fake_news_detection.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4338026c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import itertools\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ceb92d01",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('fake_or_real_news.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3f49dce1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2b621669",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "079ede7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=df.label\n",
    "# labels.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4c0e7add",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ba3f2b26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46d4b8b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7c60c048",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf =TfidfVectorizer(stop_words='english', max_df=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6993f4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_train=tfidf.fit_transform(x_train) \n",
    "tfidf_test=tfidf.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3bb5cc01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassiveAggressiveClassifier(max_iter=50)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pac=PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "697d74e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=pac.predict(tfidf_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "975447c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[591  47]\n",
      " [ 39 590]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import math\n",
    "\n",
    "cm = confusion_matrix(y_test,y_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cb8624c8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for test set = 93.21231254932913 (94 percent)\n",
      "Precision for test set = 92.6332288401254 (93 percent)\n",
      "Recall for test set = 93.80952380952381 (94 percent)\n",
      "F1 for test set = 93.21766561514197 (94 percent)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import math\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "TP = cm[0,0]\n",
    "FP = cm[0,1]\n",
    "FN = cm[1,0]\n",
    "TN = cm[1,1]\n",
    "accuracy=((TP+TN)/(TP+TN+FP+FN))*100\n",
    "precision=(TP/(TP+FP))*100\n",
    "recall=(TP/(TP+FN))*100\n",
    "F1=2*(1/((1/precision)+(1/recall)))\n",
    "print('Accuracy for test set =', accuracy , \"(%d percent)\" %math.ceil(accuracy))\n",
    "print('Precision for test set =', precision, \"(%d percent)\" %math.ceil(precision))\n",
    "print('Recall for test set =',recall, \"(%d percent)\" %math.ceil(recall))\n",
    "print('F1 for test set =', F1, \"(%d percent)\" %math.ceil(F1))\n",
    "\n",
    "plt.figure(figsize = (6,6))\n",
    "sns.heatmap(cm, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ded0ffd",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}