Fake_News_LSTM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "84f60ca0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import Embedding,LSTM,Dense,Dropout\n",
    "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.preprocessing.text import one_hot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87334d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\mayan\\Downloads\\train.csv\\train.csv\")\n",
    "test = pd.read_csv(r\"C:\\Users\\mayan\\Downloads\\fake-news\\test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ddbb003",
   "metadata": {},
   "outputs": [
    {
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       "  <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",
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       "      <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",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
       "      <td>Howard Portnoy</td>\n",
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       "   id                                              title              author  \\\n",
       "0   0  House Dem Aide: We Didn’t Even See Comey’s Let...       Darrell Lucus   \n",
       "1   1  FLYNN: Hillary Clinton, Big Woman on Campus - ...     Daniel J. Flynn   \n",
       "2   2                  Why the Truth Might Get You Fired  Consortiumnews.com   \n",
       "3   3  15 Civilians Killed In Single US Airstrike Hav...     Jessica Purkiss   \n",
       "4   4  Iranian woman jailed for fictional unpublished...      Howard Portnoy   \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  "
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     "execution_count": 3,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f3a3738d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.fillna('')\n",
    "test=test.fillna('')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2cf6ec74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['total'] = df['title']+' '+df['author']\n",
    "test['total']=test['title']+' '+test['author']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9bc1c0c6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20800, 5)\n",
      "(20800,)\n"
     ]
    }
   ],
   "source": [
    "X = df.drop('label',axis=1)\n",
    "y=df['label']\n",
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ba7dff0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "voc_size = 5000\n",
    "msg = X.copy()\n",
    "msg_test = test.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c4f8a6c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\mayan\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
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     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "import re\n",
    "from nltk.corpus import stopwords\n",
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d8e57ffa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "corpus = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "baace2b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(msg)):\n",
    "  review = re.sub('[^a-zA-Z]',' ',msg['total'][i])\n",
    "  review = review.lower()\n",
    "  review = review.split()\n",
    "  review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n",
    "  review = ' '.join(review)\n",
    "  corpus.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c3cf14ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "corpus_test = []\n",
    "for i in range(len(msg_test)):\n",
    "  review = re.sub('[^a-zA-Z]',' ',msg_test['total'][i])\n",
    "  review = review.lower()\n",
    "  review = review.split()\n",
    "  review = [ps.stem(word) for word in review if not word in stopwords.words('english')]\n",
    "  review = ' '.join(review)\n",
    "  corpus_test.append(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "24715650",
   "metadata": {},
   "outputs": [],
   "source": [
    "onehot_rep = [one_hot(words,voc_size)for words in corpus]\n",
    "onehot_rep_test = [one_hot(words,voc_size)for words in corpus_test]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba06225f",
   "metadata": {},
   "outputs": [],
   "source": [
    "embedded_docs = pad_sequences(onehot_rep,padding='pre',maxlen=25)\n",
    "embedded_docs_test = pad_sequences(onehot_rep_test,padding='pre',maxlen=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c1630edd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 25, 40)            200000    \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 25, 40)            0         \n",
      "                                                                 \n",
      " lstm (LSTM)                 (None, 100)               56400     \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 100)               0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                6464      \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 262,929\n",
      "Trainable params: 262,929\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Embedding(voc_size,40,input_length=25))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(LSTM(100))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(64,activation='relu'))\n",
    "model.add(Dropout(0.3))\n",
    "model.add(Dense(1,activation='sigmoid'))\n",
    "model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9b52a99d",
   "metadata": {},
   "outputs": [
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     "execution_count": 15,
     "metadata": {},
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   ],
   "source": [
    "X_final = np.array(embedded_docs)\n",
    "y_final = np.array(y)\n",
    "test_final = np.array(embedded_docs_test)\n",
    "X_final.shape,y_final.shape,test_final.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "360ae79a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "325/325 [==============================] - 9s 17ms/step - loss: 0.1592 - accuracy: 0.9350\n",
      "Epoch 2/10\n",
      "325/325 [==============================] - 6s 19ms/step - loss: 0.0197 - accuracy: 0.9942\n",
      "Epoch 3/10\n",
      "325/325 [==============================] - 8s 24ms/step - loss: 0.0102 - accuracy: 0.9973\n",
      "Epoch 4/10\n",
      "325/325 [==============================] - 7s 23ms/step - loss: 0.0054 - accuracy: 0.9984\n",
      "Epoch 5/10\n",
      "325/325 [==============================] - 8s 24ms/step - loss: 0.0037 - accuracy: 0.9989\n",
      "Epoch 6/10\n",
      "325/325 [==============================] - 8s 23ms/step - loss: 0.0028 - accuracy: 0.9989\n",
      "Epoch 7/10\n",
      "325/325 [==============================] - 7s 21ms/step - loss: 0.0027 - accuracy: 0.9991\n",
      "Epoch 8/10\n",
      "325/325 [==============================] - 7s 22ms/step - loss: 0.0026 - accuracy: 0.9992\n",
      "Epoch 9/10\n",
      "325/325 [==============================] - 6s 18ms/step - loss: 0.0013 - accuracy: 0.9996\n",
      "Epoch 10/10\n",
      "325/325 [==============================] - 6s 17ms/step - loss: 0.0016 - accuracy: 0.9995\n"
     ]
    },
    {
     "data": {
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       "<keras.callbacks.History at 0x24fc77f9040>"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_final,y_final,epochs=10,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1d27a052",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "163/163 [==============================] - 1s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = np.argmax(model.predict(test_final), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c6909059",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_sub = pd.DataFrame()\n",
    "final_sub['id']=test['id']\n",
    "final_sub['label'] = y_pred\n",
    "final_sub.to_csv('final_sub.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7feeaa0f",
   "metadata": {},
   "outputs": [
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   "cell_type": "code",
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     "text": [
      "Epoch 1/10\n",
      "82/82 [==============================] - 3s 20ms/step - loss: 0.2635 - accuracy: 0.9615\n",
      "Epoch 2/10\n",
      "82/82 [==============================] - 2s 23ms/step - loss: 1.5626e-08 - accuracy: 1.0000\n",
      "Epoch 3/10\n",
      "82/82 [==============================] - 2s 22ms/step - loss: 6.8277e-10 - accuracy: 1.0000\n",
      "Epoch 4/10\n",
      "82/82 [==============================] - 2s 23ms/step - loss: 7.2569e-10 - accuracy: 1.0000\n",
      "Epoch 5/10\n",
      "82/82 [==============================] - 2s 22ms/step - loss: 1.0598e-09 - accuracy: 1.0000\n",
      "Epoch 6/10\n",
      "82/82 [==============================] - 2s 24ms/step - loss: 1.1446e-09 - accuracy: 1.0000\n",
      "Epoch 7/10\n",
      "82/82 [==============================] - 2s 22ms/step - loss: 4.2291e-10 - accuracy: 1.0000\n",
      "Epoch 8/10\n",
      "82/82 [==============================] - 2s 22ms/step - loss: 1.3507e-09 - accuracy: 1.0000\n",
      "Epoch 9/10\n",
      "82/82 [==============================] - 2s 26ms/step - loss: 5.8985e-09 - accuracy: 1.0000\n",
      "Epoch 10/10\n",
      "82/82 [==============================] - 2s 27ms/step - loss: 1.8743e-10 - accuracy: 1.0000\n"
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   "source": [
    "model.fit(test_final,y_pred,epochs=10,batch_size=64)"
   ]
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   "execution_count": null,
   "id": "fda18ecd",
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