getting started with nlp.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/nlp-getting-started/train.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fatalities               45\n",
       "deluge                   42\n",
       "armageddon               42\n",
       "sinking                  41\n",
       "body%20bags              41\n",
       "                         ..\n",
       "forest%20fire            19\n",
       "epicentre                12\n",
       "threat                   11\n",
       "inundation               10\n",
       "radiation%20emergency     9\n",
       "Name: keyword, Length: 221, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keyword.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA                          104\n",
       "New York                      71\n",
       "United States                 50\n",
       "London                        45\n",
       "Canada                        29\n",
       "                            ... \n",
       "Livonia, MI                    1\n",
       "A small federal enclave        1\n",
       "amsterdayum 120615 062415      1\n",
       "Des Moines, Iowa               1\n",
       "D(M)V                          1\n",
       "Name: location, Length: 3341, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.location.replace('United States', 'USA', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3340"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    94\n",
       "0    60\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df['location']='USA'\n",
    "df.loc[df['location'] == 'USA', 'target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7613"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.id.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.57034\n",
       "1    0.42966\n",
       "Name: target, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.target.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    5080\n",
       "True     2533\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Our Deeds are the Reason of this #earthquake May ALLAH Forgive us all'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.text[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    37\n",
       "1     4\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['keyword'] == 'harm', 'target'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>keyword</th>\n",
       "      <th>location</th>\n",
       "      <th>text</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>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>NaN</td>\n",
       "      <td>NaN</td>\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>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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  keyword location                                               text  target\n",
       "0     NaN      NaN  Our Deeds are the Reason of this #earthquake M...       1\n",
       "1     NaN      NaN             Forest fire near La Ronge Sask. Canada       1\n",
       "2     NaN      NaN  All residents asked to 'shelter in place' are ...       1\n",
       "3     NaN      NaN  13,000 people receive #wildfires evacuation or...       1\n",
       "4     NaN      NaN  Just got sent this photo from Ruby #Alaska as ...       1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Birmingham', 'Est. September 2012 - Bristol', 'AFRICA', ...,\n",
       "       'Vancouver, Canada', 'London ', 'Lincoln'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.location.unique()[1:]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['location',\n",
    "                 'keyword',\n",
    "                 ], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\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>target</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",
       "    </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": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1min 38s\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "import nltk\n",
    "import re\n",
    "\n",
    "corpus = []\n",
    "ps = PorterStemmer()\n",
    "\n",
    "for i in range(7613):\n",
    "    review = re.sub('[^a-zA-Z]', ' ', df['text'][i])\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    review = [ps.stem(word)\n",
    "              for word in review if word not in stopwords.words('english')]\n",
    "    review = ' '.join(review)\n",
    "    corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'deed reason earthquak may allah forgiv us'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corpus[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "cv = CountVectorizer(max_features=2000)\n",
    "tfidf = TfidfVectorizer(max_features=3000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_bog = cv.fit_transform(corpus).toarray()\n",
    "x_tfidf = tfidf.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((7613, 2000), (7613, 3000))"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_bog.shape, x_tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sklearn.naive_bayes import GaussianNB,MultinomialNB\n",
    "# from sklearn.metrics import classification_report,accuracy_score\n",
    "\n",
    "# gb=GaussianNB()\n",
    "# mb=MultinomialNB()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gb.fit(x_bog,df.target.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mb.fit(x_bog,df.target.ravel())\n",
    "# mb.score(x_bog,df.target.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# gb.score(x_bog,df.target.ravel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\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",
       "    </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": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('data/nlp-getting-started/test.csv')\n",
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test.drop(columns=['keyword', 'location'], inplace=True)\n",
    "test_corpus = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Just happened a terrible car crash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</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>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>Apocalypse lighting. #Spokane #wildfires</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3258</th>\n",
       "      <td>10861</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>Storm in RI worse than last hurricane. My city...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3260</th>\n",
       "      <td>10868</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>MEG issues Hazardous Weather Outlook (HWO) htt...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3262</th>\n",
       "      <td>10875</td>\n",
       "      <td>#CityofCalgary has activated its Municipal Eme...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3263 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id                                               text\n",
       "0         0                 Just happened a terrible car crash\n",
       "1         2  Heard about #earthquake is different cities, s...\n",
       "2         3  there is a forest fire at spot pond, geese are...\n",
       "3         9           Apocalypse lighting. #Spokane #wildfires\n",
       "4        11      Typhoon Soudelor kills 28 in China and Taiwan\n",
       "...     ...                                                ...\n",
       "3258  10861  EARTHQUAKE SAFETY LOS ANGELES ÛÒ SAFETY FASTE...\n",
       "3259  10865  Storm in RI worse than last hurricane. My city...\n",
       "3260  10868  Green Line derailment in Chicago http://t.co/U...\n",
       "3261  10874  MEG issues Hazardous Weather Outlook (HWO) htt...\n",
       "3262  10875  #CityofCalgary has activated its Municipal Eme...\n",
       "\n",
       "[3263 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 44.6 s\n"
     ]
    }
   ],
   "source": [
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "import nltk\n",
    "import re\n",
    "\n",
    "ps = PorterStemmer()\n",
    "\n",
    "for i in range(3263):\n",
    "    review = re.sub('[^a-zA-Z]', ' ', df_test['text'][i])\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    review = [ps.stem(word)\n",
    "              for word in review if word not in stopwords.words('english')]\n",
    "    review = ' '.join(review)\n",
    "    test_corpus.append(review)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_bog_test = cv.transform(test_corpus).toarray()\n",
    "x_tfidf_test = tfidf.transform(test_corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_pred=mb.predict(x_test)\n",
    "# y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# output = pd.DataFrame({'id': df_test.id, 'target': y_pred})\n",
    "# output.to_csv('sample_submission.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Try other models\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9158019177722317"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# y=df.target.ravel()\n",
    "# from sklearn.svm import SVC\n",
    "# svc=SVC()\n",
    "# svc.fit(x_bog,y)\n",
    "# svc.score(x_bog,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y_svc_pred=svc.predict(x_test)\n",
    "# output =pd.DataFrame({'id':df_test.id,'target':y_svc_pred})\n",
    "# output.to_csv('sample_submission.csv', index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.ensemble import VotingClassifier\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=SVC(),\n",
       "             param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# parameters = {'kernel':('linear','sigmoid','poly','rbf'), 'C':[1, 5,10,20]}\n",
    "# clf = GridSearchCV(svc, parameters)\n",
    "# clf.fit(x_bog,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time', 'param_C', 'param_kernel', 'params', 'split0_test_score', 'split1_test_score', 'split2_test_score', 'split3_test_score', 'split4_test_score', 'mean_test_score', 'std_test_score', 'rank_test_score'])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clf.cv_results_.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "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",
       "\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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23.857344</td>\n",
       "      <td>0.763693</td>\n",
       "      <td>4.595492</td>\n",
       "      <td>0.182498</td>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>0.708470</td>\n",
       "      <td>0.597505</td>\n",
       "      <td>0.646093</td>\n",
       "      <td>0.637319</td>\n",
       "      <td>0.744415</td>\n",
       "      <td>0.666761</td>\n",
       "      <td>0.052650</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>33.920913</td>\n",
       "      <td>3.568469</td>\n",
       "      <td>10.017005</td>\n",
       "      <td>0.560905</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.726855</td>\n",
       "      <td>0.663165</td>\n",
       "      <td>0.685489</td>\n",
       "      <td>0.698423</td>\n",
       "      <td>0.798949</td>\n",
       "      <td>0.714576</td>\n",
       "      <td>0.046946</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>32.251862</td>\n",
       "      <td>2.222124</td>\n",
       "      <td>3.874929</td>\n",
       "      <td>0.375307</td>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>0.655942</td>\n",
       "      <td>0.593565</td>\n",
       "      <td>0.619173</td>\n",
       "      <td>0.629435</td>\n",
       "      <td>0.675427</td>\n",
       "      <td>0.634708</td>\n",
       "      <td>0.028534</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37.615784</td>\n",
       "      <td>0.970159</td>\n",
       "      <td>10.117270</td>\n",
       "      <td>0.608552</td>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
       "      <td>0.709783</td>\n",
       "      <td>0.631648</td>\n",
       "      <td>0.653316</td>\n",
       "      <td>0.670171</td>\n",
       "      <td>0.749671</td>\n",
       "      <td>0.682918</td>\n",
       "      <td>0.042060</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
       "0      23.857344      0.763693         4.595492        0.182498       1   \n",
       "1      33.920913      3.568469        10.017005        0.560905       1   \n",
       "2      32.251862      2.222124         3.874929        0.375307      10   \n",
       "3      37.615784      0.970159        10.117270        0.608552      10   \n",
       "\n",
       "  param_kernel                         params  split0_test_score  \\\n",
       "0       linear   {'C': 1, 'kernel': 'linear'}           0.708470   \n",
       "1          rbf      {'C': 1, 'kernel': 'rbf'}           0.726855   \n",
       "2       linear  {'C': 10, 'kernel': 'linear'}           0.655942   \n",
       "3          rbf     {'C': 10, 'kernel': 'rbf'}           0.709783   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0           0.597505           0.646093           0.637319           0.744415   \n",
       "1           0.663165           0.685489           0.698423           0.798949   \n",
       "2           0.593565           0.619173           0.629435           0.675427   \n",
       "3           0.631648           0.653316           0.670171           0.749671   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.666761        0.052650                3  \n",
       "1         0.714576        0.046946                1  \n",
       "2         0.634708        0.028534                4  \n",
       "3         0.682918        0.042060                2  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pd.DataFrame(clf.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC(C=1, kernel='rbf')\n",
    "rf = RandomForestClassifier(n_estimators=200, max_depth=100)\n",
    "xgb = XGBClassifier()\n",
    "mb = MultinomialNB()\n",
    "lr = LogisticRegression(max_iter=1000)\n",
    "clf = VotingClassifier([('svc', svc), ('rf', rf), ('xgb', xgb)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\kurma\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[15:48:41] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "              colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=-1,\n",
       "              importance_type='gain', interaction_constraints='',\n",
       "              learning_rate=0.300000012, max_delta_step=0, max_depth=6,\n",
       "              min_child_weight=1, missing=nan, monotone_constraints='()',\n",
       "              n_estimators=100, n_jobs=8, num_parallel_tree=1, random_state=0,\n",
       "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=1,\n",
       "              tree_method='exact', validate_parameters=1, verbosity=None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rf=RandomForestClassifier(n_estimators=500,n_jobs=-1)\n",
    "# rf.fit(x_bog,y)\n",
    "y = df.target.ravel()\n",
    "xgb.fit(x_bog, y)\n",
    "xgb.score(x_bog, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\kurma\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:04:03] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.861289898857218"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df.target.ravel()\n",
    "xgb.fit(x_tfidf, y)\n",
    "xgb.score(x_tfidf, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_xgb_pred = xgb.predict(x_tfidf_test)\n",
    "output = pd.DataFrame({'id': df_test.id, 'target': y_xgb_pred})\n",
    "output.to_csv('sample_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\kurma\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\xgboost\\sklearn.py:1146: UserWarning: The use of label encoder in XGBClassifier is deprecated and will be removed in a future release. To remove this warning, do the following: 1) Pass option use_label_encoder=False when constructing XGBClassifier object; and 2) Encode your labels (y) as integers starting with 0, i.e. 0, 1, 2, ..., [num_class - 1].\n",
      "  warnings.warn(label_encoder_deprecation_msg, UserWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[17:17:12] WARNING: C:/Users/Administrator/workspace/xgboost-win64_release_1.4.0/src/learner.cc:1095: Starting in XGBoost 1.3.0, the default evaluation metric used with the objective 'binary:logistic' was changed from 'error' to 'logloss'. Explicitly set eval_metric if you'd like to restore the old behavior.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "VotingClassifier(estimators=[('svc', SVC(C=1)),\n",
       "                             ('rf',\n",
       "                              RandomForestClassifier(max_depth=100,\n",
       "                                                     n_estimators=200)),\n",
       "                             ('xgb',\n",
       "                              XGBClassifier(base_score=None, booster=None,\n",
       "                                            colsample_bylevel=None,\n",
       "                                            colsample_bynode=None,\n",
       "                                            colsample_bytree=None, gamma=None,\n",
       "                                            gpu_id=None, importance_type='gain',\n",
       "                                            interaction_constraints=None,\n",
       "                                            learning_rate=None,\n",
       "                                            max_delta_step=None, max_depth=None,\n",
       "                                            min_child_weight=None, missing=nan,\n",
       "                                            monotone_constraints=None,\n",
       "                                            n_estimators=100, n_jobs=None,\n",
       "                                            num_parallel_tree=None,\n",
       "                                            random_state=None, reg_alpha=None,\n",
       "                                            reg_lambda=None,\n",
       "                                            scale_pos_weight=None,\n",
       "                                            subsample=None, tree_method=None,\n",
       "                                            validate_parameters=None,\n",
       "                                            verbosity=None))])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x_tfidf, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9146197294102194"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(x_tfidf, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_best = clf.predict(x_tfidf_test)\n",
    "output = pd.DataFrame({'id': df_test.id, 'target': y_best})\n",
    "output.to_csv('sample_submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mb.fit(x_bog, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8295021673453303"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mb.score(x_bog, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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