News Classification using NLP part 1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AG News Classification Dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"news.jpeg\" width=\"1000\" height=\"500\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# News Classification Dataset\n",
    "\n",
    "### Data Source:https://www.kaggle.com/amananandrai/ag-news-classification-dataset/notebooks\n",
    "\n",
    "### News Classification dataset consist of News articles of class \"world\", \"sports\", \"business\", and \"Science\"\n",
    "\n",
    "## **1. Objective**\n",
    "\n",
    "Given a Title and description we have to determine wheater it belongs to which news article category .\n",
    "\n",
    "### 1.1 Type of Machine learning problem:\n",
    "We have to predict the news article on given informartion so it is multiclass Classification problem\n",
    "\n",
    "\n",
    "### 1.2 How to determine wheater it belongs to which news article category ?\n",
    "We can use Natural Language processing technique to convert the given texts to vectors as machine inderstands numbers so by using some of the Data preprossesingtechniques such as Word2Vec, BagofWords ,Tfidf etc etc , and then by using Classification machine learning Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. load Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120000, 3)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train = pd.read_csv('train.csv')\n",
    "train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7600, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Concating Both train and test dataset in single dataframe for text preprocessing purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = [train,test]\n",
    "df = pd.concat(frames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(127600, 3)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Class Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Wall St. Bears Claw Back Into the Black (Reuters)</td>\n",
       "      <td>Reuters - Short-sellers, Wall Street's dwindli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
       "      <td>Reuters - Private investment firm Carlyle Grou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n",
       "      <td>Reuters - Soaring crude prices plus worries\\ab...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class Index                                              Title  \\\n",
       "0            3  Wall St. Bears Claw Back Into the Black (Reuters)   \n",
       "1            3  Carlyle Looks Toward Commercial Aerospace (Reu...   \n",
       "2            3    Oil and Economy Cloud Stocks' Outlook (Reuters)   \n",
       "\n",
       "                                         Description  \n",
       "0  Reuters - Short-sellers, Wall Street's dwindli...  \n",
       "1  Reuters - Private investment firm Carlyle Grou...  \n",
       "2  Reuters - Soaring crude prices plus worries\\ab...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total shape (127600, 3)\n",
      "total columns Index(['Class Index', 'Title', 'Description'], dtype='object')\n",
      "**************************************************\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 127600 entries, 0 to 7599\n",
      "Data columns (total 3 columns):\n",
      " #   Column       Non-Null Count   Dtype \n",
      "---  ------       --------------   ----- \n",
      " 0   Class Index  127600 non-null  int64 \n",
      " 1   Title        127600 non-null  object\n",
      " 2   Description  127600 non-null  object\n",
      "dtypes: int64(1), object(2)\n",
      "memory usage: 3.9+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print('total shape',df.shape)\n",
    "print('total columns',df.columns)\n",
    "print('*'*50)\n",
    "print(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2 Attribute information:\n",
    "1: Class Index: labels are given w.r.to news article of class i.e sports:1,science:2 etc etc\n",
    "\n",
    "2: Title: Heading of news.\n",
    "\n",
    "3: Description: News realted to above articles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    31900\n",
       "3    31900\n",
       "2    31900\n",
       "1    31900\n",
       "Name: Class Index, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Class Index'].value_counts()   # Balanced dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Lets check which news article belongs to which with help of groupby"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>2</td>\n",
       "      <td>Phelps, Thorpe Advance in 200 Freestyle (AP)</td>\n",
       "      <td>AP - Michael Phelps took care of qualifying fo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>2</td>\n",
       "      <td>Reds Knock Padres Out of Wild-Card Lead (AP)</td>\n",
       "      <td>AP - Wily Mo Pena homered twice and drove in f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>2</td>\n",
       "      <td>Dreaming done, NBA stars awaken to harsh Olymp...</td>\n",
       "      <td>AFP - National Basketball Association players ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class Index                                              Title  \\\n",
       "448            2       Phelps, Thorpe Advance in 200 Freestyle (AP)   \n",
       "449            2       Reds Knock Padres Out of Wild-Card Lead (AP)   \n",
       "450            2  Dreaming done, NBA stars awaken to harsh Olymp...   \n",
       "\n",
       "                                           Description  \n",
       "448  AP - Michael Phelps took care of qualifying fo...  \n",
       "449  AP - Wily Mo Pena homered twice and drove in f...  \n",
       "450  AFP - National Basketball Association players ...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df.groupby(['Class Index'])\n",
    "s = g.get_group(2)  \n",
    "s.head(3)    # 2 : Sports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Wall St. Bears Claw Back Into the Black (Reuters)</td>\n",
       "      <td>Reuters - Short-sellers, Wall Street's dwindli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
       "      <td>Reuters - Private investment firm Carlyle Grou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n",
       "      <td>Reuters - Soaring crude prices plus worries\\ab...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class Index                                              Title  \\\n",
       "0            3  Wall St. Bears Claw Back Into the Black (Reuters)   \n",
       "1            3  Carlyle Looks Toward Commercial Aerospace (Reu...   \n",
       "2            3    Oil and Economy Cloud Stocks' Outlook (Reuters)   \n",
       "\n",
       "                                         Description  \n",
       "0  Reuters - Short-sellers, Wall Street's dwindli...  \n",
       "1  Reuters - Private investment firm Carlyle Grou...  \n",
       "2  Reuters - Soaring crude prices plus worries\\ab...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = g.get_group(3)   \n",
    "b.head(3)     # 3: Buisness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>4</td>\n",
       "      <td>'Madden,' 'ESPN' Football Score in Different W...</td>\n",
       "      <td>Reuters - Was absenteeism a little high\\on Tue...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>4</td>\n",
       "      <td>Group to Propose New High-Speed Wireless Forma...</td>\n",
       "      <td>Reuters - A group of technology companies\\incl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>4</td>\n",
       "      <td>AOL to Sell Cheap PCs to Minorities and Senior...</td>\n",
       "      <td>Reuters - America Online on Thursday said it\\p...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Class Index                                              Title  \\\n",
       "78            4  'Madden,' 'ESPN' Football Score in Different W...   \n",
       "79            4  Group to Propose New High-Speed Wireless Forma...   \n",
       "80            4  AOL to Sell Cheap PCs to Minorities and Senior...   \n",
       "\n",
       "                                          Description  \n",
       "78  Reuters - Was absenteeism a little high\\on Tue...  \n",
       "79  Reuters - A group of technology companies\\incl...  \n",
       "80  Reuters - America Online on Thursday said it\\p...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc = g.get_group(4) \n",
    "sc.head(3)    # 4: Science"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>1</td>\n",
       "      <td>Venezuelans Vote Early in Referendum on Chavez...</td>\n",
       "      <td>Reuters - Venezuelans turned out early\\and in ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>1</td>\n",
       "      <td>S.Koreans Clash with Police on Iraq Troop Disp...</td>\n",
       "      <td>Reuters - South Korean police used water canno...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>1</td>\n",
       "      <td>Palestinians in Israeli Jails Start Hunger Str...</td>\n",
       "      <td>Reuters - Thousands of Palestinian\\prisoners i...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Class Index                                              Title  \\\n",
       "492            1  Venezuelans Vote Early in Referendum on Chavez...   \n",
       "493            1  S.Koreans Clash with Police on Iraq Troop Disp...   \n",
       "494            1  Palestinians in Israeli Jails Start Hunger Str...   \n",
       "\n",
       "                                           Description  \n",
       "492  Reuters - Venezuelans turned out early\\and in ...  \n",
       "493  Reuters - South Korean police used water canno...  \n",
       "494  Reuters - Thousands of Palestinian\\prisoners i...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w = g.get_group(1) \n",
    "w.head(3)    # 1: World news"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 lets assign labels to each and every class index and create another columns with help of Dictonary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = {1:'World',2:'Sports',3:'Buisness',4:'Science & Tech'}\n",
    "df['labels'] = df['Class Index'].map(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Class Index</th>\n",
       "      <th>Title</th>\n",
       "      <th>Description</th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Wall St. Bears Claw Back Into the Black (Reuters)</td>\n",
       "      <td>Reuters - Short-sellers, Wall Street's dwindli...</td>\n",
       "      <td>Buisness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
       "      <td>Reuters - Private investment firm Carlyle Grou...</td>\n",
       "      <td>Buisness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters)</td>\n",
       "      <td>Reuters - Soaring crude prices plus worries\\ab...</td>\n",
       "      <td>Buisness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Iraq Halts Oil Exports from Main Southern Pipe...</td>\n",
       "      <td>Reuters - Authorities have halted oil export\\f...</td>\n",
       "      <td>Buisness</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>Oil prices soar to all-time record, posing new...</td>\n",
       "      <td>AFP - Tearaway world oil prices, toppling reco...</td>\n",
       "      <td>Buisness</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class Index                                              Title  \\\n",
       "0            3  Wall St. Bears Claw Back Into the Black (Reuters)   \n",
       "1            3  Carlyle Looks Toward Commercial Aerospace (Reu...   \n",
       "2            3    Oil and Economy Cloud Stocks' Outlook (Reuters)   \n",
       "3            3  Iraq Halts Oil Exports from Main Southern Pipe...   \n",
       "4            3  Oil prices soar to all-time record, posing new...   \n",
       "\n",
       "                                         Description    labels  \n",
       "0  Reuters - Short-sellers, Wall Street's dwindli...  Buisness  \n",
       "1  Reuters - Private investment firm Carlyle Grou...  Buisness  \n",
       "2  Reuters - Soaring crude prices plus worries\\ab...  Buisness  \n",
       "3  Reuters - Authorities have halted oil export\\f...  Buisness  \n",
       "4  AFP - Tearaway world oil prices, toppling reco...  Buisness  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Now Combine Title and Description to single columns ['Summary']\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Summary'] = df['Title'] + ' ' + df['Description']\n",
    "df.drop(columns=['Title', 'Description'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Class Index</th>\n",
       "      <th>labels</th>\n",
       "      <th>Summary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Buisness</td>\n",
       "      <td>Wall St. Bears Claw Back Into the Black (Reute...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Buisness</td>\n",
       "      <td>Carlyle Looks Toward Commercial Aerospace (Reu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Buisness</td>\n",
       "      <td>Oil and Economy Cloud Stocks' Outlook (Reuters...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Class Index    labels                                            Summary\n",
       "0            3  Buisness  Wall St. Bears Claw Back Into the Black (Reute...\n",
       "1            3  Buisness  Carlyle Looks Toward Commercial Aerospace (Reu...\n",
       "2            3  Buisness  Oil and Economy Cloud Stocks' Outlook (Reuters..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Science & Tech    31900\n",
       "Buisness          31900\n",
       "Sports            31900\n",
       "World             31900\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.labels.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Distribution of data points among News Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f3b55129c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.labels.value_counts().plot(kind = 'bar')     # Balanced Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What we have done so far ?\n",
    "1:Concat train + test into single\n",
    "\n",
    "2:Created new column = labels\n",
    "\n",
    "3: added title and description into 1 columns = summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 Check for Null value if any:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Class Index    0\n",
       "labels         0\n",
       "Summary        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6 Exporting File to csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('df.csv',index = 'False')"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}