Project - Fake News Classifier - Simon Lowry.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2a89a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy\n",
    "\n",
    "# disabling tagger and parser of spacy for time considerations\n",
    "nlp = spacy.load('en_core_web_sm', disable=[ \"parser\", \"ner\"])\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a7907ffe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# read in the test and training data to dataframes\n",
    "trainingData = pd.read_csv(\"C:/Users/simon/Documents/AI/Foundatoins in AI - TU Dublin Course/Project/Project Data/train.csv\")\n",
    "testData = pd.read_csv(\"C:/Users/simon/Documents/AI/Foundatoins in AI - TU Dublin Course/Project/Project Data/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "200be001",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   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      "4   4  Iranian woman jailed for fictional unpublished...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  \n",
      "      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...  \n"
     ]
    }
   ],
   "source": [
    "print(trainingData.head())\n",
    "print(testData.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "abb0e3f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set Number of Rows and columns (Rows, Columns): \n",
      "Test set Number of Rows and columns (Rows, Columns): \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5200, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Training set Number of Rows and columns (Rows, Columns): \")\n",
    "trainingData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "b8fc5e7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set Number of Rows and columns (Rows, Columns): \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5200, 4)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"Test set Number of Rows and columns (Rows, Columns): \")\n",
    "testData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2c267c74",
   "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": [
    "trainingData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "10781f7b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\simon\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: 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",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='label', ylabel='count'>"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn  as sns\n",
    "\n",
    "sns.countplot(trainingData['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e063d81d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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      "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...   \n",
       "\n",
       "   labels  \n",
       "0       0  \n",
       "1       1  \n",
       "2       0  \n",
       "3       1  \n",
       "4       1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take test labels from submit csv dataframe and add them to the test dataframe (they were originally separate in two csv files)\n",
    "testLabels = pd.read_csv('C:/Users/simon/Documents/AI/Foundatoins in AI - TU Dublin Course/Project/Project Data/submit.csv')\n",
    "testData['labels'] = testLabels['label']\n",
    "testData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "9a9e947c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data nulls:  0\n",
      "Test data nulls:  0\n"
     ]
    }
   ],
   "source": [
    "print(\"Training data nulls: \", trainingData['text'].isnull().sum())\n",
    "print(\"Test data nulls: \", testData['text'].isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5f7df6b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 20761 entries, 0 to 20799\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      20761 non-null  int64 \n",
      " 1   title   20203 non-null  object\n",
      " 2   author  18843 non-null  object\n",
      " 3   text    20761 non-null  object\n",
      " 4   label   20761 non-null  int64 \n",
      "dtypes: int64(2), object(3)\n",
      "memory usage: 973.2+ KB\n"
     ]
    }
   ],
   "source": [
    "trainingData = trainingData.dropna(subset=['text'])\n",
    "trainingData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "17c3b68a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5193 entries, 0 to 5199\n",
      "Data columns (total 4 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      5193 non-null   int64 \n",
      " 1   title   5071 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: 202.9+ KB\n"
     ]
    }
   ],
   "source": [
    "testData = testData.dropna(subset=['text'])\n",
    "testData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "df00d935",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5193, 5)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6aa4221a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        House Dem Aide: We Didn’t Even See Comey’s Let...\n",
       "1        Ever get the feeling your life circles the rou...\n",
       "2        Why the Truth Might Get You Fired October 29, ...\n",
       "3        Videos 15 Civilians Killed In Single US Airstr...\n",
       "4        Print \\nAn Iranian woman has been sentenced to...\n",
       "                               ...                        \n",
       "20795    Rapper T. I. unloaded on black celebrities who...\n",
       "20796    When the Green Bay Packers lost to the Washing...\n",
       "20797    The Macy’s of today grew from the union of sev...\n",
       "20798    NATO, Russia To Hold Parallel Exercises In Bal...\n",
       "20799      David Swanson is an author, activist, journa...\n",
       "Name: text, Length: 20800, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainingData['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "71e6196f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'House Dem Aide: We Didn’t Even See Comey’s Letter Until Jason Chaffetz Tweeted It By Darrell Lucus on October 30, 2016 Subscribe Jason Chaffetz on the stump in American Fork, Utah ( image courtesy Michael Jolley, available under a Creative Commons-BY license) \\nWith apologies to Keith Olbermann, there is no doubt who the Worst Person in The World is this week–FBI Director James Comey. But according to a House Democratic aide, it looks like we also know who the second-worst person is as well. It turns out that when Comey sent his now-infamous letter announcing that the FBI was looking into emails that may be related to Hillary Clinton’s email server, the ranking Democrats on the relevant committees didn’t hear about it from Comey. They found out via a tweet from one of the Republican committee chairmen. \\nAs we now know, Comey notified the Republican chairmen and Democratic ranking members of the House Intelligence, Judiciary, and Oversight committees that his agency was reviewing emails it had recently discovered in order to see if they contained classified information. Not long after this letter went out, Oversight Committee Chairman Jason Chaffetz set the political world ablaze with this tweet. FBI Dir just informed me, \"The FBI has learned of the existence of emails that appear to be pertinent to the investigation.\" Case reopened \\n— Jason Chaffetz (@jasoninthehouse) October 28, 2016 \\nOf course, we now know that this was not the case . Comey was actually saying that it was reviewing the emails in light of “an unrelated case”–which we now know to be Anthony Weiner’s sexting with a teenager. But apparently such little things as facts didn’t matter to Chaffetz. The Utah Republican had already vowed to initiate a raft of investigations if Hillary wins–at least two years’ worth, and possibly an entire term’s worth of them. Apparently Chaffetz thought the FBI was already doing his work for him–resulting in a tweet that briefly roiled the nation before cooler heads realized it was a dud. \\nBut according to a senior House Democratic aide, misreading that letter may have been the least of Chaffetz’ sins. That aide told Shareblue that his boss and other Democrats didn’t even know about Comey’s letter at the time–and only found out when they checked Twitter. “Democratic Ranking Members on the relevant committees didn’t receive Comey’s letter until after the Republican Chairmen. In fact, the Democratic Ranking Members didn’ receive it until after the Chairman of the Oversight and Government Reform Committee, Jason Chaffetz, tweeted it out and made it public.” \\nSo let’s see if we’ve got this right. The FBI director tells Chaffetz and other GOP committee chairmen about a major development in a potentially politically explosive investigation, and neither Chaffetz nor his other colleagues had the courtesy to let their Democratic counterparts know about it. Instead, according to this aide, he made them find out about it on Twitter. \\nThere has already been talk on Daily Kos that Comey himself provided advance notice of this letter to Chaffetz and other Republicans, giving them time to turn on the spin machine. That may make for good theater, but there is nothing so far that even suggests this is the case. After all, there is nothing so far that suggests that Comey was anything other than grossly incompetent and tone-deaf. \\nWhat it does suggest, however, is that Chaffetz is acting in a way that makes Dan Burton and Darrell Issa look like models of responsibility and bipartisanship. He didn’t even have the decency to notify ranking member Elijah Cummings about something this explosive. If that doesn’t trample on basic standards of fairness, I don’t know what does. \\nGranted, it’s not likely that Chaffetz will have to answer for this. He sits in a ridiculously Republican district anchored in Provo and Orem; it has a Cook Partisan Voting Index of R+25, and gave Mitt Romney a punishing 78 percent of the vote in 2012. Moreover, the Republican House leadership has given its full support to Chaffetz’ planned fishing expedition. But that doesn’t mean we can’t turn the hot lights on him. After all, he is a textbook example of what the House has become under Republican control. And he is also the Second Worst Person in the World. About Darrell Lucus \\nDarrell is a 30-something graduate of the University of North Carolina who considers himself a journalist of the old school. An attempt to turn him into a member of the religious right in college only succeeded in turning him into the religious right\\'s worst nightmare--a charismatic Christian who is an unapologetic liberal. His desire to stand up for those who have been scared into silence only increased when he survived an abusive three-year marriage. You may know him on Daily Kos as Christian Dem in NC . Follow him on Twitter @DarrellLucus or connect with him on Facebook . Click here to buy Darrell a Mello Yello. Connect'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainingData['text'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3763f706",
   "metadata": {},
   "source": [
    "## Removing Stop Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9db14f3d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4eebf9ce",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'to', 'not', 'full', 'enough', 'this', 'may', 'third', 'part', 'seemed', 'via', 'go', 'nowhere', 'out', 'done', 'anyhow', 'toward', 'across', 'still', 'here', 'someone', 'fifteen', 'where', 'somewhere', '‘d', 'only', \"'ll\", 'first', 'am', 'whereby', 'at', \"'s\", 'does', 'whence', 'perhaps', 'amount', 'of', 'such', 'itself', 'could', 'anyone', 'thereafter', 'or', 'by', 'between', 'afterwards', 'five', 'too', 'below', 'if', 'some', 'last', 'nine', 'nothing', 'give', 'were', 'because', 'can', 'what', 'behind', 'ten', 'several', 'mine', 'except', 'before', 'say', 'hence', 'beforehand', 'seeming', 'about', 'a', 'was', 'n’t', 'one', 'something', 'always', '’d', 'so', 'indeed', 'often', 'nevertheless', 'namely', 'under', 'other', 'within', 'four', 'therefore', 'but', 'much', 'none', 'your', 'others', 'on', 'latterly', 'the', 'ever', '‘s', 'everything', 'same', 'own', 'used', 'empty', 'meanwhile', 'myself', 'with', 'which', 'next', 'become', 'do', 'you', 'therein', 'his', 'have', 'an', 'nobody', 'once', 're', 'twelve', 'upon', 'hereby', 'is', 'whither', '’m', '‘ll', 'everyone', 'regarding', 'been', 'thus', 'towards', 'call', 'doing', 'has', 'bottom', 'throughout', 'above', \"'re\", 'just', 'she', 'that', 'fifty', 'together', 'her', 'sometimes', 'against', 'he', 'being', 'without', 'sometime', 'yourself', 'else', 'each', 'him', 'while', 'became', 'me', 'now', 'yours', 'us', 'its', 'least', 'unless', 'six', 'whereas', 'never', 'per', 'did', 'front', 'should', 'cannot', 'over', \"'m\", 'quite', 'down', 'however', 'somehow', 'forty', 'my', 'then', 'thereupon', 'yet', 'yourselves', 'whom', 'any', 'becoming', 'and', 'see', 'mostly', 'formerly', 'latter', 'put', 'show', 'since', 'side', 'both', 'keep', 'until', 'many', 'move', 'even', 'whose', 'former', '’ll', 'less', 'herein', 'along', 'there', 'almost', 'we', 'for', 'who', 'made', 'whoever', 'again', 'beyond', 'few', 'eleven', 'in', 'otherwise', \"n't\", 'themselves', 'twenty', '‘re', 'himself', 'whole', '‘m', 'those', 'anywhere', 'will', 'into', 'thereby', 'their', 'two', 'after', 'had', 'further', 'seems', 'whereafter', 'due', '‘ve', 'hereafter', 'get', 'whether', 'whenever', 'though', 'hers', 'whatever', 'onto', 'when', 'seem', 'elsewhere', 'how', 'whereupon', 'during', 'it', 'eight', 'up', 'are', 'would', 'they', 'using', 'sixty', 'besides', 'ourselves', 'anything', 'as', 'various', 'three', 'must', 'take', 'among', 'might', '’ve', 'anyway', 'wherever', '’re', 'from', '’s', 'noone', 'please', \"'ve\", 'either', 'herself', 'wherein', 'already', 'hereupon', 'although', 'make', 'through', 'beside', 'becomes', 'really', 'why', 'these', 'rather', 'hundred', 'our', 'very', 'well', 'serious', 'thence', 'also', 'them', 'more', 'thru', 'another', 'back', 'off', 'ours', 'than', 'be', 'all', 'alone', 'neither', 'nor', 'n‘t', 'top', \"'d\", 'most', 'every', 'ca', 'everywhere', 'name', 'moreover', 'amongst', 'around', 'i', 'no'}\n"
     ]
    }
   ],
   "source": [
    "print(nlp.Defaults.stop_words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "f928bdee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def tokenizeAndNormalizeArticle(article):\n",
    "    article_split = article.split()\n",
    "    normalizedTokens = []\n",
    "    \n",
    "    for word in article_split:\n",
    "        word = re.sub(r\"[\\n\\t]*\", \"\", word).strip()   # remove newlines, tabs and any additional spaces\n",
    "        word = word.lower()                           # set to lower case\n",
    "        if (nlp.vocab[word].is_stop == False):        # remove stop words\n",
    "            normalizedTokens.append(word)\n",
    "        word = re.sub('[^A-Za-z0-9]+', '', word)      # remove all special characters & punctutation\n",
    "    \n",
    "    return \" \".join(normalizedTokens)\n",
    "\n",
    "\n",
    "# call method removeStopWordsInTweet for each row of the text\n",
    "# parameter to remove all stop words from the dataset\n",
    "articleData = trainingData[\"text\"]\n",
    "normalizedArticleData = []\n",
    "\n",
    "counter = 0\n",
    "for article in articleData:\n",
    "    if (type(article) is str):\n",
    "        normalizedArticleData.append(tokenizeAndNormalizeArticle(article))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "58385ae1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20761\n"
     ]
    }
   ],
   "source": [
    "print(len(normalizedArticleData))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7feb371c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5193\n"
     ]
    }
   ],
   "source": [
    "# repeat for test data\n",
    "articleData = testData[\"text\"]\n",
    "normalizedTestData = []\n",
    "\n",
    "counter = 0\n",
    "for article in articleData:\n",
    "    if (type(article) is str):\n",
    "        normalizedTestData.append(tokenizeAndNormalizeArticle(article))\n",
    "        \n",
    "print(len(normalizedTestData))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17da54b4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('tok2vec', <spacy.pipeline.tok2vec.Tok2Vec at 0x241885ac860>),\n",
       " ('tagger', <spacy.pipeline.tagger.Tagger at 0x241885c1e00>),\n",
       " ('parser', <spacy.pipeline.dep_parser.DependencyParser at 0x2418842fe20>),\n",
       " ('ner', <spacy.pipeline.ner.EntityRecognizer at 0x241885ce460>),\n",
       " ('attribute_ruler',\n",
       "  <spacy.pipeline.attributeruler.AttributeRuler at 0x24188620cc0>),\n",
       " ('lemmatizer', <spacy.lang.en.lemmatizer.EnglishLemmatizer at 0x24188620b00>)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nlp.pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d4e176",
   "metadata": {},
   "source": [
    "# Vectorization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "88fbe719",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 27.9 GiB for an array with shape (20761, 180438) and data type float64",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-91-3fa09eddeb2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mxTrainingTf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtfidfVectorizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnormalizedArticleData\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mxTrainingTf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtfidfVectorizer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_feature_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;31m# xTestTf = tfidfVectorizer.transform(normalizedTestData)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\scipy\\sparse\\compressed.py\u001b[0m in \u001b[0;36mtoarray\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m   1029\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0morder\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1030\u001b[0m             \u001b[0morder\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_swap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'cf'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1031\u001b[1;33m         \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_process_toarray_args\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1032\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mc_contiguous\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf_contiguous\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1033\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Output array must be C or F contiguous'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\scipy\\sparse\\base.py\u001b[0m in \u001b[0;36m_process_toarray_args\u001b[1;34m(self, order, out)\u001b[0m\n\u001b[0;32m   1200\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mout\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1201\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1202\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1203\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1204\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 27.9 GiB for an array with shape (20761, 180438) and data type float64"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer \n",
    " \n",
    "tfidfVectorizer=TfidfVectorizer(use_idf=True) \n",
    " \n",
    "# vectorize training & test data\n",
    "xTrainingTf = tfidfVectorizer.fit_transform(normalizedArticleData)\n",
    "\n",
    "xTestTf = tfidfVectorizer.transform(normalizedTestData)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "7eb922d4",
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "34d9605d",
   "metadata": {},
   "source": [
    "# Applying Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1e9e19aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Naive Bayes Model:\n",
      "----------------------------\n",
      "accuracy:   0.582\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Positive       0.53      0.73      0.61      2339\n",
      "    Negative       0.68      0.46      0.55      2854\n",
      "\n",
      "    accuracy                           0.58      5193\n",
      "   macro avg       0.60      0.60      0.58      5193\n",
      "weighted avg       0.61      0.58      0.58      5193\n",
      "\n",
      "\n",
      "Time taken to apply model: 0.262702 seconds\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from time import time\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "\n",
    "t = time()\n",
    "\n",
    "yTraining = trainingData['label']\n",
    "naiveBayesClassifier = MultinomialNB()\n",
    "naiveBayesClassifier.fit(xTrainingTf, yTraining)\n",
    "\n",
    "# predict the new document from the testing dataset\n",
    "yPredictions = naiveBayesClassifier.predict(xTestTf)\n",
    "yTest = testData['labels']\n",
    "\n",
    "print(\"Applying Naive Bayes Model:\")\n",
    "print(\"----------------------------\")\n",
    "\n",
    "# compute the performance measures\n",
    "score1 = metrics.accuracy_score(yTest, yPredictions)\n",
    "print(\"accuracy:   %0.3f\" % score1)\n",
    "\n",
    "print(metrics.classification_report(yTest, yPredictions,\n",
    "     target_names=['Positive', 'Negative']))\n",
    "\n",
    "naiveBayesConfusionMatrix = metrics.confusion_matrix(yTest, yPredictions)\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(naiveBayesConfusionMatrix, annot=True, fmt='g', ax=ax);\n",
    "\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');\n",
    "ax.set_title('Confusion Matrix');\n",
    "ax.xaxis.set_ticklabels(['TRUE', 'FAKE']); \n",
    "ax.yaxis.set_ticklabels(['TRUE', 'FAKE']);\n",
    "\n",
    "\n",
    "duration = time() - t\n",
    "print(\"\\nTime taken to apply model: %f seconds\" % (duration))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "d655475c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(naiveBayesConfusionMatrix, annot=True, fmt='g', ax=ax);\n",
    "\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');\n",
    "ax.set_title('Confusion Matrix');\n",
    "ax.xaxis.set_ticklabels(['TRUE', 'FAKE']); \n",
    "ax.yaxis.set_ticklabels(['TRUE', 'FAKE']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "369ed26c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.582\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Positive       0.53      0.73      0.61      2339\n",
      "    Negative       0.68      0.46      0.55      2854\n",
      "\n",
      "    accuracy                           0.58      5193\n",
      "   macro avg       0.60      0.60      0.58      5193\n",
      "weighted avg       0.61      0.58      0.58      5193\n",
      "\n",
      "confusion matrix:\n",
      "[[1719  620]\n",
      " [1551 1303]]\n",
      "------------------------------\n"
     ]
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d82b701f",
   "metadata": {},
   "source": [
    "# Random Forrest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "dbd1b203",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying Random Forrest Model:\n",
      "-------------------------------\n",
      "accuracy:   0.668\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Positive       0.61      0.71      0.66      2339\n",
      "    Negative       0.73      0.63      0.68      2854\n",
      "\n",
      "    accuracy                           0.67      5193\n",
      "   macro avg       0.67      0.67      0.67      5193\n",
      "weighted avg       0.68      0.67      0.67      5193\n",
      "\n",
      "\n",
      "Time taken to apply model: 36.406860 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "t = time()\n",
    "randomForrestClassifier = RandomForestClassifier(n_estimators=100, random_state=0, n_jobs=-1)\n",
    "randomForrestClassifier.fit(xTrainingTf, yTraining)\n",
    "yPredictions = randomForrestClassifier.predict(xTestTf)\n",
    "\n",
    "print(\"Applying Random Forrest Model:\")\n",
    "print(\"-------------------------------\")\n",
    "\n",
    "# compute the performance measures\n",
    "score1 = metrics.accuracy_score(yTest, yPredictions)\n",
    "print(\"accuracy:   %0.3f\" % score1)\n",
    "\n",
    "print(metrics.classification_report(yTest, yPredictions,\n",
    "     target_names=['Positive', 'Negative']))\n",
    "\n",
    "randomForrestConfusionMatrix = metrics.confusion_matrix(yTest, yPredictions)\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(randomForrestConfusionMatrix, annot=True, fmt='g', ax=ax);\n",
    "\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');\n",
    "ax.set_title('Confusion Matrix');\n",
    "ax.xaxis.set_ticklabels(['TRUE', 'FAKE']); \n",
    "ax.yaxis.set_ticklabels(['TRUE', 'FAKE']);\n",
    "\n",
    "duration = time() - t\n",
    "print(\"\\nTime taken to apply model: %f seconds\" % (duration))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1756ac1e",
   "metadata": {},
   "source": [
    "# Applying SVM Model Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "b8f9e270",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Applying SVM Model:\n",
      "----------------------------\n",
      "accuracy:   0.637\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "    Positive       0.59      0.65      0.62      2339\n",
      "    Negative       0.69      0.62      0.65      2854\n",
      "\n",
      "    accuracy                           0.64      5193\n",
      "   macro avg       0.64      0.64      0.64      5193\n",
      "weighted avg       0.64      0.64      0.64      5193\n",
      "\n",
      "\n",
      "Time taken to apply model: 1305.187009 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "#Create a svm Classifier\n",
    "svmClassifier = svm.SVC(kernel='linear') # Linear Kernel\n",
    "\n",
    "# Perform training for the svm model\n",
    "svmClassifier.fit(xTrainingTf, yTraining)\n",
    "\n",
    "# Predict the response for test dataset \n",
    "yPredictions = svmClassifier.predict(xTestTf)\n",
    "\n",
    "print(\"Applying SVM Model:\")\n",
    "print(\"----------------------------\")\n",
    "\n",
    "# compute the performance measures\n",
    "score1 = metrics.accuracy_score(yTest, yPredictions)\n",
    "print(\"accuracy:   %0.3f\" % score1)\n",
    "\n",
    "print(metrics.classification_report(yTest, yPredictions,\n",
    "     target_names=['Positive', 'Negative']))\n",
    "\n",
    "svmConfusionMatrix = metrics.confusion_matrix(yTest, yPredictions)\n",
    "\n",
    "ax= plt.subplot()\n",
    "sns.heatmap(svmConfusionMatrix, annot=True, fmt='g', ax=ax);\n",
    "\n",
    "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels');\n",
    "ax.set_title('Confusion Matrix');\n",
    "ax.xaxis.set_ticklabels(['TRUE', 'FAKE']); \n",
    "ax.yaxis.set_ticklabels(['TRUE', 'FAKE']);\n",
    "\n",
    "duration = time() - t\n",
    "print(\"\\nTime taken to apply model: %f seconds\" % (duration))"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}