Bernoulli Method .ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bba65747",
   "metadata": {},
   "source": [
    "# Bernoulli Method "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "47b0ddd3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Important libraries loaded successfully\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import re\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "print(\"Important libraries loaded successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "f337c505",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Heard about #earthquake is different cities, s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>9</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>10861</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>10865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Storm in RI worse than last hurricane. My city...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>10868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Green Line derailment in Chicago http://t.co/U...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3261</th>\n",
       "      <td>10874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MEG issues Hazardous Weather Outlook (HWO) htt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>10875</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>#CityofCalgary has activated its Municipal Eme...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3263 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id keyword location  \\\n",
       "0         0     NaN      NaN   \n",
       "1         2     NaN      NaN   \n",
       "2         3     NaN      NaN   \n",
       "3         9     NaN      NaN   \n",
       "4        11     NaN      NaN   \n",
       "...     ...     ...      ...   \n",
       "3258  10861     NaN      NaN   \n",
       "3259  10865     NaN      NaN   \n",
       "3260  10868     NaN      NaN   \n",
       "3261  10874     NaN      NaN   \n",
       "3262  10875     NaN      NaN   \n",
       "\n",
       "                                                   text  \n",
       "0                    Just happened a terrible car crash  \n",
       "1     Heard about #earthquake is different cities, s...  \n",
       "2     there is a forest fire at spot pond, geese are...  \n",
       "3              Apocalypse lighting. #Spokane #wildfires  \n",
       "4         Typhoon Soudelor kills 28 in China and Taiwan  \n",
       "...                                                 ...  \n",
       "3258  EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...  \n",
       "3259  Storm in RI worse than last hurricane. My city...  \n",
       "3260  Green Line derailment in Chicago http://t.co/U...  \n",
       "3261  MEG issues Hazardous Weather Outlook (HWO) htt...  \n",
       "3262  #CityofCalgary has activated its Municipal Eme...  \n",
       "\n",
       "[3263 rows x 4 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = (\"Resources/test.csv\")\n",
    "test_df = pd.read_csv(file_path)\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3cc78286",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>10869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Two giant cranes holding a bridge collapse int...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>10870</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>@aria_ahrary @TheTawniest The out of control w...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>10871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>10872</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Police investigating after an e-bike collided ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>10873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>The Latest: More Homes Razed by Northern Calif...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id keyword location  \\\n",
       "0         1     NaN      NaN   \n",
       "1         4     NaN      NaN   \n",
       "2         5     NaN      NaN   \n",
       "3         6     NaN      NaN   \n",
       "4         7     NaN      NaN   \n",
       "...     ...     ...      ...   \n",
       "7608  10869     NaN      NaN   \n",
       "7609  10870     NaN      NaN   \n",
       "7610  10871     NaN      NaN   \n",
       "7611  10872     NaN      NaN   \n",
       "7612  10873     NaN      NaN   \n",
       "\n",
       "                                                   text  target  \n",
       "0     Our Deeds are the Reason of this #earthquake M...       1  \n",
       "1                Forest fire near La Ronge Sask. Canada       1  \n",
       "2     All residents asked to 'shelter in place' are ...       1  \n",
       "3     13,000 people receive #wildfires evacuation or...       1  \n",
       "4     Just got sent this photo from Ruby #Alaska as ...       1  \n",
       "...                                                 ...     ...  \n",
       "7608  Two giant cranes holding a bridge collapse int...       1  \n",
       "7609  @aria_ahrary @TheTawniest The out of control w...       1  \n",
       "7610  M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...       1  \n",
       "7611  Police investigating after an e-bike collided ...       1  \n",
       "7612  The Latest: More Homes Razed by Northern Calif...       1  \n",
       "\n",
       "[7613 rows x 5 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_path = (\"Resources/train.csv\") \n",
    "train_df = pd.read_csv(file_path)\n",
    "train_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20acfacc",
   "metadata": {},
   "source": [
    "# Total Missing Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ba9f4932",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Train Data Table\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>2533</td>\n",
       "      <td>0.332720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyword</th>\n",
       "      <td>61</td>\n",
       "      <td>0.008013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total   Percent\n",
       "location   2533  0.332720\n",
       "keyword      61  0.008013\n",
       "id            0  0.000000\n",
       "text          0  0.000000\n",
       "target        0  0.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = train_df.isnull().sum().sort_values(ascending = False)\n",
    "\n",
    "percent = (train_df.isnull().sum()/train_df.isnull().count()).sort_values(ascending = False)\n",
    "\n",
    "print(\"Missing Train Data Table\")\n",
    "missing_data = pd.concat([total, percent], axis = 1, keys=['Total', 'Percent'])\n",
    "missing_data\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "dc786239",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing Test Data Table\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>1105</td>\n",
       "      <td>0.338645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keyword</th>\n",
       "      <td>26</td>\n",
       "      <td>0.007968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>text</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Total   Percent\n",
       "location   1105  0.338645\n",
       "keyword      26  0.007968\n",
       "id            0  0.000000\n",
       "text          0  0.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = test_df.isnull().sum().sort_values(ascending = False)\n",
    "\n",
    "percent = (test_df.isnull().sum()/test_df.isnull().count()).sort_values(ascending = False)\n",
    "\n",
    "print(\"Missing Test Data Table\")\n",
    "missing_data = pd.concat([total, percent], axis = 1, keys=['Total', 'Percent'])\n",
    "missing_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4daa70c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "location, id, and keyword columns droped successfully\n"
     ]
    },
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>Two giant cranes holding a bridge collapse int...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>@aria_ahrary @TheTawniest The out of control w...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>Police investigating after an e-bike collided ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>The Latest: More Homes Razed by Northern Calif...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text  target\n",
       "0     Our Deeds are the Reason of this #earthquake M...       1\n",
       "1                Forest fire near La Ronge Sask. Canada       1\n",
       "2     All residents asked to 'shelter in place' are ...       1\n",
       "3     13,000 people receive #wildfires evacuation or...       1\n",
       "4     Just got sent this photo from Ruby #Alaska as ...       1\n",
       "...                                                 ...     ...\n",
       "7608  Two giant cranes holding a bridge collapse int...       1\n",
       "7609  @aria_ahrary @TheTawniest The out of control w...       1\n",
       "7610  M1.94 [01:04 UTC]?5km S of Volcano Hawaii. htt...       1\n",
       "7611  Police investigating after an e-bike collided ...       1\n",
       "7612  The Latest: More Homes Razed by Northern Calif...       1\n",
       "\n",
       "[7613 rows x 2 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dropped_df = train_df.drop([\"location\", \"keyword\", \"id\"], axis = 1)\n",
    "print(\"location, id, and keyword columns droped successfully\")\n",
    "train_dropped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "588931c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "location, id, and keyword columns droped successfully\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Heard about #earthquake is different cities, s...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3259</th>\n",
       "      <td>Storm in RI worse than last hurricane. My city...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>Green Line derailment in Chicago http://t.co/U...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3261</th>\n",
       "      <td>MEG issues Hazardous Weather Outlook (HWO) htt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>#CityofCalgary has activated its Municipal Eme...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3263 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                   text\n",
       "0                    Just happened a terrible car crash\n",
       "1     Heard about #earthquake is different cities, s...\n",
       "2     there is a forest fire at spot pond, geese are...\n",
       "3              Apocalypse lighting. #Spokane #wildfires\n",
       "4         Typhoon Soudelor kills 28 in China and Taiwan\n",
       "...                                                 ...\n",
       "3258  EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...\n",
       "3259  Storm in RI worse than last hurricane. My city...\n",
       "3260  Green Line derailment in Chicago http://t.co/U...\n",
       "3261  MEG issues Hazardous Weather Outlook (HWO) htt...\n",
       "3262  #CityofCalgary has activated its Municipal Eme...\n",
       "\n",
       "[3263 rows x 1 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dropped_df = test_df.drop([\"location\", \"keyword\", \"id\"], axis = 1)\n",
    "print(\"location, id, and keyword columns droped successfully\")\n",
    "test_dropped_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "03f6df03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corpus created successfully\n"
     ]
    }
   ],
   "source": [
    "def clean_tweets(df):\n",
    "    corpus  = []\n",
    "    pstem = PorterStemmer()\n",
    "    for i in range(df.shape[0]):\n",
    "        #Remove unwanted words\n",
    "        tweet = re.sub(\"[^a-zA-Z]\", ' ', df[i])\n",
    "        #Transform words to lowercase\n",
    "        tweet = tweet.lower()\n",
    "        tweet = tweet.split()\n",
    "        #Remove stopwords then Stemming it\n",
    "        tweet = [pstem.stem(word) for word in tweet if not word in set(stopwords.words('english'))]\n",
    "        tweet = ' '.join(tweet)\n",
    "        #Append cleaned tweet to corpus\n",
    "        corpus.append(tweet)\n",
    "    return corpus\n",
    "print(\"Corpus created successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e9077a6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>deed reason earthquak may allah forgiv us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>forest fire near la rong sask canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>resid ask shelter place notifi offic evacu she...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>peopl receiv wildfir evacu order california</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>got sent photo rubi alaska smoke wildfir pour ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>two giant crane hold bridg collaps nearbi home...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>aria ahrari thetawniest control wild fire cali...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>utc km volcano hawaii http co zdtoyd ebj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>polic investig e bike collid car littl portug ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>latest home raze northern california wildfir a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0\n",
       "0             deed reason earthquak may allah forgiv us\n",
       "1                  forest fire near la rong sask canada\n",
       "2     resid ask shelter place notifi offic evacu she...\n",
       "3           peopl receiv wildfir evacu order california\n",
       "4     got sent photo rubi alaska smoke wildfir pour ...\n",
       "...                                                 ...\n",
       "7608  two giant crane hold bridg collaps nearbi home...\n",
       "7609  aria ahrari thetawniest control wild fire cali...\n",
       "7610           utc km volcano hawaii http co zdtoyd ebj\n",
       "7611  polic investig e bike collid car littl portug ...\n",
       "7612  latest home raze northern california wildfir a...\n",
       "\n",
       "[7613 rows x 1 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean Training Data \n",
    "train_tweet = clean_tweets(train_dropped_df['text'])\n",
    "train_tweet = pd.DataFrame(train_tweet)\n",
    "train_tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "87e5bafe",
   "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>text</th>\n",
       "      <th>target</th>\n",
       "      <th>clean_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Our Deeds are the Reason of this #earthquake M...</td>\n",
       "      <td>1</td>\n",
       "      <td>deed reason earthquak may allah forgiv us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Forest fire near La Ronge Sask. Canada</td>\n",
       "      <td>1</td>\n",
       "      <td>forest fire near la rong sask canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>All residents asked to 'shelter in place' are ...</td>\n",
       "      <td>1</td>\n",
       "      <td>resid ask shelter place notifi offic evacu she...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13,000 people receive #wildfires evacuation or...</td>\n",
       "      <td>1</td>\n",
       "      <td>peopl receiv wildfir evacu order california</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Just got sent this photo from Ruby #Alaska as ...</td>\n",
       "      <td>1</td>\n",
       "      <td>got sent photo rubi alaska smoke wildfir pour ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  target  \\\n",
       "0  Our Deeds are the Reason of this #earthquake M...       1   \n",
       "1             Forest fire near La Ronge Sask. Canada       1   \n",
       "2  All residents asked to 'shelter in place' are ...       1   \n",
       "3  13,000 people receive #wildfires evacuation or...       1   \n",
       "4  Just got sent this photo from Ruby #Alaska as ...       1   \n",
       "\n",
       "                                         clean_tweet  \n",
       "0          deed reason earthquak may allah forgiv us  \n",
       "1               forest fire near la rong sask canada  \n",
       "2  resid ask shelter place notifi offic evacu she...  \n",
       "3        peopl receiv wildfir evacu order california  \n",
       "4  got sent photo rubi alaska smoke wildfir pour ...  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append cleaned tweets to the trained data \n",
    "train_dropped_df['clean_tweet'] = train_tweet\n",
    "\n",
    "# Compare the cleaned and uncleaned tweets \n",
    "train_dropped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9dead193",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>deed reason earthquak may allah forgiv us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>forest fire near la rong sask canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>resid ask shelter place notifi offic evacu she...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>peopl receiv wildfir evacu order california</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>got sent photo rubi alaska smoke wildfir pour ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7608</th>\n",
       "      <td>two giant crane hold bridg collaps nearbi home...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7609</th>\n",
       "      <td>aria ahrari thetawniest control wild fire cali...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>utc km volcano hawaii http co zdtoyd ebj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>polic investig e bike collid car littl portug ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>latest home raze northern california wildfir a...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7613 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      0\n",
       "0             deed reason earthquak may allah forgiv us\n",
       "1                  forest fire near la rong sask canada\n",
       "2     resid ask shelter place notifi offic evacu she...\n",
       "3           peopl receiv wildfir evacu order california\n",
       "4     got sent photo rubi alaska smoke wildfir pour ...\n",
       "...                                                 ...\n",
       "7608  two giant crane hold bridg collaps nearbi home...\n",
       "7609  aria ahrari thetawniest control wild fire cali...\n",
       "7610           utc km volcano hawaii http co zdtoyd ebj\n",
       "7611  polic investig e bike collid car littl portug ...\n",
       "7612  latest home raze northern california wildfir a...\n",
       "\n",
       "[7613 rows x 1 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean Testing Data \n",
    "test_tweet = clean_tweets(test_dropped_df['text'])\n",
    "test_tweet = pd.DataFrame(train_tweet)\n",
    "test_tweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "b880d095",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>text</th>\n",
       "      <th>clean_tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "      <td>deed reason earthquak may allah forgiv us</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Heard about #earthquake is different cities, s...</td>\n",
       "      <td>forest fire near la rong sask canada</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>there is a forest fire at spot pond, geese are...</td>\n",
       "      <td>resid ask shelter place notifi offic evacu she...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apocalypse lighting. #Spokane #wildfires</td>\n",
       "      <td>peopl receiv wildfir evacu order california</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Typhoon Soudelor kills 28 in China and Taiwan</td>\n",
       "      <td>got sent photo rubi alaska smoke wildfir pour ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  \\\n",
       "0                 Just happened a terrible car crash   \n",
       "1  Heard about #earthquake is different cities, s...   \n",
       "2  there is a forest fire at spot pond, geese are...   \n",
       "3           Apocalypse lighting. #Spokane #wildfires   \n",
       "4      Typhoon Soudelor kills 28 in China and Taiwan   \n",
       "\n",
       "                                         clean_tweet  \n",
       "0          deed reason earthquak may allah forgiv us  \n",
       "1               forest fire near la rong sask canada  \n",
       "2  resid ask shelter place notifi offic evacu she...  \n",
       "3        peopl receiv wildfir evacu order california  \n",
       "4  got sent photo rubi alaska smoke wildfir pour ...  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Append cleaned tweets to the trained data \n",
    "test_dropped_df['clean_tweet'] = test_tweet\n",
    "\n",
    "# Compare the cleaned and uncleaned tweets \n",
    "test_dropped_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "aa59bdde",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create our dictionary \n",
    "def unique_words(df):\n",
    "    uniqueWordFrequents = {}\n",
    "    for tweet in df:\n",
    "        for word in tweet.split():\n",
    "            if(word in uniqueWordFrequents.keys()):\n",
    "                uniqueWordFrequents[word] += 1\n",
    "            else:\n",
    "                uniqueWordFrequents[word] = 1\n",
    "\n",
    "    #Convert dictionary to dataFrame\n",
    "    uniqueWordFrequents = pd.DataFrame.from_dict(uniqueWordFrequents,orient='index',columns=['Word Frequent'])\n",
    "    uniqueWordFrequents.sort_values(by=['Word Frequent'], inplace=True, ascending=False)\n",
    "    uniqueWordFrequents.head(10)\n",
    "    return uniqueWordFrequents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "c737b00d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word Frequent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>co</th>\n",
       "      <td>4746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>http</th>\n",
       "      <td>4721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like</th>\n",
       "      <td>411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fire</th>\n",
       "      <td>363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amp</th>\n",
       "      <td>344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>get</th>\n",
       "      <td>311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bomb</th>\n",
       "      <td>239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new</th>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>via</th>\n",
       "      <td>220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>u</th>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Word Frequent\n",
       "co             4746\n",
       "http           4721\n",
       "like            411\n",
       "fire            363\n",
       "amp             344\n",
       "get             311\n",
       "bomb            239\n",
       "new             228\n",
       "via             220\n",
       "u               216"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique words for Training Data \n",
    "train_unique = unique_words(train_dropped_df['clean_tweet'])\n",
    "train_unique = pd.DataFrame(train_unique)\n",
    "train_unique.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "845ba42c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word Frequent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>co</th>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>http</th>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like</th>\n",
       "      <td>176</td>\n",
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       "    <tr>\n",
       "      <th>amp</th>\n",
       "      <td>172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>burn</th>\n",
       "      <td>161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>get</th>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bomb</th>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emerg</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>build</th>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fire</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Word Frequent\n",
       "co              1960\n",
       "http            1954\n",
       "like             176\n",
       "amp              172\n",
       "burn             161\n",
       "get              146\n",
       "bomb             139\n",
       "emerg            137\n",
       "build            130\n",
       "fire             120"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Unique words for Testing Data \n",
    "test_unique = unique_words(test_dropped_df['clean_tweet'])\n",
    "test_unique = pd.DataFrame(test_unique)\n",
    "test_unique.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "98407f34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4746, 4721,  411,  363,  344,  311,  239,  228,  220,  216,  213,\n",
       "        210,  209,  201,  183,  181,  180,  178,  175,  169,  166,  164,\n",
       "        162,  156,  155,  153,  151,  145,  144,  143,  137,  133,  132,\n",
       "        131,  130,  129,  128,  125,  124,  123,  122,  121,  120,  119,\n",
       "        118,  117,  116,  114,  111,  110,  109,  108,  106,  105,  104,\n",
       "        103,  102,  101,  100,   99,   98,   97,   96,   95,   94,   93,\n",
       "         91,   90,   89,   88,   87,   86,   84,   83,   82,   79,   78,\n",
       "         77,   76,   75,   74,   73,   72,   71,   70,   69,   68,   67,\n",
       "         66,   65,   64,   63,   62,   61,   60,   59,   58,   57,   56,\n",
       "         55,   54,   53,   52,   51,   50,   49,   48,   47,   46,   45,\n",
       "         44,   43,   42,   41,   40,   39,   38,   37,   36,   35,   34,\n",
       "         33,   32,   31,   30,   29,   28,   27,   26,   25,   24,   23,\n",
       "         22,   21,   20,   19,   18,   17,   16,   15,   14,   13,   12,\n",
       "         11,   10,    9,    8,    7,    6,    5,    4,    3,    2,    1])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Word frequency for training data\n",
    "train_unique[\"Word Frequent\"].unique( )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "0f620969",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1960, 1954,  176,  172,  161,  146,  139,  137,  130,  120,  119,\n",
       "        115,  112,  110,  109,  108,   99,   97,   95,   94,   93,   91,\n",
       "         89,   88,   86,   85,   84,   77,   72,   71,   70,   69,   67,\n",
       "         66,   65,   64,   63,   62,   61,   60,   59,   58,   57,   56,\n",
       "         55,   54,   53,   52,   51,   49,   48,   47,   46,   45,   44,\n",
       "         43,   42,   41,   40,   39,   38,   37,   36,   35,   34,   33,\n",
       "         32,   31,   30,   29,   28,   27,   26,   25,   24,   23,   22,\n",
       "         21,   20,   19,   18,   17,   16,   15,   14,   13,   12,   11,\n",
       "         10,    9,    8,    7,    6,    5,    4,    3,    2,    1])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Word frequency for testing data\n",
    "test_unique[\"Word Frequent\"].unique( )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ff236cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will get only words that repeated more than or equal 20 once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "3ac32409",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(787, 1)\n"
     ]
    },
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <th>cnn</th>\n",
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       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>787 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Word Frequent\n",
       "co               4746\n",
       "http             4721\n",
       "like              411\n",
       "fire              363\n",
       "amp               344\n",
       "...               ...\n",
       "cnn                20\n",
       "gem                20\n",
       "captur             20\n",
       "arriv              20\n",
       "carri              20\n",
       "\n",
       "[787 rows x 1 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unique words for training data with words that repeated more than or equal 20 once.\n",
    "train_unique = train_unique[train_unique['Word Frequent'] >= 20]\n",
    "print(train_unique.shape)\n",
    "train_unique\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8a5f991d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(287, 1)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Word Frequent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>co</th>\n",
       "      <td>1960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>http</th>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>like</th>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amp</th>\n",
       "      <td>172</td>\n",
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       "      <th>burn</th>\n",
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       "      <th>rise</th>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>confirm</th>\n",
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       "      <td>20</td>\n",
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       "      <th>river</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>287 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Word Frequent\n",
       "co                1960\n",
       "http              1954\n",
       "like               176\n",
       "amp                172\n",
       "burn               161\n",
       "...                ...\n",
       "rise                20\n",
       "confirm             20\n",
       "mishap              20\n",
       "believ              20\n",
       "river               20\n",
       "\n",
       "[287 rows x 1 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unique words for testing data with words that repeated more than or equal 20 once.\n",
    "test_unique = test_unique[test_unique['Word Frequent'] >= 20]\n",
    "print(test_unique.shape)\n",
    "test_unique\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "db16564b",
   "metadata": {},
   "outputs": [],
   "source": [
    "counVec = CountVectorizer(max_features = train_unique.shape[0])\n",
    "bagOfWords = counVec.fit_transform(train_dropped_df[\"clean_tweet\"]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "a768ba16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape =  (7613, 787)\n",
      "y shape =  (7613,)\n",
      "data splitting successfully\n"
     ]
    }
   ],
   "source": [
    "X = bagOfWords\n",
    "y = train_df['target']\n",
    "\n",
    "print(\"X shape = \",X.shape)\n",
    "print(\"y shape = \",y.shape)\n",
    "\n",
    "\n",
    "\n",
    "X_train , X_test , y_train , y_test = train_test_split(X,y,test_size=0.20, random_state=55, shuffle =True)\n",
    "print('data splitting successfully')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "136e2fa8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bernoulliNB model run successfully\n"
     ]
    }
   ],
   "source": [
    "bernoulliNBModel = BernoulliNB(alpha=0.1)\n",
    "bernoulliNBModel.fit(X_train,y_train)\n",
    "\n",
    "print(\"bernoulliNB model run successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "da5bbe8e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gaussianNB model run successfully\n"
     ]
    }
   ],
   "source": [
    "gaussianNBModel = GaussianNB()\n",
    "gaussianNBModel.fit(X_train,y_train)\n",
    "\n",
    "print(\"gaussianNB model run successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3180e8f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multinomialNB model run successfully\n"
     ]
    }
   ],
   "source": [
    "multinomialNBModel = MultinomialNB(alpha=0.1)\n",
    "multinomialNBModel.fit(X_train,y_train)\n",
    "\n",
    "print(\"multinomialNB model run successfully\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "3cbde875",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BernoulliNB  Train Score is   :  0.8091954022988506\n",
      "BernoulliNB  Test Score is    :  0.7774130006565988\n",
      "BernoulliNB  F1 Score is      :  0.7129551227773073\n",
      "--------------------------------------------------------------------------\n",
      "GaussianNB  Train Score is   :  0.7893267651888342\n",
      "GaussianNB  Test Score is    :  0.7669074195666448\n",
      "GaussianNB  F1 Score is      :  0.6728110599078342\n",
      "--------------------------------------------------------------------------\n",
      "MultinomialNB  Train Score is   :  0.8022988505747126\n",
      "MultinomialNB  Test Score is    :  0.7734734077478661\n",
      "MultinomialNB  F1 Score is      :  0.7165160230073953\n",
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "#evaluation Details\n",
    "models = [ bernoulliNBModel, gaussianNBModel, multinomialNBModel]\n",
    "\n",
    "for model in models:\n",
    "    print(type(model).__name__,' Train Score is   : ' ,model.score(X_train, y_train))\n",
    "    print(type(model).__name__,' Test Score is    : ' ,model.score(X_test, y_test))\n",
    "    \n",
    "    y_pred = model.predict(X_test)\n",
    "    print(type(model).__name__,' F1 Score is      : ' ,f1_score(y_test,y_pred))\n",
    "    print('--------------------------------------------------------------------------')\n"
   ]
  }
 ],
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