fakeNewsEDA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fake News Classification\n",
    "\n",
    "The idea is to develop a machine learning program to identify whether an article might be fake news or not.\n",
    "\n",
    "Dataset link: https://www.kaggle.com/c/fake-news/data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the train dataset\n",
    "train=pd.read_csv(\"train.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
       "      <td>Howard Portnoy</td>\n",
       "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 values from the top\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>id</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>20800.000000</td>\n",
       "      <td>20800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10399.500000</td>\n",
       "      <td>0.500625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6004.587135</td>\n",
       "      <td>0.500012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5199.750000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10399.500000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15599.250000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20799.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id         label\n",
       "count  20800.000000  20800.000000\n",
       "mean   10399.500000      0.500625\n",
       "std     6004.587135      0.500012\n",
       "min        0.000000      0.000000\n",
       "25%     5199.750000      0.000000\n",
       "50%    10399.500000      1.000000\n",
       "75%    15599.250000      1.000000\n",
       "max    20799.000000      1.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of data\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 20800 entries, 0 to 20799\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      20800 non-null  int64 \n",
      " 1   title   20242 non-null  object\n",
      " 2   author  18843 non-null  object\n",
      " 3   text    20761 non-null  object\n",
      " 4   label   20800 non-null  int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 812.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# information about the data\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the test dataset\n",
    "test=pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20800</td>\n",
       "      <td>Specter of Trump Loosens Tongues, if Not Purse...</td>\n",
       "      <td>David Streitfeld</td>\n",
       "      <td>PALO ALTO, Calif.  —   After years of scorning...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20801</td>\n",
       "      <td>Russian warships ready to strike terrorists ne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Russian warships ready to strike terrorists ne...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20802</td>\n",
       "      <td>#NoDAPL: Native American Leaders Vow to Stay A...</td>\n",
       "      <td>Common Dreams</td>\n",
       "      <td>Videos #NoDAPL: Native American Leaders Vow to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20803</td>\n",
       "      <td>Tim Tebow Will Attempt Another Comeback, This ...</td>\n",
       "      <td>Daniel Victor</td>\n",
       "      <td>If at first you don’t succeed, try a different...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20804</td>\n",
       "      <td>Keiser Report: Meme Wars (E995)</td>\n",
       "      <td>Truth Broadcast Network</td>\n",
       "      <td>42 mins ago 1 Views 0 Comments 0 Likes 'For th...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                                              title  \\\n",
       "0  20800  Specter of Trump Loosens Tongues, if Not Purse...   \n",
       "1  20801  Russian warships ready to strike terrorists ne...   \n",
       "2  20802  #NoDAPL: Native American Leaders Vow to Stay A...   \n",
       "3  20803  Tim Tebow Will Attempt Another Comeback, This ...   \n",
       "4  20804                    Keiser Report: Meme Wars (E995)   \n",
       "\n",
       "                    author                                               text  \n",
       "0         David Streitfeld  PALO ALTO, Calif.  —   After years of scorning...  \n",
       "1                      NaN  Russian warships ready to strike terrorists ne...  \n",
       "2            Common Dreams  Videos #NoDAPL: Native American Leaders Vow to...  \n",
       "3            Daniel Victor  If at first you don’t succeed, try a different...  \n",
       "4  Truth Broadcast Network  42 mins ago 1 Views 0 Comments 0 Likes 'For th...  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 5 elements from the top\n",
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23399.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1501.255031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22099.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23399.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24699.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id\n",
       "count   5200.000000\n",
       "mean   23399.500000\n",
       "std     1501.255031\n",
       "min    20800.000000\n",
       "25%    22099.750000\n",
       "50%    23399.500000\n",
       "75%    24699.250000\n",
       "max    25999.000000"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# description of test dataset\n",
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ankit\\anaconda3\\envs\\tf_gpu\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.countplot(train['label'])\n",
    "plt.title(\"Countplot for Label\")\n",
    "plt.xlabel('Sentiment')\n",
    "plt.ylabel('Count')\n",
    "plt.savefig('countplot.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The data is equally distributed according to the sentiment.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5200 entries, 0 to 5199\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      5200 non-null   int64 \n",
      " 1   title   5078 non-null   object\n",
      " 2   author  4697 non-null   object\n",
      " 3   text    5193 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 162.6+ KB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23399.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1501.255031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22099.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23399.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>24699.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25999.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id\n",
       "count   5200.000000\n",
       "mean   23399.500000\n",
       "std     1501.255031\n",
       "min    20800.000000\n",
       "25%    22099.750000\n",
       "50%    23399.500000\n",
       "75%    24699.250000\n",
       "max    25999.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting....\n",
      "Length calculated....\n"
     ]
    }
   ],
   "source": [
    "def len_cal(x):\n",
    "    x=str(x)\n",
    "    x=x.split()\n",
    "    return len(x)\n",
    "\n",
    "print('Starting....')\n",
    "\n",
    "train['Text_length'] = train['text'].map(len_cal)\n",
    "train['Title_length'] = train['title'].map(len_cal)\n",
    "\n",
    "print(\"Length calculated....\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>id</th>\n",
       "      <th>title</th>\n",
       "      <th>author</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>Text_length</th>\n",
       "      <th>Title_length</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",
       "      <td>820</td>\n",
       "      <td>14</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",
       "      <td>710</td>\n",
       "      <td>9</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",
       "      <td>1266</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>557</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
       "      <td>Howard Portnoy</td>\n",
       "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
       "      <td>1</td>\n",
       "      <td>154</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   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  Text_length  \\\n",
       "0  House Dem Aide: We Didn’t Even See Comey’s Let...      1          820   \n",
       "1  Ever get the feeling your life circles the rou...      0          710   \n",
       "2  Why the Truth Might Get You Fired October 29, ...      1         1266   \n",
       "3  Videos 15 Civilians Killed In Single US Airstr...      1          557   \n",
       "4  Print \\nAn Iranian woman has been sentenced to...      1          154   \n",
       "\n",
       "   Title_length  \n",
       "0            14  \n",
       "1             9  \n",
       "2             7  \n",
       "3            10  \n",
       "4            14  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.histplot(train['Text_length'],kde=True)\n",
    "plt.title('Distribution Plot of Length of Text')\n",
    "plt.xlabel('Length')\n",
    "plt.ylabel('Density')\n",
    "plt.savefig('textLenDis.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "sns.histplot(train['Title_length'],kde=True)\n",
    "plt.title('Distribution Plot of Length of Title')\n",
    "plt.xlabel('Length')\n",
    "plt.ylabel('Density')\n",
    "plt.savefig('titleLenDis.jpg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}