News.detection.project .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc623421",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import re\n",
    "import nltk\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "42d34a5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import PorterStemmer, WordNetLemmatizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "44a75b60",
   "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>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Donald Trump Sends Out Embarrassing New Year’...</td>\n",
       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Drunk Bragging Trump Staffer Started Russian ...</td>\n",
       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sheriff David Clarke Becomes An Internet Joke...</td>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 30, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 29, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pope Francis Just Called Out Donald Trump Dur...</td>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 25, 2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0   Donald Trump Sends Out Embarrassing New Year’...   \n",
       "1   Drunk Bragging Trump Staffer Started Russian ...   \n",
       "2   Sheriff David Clarke Becomes An Internet Joke...   \n",
       "3   Trump Is So Obsessed He Even Has Obama’s Name...   \n",
       "4   Pope Francis Just Called Out Donald Trump Dur...   \n",
       "\n",
       "                                                text subject  \\\n",
       "0  Donald Trump just couldn t wish all Americans ...    News   \n",
       "1  House Intelligence Committee Chairman Devin Nu...    News   \n",
       "2  On Friday, it was revealed that former Milwauk...    News   \n",
       "3  On Christmas day, Donald Trump announced that ...    News   \n",
       "4  Pope Francis used his annual Christmas Day mes...    News   \n",
       "\n",
       "                date  \n",
       "0  December 31, 2017  \n",
       "1  December 31, 2017  \n",
       "2  December 30, 2017  \n",
       "3  December 29, 2017  \n",
       "4  December 25, 2017  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.read_csv('New_dataset.csv.zip')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f70f34e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23481 entries, 0 to 23480\n",
      "Data columns (total 4 columns):\n",
      " #   Column   Non-Null Count  Dtype \n",
      "---  ------   --------------  ----- \n",
      " 0   title    23481 non-null  object\n",
      " 1   text     23481 non-null  object\n",
      " 2   subject  23481 non-null  object\n",
      " 3   date     23481 non-null  object\n",
      "dtypes: object(4)\n",
      "memory usage: 733.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2e52cb64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "May 10, 2017         46\n",
       "May 26, 2016         44\n",
       "May 6, 2016          44\n",
       "May 5, 2016          44\n",
       "May 11, 2016         43\n",
       "                     ..\n",
       "December 9, 2017      1\n",
       "December 4, 2017      1\n",
       "November 19, 2017     1\n",
       "November 20, 2017     1\n",
       "Jul 19, 2015          1\n",
       "Name: date, Length: 1681, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd0ec9ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23481, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6612f190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title      0\n",
       "text       0\n",
       "subject    0\n",
       "date       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3aeca712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23481, 4)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "498bfa51",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a25b2ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title      0\n",
       "text       0\n",
       "subject    0\n",
       "date       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2c44c765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23481, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fcd076e7",
   "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>index</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>subject</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Donald Trump Sends Out Embarrassing New Year’...</td>\n",
       "      <td>Donald Trump just couldn t wish all Americans ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Drunk Bragging Trump Staffer Started Russian ...</td>\n",
       "      <td>House Intelligence Committee Chairman Devin Nu...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Sheriff David Clarke Becomes An Internet Joke...</td>\n",
       "      <td>On Friday, it was revealed that former Milwauk...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 30, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Trump Is So Obsessed He Even Has Obama’s Name...</td>\n",
       "      <td>On Christmas day, Donald Trump announced that ...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 29, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Pope Francis Just Called Out Donald Trump Dur...</td>\n",
       "      <td>Pope Francis used his annual Christmas Day mes...</td>\n",
       "      <td>News</td>\n",
       "      <td>December 25, 2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index                                              title  \\\n",
       "0      0   Donald Trump Sends Out Embarrassing New Year’...   \n",
       "1      1   Drunk Bragging Trump Staffer Started Russian ...   \n",
       "2      2   Sheriff David Clarke Becomes An Internet Joke...   \n",
       "3      3   Trump Is So Obsessed He Even Has Obama’s Name...   \n",
       "4      4   Pope Francis Just Called Out Donald Trump Dur...   \n",
       "\n",
       "                                                text subject  \\\n",
       "0  Donald Trump just couldn t wish all Americans ...    News   \n",
       "1  House Intelligence Committee Chairman Devin Nu...    News   \n",
       "2  On Friday, it was revealed that former Milwauk...    News   \n",
       "3  On Christmas day, Donald Trump announced that ...    News   \n",
       "4  Pope Francis used his annual Christmas Day mes...    News   \n",
       "\n",
       "                date  \n",
       "0  December 31, 2017  \n",
       "1  December 31, 2017  \n",
       "2  December 30, 2017  \n",
       "3  December 29, 2017  \n",
       "4  December 25, 2017  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.reset_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "edad50d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Donald Trump Sends Out Embarrassing New Year’s Eve Message; This is Disturbing'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['title'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4f6bf262",
   "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>index</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>December 31, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>December 30, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>December 29, 2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>December 25, 2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index               date\n",
       "0      0  December 31, 2017\n",
       "1      1  December 31, 2017\n",
       "2      2  December 30, 2017\n",
       "3      3  December 29, 2017\n",
       "4      4  December 25, 2017"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['title','text','subject'],axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eaec104c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data = 'the quick brown fox jumps over the lazy'\n",
    "sample_data = sample_data.split()\n",
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bd4d39c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a7a2f0a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the', 'quick', 'brown', 'fox', 'jumps', 'over', 'the', 'lazy']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample_data = [data.lower() for data in sample_data]\n",
    "sample_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b5142235",
   "metadata": {},
   "outputs": [],
   "source": [
    "#stopwords = stopwords.words('english')\n",
    "#print(stopwords[0:10])\n",
    "#print(len(stopwords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd5477ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46b604dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#sample_data = [data for data in sample_data if data not in stopwords]\n",
    "#print(sample_data)\n",
    "#len(sample_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62a70fb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4.Stemming"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e76f3ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#ps = PorterStemmer()\n",
    "#sample_data_stemming = [ps.stem(data) for data in sample_data]\n",
    "#print(sample_data_stemming)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ae787653",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5. Lemmatization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad464bc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lm = WordNetLemmatizer()\n",
    "#sample_data_lemma = [lm.lemmatize(data) for data in sample_data]\n",
    "#print(sample_data_lemma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7d7b018a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lm = WordNetLemmatizer()\n",
    "#corpus = []\n",
    "#for i in range (len(df)):\n",
    "    #review = re.sub('^a-zA-Z0-9',' ', df['title'][i])\n",
    "    #review = review.lower()\n",
    "    #review = review.split()\n",
    "    #review = [lm.lemmatize(x) for x in review if x not in stopwords]\n",
    "    #review = \" \".join(review)\n",
    "    #corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45669981",
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60e79be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['title'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdd0e949",
   "metadata": {},
   "outputs": [],
   "source": [
    "#corpus[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a7bbeaf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4. Vectorization (Convert Text data into the Vector)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec7455b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tf = TfidfVectorizer()\n",
    "#x = tf.fit_transform(corpus).toarray()\n",
    "#x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6de36788",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y = df['label']\n",
    "#y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a2706e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2976195",
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_train, x_test, y_train, y_test = train_test_split(x,y, test_size = 0.3, random_state = 10, stratify ="
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "280c1eec",
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(x_train),len(y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2ce9e1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(x_test), len(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8880abc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5. Model Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b166a228",
   "metadata": {},
   "outputs": [],
   "source": [
    "#rf = RandomForestClassifier()\n",
    "#rf.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e4544961",
   "metadata": {},
   "outputs": [],
   "source": [
    "#6. Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "feb69782",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_pred = rf.predict(x_test)\n",
    "#accuracy_score_ = accuracy_score(y_test,y_pred) \n",
    "#accuracy_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3f4ae86",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Evaluation:\n",
    "    \n",
    "    def __init__(self,model,x_train,x_test,y_train,y_test):\n",
    "        self.model = model\n",
    "        self.x_train = x_train\n",
    "        self.x_test = x_test\n",
    "        self.y_train = y_train\n",
    "        self.y_test = y_test\n",
    "        \n",
    "    def train_evaluation(self):\n",
    "        y_pred_train = self.model.predict(self.x_train)\n",
    "        \n",
    "        acc_scr_train = accuracy_score(self.y_train,y_pred_train)\n",
    "        print(\"Accuracy Score On Training Data Set :\",acc_scr_train)\n",
    "        print()\n",
    "        \n",
    "        con_mat_train = confusion_matrix(self.y_train,y_pred_train)\n",
    "        print(\"Confusion Matrix On Training Data Set :\\n\",con_mat_train)\n",
    "        print()\n",
    "        \n",
    "        class_rep_train = classification_report(self.y_train,y_pred_train)\n",
    "        print(\"Classification Report On Training Data Set :\\n\",class_rep_train)\n",
    "        \n",
    "            \n",
    "    def test_evaluation(self):\n",
    "        y_pred_test = self.model.predict(self.x_test)\n",
    "        \n",
    "        acc_scr_test = accuracy_score(self.y_test,y_pred_test)\n",
    "        print(\"Accuracy Score On Testing Data Set :\",acc_scr_train)\n",
    "        print()\n",
    "        \n",
    "        con_mat_test = confusion_matrix(self.y_test,y_pred_test)\n",
    "        print(\"Confusion Matrix On Testing Data Set :\\n\",con_mat_train)\n",
    "        print()\n",
    "        \n",
    "        class_rep_test = classification_report(self.y_test,y_pred_test)\n",
    "        print(\"Classification Report On Testing Data Set :\\n\",class_rep_train)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b7249c65",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking the accuracy on training dataset\n",
    "\n",
    "Evaluation(rf,x_train, x_test, y_train, y_test).train_evaluation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79ddf738",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking the accuracy on testing dataset\n",
    "Evaluation(rf,x_train, x_test, y_train, y_test).test_evaluation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8a48767",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Preprocessing:\n",
    "    \n",
    "    def __init__(self,data):\n",
    "        self.data = data\n",
    "        \n",
    "    def text_preprocessing_user(self):\n",
    "        lm = WordNetLemmatizer()\n",
    "        pred_data = [self.data]    \n",
    "        preprocess_data = []\n",
    "        for data in pred_data:\n",
    "            review = re.sub('^a-zA-Z0-9',' ', data)\n",
    "            review = review.lower()\n",
    "            review = review.split()\n",
    "            review = [lm.lemmatize(x) for x in review if x not in stopwords]\n",
    "            review = \" \".join(review)\n",
    "            preprocess_data.append(review)\n",
    "        return preprocess_data   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "144c494c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['title'][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3995dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = 'FLYNN: Hillary Clinton, Big Woman on Campus - Breitbart'\n",
    "Preprocessing(data).text_preprocessing_user()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cd35639",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Prediction:\n",
    "    \n",
    "    def __init__(self,pred_data, model):\n",
    "        self.pred_data = pred_data\n",
    "        self.model = model\n",
    "        \n",
    "    def prediction_model(self):\n",
    "        preprocess_data = Preprocessing(self.pred_data).text_preprocessing_user()\n",
    "        data = tf.transform(preprocess_data)\n",
    "        prediction = self.model.predict(data)\n",
    "        \n",
    "        if prediction [0] == 0 :\n",
    "            return \"The News Is Fake\"\n",
    "        \n",
    "        else:\n",
    "            return \"The News Is Real\"\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "961db74c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = 'FLYNN: Hillary Clinton, Big Woman on Campus - Breitbart'\n",
    "Prediction(data,rf).prediction_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d97862",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['title'][3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cfeb7018",
   "metadata": {},
   "outputs": [],
   "source": [
    "user_data = '15 Civilians Killed In Single US Airstrike Have Been Identified' \n",
    "Prediction(user_data,rf).prediction_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb95b439",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34289778",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}