",
    "    tweet = re.sub(r\"justinbieber\", \"justin bieber\", tweet)  \n",
    "    tweet = re.sub(r\"UTC2015\", \"UTC 2015\", tweet)\n",
    "    tweet = re.sub(r\"Time2015\", \"Time 2015\", tweet)\n",
    "    tweet = re.sub(r\"djicemoon\", \"dj icemoon\", tweet)\n",
    "    tweet = re.sub(r\"LivingSafely\", \"Living Safely\", tweet)\n",
    "    tweet = re.sub(r\"FIFA16\", \"Fifa 2016\", tweet)\n",
    "    tweet = re.sub(r\"thisiswhywecanthavenicethings\", \"this is why we cannot have nice things\", tweet)\n",
    "    tweet = re.sub(r\"bbcnews\", \"bbc news\", tweet)\n",
    "    tweet = re.sub(r\"UndergroundRailraod\", \"Underground Railraod\", tweet)\n",
    "    tweet = re.sub(r\"c4news\", \"c4 news\", tweet)\n",
    "    tweet = re.sub(r\"OBLITERATION\", \"obliteration\", tweet)\n",
    "    tweet = re.sub(r\"MUDSLIDE\", \"mudslide\", tweet)\n",
    "    tweet = re.sub(r\"NoSurrender\", \"No Surrender\", tweet)\n",
    "    tweet = re.sub(r\"NotExplained\", \"Not Explained\", tweet)\n",
    "    tweet = re.sub(r\"greatbritishbakeoff\", \"great british bake off\", tweet)\n",
    "    tweet = re.sub(r\"LondonFire\", \"London Fire\", tweet)\n",
    "    tweet = re.sub(r\"KOTAWeather\", \"KOTA Weather\", tweet)\n",
    "    tweet = re.sub(r\"LuchaUnderground\", \"Lucha Underground\", tweet)\n",
    "    tweet = re.sub(r\"KOIN6News\", \"KOIN 6 News\", tweet)\n",
    "    tweet = re.sub(r\"LiveOnK2\", \"Live On K2\", tweet)\n",
    "    tweet = re.sub(r\"9NewsGoldCoast\", \"9 News Gold Coast\", tweet)\n",
    "    tweet = re.sub(r\"nikeplus\", \"nike plus\", tweet)\n",
    "    tweet = re.sub(r\"david_cameron\", \"David Cameron\", tweet)\n",
    "    tweet = re.sub(r\"peterjukes\", \"Peter Jukes\", tweet)\n",
    "    tweet = re.sub(r\"JamesMelville\", \"James Melville\", tweet)\n",
    "    tweet = re.sub(r\"megynkelly\", \"Megyn Kelly\", tweet)\n",
    "    tweet = re.sub(r\"cnewslive\", \"C News Live\", tweet)\n",
    "    tweet = re.sub(r\"JamaicaObserver\", \"Jamaica Observer\", tweet)\n",
    "    tweet = re.sub(r\"TweetLikeItsSeptember11th2001\", \"Tweet like it is september 11th 2001\", tweet)\n",
    "    tweet = re.sub(r\"cbplawyers\", \"cbp lawyers\", tweet)\n",
    "    tweet = re.sub(r\"fewmoretweets\", \"few more tweets\", tweet)\n",
    "    tweet = re.sub(r\"BlackLivesMatter\", \"Black Lives Matter\", tweet)\n",
    "    tweet = re.sub(r\"cjoyner\", \"Chris Joyner\", tweet)\n",
    "    tweet = re.sub(r\"ENGvAUS\", \"England vs Australia\", tweet)\n",
    "    tweet = re.sub(r\"ScottWalker\", \"Scott Walker\", tweet)\n",
    "    tweet = re.sub(r\"MikeParrActor\", \"Michael Parr\", tweet)\n",
    "    tweet = re.sub(r\"4PlayThursdays\", \"Foreplay Thursdays\", tweet)\n",
    "    tweet = re.sub(r\"TGF2015\", \"Tontitown Grape Festival\", tweet)\n",
    "    tweet = re.sub(r\"realmandyrain\", \"Mandy Rain\", tweet)\n",
    "    tweet = re.sub(r\"GraysonDolan\", \"Grayson Dolan\", tweet)\n",
    "    tweet = re.sub(r\"ApolloBrown\", \"Apollo Brown\", tweet)\n",
    "    tweet = re.sub(r\"saddlebrooke\", \"Saddlebrooke\", tweet)\n",
    "    tweet = re.sub(r\"TontitownGrape\", \"Tontitown Grape\", tweet)\n",
    "    tweet = re.sub(r\"AbbsWinston\", \"Abbs Winston\", tweet)\n",
    "    tweet = re.sub(r\"ShaunKing\", \"Shaun King\", tweet)\n",
    "    tweet = re.sub(r\"MeekMill\", \"Meek Mill\", tweet)\n",
    "    tweet = re.sub(r\"TornadoGiveaway\", \"Tornado Giveaway\", tweet)\n",
    "    tweet = re.sub(r\"GRupdates\", \"GR updates\", tweet)\n",
    "    tweet = re.sub(r\"SouthDowns\", \"South Downs\", tweet)\n",
    "    tweet = re.sub(r\"braininjury\", \"brain injury\", tweet)\n",
    "    tweet = re.sub(r\"auspol\", \"Australian politics\", tweet)\n",
    "    tweet = re.sub(r\"PlannedParenthood\", \"Planned Parenthood\", tweet)\n",
    "    tweet = re.sub(r\"calgaryweather\", \"Calgary Weather\", tweet)\n",
    "    tweet = re.sub(r\"weallheartonedirection\", \"we all heart one direction\", tweet)\n",
    "    tweet = re.sub(r\"edsheeran\", \"Ed Sheeran\", tweet)\n",
    "    tweet = re.sub(r\"TrueHeroes\", \"True Heroes\", tweet)\n",
    "    tweet = re.sub(r\"S3XLEAK\", \"sex leak\", tweet)\n",
    "    tweet = re.sub(r\"ComplexMag\", \"Complex Magazine\", tweet)\n",
    "    tweet = re.sub(r\"TheAdvocateMag\", \"The Advocate Magazine\", tweet)\n",
    "    tweet = re.sub(r\"CityofCalgary\", \"City of Calgary\", tweet)\n",
    "    tweet = re.sub(r\"EbolaOutbreak\", \"Ebola Outbreak\", tweet)\n",
    "    tweet = re.sub(r\"SummerFate\", \"Summer Fate\", tweet)\n",
    "    tweet = re.sub(r\"RAmag\", \"Royal Academy Magazine\", tweet)\n",
    "    tweet = re.sub(r\"offers2go\", \"offers to go\", tweet)\n",
    "    tweet = re.sub(r\"foodscare\", \"food scare\", tweet)\n",
    "    tweet = re.sub(r\"MNPDNashville\", \"Metropolitan Nashville Police Department\", tweet)\n",
    "    tweet = re.sub(r\"TfLBusAlerts\", \"TfL Bus Alerts\", tweet)\n",
    "    tweet = re.sub(r\"GamerGate\", \"Gamer Gate\", tweet)\n",
    "    tweet = re.sub(r\"IHHen\", \"Humanitarian Relief\", tweet)\n",
    "    tweet = re.sub(r\"spinningbot\", \"spinning bot\", tweet)\n",
    "    tweet = re.sub(r\"ModiMinistry\", \"Modi Ministry\", tweet)\n",
    "    tweet = re.sub(r\"TAXIWAYS\", \"taxi ways\", tweet)\n",
    "    tweet = re.sub(r\"Calum5SOS\", \"Calum Hood\", tweet)\n",
    "    tweet = re.sub(r\"po_st\", \"po.st\", tweet)\n",
    "    tweet = re.sub(r\"scoopit\", \"scoop.it\", tweet)\n",
    "    tweet = re.sub(r\"UltimaLucha\", \"Ultima Lucha\", tweet)\n",
    "    tweet = re.sub(r\"JonathanFerrell\", \"Jonathan Ferrell\", tweet)\n",
    "    tweet = re.sub(r\"aria_ahrary\", \"Aria Ahrary\", tweet)\n",
    "    tweet = re.sub(r\"rapidcity\", \"Rapid City\", tweet)\n",
    "    tweet = re.sub(r\"OutBid\", \"outbid\", tweet)\n",
    "    tweet = re.sub(r\"lavenderpoetrycafe\", \"lavender poetry cafe\", tweet)\n",
    "    tweet = re.sub(r\"EudryLantiqua\", \"Eudry Lantiqua\", tweet)\n",
    "    tweet = re.sub(r\"15PM\", \"15 PM\", tweet)\n",
    "    tweet = re.sub(r\"OriginalFunko\", \"Funko\", tweet)\n",
    "    tweet = re.sub(r\"rightwaystan\", \"Richard Tan\", tweet)\n",
    "    tweet = re.sub(r\"CindyNoonan\", \"Cindy Noonan\", tweet)\n",
    "    tweet = re.sub(r\"RT_America\", \"RT America\", tweet)\n",
    "    tweet = re.sub(r\"narendramodi\", \"Narendra Modi\", tweet)\n",
    "    tweet = re.sub(r\"BakeOffFriends\", \"Bake Off Friends\", tweet)\n",
    "    tweet = re.sub(r\"TeamHendrick\", \"Hendrick Motorsports\", tweet)\n",
    "    tweet = re.sub(r\"alexbelloli\", \"Alex Belloli\", tweet)\n",
    "    tweet = re.sub(r\"itsjustinstuart\", \"Justin Stuart\", tweet)\n",
    "    tweet = re.sub(r\"gunsense\", \"gun sense\", tweet)\n",
    "    tweet = re.sub(r\"DebateQuestionsWeWantToHear\", \"debate questions we want to hear\", tweet)\n",
    "    tweet = re.sub(r\"RoyalCarribean\", \"Royal Carribean\", tweet)\n",
    "    tweet = re.sub(r\"samanthaturne19\", \"Samantha Turner\", tweet)\n",
    "    tweet = re.sub(r\"JonVoyage\", \"Jon Stewart\", tweet)\n",
    "    tweet = re.sub(r\"renew911health\", \"renew 911 health\", tweet)\n",
    "    tweet = re.sub(r\"SuryaRay\", \"Surya Ray\", tweet)\n",
    "    tweet = re.sub(r\"pattonoswalt\", \"Patton Oswalt\", tweet)\n",
    "    tweet = re.sub(r\"minhazmerchant\", \"Minhaz Merchant\", tweet)\n",
    "    tweet = re.sub(r\"TLVFaces\", \"Israel Diaspora Coalition\", tweet)\n",
    "    tweet = re.sub(r\"pmarca\", \"Marc Andreessen\", tweet)\n",
    "    tweet = re.sub(r\"pdx911\", \"Portland Police\", tweet)\n",
    "    tweet = re.sub(r\"jamaicaplain\", \"Jamaica Plain\", tweet)\n",
    "    tweet = re.sub(r\"Japton\", \"Arkansas\", tweet)\n",
    "    tweet = re.sub(r\"RouteComplex\", \"Route Complex\", tweet)\n",
    "    tweet = re.sub(r\"INSubcontinent\", \"Indian Subcontinent\", tweet)\n",
    "    tweet = re.sub(r\"NJTurnpike\", \"New Jersey Turnpike\", tweet)\n",
    "    tweet = re.sub(r\"Politifiact\", \"PolitiFact\", tweet)\n",
    "    tweet = re.sub(r\"Hiroshima70\", \"Hiroshima\", tweet)\n",
    "    tweet = re.sub(r\"GMMBC\", \"Greater Mt Moriah Baptist Church\", tweet)\n",
    "    tweet = re.sub(r\"versethe\", \"verse the\", tweet)\n",
    "    tweet = re.sub(r\"TubeStrike\", \"Tube Strike\", tweet)\n",
    "    tweet = re.sub(r\"MissionHills\", \"Mission Hills\", tweet)\n",
    "    tweet = re.sub(r\"ProtectDenaliWolves\", \"Protect Denali Wolves\", tweet)\n",
    "    tweet = re.sub(r\"NANKANA\", \"Nankana\", tweet)\n",
    "    tweet = re.sub(r\"SAHIB\", \"Sahib\", tweet)\n",
    "    tweet = re.sub(r\"PAKPATTAN\", \"Pakpattan\", tweet)\n",
    "    tweet = re.sub(r\"Newz_Sacramento\", \"News Sacramento\", tweet)\n",
    "    tweet = re.sub(r\"gofundme\", \"go fund me\", tweet)\n",
    "    tweet = re.sub(r\"pmharper\", \"Stephen Harper\", tweet)\n",
    "    tweet = re.sub(r\"IvanBerroa\", \"Ivan Berroa\", tweet)\n",
    "    tweet = re.sub(r\"LosDelSonido\", \"Los Del Sonido\", tweet)\n",
    "    tweet = re.sub(r\"bancodeseries\", \"banco de series\", tweet)\n",
    "    tweet = re.sub(r\"timkaine\", \"Tim Kaine\", tweet)\n",
    "    tweet = re.sub(r\"IdentityTheft\", \"Identity Theft\", tweet)\n",
    "    tweet = re.sub(r\"AllLivesMatter\", \"All Lives Matter\", tweet)\n",
    "    tweet = re.sub(r\"mishacollins\", \"Misha Collins\", tweet)\n",
    "    tweet = re.sub(r\"BillNeelyNBC\", \"Bill Neely\", tweet)\n",
    "    tweet = re.sub(r\"BeClearOnCancer\", \"be clear on cancer\", tweet)\n",
    "    tweet = re.sub(r\"Kowing\", \"Knowing\", tweet)\n",
    "    tweet = re.sub(r\"ScreamQueens\", \"Scream Queens\", tweet)\n",
    "    tweet = re.sub(r\"AskCharley\", \"Ask Charley\", tweet)\n",
    "    tweet = re.sub(r\"BlizzHeroes\", \"Heroes of the Storm\", tweet)\n",
    "    tweet = re.sub(r\"BradleyBrad47\", \"Bradley Brad\", tweet)\n",
    "    tweet = re.sub(r\"HannaPH\", \"Typhoon Hanna\", tweet)\n",
    "    tweet = re.sub(r\"meinlcymbals\", \"MEINL Cymbals\", tweet)\n",
    "    tweet = re.sub(r\"Ptbo\", \"Peterborough\", tweet)\n",
    "    tweet = re.sub(r\"cnnbrk\", \"CNN Breaking News\", tweet)\n",
    "    tweet = re.sub(r\"IndianNews\", \"Indian News\", tweet)\n",
    "    tweet = re.sub(r\"savebees\", \"save bees\", tweet)\n",
    "    tweet = re.sub(r\"GreenHarvard\", \"Green Harvard\", tweet)\n",
    "    tweet = re.sub(r\"StandwithPP\", \"Stand with planned parenthood\", tweet)\n",
    "    tweet = re.sub(r\"hermancranston\", \"Herman Cranston\", tweet)\n",
    "    tweet = re.sub(r\"WMUR9\", \"WMUR-TV\", tweet)\n",
    "    tweet = re.sub(r\"RockBottomRadFM\", \"Rock Bottom Radio\", tweet)\n",
    "    tweet = re.sub(r\"ameenshaikh3\", \"Ameen Shaikh\", tweet)\n",
    "    tweet = re.sub(r\"ProSyn\", \"Project Syndicate\", tweet)\n",
    "    tweet = re.sub(r\"Daesh\", \"ISIS\", tweet)\n",
    "    tweet = re.sub(r\"s2g\", \"swear to god\", tweet)\n",
    "    tweet = re.sub(r\"listenlive\", \"listen live\", tweet)\n",
    "    tweet = re.sub(r\"CDCgov\", \"Centers for Disease Control and Prevention\", tweet)\n",
    "    tweet = re.sub(r\"FoxNew\", \"Fox News\", tweet)\n",
    "    tweet = re.sub(r\"CBSBigBrother\", \"Big Brother\", tweet)\n",
    "    tweet = re.sub(r\"JulieDiCaro\", \"Julie DiCaro\", tweet)\n",
    "    tweet = re.sub(r\"theadvocatemag\", \"The Advocate Magazine\", tweet)\n",
    "    tweet = re.sub(r\"RohnertParkDPS\", \"Rohnert Park Police Department\", tweet)\n",
    "    tweet = re.sub(r\"THISIZBWRIGHT\", \"Bonnie Wright\", tweet)\n",
    "    tweet = re.sub(r\"Popularmmos\", \"Popular MMOs\", tweet)\n",
    "    tweet = re.sub(r\"WildHorses\", \"Wild Horses\", tweet)\n",
    "    tweet = re.sub(r\"FantasticFour\", \"Fantastic Four\", tweet)\n",
    "    tweet = re.sub(r\"HORNDALE\", \"Horndale\", tweet)\n",
    "    tweet = re.sub(r\"PINER\", \"Piner\", tweet)\n",
    "    tweet = re.sub(r\"BathAndNorthEastSomerset\", \"Bath and North East Somerset\", tweet)\n",
    "    tweet = re.sub(r\"thatswhatfriendsarefor\", \"that is what friends are for\", tweet)\n",
    "    tweet = re.sub(r\"residualincome\", \"residual income\", tweet)\n",
    "    tweet = re.sub(r\"YahooNewsDigest\", \"Yahoo News Digest\", tweet)\n",
    "    tweet = re.sub(r\"MalaysiaAirlines\", \"Malaysia Airlines\", tweet)\n",
    "    tweet = re.sub(r\"AmazonDeals\", \"Amazon Deals\", tweet)\n",
    "    tweet = re.sub(r\"MissCharleyWebb\", \"Charley Webb\", tweet)\n",
    "    tweet = re.sub(r\"shoalstraffic\", \"shoals traffic\", tweet)\n",
    "    tweet = re.sub(r\"GeorgeFoster72\", \"George Foster\", tweet)\n",
    "    tweet = re.sub(r\"pop2015\", \"pop 2015\", tweet)\n",
    "    tweet = re.sub(r\"_PokemonCards_\", \"Pokemon Cards\", tweet)\n",
    "    tweet = re.sub(r\"DianneG\", \"Dianne Gallagher\", tweet)\n",
    "    tweet = re.sub(r\"KashmirConflict\", \"Kashmir Conflict\", tweet)\n",
    "    tweet = re.sub(r\"BritishBakeOff\", \"British Bake Off\", tweet)\n",
    "    tweet = re.sub(r\"FreeKashmir\", \"Free Kashmir\", tweet)\n",
    "    tweet = re.sub(r\"mattmosley\", \"Matt Mosley\", tweet)\n",
    "    tweet = re.sub(r\"BishopFred\", \"Bishop Fred\", tweet)\n",
    "    tweet = re.sub(r\"EndConflict\", \"End Conflict\", tweet)\n",
    "    tweet = re.sub(r\"EndOccupation\", \"End Occupation\", tweet)\n",
    "    tweet = re.sub(r\"UNHEALED\", \"unhealed\", tweet)\n",
    "    tweet = re.sub(r\"CharlesDagnall\", \"Charles Dagnall\", tweet)\n",
    "    tweet = re.sub(r\"Latestnews\", \"Latest news\", tweet)\n",
    "    tweet = re.sub(r\"KindleCountdown\", \"Kindle Countdown\", tweet)\n",
    "    tweet = re.sub(r\"NoMoreHandouts\", \"No More Handouts\", tweet)\n",
    "    tweet = re.sub(r\"datingtips\", \"dating tips\", tweet)\n",
    "    tweet = re.sub(r\"charlesadler\", \"Charles Adler\", tweet)\n",
    "    tweet = re.sub(r\"twia\", \"Texas Windstorm Insurance Association\", tweet)\n",
    "    tweet = re.sub(r\"txlege\", \"Texas Legislature\", tweet)\n",
    "    tweet = re.sub(r\"WindstormInsurer\", \"Windstorm Insurer\", tweet)\n",
    "    tweet = re.sub(r\"Newss\", \"News\", tweet)\n",
    "    tweet = re.sub(r\"hempoil\", \"hemp oil\", tweet)\n",
    "    tweet = re.sub(r\"CommoditiesAre\", \"Commodities are\", tweet)\n",
    "    tweet = re.sub(r\"tubestrike\", \"tube strike\", tweet)\n",
    "    tweet = re.sub(r\"JoeNBC\", \"Joe Scarborough\", tweet)\n",
    "    tweet = re.sub(r\"LiteraryCakes\", \"Literary Cakes\", tweet)\n",
    "    tweet = re.sub(r\"TI5\", \"The International 5\", tweet)\n",
    "    tweet = re.sub(r\"thehill\", \"the hill\", tweet)\n",
    "    tweet = re.sub(r\"3others\", \"3 others\", tweet)\n",
    "    tweet = re.sub(r\"stighefootball\", \"Sam Tighe\", tweet)\n",
    "    tweet = re.sub(r\"whatstheimportantvideo\", \"what is the important video\", tweet)\n",
    "    tweet = re.sub(r\"ClaudioMeloni\", \"Claudio Meloni\", tweet)\n",
    "    tweet = re.sub(r\"DukeSkywalker\", \"Duke Skywalker\", tweet)\n",
    "    tweet = re.sub(r\"carsonmwr\", \"Fort Carson\", tweet)\n",
    "    tweet = re.sub(r\"offdishduty\", \"off dish duty\", tweet)\n",
    "    tweet = re.sub(r\"andword\", \"and word\", tweet)\n",
    "    tweet = re.sub(r\"rhodeisland\", \"Rhode Island\", tweet)\n",
    "    tweet = re.sub(r\"easternoregon\", \"Eastern Oregon\", tweet)\n",
    "    tweet = re.sub(r\"WAwildfire\", \"Washington Wildfire\", tweet)\n",
    "    tweet = re.sub(r\"fingerrockfire\", \"Finger Rock Fire\", tweet)\n",
    "    tweet = re.sub(r\"57am\", \"57 am\", tweet)\n",
    "    tweet = re.sub(r\"fingerrockfire\", \"Finger Rock Fire\", tweet)\n",
    "    tweet = re.sub(r\"JacobHoggard\", \"Jacob Hoggard\", tweet)\n",
    "    tweet = re.sub(r\"newnewnew\", \"new new new\", tweet)\n",
    "    tweet = re.sub(r\"under50\", \"under 50\", tweet)\n",
    "    tweet = re.sub(r\"getitbeforeitsgone\", \"get it before it is gone\", tweet)\n",
    "    tweet = re.sub(r\"freshoutofthebox\", \"fresh out of the box\", tweet)\n",
    "    tweet = re.sub(r\"amwriting\", \"am writing\", tweet)\n",
    "    tweet = re.sub(r\"Bokoharm\", \"Boko Haram\", tweet)\n",
    "    tweet = re.sub(r\"Nowlike\", \"Now like\", tweet)\n",
    "    tweet = re.sub(r\"seasonfrom\", \"season from\", tweet)\n",
    "    tweet = re.sub(r\"epicente\", \"epicenter\", tweet)\n",
    "    tweet = re.sub(r\"epicenterr\", \"epicenter\", tweet)\n",
    "    tweet = re.sub(r\"sicklife\", \"sick life\", tweet)\n",
    "    tweet = re.sub(r\"yycweather\", \"Calgary Weather\", tweet)\n",
    "    tweet = re.sub(r\"calgarysun\", \"Calgary Sun\", tweet)\n",
    "    tweet = re.sub(r\"approachng\", \"approaching\", tweet)\n",
    "    tweet = re.sub(r\"evng\", \"evening\", tweet)\n",
    "    tweet = re.sub(r\"Sumthng\", \"something\", tweet)\n",
    "    tweet = re.sub(r\"EllenPompeo\", \"Ellen Pompeo\", tweet)\n",
    "    tweet = re.sub(r\"shondarhimes\", \"Shonda Rhimes\", tweet)\n",
    "    tweet = re.sub(r\"ABCNetwork\", \"ABC Network\", tweet)\n",
    "    tweet = re.sub(r\"SushmaSwaraj\", \"Sushma Swaraj\", tweet)\n",
    "    tweet = re.sub(r\"pray4japan\", \"Pray for Japan\", tweet)\n",
    "    tweet = re.sub(r\"hope4japan\", \"Hope for Japan\", tweet)\n",
    "    tweet = re.sub(r\"Illusionimagess\", \"Illusion images\", tweet)\n",
    "    tweet = re.sub(r\"SummerUnderTheStars\", \"Summer Under The Stars\", tweet)\n",
    "    tweet = re.sub(r\"ShallWeDance\", \"Shall We Dance\", tweet)\n",
    "    tweet = re.sub(r\"TCMParty\", \"TCM Party\", tweet)\n",
    "    tweet = re.sub(r\"marijuananews\", \"marijuana news\", tweet)\n",
    "    tweet = re.sub(r\"onbeingwithKristaTippett\", \"on being with Krista Tippett\", tweet)\n",
    "    tweet = re.sub(r\"Beingtweets\", \"Being tweets\", tweet)\n",
    "    tweet = re.sub(r\"newauthors\", \"new authors\", tweet)\n",
    "    tweet = re.sub(r\"remedyyyy\", \"remedy\", tweet)\n",
    "    tweet = re.sub(r\"44PM\", \"44 PM\", tweet)\n",
    "    tweet = re.sub(r\"HeadlinesApp\", \"Headlines App\", tweet)\n",
    "    tweet = re.sub(r\"40PM\", \"40 PM\", tweet)\n",
    "    tweet = re.sub(r\"myswc\", \"Severe Weather Center\", tweet)\n",
    "    tweet = re.sub(r\"ithats\", \"that is\", tweet)\n",
    "    tweet = re.sub(r\"icouldsitinthismomentforever\", \"I could sit in this moment forever\", tweet)\n",
    "    tweet = re.sub(r\"FatLoss\", \"Fat Loss\", tweet)\n",
    "    tweet = re.sub(r\"02PM\", \"02 PM\", tweet)\n",
    "    tweet = re.sub(r\"MetroFmTalk\", \"Metro Fm Talk\", tweet)\n",
    "    tweet = re.sub(r\"Bstrd\", \"bastard\", tweet)\n",
    "    tweet = re.sub(r\"bldy\", \"bloody\", tweet)\n",
    "    tweet = re.sub(r\"MetrofmTalk\", \"Metro Fm Talk\", tweet)\n",
    "    tweet = re.sub(r\"terrorismturn\", \"terrorism turn\", tweet)\n",
    "    tweet = re.sub(r\"BBCNewsAsia\", \"BBC News Asia\", tweet)\n",
    "    tweet = re.sub(r\"BehindTheScenes\", \"Behind The Scenes\", tweet)\n",
    "    tweet = re.sub(r\"GeorgeTakei\", \"George Takei\", tweet)\n",
    "    tweet = re.sub(r\"WomensWeeklyMag\", \"Womens Weekly Magazine\", tweet)\n",
    "    tweet = re.sub(r\"SurvivorsGuidetoEarth\", \"Survivors Guide to Earth\", tweet)\n",
    "    tweet = re.sub(r\"incubusband\", \"incubus band\", tweet)\n",
    "    tweet = re.sub(r\"Babypicturethis\", \"Baby picture this\", tweet)\n",
    "    tweet = re.sub(r\"BombEffects\", \"Bomb Effects\", tweet)\n",
    "    tweet = re.sub(r\"win10\", \"Windows 10\", tweet)\n",
    "    tweet = re.sub(r\"idkidk\", \"I do not know I do not know\", tweet)\n",
    "    tweet = re.sub(r\"TheWalkingDead\", \"The Walking Dead\", tweet)\n",
    "    tweet = re.sub(r\"amyschumer\", \"Amy Schumer\", tweet)\n",
    "    tweet = re.sub(r\"crewlist\", \"crew list\", tweet)\n",
    "    tweet = re.sub(r\"Erdogans\", \"Erdogan\", tweet)\n",
    "    tweet = re.sub(r\"BBCLive\", \"BBC Live\", tweet)\n",
    "    tweet = re.sub(r\"TonyAbbottMHR\", \"Tony Abbott\", tweet)\n",
    "    tweet = re.sub(r\"paulmyerscough\", \"Paul Myerscough\", tweet)\n",
    "    tweet = re.sub(r\"georgegallagher\", \"George Gallagher\", tweet)\n",
    "    tweet = re.sub(r\"JimmieJohnson\", \"Jimmie Johnson\", tweet)\n",
    "    tweet = re.sub(r\"pctool\", \"pc tool\", tweet)\n",
    "    tweet = re.sub(r\"DoingHashtagsRight\", \"Doing Hashtags Right\", tweet)\n",
    "    tweet = re.sub(r\"ThrowbackThursday\", \"Throwback Thursday\", tweet)\n",
    "    tweet = re.sub(r\"SnowBackSunday\", \"Snowback Sunday\", tweet)\n",
    "    tweet = re.sub(r\"LakeEffect\", \"Lake Effect\", tweet)\n",
    "    tweet = re.sub(r\"RTphotographyUK\", \"Richard Thomas Photography UK\", tweet)\n",
    "    tweet = re.sub(r\"BigBang_CBS\", \"Big Bang CBS\", tweet)\n",
    "    tweet = re.sub(r\"writerslife\", \"writers life\", tweet)\n",
    "    tweet = re.sub(r\"NaturalBirth\", \"Natural Birth\", tweet)\n",
    "    tweet = re.sub(r\"UnusualWords\", \"Unusual Words\", tweet)\n",
    "    tweet = re.sub(r\"wizkhalifa\", \"Wiz Khalifa\", tweet)\n",
    "    tweet = re.sub(r\"acreativedc\", \"a creative DC\", tweet)\n",
    "    tweet = re.sub(r\"vscodc\", \"vsco DC\", tweet)\n",
    "    tweet = re.sub(r\"VSCOcam\", \"vsco camera\", tweet)\n",
    "    tweet = re.sub(r\"TheBEACHDC\", \"The beach DC\", tweet)\n",
    "    tweet = re.sub(r\"buildingmuseum\", \"building museum\", tweet)\n",
    "    tweet = re.sub(r\"WorldOil\", \"World Oil\", tweet)\n",
    "    tweet = re.sub(r\"redwedding\", \"red wedding\", tweet)\n",
    "    tweet = re.sub(r\"AmazingRaceCanada\", \"Amazing Race Canada\", tweet)\n",
    "    tweet = re.sub(r\"WakeUpAmerica\", \"Wake Up America\", tweet)\n",
    "    tweet = re.sub(r\"\\\\Allahuakbar\\\\\", \"Allahu Akbar\", tweet)\n",
    "    tweet = re.sub(r\"bleased\", \"blessed\", tweet)\n",
    "    tweet = re.sub(r\"nigeriantribune\", \"Nigerian Tribune\", tweet)\n",
    "    tweet = re.sub(r\"HIDEO_KOJIMA_EN\", \"Hideo Kojima\", tweet)\n",
    "    tweet = re.sub(r\"FusionFestival\", \"Fusion Festival\", tweet)\n",
    "    tweet = re.sub(r\"50Mixed\", \"50 Mixed\", tweet)\n",
    "    tweet = re.sub(r\"NoAgenda\", \"No Agenda\", tweet)\n",
    "    tweet = re.sub(r\"WhiteGenocide\", \"White Genocide\", tweet)\n",
    "    tweet = re.sub(r\"dirtylying\", \"dirty lying\", tweet)\n",
    "    tweet = re.sub(r\"SyrianRefugees\", \"Syrian Refugees\", tweet)\n",
    "    tweet = re.sub(r\"changetheworld\", \"change the world\", tweet)\n",
    "    tweet = re.sub(r\"Ebolacase\", \"Ebola case\", tweet)\n",
    "    tweet = re.sub(r\"mcgtech\", \"mcg technologies\", tweet)\n",
    "    tweet = re.sub(r\"withweapons\", \"with weapons\", tweet)\n",
    "    tweet = re.sub(r\"advancedwarfare\", \"advanced warfare\", tweet)\n",
    "    tweet = re.sub(r\"letsFootball\", \"let us Football\", tweet)\n",
    "    tweet = re.sub(r\"LateNiteMix\", \"late night mix\", tweet)\n",
    "    tweet = re.sub(r\"PhilCollinsFeed\", \"Phil Collins\", tweet)\n",
    "    tweet = re.sub(r\"RudyHavenstein\", \"Rudy Havenstein\", tweet)\n",
    "    tweet = re.sub(r\"22PM\", \"22 PM\", tweet)\n",
    "    tweet = re.sub(r\"54am\", \"54 AM\", tweet)\n",
    "    tweet = re.sub(r\"38am\", \"38 AM\", tweet)\n",
    "    tweet = re.sub(r\"OldFolkExplainStuff\", \"Old Folk Explain Stuff\", tweet)\n",
    "    tweet = re.sub(r\"BlacklivesMatter\", \"Black Lives Matter\", tweet)\n",
    "    tweet = re.sub(r\"InsaneLimits\", \"Insane Limits\", tweet)\n",
    "    tweet = re.sub(r\"youcantsitwithus\", \"you cannot sit with us\", tweet)\n",
    "    tweet = re.sub(r\"2k15\", \"2015\", tweet)\n",
    "    tweet = re.sub(r\"TheIran\", \"Iran\", tweet)\n",
    "    tweet = re.sub(r\"JimmyFallon\", \"Jimmy Fallon\", tweet)\n",
    "    tweet = re.sub(r\"AlbertBrooks\", \"Albert Brooks\", tweet)\n",
    "    tweet = re.sub(r\"defense_news\", \"defense news\", tweet)\n",
    "    tweet = re.sub(r\"nuclearrcSA\", \"Nuclear Risk Control Self Assessment\", tweet)\n",
    "    tweet = re.sub(r\"Auspol\", \"Australia Politics\", tweet)\n",
    "    tweet = re.sub(r\"NuclearPower\", \"Nuclear Power\", tweet)\n",
    "    tweet = re.sub(r\"WhiteTerrorism\", \"White Terrorism\", tweet)\n",
    "    tweet = re.sub(r\"truthfrequencyradio\", \"Truth Frequency Radio\", tweet)\n",
    "    tweet = re.sub(r\"ErasureIsNotEquality\", \"Erasure is not equality\", tweet)\n",
    "    tweet = re.sub(r\"ProBonoNews\", \"Pro Bono News\", tweet)\n",
    "    tweet = re.sub(r\"JakartaPost\", \"Jakarta Post\", tweet)\n",
    "    tweet = re.sub(r\"toopainful\", \"too painful\", tweet)\n",
    "    tweet = re.sub(r\"melindahaunton\", \"Melinda Haunton\", tweet)\n",
    "    tweet = re.sub(r\"NoNukes\", \"No Nukes\", tweet)\n",
    "    tweet = re.sub(r\"curryspcworld\", \"Currys PC World\", tweet)\n",
    "    tweet = re.sub(r\"ineedcake\", \"I need cake\", tweet)\n",
    "    tweet = re.sub(r\"blackforestgateau\", \"black forest gateau\", tweet)\n",
    "    tweet = re.sub(r\"BBCOne\", \"BBC One\", tweet)\n",
    "    tweet = re.sub(r\"AlexxPage\", \"Alex Page\", tweet)\n",
    "    tweet = re.sub(r\"jonathanserrie\", \"Jonathan Serrie\", tweet)\n",
    "    tweet = re.sub(r\"SocialJerkBlog\", \"Social Jerk Blog\", tweet)\n",
    "    tweet = re.sub(r\"ChelseaVPeretti\", \"Chelsea Peretti\", tweet)\n",
    "    tweet = re.sub(r\"irongiant\", \"iron giant\", tweet)\n",
    "    tweet = re.sub(r\"RonFunches\", \"Ron Funches\", tweet)\n",
    "    tweet = re.sub(r\"TimCook\", \"Tim Cook\", tweet)\n",
    "    tweet = re.sub(r\"sebastianstanisaliveandwell\", \"Sebastian Stan is alive and well\", tweet)\n",
    "    tweet = re.sub(r\"Madsummer\", \"Mad summer\", tweet)\n",
    "    tweet = re.sub(r\"NowYouKnow\", \"Now you know\", tweet)\n",
    "    tweet = re.sub(r\"concertphotography\", \"concert photography\", tweet)\n",
    "    tweet = re.sub(r\"TomLandry\", \"Tom Landry\", tweet)\n",
    "    tweet = re.sub(r\"showgirldayoff\", \"show girl day off\", tweet)\n",
    "    tweet = re.sub(r\"Yougslavia\", \"Yugoslavia\", tweet)\n",
    "    tweet = re.sub(r\"QuantumDataInformatics\", \"Quantum Data Informatics\", tweet)\n",
    "    tweet = re.sub(r\"FromTheDesk\", \"From The Desk\", tweet)\n",
    "    tweet = re.sub(r\"TheaterTrial\", \"Theater Trial\", tweet)\n",
    "    tweet = re.sub(r\"CatoInstitute\", \"Cato Institute\", tweet)\n",
    "    tweet = re.sub(r\"EmekaGift\", \"Emeka Gift\", tweet)\n",
    "    tweet = re.sub(r\"LetsBe_Rational\", \"Let us be rational\", tweet)\n",
    "    tweet = re.sub(r\"Cynicalreality\", \"Cynical reality\", tweet)\n",
    "    tweet = re.sub(r\"FredOlsenCruise\", \"Fred Olsen Cruise\", tweet)\n",
    "    tweet = re.sub(r\"NotSorry\", \"not sorry\", tweet)\n",
    "    tweet = re.sub(r\"UseYourWords\", \"use your words\", tweet)\n",
    "    tweet = re.sub(r\"WordoftheDay\", \"word of the day\", tweet)\n",
    "    tweet = re.sub(r\"Dictionarycom\", \"Dictionary.com\", tweet)\n",
    "    tweet = re.sub(r\"TheBrooklynLife\", \"The Brooklyn Life\", tweet)\n",
    "    tweet = re.sub(r\"jokethey\", \"joke they\", tweet)\n",
    "    tweet = re.sub(r\"nflweek1picks\", \"NFL week 1 picks\", tweet)\n",
    "    tweet = re.sub(r\"uiseful\", \"useful\", tweet)\n",
    "    tweet = re.sub(r\"JusticeDotOrg\", \"The American Association for Justice\", tweet)\n",
    "    tweet = re.sub(r\"autoaccidents\", \"auto accidents\", tweet)\n",
    "    tweet = re.sub(r\"SteveGursten\", \"Steve Gursten\", tweet)\n",
    "    tweet = re.sub(r\"MichiganAutoLaw\", \"Michigan Auto Law\", tweet)\n",
    "    tweet = re.sub(r\"birdgang\", \"bird gang\", tweet)\n",
    "    tweet = re.sub(r\"nflnetwork\", \"NFL Network\", tweet)\n",
    "    tweet = re.sub(r\"NYDNSports\", \"NY Daily News Sports\", tweet)\n",
    "    tweet = re.sub(r\"RVacchianoNYDN\", \"Ralph Vacchiano NY Daily News\", tweet)\n",
    "    tweet = re.sub(r\"EdmontonEsks\", \"Edmonton Eskimos\", tweet)\n",
    "    tweet = re.sub(r\"david_brelsford\", \"David Brelsford\", tweet)\n",
    "    tweet = re.sub(r\"TOI_India\", \"The Times of India\", tweet)\n",
    "    tweet = re.sub(r\"hegot\", \"he got\", tweet)\n",
    "    tweet = re.sub(r\"SkinsOn9\", \"Skins on 9\", tweet)\n",
    "    tweet = re.sub(r\"sothathappened\", \"so that happened\", tweet)\n",
    "    tweet = re.sub(r\"LCOutOfDoors\", \"LC Out Of Doors\", tweet)\n",
    "    tweet = re.sub(r\"NationFirst\", \"Nation First\", tweet)\n",
    "    tweet = re.sub(r\"IndiaToday\", \"India Today\", tweet)\n",
    "    tweet = re.sub(r\"HLPS\", \"helps\", tweet)\n",
    "    tweet = re.sub(r\"HOSTAGESTHROSW\", \"hostages throw\", tweet)\n",
    "    tweet = re.sub(r\"SNCTIONS\", \"sanctions\", tweet)\n",
    "    tweet = re.sub(r\"BidTime\", \"Bid Time\", tweet)\n",
    "    tweet = re.sub(r\"crunchysensible\", \"crunchy sensible\", tweet)\n",
    "    tweet = re.sub(r\"RandomActsOfRomance\", \"Random acts of romance\", tweet)\n",
    "    tweet = re.sub(r\"MomentsAtHill\", \"Moments at hill\", tweet)\n",
    "    tweet = re.sub(r\"eatshit\", \"eat shit\", tweet)\n",
    "    tweet = re.sub(r\"liveleakfun\", \"live leak fun\", tweet)\n",
    "    tweet = re.sub(r\"SahelNews\", \"Sahel News\", tweet)\n",
    "    tweet = re.sub(r\"abc7newsbayarea\", \"ABC 7 News Bay Area\", tweet)\n",
    "    tweet = re.sub(r\"facilitiesmanagement\", \"facilities management\", tweet)\n",
    "    tweet = re.sub(r\"facilitydude\", \"facility dude\", tweet)\n",
    "    tweet = re.sub(r\"CampLogistics\", \"Camp logistics\", tweet)\n",
    "    tweet = re.sub(r\"alaskapublic\", \"Alaska public\", tweet)\n",
    "    tweet = re.sub(r\"MarketResearch\", \"Market Research\", tweet)\n",
    "    tweet = re.sub(r\"AccuracyEsports\", \"Accuracy Esports\", tweet)\n",
    "    tweet = re.sub(r\"TheBodyShopAust\", \"The Body Shop Australia\", tweet)\n",
    "    tweet = re.sub(r\"yychail\", \"Calgary hail\", tweet)\n",
    "    tweet = re.sub(r\"yyctraffic\", \"Calgary traffic\", tweet)\n",
    "    tweet = re.sub(r\"eliotschool\", \"eliot school\", tweet)\n",
    "    tweet = re.sub(r\"TheBrokenCity\", \"The Broken City\", tweet)\n",
    "    tweet = re.sub(r\"OldsFireDept\", \"Olds Fire Department\", tweet)\n",
    "    tweet = re.sub(r\"RiverComplex\", \"River Complex\", tweet)\n",
    "    tweet = re.sub(r\"fieldworksmells\", \"field work smells\", tweet)\n",
    "    tweet = re.sub(r\"IranElection\", \"Iran Election\", tweet)\n",
    "    tweet = re.sub(r\"glowng\", \"glowing\", tweet)\n",
    "    tweet = re.sub(r\"kindlng\", \"kindling\", tweet)\n",
    "    tweet = re.sub(r\"riggd\", \"rigged\", tweet)\n",
    "    tweet = re.sub(r\"slownewsday\", \"slow news day\", tweet)\n",
    "    tweet = re.sub(r\"MyanmarFlood\", \"Myanmar Flood\", tweet)\n",
    "    tweet = re.sub(r\"abc7chicago\", \"ABC 7 Chicago\", tweet)\n",
    "    tweet = re.sub(r\"copolitics\", \"Colorado Politics\", tweet)\n",
    "    tweet = re.sub(r\"AdilGhumro\", \"Adil Ghumro\", tweet)\n",
    "    tweet = re.sub(r\"netbots\", \"net bots\", tweet)\n",
    "    tweet = re.sub(r\"byebyeroad\", \"bye bye road\", tweet)\n",
    "    tweet = re.sub(r\"massiveflooding\", \"massive flooding\", tweet)\n",
    "    tweet = re.sub(r\"EndofUS\", \"End of United States\", tweet)\n",
    "    tweet = re.sub(r\"35PM\", \"35 PM\", tweet)\n",
    "    tweet = re.sub(r\"greektheatrela\", \"Greek Theatre Los Angeles\", tweet)\n",
    "    tweet = re.sub(r\"76mins\", \"76 minutes\", tweet)\n",
    "    tweet = re.sub(r\"publicsafetyfirst\", \"public safety first\", tweet)\n",
    "    tweet = re.sub(r\"livesmatter\", \"lives matter\", tweet)\n",
    "    tweet = re.sub(r\"myhometown\", \"my hometown\", tweet)\n",
    "    tweet = re.sub(r\"tankerfire\", \"tanker fire\", tweet)\n",
    "    tweet = re.sub(r\"MEMORIALDAY\", \"memorial day\", tweet)\n",
    "    tweet = re.sub(r\"MEMORIAL_DAY\", \"memorial day\", tweet)\n",
    "    tweet = re.sub(r\"instaxbooty\", \"instagram booty\", tweet)\n",
    "    tweet = re.sub(r\"Jerusalem_Post\", \"Jerusalem Post\", tweet)\n",
    "    tweet = re.sub(r\"WayneRooney_INA\", \"Wayne Rooney\", tweet)\n",
    "    tweet = re.sub(r\"VirtualReality\", \"Virtual Reality\", tweet)\n",
    "    tweet = re.sub(r\"OculusRift\", \"Oculus Rift\", tweet)\n",
    "    tweet = re.sub(r\"OwenJones84\", \"Owen Jones\", tweet)\n",
    "    tweet = re.sub(r\"jeremycorbyn\", \"Jeremy Corbyn\", tweet)\n",
    "    tweet = re.sub(r\"paulrogers002\", \"Paul Rogers\", tweet)\n",
    "    tweet = re.sub(r\"mortalkombatx\", \"Mortal Kombat X\", tweet)\n",
    "    tweet = re.sub(r\"mortalkombat\", \"Mortal Kombat\", tweet)\n",
    "    tweet = re.sub(r\"FilipeCoelho92\", \"Filipe Coelho\", tweet)\n",
    "    tweet = re.sub(r\"OnlyQuakeNews\", \"Only Quake News\", tweet)\n",
    "    tweet = re.sub(r\"kostumes\", \"costumes\", tweet)\n",
    "    tweet = re.sub(r\"YEEESSSS\", \"yes\", tweet)\n",
    "    tweet = re.sub(r\"ToshikazuKatayama\", \"Toshikazu Katayama\", tweet)\n",
    "    tweet = re.sub(r\"IntlDevelopment\", \"Intl Development\", tweet)\n",
    "    tweet = re.sub(r\"ExtremeWeather\", \"Extreme Weather\", tweet)\n",
    "    tweet = re.sub(r\"WereNotGruberVoters\", \"We are not gruber voters\", tweet)\n",
    "    tweet = re.sub(r\"NewsThousands\", \"News Thousands\", tweet)\n",
    "    tweet = re.sub(r\"EdmundAdamus\", \"Edmund Adamus\", tweet)\n",
    "    tweet = re.sub(r\"EyewitnessWV\", \"Eye witness WV\", tweet)\n",
    "    tweet = re.sub(r\"PhiladelphiaMuseu\", \"Philadelphia Museum\", tweet)\n",
    "    tweet = re.sub(r\"DublinComicCon\", \"Dublin Comic Con\", tweet)\n",
    "    tweet = re.sub(r\"NicholasBrendon\", \"Nicholas Brendon\", tweet)\n",
    "    tweet = re.sub(r\"Alltheway80s\", \"All the way 80s\", tweet)\n",
    "    tweet = re.sub(r\"FromTheField\", \"From the field\", tweet)\n",
    "    tweet = re.sub(r\"NorthIowa\", \"North Iowa\", tweet)\n",
    "    tweet = re.sub(r\"WillowFire\", \"Willow Fire\", tweet)\n",
    "    tweet = re.sub(r\"MadRiverComplex\", \"Mad River Complex\", tweet)\n",
    "    tweet = re.sub(r\"feelingmanly\", \"feeling manly\", tweet)\n",
    "    tweet = re.sub(r\"stillnotoverit\", \"still not over it\", tweet)\n",
    "    tweet = re.sub(r\"FortitudeValley\", \"Fortitude Valley\", tweet)\n",
    "    tweet = re.sub(r\"CoastpowerlineTramTr\", \"Coast powerline\", tweet)\n",
    "    tweet = re.sub(r\"ServicesGold\", \"Services Gold\", tweet)\n",
    "    tweet = re.sub(r\"NewsbrokenEmergency\", \"News broken emergency\", tweet)\n",
    "    tweet = re.sub(r\"Evaucation\", \"evacuation\", tweet)\n",
    "    tweet = re.sub(r\"leaveevacuateexitbe\", \"leave evacuate exit be\", tweet)\n",
    "    tweet = re.sub(r\"P_EOPLE\", \"PEOPLE\", tweet)\n",
    "    tweet = re.sub(r\"Tubestrike\", \"tube strike\", tweet)\n",
    "    tweet = re.sub(r\"CLASS_SICK\", \"CLASS SICK\", tweet)\n",
    "    tweet = re.sub(r\"localplumber\", \"local plumber\", tweet)\n",
    "    tweet = re.sub(r\"awesomejobsiri\", \"awesome job siri\", tweet)\n",
    "    tweet = re.sub(r\"PayForItHow\", \"Pay for it how\", tweet)\n",
    "    tweet = re.sub(r\"ThisIsAfrica\", \"This is Africa\", tweet)\n",
    "    tweet = re.sub(r\"crimeairnetwork\", \"crime air network\", tweet)\n",
    "    tweet = re.sub(r\"KimAcheson\", \"Kim Acheson\", tweet)\n",
    "    tweet = re.sub(r\"cityofcalgary\", \"City of Calgary\", tweet)\n",
    "    tweet = re.sub(r\"prosyndicate\", \"pro syndicate\", tweet)\n",
    "    tweet = re.sub(r\"660NEWS\", \"660 NEWS\", tweet)\n",
    "    tweet = re.sub(r\"BusInsMagazine\", \"Business Insurance Magazine\", tweet)\n",
    "    tweet = re.sub(r\"wfocus\", \"focus\", tweet)\n",
    "    tweet = re.sub(r\"ShastaDam\", \"Shasta Dam\", tweet)\n",
    "    tweet = re.sub(r\"go2MarkFranco\", \"Mark Franco\", tweet)\n",
    "    tweet = re.sub(r\"StephGHinojosa\", \"Steph Hinojosa\", tweet)\n",
    "    tweet = re.sub(r\"Nashgrier\", \"Nash Grier\", tweet)\n",
    "    tweet = re.sub(r\"NashNewVideo\", \"Nash new video\", tweet)\n",
    "    tweet = re.sub(r\"IWouldntGetElectedBecause\", \"I would not get elected because\", tweet)\n",
    "    tweet = re.sub(r\"SHGames\", \"Sledgehammer Games\", tweet)\n",
    "    tweet = re.sub(r\"bedhair\", \"bed hair\", tweet)\n",
    "    tweet = re.sub(r\"JoelHeyman\", \"Joel Heyman\", tweet)\n",
    "    tweet = re.sub(r\"viaYouTube\", \"via YouTube\", tweet)\n",
    "           \n",
    "    # Urls\n",
    "    tweet = re.sub(r\"https?:\\/\\/t.co\\/[A-Za-z0-9]+\", \"\", tweet)\n",
    "        \n",
    "    # Words with punctuations and special characters\n",
    "    punctuations = '@#!?+&*[]-%.:/();$=><|{}^' + \"'`\"\n",
    "    for p in punctuations:\n",
    "        tweet = tweet.replace(p, f' {p} ')\n",
    "        \n",
    "    # ... and ..\n",
    "    tweet = tweet.replace('...', ' ... ')\n",
    "    if '...' not in tweet:\n",
    "        tweet = tweet.replace('..', ' ... ')      \n",
    "        \n",
    "    # Acronyms\n",
    "    tweet = re.sub(r\"MH370\", \"Malaysia Airlines Flight 370\", tweet)\n",
    "    tweet = re.sub(r\"mÌ¼sica\", \"music\", tweet)\n",
    "    tweet = re.sub(r\"okwx\", \"Oklahoma City Weather\", tweet)\n",
    "    tweet = re.sub(r\"arwx\", \"Arkansas Weather\", tweet)    \n",
    "    tweet = re.sub(r\"gawx\", \"Georgia Weather\", tweet)  \n",
    "    tweet = re.sub(r\"scwx\", \"South Carolina Weather\", tweet)  \n",
    "    tweet = re.sub(r\"cawx\", \"California Weather\", tweet)\n",
    "    tweet = re.sub(r\"tnwx\", \"Tennessee Weather\", tweet)\n",
    "    tweet = re.sub(r\"azwx\", \"Arizona Weather\", tweet)  \n",
    "    tweet = re.sub(r\"alwx\", \"Alabama Weather\", tweet)\n",
    "    tweet = re.sub(r\"wordpressdotcom\", \"wordpress\", tweet)    \n",
    "    tweet = re.sub(r\"usNWSgov\", \"United States National Weather Service\", tweet)\n",
    "    tweet = re.sub(r\"Suruc\", \"Sanliurfa\", tweet)   \n",
    "    \n",
    "    # Grouping same words without embeddings\n",
    "    tweet = re.sub(r\"Bestnaijamade\", \"bestnaijamade\", tweet)\n",
    "    tweet = re.sub(r\"SOUDELOR\", \"Soudelor\", tweet)\n",
    "    \n",
    "    return tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removing special characters and numbers\n",
    "df_train['text_cleaned'] = df_train['text'].apply(lambda s : clean(s))\n",
    "df_test['text_cleaned'] = df_test['text'].apply(lambda s : clean(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GloVe Embeddings cover 71.65% of vocabulary and 95.01% of text in Training Set\n",
      "GloVe Embeddings cover 76.87% of vocabulary and 94.89% of text in Test Set\n"
     ]
    }
   ],
   "source": [
    "train_glove_vocab_coverage, train_glove_text_coverage = check_embeddings_coverage(df_train['text_cleaned'], glove_vectors)\n",
    "test_glove_vocab_coverage, test_glove_text_coverage = check_embeddings_coverage(df_test['text_cleaned'], glove_vectors)\n",
    "print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Training Set'.format(train_glove_vocab_coverage, train_glove_text_coverage))\n",
    "print('GloVe Embeddings cover {:.2%} of vocabulary and {:.2%} of text in Test Set'.format(test_glove_vocab_coverage, test_glove_text_coverage))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['vec'] = df_train['text'].apply(lambda x: get_vec(x))\n",
    "df_test['vec'] = df_train['text'].apply(lambda x: get_vec(x))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [0.022177076923076928, 0.27797830769230775, 0....\n",
       "1       [-0.09050485714285712, -0.2712157142857143, 0....\n",
       "2       [-0.017607409090909076, 0.17308627272727273, 0...\n",
       "3       [0.245866625, 0.275276625, 0.00756562500000000...\n",
       "4       [0.0046112437500000075, 0.18196333124999997, 0...\n",
       "                              ...                        \n",
       "7608    [0.07085272727272728, 0.2873204545454545, -0.0...\n",
       "7609    [-0.15719479999999997, -0.06036769999999999, 0...\n",
       "7610    [-0.034623749999999995, 0.10597499999999999, 0...\n",
       "7611    [0.055600157894736844, -0.15937647368421057, 0...\n",
       "7612    [-0.20643718461538466, 0.04325961538461539, 0....\n",
       "Name: vec, Length: 7613, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['vec']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "      <th>location_str</th>\n",
       "      <th>char_count</th>\n",
       "      <th>words_count</th>\n",
       "      <th>unique_word_count</th>\n",
       "      <th>stop_word_count</th>\n",
       "      <th>hashtag_count</th>\n",
       "      <th>text_cleaned</th>\n",
       "      <th>vec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>our deeds are the reason of this #earthquake m...</td>\n",
       "      <td>1</td>\n",
       "      <td>no data</td>\n",
       "      <td>69</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>our deeds are the reason of this  # earthquake...</td>\n",
       "      <td>[0.022177076923076928, 0.27797830769230775, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>forest fire near la ronge sask. canada</td>\n",
       "      <td>1</td>\n",
       "      <td>no data</td>\n",
       "      <td>38</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>forest fire near la ronge sask .  canada</td>\n",
       "      <td>[-0.09050485714285712, -0.2712157142857143, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>all residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "      <td>no data</td>\n",
       "      <td>133</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>0</td>\n",
       "      <td>all residents asked to  ' shelter in place '  ...</td>\n",
       "      <td>[-0.017607409090909076, 0.17308627272727273, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "      <td>no data</td>\n",
       "      <td>65</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>13,000 people receive  # wildfires evacuation ...</td>\n",
       "      <td>[0.245866625, 0.275276625, 0.00756562500000000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>no_data</td>\n",
       "      <td>no_data</td>\n",
       "      <td>just got sent this photo from ruby #alaska as ...</td>\n",
       "      <td>1</td>\n",
       "      <td>no data</td>\n",
       "      <td>88</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>just got sent this photo from ruby  # alaska a...</td>\n",
       "      <td>[0.0046112437500000075, 0.18196333124999997, 0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  keyword location                                               text  \\\n",
       "0   1  no_data  no_data  our deeds are the reason of this #earthquake m...   \n",
       "1   4  no_data  no_data             forest fire near la ronge sask. canada   \n",
       "2   5  no_data  no_data  all residents asked to 'shelter in place' are ...   \n",
       "3   6  no_data  no_data  13,000 people receive #wildfires evacuation or...   \n",
       "4   7  no_data  no_data  just got sent this photo from ruby #alaska as ...   \n",
       "\n",
       "   target location_str  char_count  words_count  unique_word_count  \\\n",
       "0       1      no data          69           13                 13   \n",
       "1       1      no data          38            7                  7   \n",
       "2       1      no data         133           22                 20   \n",
       "3       1      no data          65            8                  8   \n",
       "4       1      no data          88           16                 15   \n",
       "\n",
       "   stop_word_count  hashtag_count  \\\n",
       "0               13              1   \n",
       "1                7              0   \n",
       "2               20              0   \n",
       "3                8              1   \n",
       "4               15              2   \n",
       "\n",
       "                                        text_cleaned  \\\n",
       "0  our deeds are the reason of this  # earthquake...   \n",
       "1           forest fire near la ronge sask .  canada   \n",
       "2  all residents asked to  ' shelter in place '  ...   \n",
       "3  13,000 people receive  # wildfires evacuation ...   \n",
       "4  just got sent this photo from ruby  # alaska a...   \n",
       "\n",
       "                                                 vec  \n",
       "0  [0.022177076923076928, 0.27797830769230775, 0....  \n",
       "1  [-0.09050485714285712, -0.2712157142857143, 0....  \n",
       "2  [-0.017607409090909076, 0.17308627272727273, 0...  \n",
       "3  [0.245866625, 0.275276625, 0.00756562500000000...  \n",
       "4  [0.0046112437500000075, 0.18196333124999997, 0...  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML Model Building, Training, and Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [0.022177076923076928, 0.27797830769230775, 0....\n",
       "1       [-0.09050485714285712, -0.2712157142857143, 0....\n",
       "2       [-0.017607409090909076, 0.17308627272727273, 0...\n",
       "3       [0.245866625, 0.275276625, 0.00756562500000000...\n",
       "4       [0.0046112437500000075, 0.18196333124999997, 0...\n",
       "                              ...                        \n",
       "7608    [0.07085272727272728, 0.2873204545454545, -0.0...\n",
       "7609    [-0.15719479999999997, -0.06036769999999999, 0...\n",
       "7610    [-0.034623749999999995, 0.10597499999999999, 0...\n",
       "7611    [0.055600157894736844, -0.15937647368421057, 0...\n",
       "7612    [-0.20643718461538466, 0.04325961538461539, 0....\n",
       "Name: vec, Length: 7613, dtype: object"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train = df_train['vec']\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = np.concatenate(X_train, axis = 0).reshape(-1, vec_shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6090, 100)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec_shape = 100\n",
    "X_train = np.concatenate(X_train, axis = 0).reshape(-1, vec_shape)\n",
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3263,)\n",
      "(3263, 100)\n"
     ]
    }
   ],
   "source": [
    "X_test = df_test['vec']\n",
    "print(X_test.shape)\n",
    "X_test= np.concatenate(X_test, axis = 0).reshape(-1, vec_shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = df_train['target']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = LogisticRegression(solver = 'liblinear', multi_class='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/localdisk/jafraust/miniforge/envs/nlp_tweets/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(multi_class=&#x27;auto&#x27;, solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(multi_class=&#x27;auto&#x27;, solver=&#x27;liblinear&#x27;)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(multi_class='auto', solver='liblinear')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "output = pd.DataFrame(y_pred, columns=['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   target\n",
       "0       0\n",
       "1       1\n",
       "2       1\n",
       "3       1\n",
       "4       0"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  target\n",
       "0   0       0\n",
       "1   2       1\n",
       "2   3       1\n",
       "3   9       1\n",
       "4  11       0"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [df_test['id'], output]\n",
    "result = pd.concat(frames, axis=1, join='inner')\n",
    "result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv('submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression only with Train Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X_train # test dataframe does not have target column\n",
    "y = y_train\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0, stratify = y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.79      0.82      0.81       695\n",
      "           1       0.75      0.70      0.73       523\n",
      "\n",
      "    accuracy                           0.77      1218\n",
      "   macro avg       0.77      0.76      0.77      1218\n",
      "weighted avg       0.77      0.77      0.77      1218\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/localdisk/jafraust/miniforge/envs/nlp_tweets/lib/python3.12/site-packages/sklearn/linear_model/_logistic.py:1247: FutureWarning: 'multi_class' was deprecated in version 1.5 and will be removed in 1.7. From then on, it will always use 'multinomial'. Leave it to its default value to avoid this warning.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)\n",
    "y_pred = clf.predict(X_test)\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearSVC()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearSVC<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.svm.LinearSVC.html\">?<span>Documentation for LinearSVC</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearSVC()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearSVC()"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = LinearSVC()\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(clf, open('model_svm.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict Text Emotion with Custom Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pickle.load(open('model_svm.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = 'this is an awesome adventure'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pred(x):\n",
    "    vec = get_vec(x).reshape(-1, vec_shape)\n",
    "    \n",
    "    emotion = model.predict(vec)\n",
    "    \n",
    "    return emotion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_pred(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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