Articles_Visualisation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bf622e21",
   "metadata": {},
   "source": [
    "## Visualisation of training and testing data for Fake News Detection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62bf4fe0",
   "metadata": {},
   "source": [
    "#### Import libraries for visualisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5b297210",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43a6efdc",
   "metadata": {},
   "source": [
    "#### Load dataset into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "83692fbe",
   "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>news_text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Get the latest from TODAY Sign up for our news...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2d  Conan On The Funeral Trump Will Be Invited...</td>\n",
       "      <td>fake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>It’s safe to say that Instagram Stories has fa...</td>\n",
       "      <td>real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Much like a certain Amazon goddess with a lass...</td>\n",
       "      <td>real</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>At a time when the perfect outfit is just one ...</td>\n",
       "      <td>real</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           news_text label\n",
       "0  Get the latest from TODAY Sign up for our news...  fake\n",
       "1  2d  Conan On The Funeral Trump Will Be Invited...  fake\n",
       "2  It’s safe to say that Instagram Stories has fa...  real\n",
       "3  Much like a certain Amazon goddess with a lass...  real\n",
       "4  At a time when the perfect outfit is just one ...  real"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train = pd.read_csv(r\"fake_news_train.csv\")\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eeac0dd",
   "metadata": {},
   "source": [
    "#### Visualise distribution of labels in pie-chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2891537e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "real    2972\n",
       "fake    2014\n",
       "Name: label, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.label.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5f0c2d6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = df_train.label.value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "03e744d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "count = df_train.label.value_counts().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "43bcfdf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Seaborn color palette to plot pie chart\n",
    "colors = sns.color_palette('pastel')\n",
    "\n",
    "# create pie chart using matplotlib\n",
    "plt.pie(count, labels=labels, colors=colors, autopct='%.0f%%', shadow=True, rotatelabels='true')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ccca3c5",
   "metadata": {},
   "source": [
    "#### Load predicted set into dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10ce01de",
   "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>news_text</th>\n",
       "      <th>label</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>unicorn in the city</td>\n",
       "      <td>real</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>An alien was found roaming in Wall Street</td>\n",
       "      <td>fake</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Twitter has not taken any legal action against...</td>\n",
       "      <td>real</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>It appears that the index has embarked on a su...</td>\n",
       "      <td>real</td>\n",
       "      <td>0.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Get the latest from TODAY Sign up for our news...</td>\n",
       "      <td>fake</td>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           news_text label  probability\n",
       "0                                unicorn in the city  real         0.73\n",
       "1          An alien was found roaming in Wall Street  fake         0.51\n",
       "2  Twitter has not taken any legal action against...  real         0.51\n",
       "3  It appears that the index has embarked on a su...  real         0.55\n",
       "4  Get the latest from TODAY Sign up for our news...  fake         0.52"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv(r\"fake_news_test_output.csv\")\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f96d50d",
   "metadata": {},
   "source": [
    "#### Visualise predicted data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "987f55f3",
   "metadata": {},
   "source": [
    "We use distribution plot to know how the probabilities of labels are distributed in both the histogram and the density view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fdf2c402",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files\\Python39\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='probability', ylabel='Density'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(df_test.probability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89d77877",
   "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": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}