Model.ipynb

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     "text": [
      "/kaggle/input/fake-news/fake_train.csv\n"
     ]
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   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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       "      <th></th>\n",
       "      <th>id</th>\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       "4   4  Iranian woman jailed for fictional unpublished...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",
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       "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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   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"/kaggle/input/fake-news/fake_train.csv\")\n",
    "df.head()"
   ]
  },
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   "source": [
    "df=df.dropna()"
   ]
  },
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   "source": [
    "X=df.drop('label',axis=1)\n",
    "y=df['label']"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "(18285, 4)"
      ]
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     "execution_count": 5,
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   "source": [
    "X.shape"
   ]
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    {
     "data": {
      "text/plain": [
       "'2.6.4'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "614b161c",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Embedding\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\n",
    "from tensorflow.keras.layers import LSTM\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60138aa7",
   "metadata": {
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   "outputs": [],
   "source": [
    "messages=df.copy()\n",
    "messages.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c7a7923f",
   "metadata": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import nltk\n",
    "import re\n",
    "from nltk.corpus import stopwords\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1010226a",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /usr/share/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "nltk.download('stopwords')\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "ps = PorterStemmer()\n",
    "corpus = []\n",
    "for i in range(0, len(messages)):\n",
    "    review = re.sub('[^a-zA-Z]', ' ', messages['title'][i])\n",
    "    review = review.lower()\n",
    "    review = review.split()\n",
    "    \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": "4883438f",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1961, 1790, 862, 3336, 4042, 1245, 4417, 2912, 3204, 2293], [3341, 3729, 353, 2365, 4789, 1064, 2188], [1490, 4139, 2589, 3662], [1366, 1137, 1428, 117, 4979, 2760], [93, 4789, 3164, 535, 1474, 3362, 4789, 4721, 3275, 1910]]\n"
     ]
    }
   ],
   "source": [
    "voc_size=5000\n",
    "one_hot_rep=[one_hot(words,voc_size) for words in corpus]\n",
    "print(one_hot_rep[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a30c6518",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[   0    0    0 ... 2912 3204 2293]\n",
      " [   0    0    0 ... 4789 1064 2188]\n",
      " [   0    0    0 ... 4139 2589 3662]\n",
      " ...\n",
      " [   0    0    0 ... 4905 2465 1140]\n",
      " [   0    0    0 ... 2708 3329 1213]\n",
      " [   0    0    0 ...  246 1593 3037]]\n"
     ]
    }
   ],
   "source": [
    "sent_len=20\n",
    "embedded_docs=pad_sequences(one_hot_rep,padding='pre',maxlen=sent_len)\n",
    "print(embedded_docs)\n",
    "                    "
   ]
  },
  {
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-21 07:35:40.974392: I tensorflow/core/common_runtime/process_util.cc:146] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.\n"
     ]
    }
   ],
   "source": [
    "dimensions=40\n",
    "model=Sequential()\n",
    "model.add(Embedding(voc_size,dimensions,input_length=sent_len))\n",
    "model.add(LSTM(100))\n",
    "model.add(Dense(1,activation='sigmoid'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6681b415",
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    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "embedding (Embedding)        (None, 20, 40)            200000    \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 100)               56400     \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 1)                 101       \n",
      "=================================================================\n",
      "Total params: 256,501\n",
      "Trainable params: 256,501\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fa1c1de5",
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "X_final=np.array(embedded_docs)\n",
    "y_final=np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "23e2220a",
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     "iopub.status.busy": "2022-11-21T07:35:41.422648Z",
     "iopub.status.idle": "2022-11-21T07:35:41.431773Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_final, y_final, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "573466b0",
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    "execution": {
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-11-21 07:35:41.502794: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "192/192 [==============================] - 9s 32ms/step - loss: 0.3339 - accuracy: 0.8428 - val_loss: 0.1922 - val_accuracy: 0.9183\n",
      "Epoch 2/10\n",
      "192/192 [==============================] - 5s 29ms/step - loss: 0.1377 - accuracy: 0.9451 - val_loss: 0.1909 - val_accuracy: 0.9215\n",
      "Epoch 3/10\n",
      "192/192 [==============================] - 6s 32ms/step - loss: 0.0949 - accuracy: 0.9637 - val_loss: 0.2274 - val_accuracy: 0.9200\n",
      "Epoch 4/10\n",
      "192/192 [==============================] - 6s 30ms/step - loss: 0.0684 - accuracy: 0.9761 - val_loss: 0.2285 - val_accuracy: 0.9145\n",
      "Epoch 5/10\n",
      "192/192 [==============================] - 6s 29ms/step - loss: 0.0429 - accuracy: 0.9862 - val_loss: 0.3043 - val_accuracy: 0.9170\n",
      "Epoch 6/10\n",
      "192/192 [==============================] - 6s 29ms/step - loss: 0.0250 - accuracy: 0.9923 - val_loss: 0.4181 - val_accuracy: 0.9167\n",
      "Epoch 7/10\n",
      "192/192 [==============================] - 6s 30ms/step - loss: 0.0165 - accuracy: 0.9949 - val_loss: 0.4409 - val_accuracy: 0.9125\n",
      "Epoch 8/10\n",
      "192/192 [==============================] - 5s 29ms/step - loss: 0.0111 - accuracy: 0.9972 - val_loss: 0.4007 - val_accuracy: 0.9107\n",
      "Epoch 9/10\n",
      "192/192 [==============================] - 6s 32ms/step - loss: 0.0080 - accuracy: 0.9977 - val_loss: 0.5664 - val_accuracy: 0.9117\n",
      "Epoch 10/10\n",
      "192/192 [==============================] - 6s 29ms/step - loss: 0.0046 - accuracy: 0.9989 - val_loss: 0.5832 - val_accuracy: 0.9069\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd7342ba510>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Finally Training\n",
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=10,batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a2874301",
   "metadata": {
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     "status": "completed"
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   "source": []
  }
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