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FakeNewsDetector
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\super\\Documents\\CSE Projects\\FakeNewsDetector\\venv\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n",
      "Generating train split: 100%|██████████| 44898/44898 [00:01<00:00, 25984.86 examples/s]\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"mrm8488/fake-news\")\n",
    "dataset = dataset['train'].train_test_split(test_size=0.2)\n",
    "\n",
    "# Split out sets\n",
    "train_ds = dataset['train']\n",
    "test_ds = dataset['test']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "train_df = train_ds.to_pandas()\n",
    "test_df = test_ds.to_pandas()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label\n",
      "1    18743\n",
      "0    17175\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.countplot(data=train_df, x=\"label\")\n",
    "plt.title(\"Class Distribution (0 = Fake, 1 = Real)\")\n",
    "plt.show()\n",
    "\n",
    "# Optional: exact counts\n",
    "print(train_df['label'].value_counts())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "text     0\n",
      "label    0\n",
      "dtype: int64\n",
      "                                                    text  label\n",
      "29516  A federal aid package was all set to pass the ...      1\n",
      "26424  MOSCOW (Reuters) - Russia s lower house of par...      0\n",
      "12155  The information below is disturbing and should...      1\n",
      "14098   There are people out there who are giving the...      1\n",
      "21622  CARACAS (Reuters) - Cuba’s main regional ally,...      0\n"
     ]
    }
   ],
   "source": [
    "print(train_df.isnull().sum())\n",
    "\n",
    "# You can also visually inspect\n",
    "print(train_df.sample(5))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Add new column for text length\n",
    "train_df['text_length'] = train_df['text'].apply(lambda x: len(x.split()))\n",
    "\n",
    "# Plot distribution\n",
    "sns.histplot(train_df['text_length'], bins=50, kde=True)\n",
    "plt.title(\"Article Length Distribution (in words)\")\n",
    "plt.xlabel(\"Word Count\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min: 0\n",
      "Max: 8135\n",
      "Mean: 404.48017706999275\n"
     ]
    }
   ],
   "source": [
    "print(\"Min:\", train_df['text_length'].min())\n",
    "print(\"Max:\", train_df['text_length'].max())\n",
    "print(\"Mean:\", train_df['text_length'].mean())\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "venv",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}