Model_Training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b409485e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9cc1e701",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c80931ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
       "      <td>Darrell Lucus</td>\n",
       "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>FLYNN: Hillary Clinton, Big Woman on Campus - ...</td>\n",
       "      <td>Daniel J. Flynn</td>\n",
       "      <td>Ever get the feeling your life circles the rou...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Why the Truth Might Get You Fired</td>\n",
       "      <td>Consortiumnews.com</td>\n",
       "      <td>Why the Truth Might Get You Fired October 29, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>15 Civilians Killed In Single US Airstrike Hav...</td>\n",
       "      <td>Jessica Purkiss</td>\n",
       "      <td>Videos 15 Civilians Killed In Single US Airstr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
       "      <td>Howard Portnoy</td>\n",
       "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Jackie Mason: Hollywood Would Love Trump if He...</td>\n",
       "      <td>Daniel Nussbaum</td>\n",
       "      <td>In these trying times, Jackie Mason is the Voi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>Life: Life Of Luxury: Elton John’s 6 Favorite ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ever wonder how Britain’s most iconic pop pian...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Benoît Hamon Wins French Socialist Party’s Pre...</td>\n",
       "      <td>Alissa J. Rubin</td>\n",
       "      <td>PARIS  —   France chose an idealistic, traditi...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>Excerpts From a Draft Script for Donald Trump’...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Donald J. Trump is scheduled to make a highly ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>A Back-Channel Plan for Ukraine and Russia, Co...</td>\n",
       "      <td>Megan Twohey and Scott Shane</td>\n",
       "      <td>A week before Michael T. Flynn resigned as nat...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                              title  \\\n",
       "0   0  House Dem Aide: We Didn’t Even See Comey’s Let...   \n",
       "1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...   \n",
       "2   2                  Why the Truth Might Get You Fired   \n",
       "3   3  15 Civilians Killed In Single US Airstrike Hav...   \n",
       "4   4  Iranian woman jailed for fictional unpublished...   \n",
       "5   5  Jackie Mason: Hollywood Would Love Trump if He...   \n",
       "6   6  Life: Life Of Luxury: Elton John’s 6 Favorite ...   \n",
       "7   7  Benoît Hamon Wins French Socialist Party’s Pre...   \n",
       "8   8  Excerpts From a Draft Script for Donald Trump’...   \n",
       "9   9  A Back-Channel Plan for Ukraine and Russia, Co...   \n",
       "\n",
       "                         author  \\\n",
       "0                 Darrell Lucus   \n",
       "1               Daniel J. Flynn   \n",
       "2            Consortiumnews.com   \n",
       "3               Jessica Purkiss   \n",
       "4                Howard Portnoy   \n",
       "5               Daniel Nussbaum   \n",
       "6                           NaN   \n",
       "7               Alissa J. Rubin   \n",
       "8                           NaN   \n",
       "9  Megan Twohey and Scott Shane   \n",
       "\n",
       "                                                text  label  \n",
       "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1  \n",
       "1  Ever get the feeling your life circles the rou...      0  \n",
       "2  Why the Truth Might Get You Fired October 29, ...      1  \n",
       "3  Videos 15 Civilians Killed In Single US Airstr...      1  \n",
       "4  Print \\nAn Iranian woman has been sentenced to...      1  \n",
       "5  In these trying times, Jackie Mason is the Voi...      0  \n",
       "6  Ever wonder how Britain’s most iconic pop pian...      1  \n",
       "7  PARIS  —   France chose an idealistic, traditi...      0  \n",
       "8  Donald J. Trump is scheduled to make a highly ...      0  \n",
       "9  A week before Michael T. Flynn resigned as nat...      0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7dd905b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2007 entries, 0 to 2006\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      2007 non-null   int64 \n",
      " 1   title   1955 non-null   object\n",
      " 2   author  1798 non-null   object\n",
      " 3   text    2003 non-null   object\n",
      " 4   label   2007 non-null   int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 78.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "69444026",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id          0\n",
       "title      52\n",
       "author    209\n",
       "text        4\n",
       "label       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "62189631",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5f16a1dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id        0\n",
       "title     0\n",
       "author    0\n",
       "text      0\n",
       "label     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "417980ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'title', 'author', 'text', 'label'], dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9c89d641",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop(['id', 'title', 'author'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "16711c26",
   "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>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ever get the feeling your life circles the rou...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Why the Truth Might Get You Fired October 29, ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Videos 15 Civilians Killed In Single US Airstr...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label\n",
       "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1\n",
       "1  Ever get the feeling your life circles the rou...      0\n",
       "2  Why the Truth Might Get You Fired October 29, ...      1\n",
       "3  Videos 15 Civilians Killed In Single US Airstr...      1\n",
       "4  Print \\nAn Iranian woman has been sentenced to...      1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "09cd07fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "91d806b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "23f065f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df48ce8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "port_stem=PorterStemmer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fbaf7e80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PorterStemmer>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "059f9db2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hi my name  fdjd gh   @#$%   this   is sadiq #$%?'"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "port_stem.stem('Hi my name  fdjd gh   @#$%   this   is SADIQ #$%?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c0a03132",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stemming(content):\n",
    "    con=re.sub('[^a-zA-Z]', ' ', content)\n",
    "    con=con.lower()\n",
    "    con=con.split()\n",
    "    con=[port_stem.stem(word) for word in con if not word in stopwords.words('english')]\n",
    "    con=' '.join(con)\n",
    "    return con"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2108a825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hi name fdjd gh sadiq'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stemming('Hi my name  fdjd gh   @#$%   this   is SADIQ #$%?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4c6b3c9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['text']= df['text'].apply(stemming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "48829e81",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a7c1d398",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2cce09b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2007,)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "9efe21ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2007,)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a8dc573e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3d4ebc57",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train , x_test , y_train, y_test = train_test_split(x, y, test_size=0.20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e20c7f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ff714174",
   "metadata": {},
   "outputs": [],
   "source": [
    "vect=TfidfVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "edda7ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=vect.fit_transform(x_train)\n",
    "x_test=vect.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9bcea73e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1605, 27470)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "798ff534",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(402, 27470)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f811f2f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fec65e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6071589e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d4f1de6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8411fdd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0,\n",
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       "       1, 1, 0, 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ca014ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8383084577114428"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1334a0b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f3feefb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(vect, open('vector.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7ac918d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(model, open('model.pkl', 'wb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0767c7af",
   "metadata": {},
   "outputs": [],
   "source": [
    "vector_form=pickle.load(open('vector.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ec016e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_model=pickle.load(open('model.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "74fcb8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fake_news(news):\n",
    "    news=stemming(news)\n",
    "    input_data=[news]\n",
    "    vector_form1=vector_form.transform(input_data)\n",
    "    prediction = load_model.predict(vector_form1)\n",
    "    return prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "8280fa22",
   "metadata": {},
   "outputs": [],
   "source": [
    "val=fake_news(\"\"\" Ever get the feeling your life circles the roundabout rather than heads in a straight line toward the intended destination? [Hillary Clinton remains the big woman on campus in leafy, liberal Wellesley, Massachusetts. Everywhere else votes her most likely to don her inauguration dress for the remainder of her days the way Miss Havisham forever wore that wedding dress.  Speaking of Great Expectations, Hillary Rodham overflowed with them 48 years ago when she first addressed a Wellesley graduating class. The president of the college informed those gathered in 1969 that the students needed â€œno debate so far as I could ascertain as to who their spokesman was to beâ€ (kind of the like the Democratic primaries in 2016 minus the   terms unknown then even at a Seven Sisters school). â€œI am very glad that Miss Adams made it clear that what I am speaking for today is all of us â€”  the 400 of us,â€ Miss Rodham told her classmates. After appointing herself Edger Bergen to the Charlie McCarthys and Mortimer Snerds in attendance, the    bespectacled in granny glasses (awarding her matronly wisdom â€”  or at least John Lennon wisdom) took issue with the previous speaker. Despite becoming the first   to win election to a seat in the U. S. Senate since Reconstruction, Edward Brooke came in for criticism for calling for â€œempathyâ€ for the goals of protestors as he criticized tactics. Though Clinton in her senior thesis on Saul Alinsky lamented â€œBlack Power demagoguesâ€ and â€œelitist arrogance and repressive intoleranceâ€ within the New Left, similar words coming out of a Republican necessitated a brief rebuttal. â€œTrust,â€ Rodham ironically observed in 1969, â€œthis is one word that when I asked the class at our rehearsal what it was they wanted me to say for them, everyone came up to me and said â€˜Talk about trust, talk about the lack of trust both for us and the way we feel about others. Talk about the trust bust.â€™ What can you say about it? What can you say about a feeling that permeates a generation and that perhaps is not even understood by those who are distrusted?â€ The â€œtrust bustâ€ certainly busted Clintonâ€™s 2016 plans. She certainly did not even understand that people distrusted her. After Whitewater, Travelgate, the vast   conspiracy, Benghazi, and the missing emails, Clinton found herself the distrusted voice on Friday. There was a load of compromising on the road to the broadening of her political horizons. And distrust from the American people â€”  Trump edged her 48 percent to 38 percent on the question immediately prior to Novemberâ€™s election â€”  stood as a major reason for the closing of those horizons. Clinton described her vanquisher and his supporters as embracing a â€œlie,â€ a â€œcon,â€ â€œalternative facts,â€ and â€œa   assault on truth and reason. â€ She failed to explain why the American people chose his lies over her truth. â€œAs the history majors among you here today know all too well, when people in power invent their own facts and attack those who question them, it can mark the beginning of the end of a free society,â€ she offered. â€œThat is not hyperbole. â€ Like so many people to emerge from the 1960s, Hillary Clinton embarked upon a long, strange trip. From high school Goldwater Girl and Wellesley College Republican president to Democratic politician, Clinton drank in the times and the place that gave her a degree. More significantly, she went from idealist to cynic, as a comparison of her two Wellesley commencement addresses show. Way back when, she lamented that â€œfor too long our leaders have viewed politics as the art of the possible, and the challenge now is to practice politics as the art of making what appears to be impossible possible. â€ Now, as the big woman on campus but the odd woman out of the White House, she wonders how her current station is even possible. â€œWhy arenâ€™t I 50 points ahead?â€ she asked in September. In May she asks why she isnâ€™t president. The woman famously dubbed a â€œcongenital liarâ€ by Bill Safire concludes that lies did her in â€”  theirs, mind you, not hers. Getting stood up on Election Day, like finding yourself the jilted bride on your wedding day, inspires dangerous delusions.\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8889f6a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "reliable\n"
     ]
    }
   ],
   "source": [
    "if val==[0]:\n",
    "    print('reliable')\n",
    "else:\n",
    "    print('unreliable')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4af7c7d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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