Fake News Prediction (2).ipynb

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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "import itertools\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import re\n",
    "import nltk\n",
    "import pickle\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.metrics import accuracy_score,f1_score,recall_score,precision_score"
   ]
  },
  {
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   "id": "5ef2b1c9-8b50-4a11-b209-483318d2bb11",
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       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
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       "      <th>4</th>\n",
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       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
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       "      <td>REAL</td>\n",
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       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                text label  \n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "4  It's primary day in New York and front-runners...  REAL  "
      ]
     },
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   ],
   "source": [
    "df=pd.read_csv(r'C:\\Users\\Public\\Datasets\\news.csv')\n",
    "df.head()"
   ]
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      "text/plain": [
       "      Unnamed: 0                                              title  \\\n",
       "0           8476                       You Can Smell Hillary’s Fear   \n",
       "1          10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2           3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3          10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4            875   The Battle of New York: Why This Primary Matters   \n",
       "...          ...                                                ...   \n",
       "6330        4490  State Department says it can't find emails fro...   \n",
       "6331        8062  The ‘P’ in PBS Should Stand for ‘Plutocratic’ ...   \n",
       "6332        8622  Anti-Trump Protesters Are Tools of the Oligarc...   \n",
       "6333        4021  In Ethiopia, Obama seeks progress on peace, se...   \n",
       "6334        4330  Jeb Bush Is Suddenly Attacking Trump. Here's W...   \n",
       "\n",
       "                                                   text label  \n",
       "0     Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "1     Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "2     U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "3     — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "4     It's primary day in New York and front-runners...  REAL  \n",
       "...                                                 ...   ...  \n",
       "6330  The State Department told the Republican Natio...  REAL  \n",
       "6331  The ‘P’ in PBS Should Stand for ‘Plutocratic’ ...  FAKE  \n",
       "6332   Anti-Trump Protesters Are Tools of the Oligar...  FAKE  \n",
       "6333  ADDIS ABABA, Ethiopia —President Obama convene...  REAL  \n",
       "6334  Jeb Bush Is Suddenly Attacking Trump. Here's W...  REAL  \n",
       "\n",
       "[6335 rows x 4 columns]"
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   "id": "be62b46e-963a-4bd6-84b3-900a2675b1a1",
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   "source": [
    "lemmatizer=WordNetLemmatizer()\n",
    "stpwords=list(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "484bb57d-0862-4386-98eb-4d00983cd025",
   "metadata": {},
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    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 5,
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    }
   ],
   "source": [
    "stpwords"
   ]
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    {
     "name": "stdout",
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     "text": [
      "checking the qualities...\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6335 entries, 0 to 6334\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  6335 non-null   int64 \n",
      " 1   title       6335 non-null   object\n",
      " 2   text        6335 non-null   object\n",
      " 3   label       6335 non-null   object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 198.1+ KB\n",
      "check finished\n"
     ]
    }
   ],
   "source": [
    "def data_qualityCheck():\n",
    "    print(\"checking the qualities...\")\n",
    "    df.isnull().sum()\n",
    "    df.info()\n",
    "    print(\"check finished\")\n",
    "data_qualityCheck()"
   ]
  },
  {
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   "execution_count": 7,
   "id": "32e14735-bd96-4261-b7d6-f715cc32d3dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_train=df.label"
   ]
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   "cell_type": "code",
   "execution_count": 8,
   "id": "2ac54b6d-81bc-4131-95ae-16fd34efdd90",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "0    FAKE\n",
       "1    FAKE\n",
       "2    REAL\n",
       "3    FAKE\n",
       "4    REAL\n",
       "5    FAKE\n",
       "6    FAKE\n",
       "7    REAL\n",
       "8    REAL\n",
       "9    REAL\n",
       "Name: label, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "label_train.head(10)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2d60361e-a005-48da-8acc-f4e7f6f1bcfc",
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   "outputs": [],
   "source": [
    "df=df.drop(\"label\",axis=1)"
   ]
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  {
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   "execution_count": 10,
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      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                text  \n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  \n",
       "4  It's primary day in New York and front-runners...  "
      ]
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     "execution_count": 10,
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "38602660-4311-4e3c-8ad1-aa37508b064d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Nearly simultaneous explosions targeted a Turkish peace rally Saturday in Ankara, killing at least 95 people and wounding hundreds in Turkey\\'s deadliest attack in years — one that threatens to inflame the nation\\'s ethnic tensions.\\n\\nThere was no immediate claim of responsibility but Prime Minister Ahmet Davutoglu said there were \"strong signs\" that the two explosions — which struck 50 meters (yards) apart just after 10 a.m. — were suicide bombings. He suggested that Kurdish rebels or Islamic State group militants were to blame.\\n\\nThe two explosions occurred seconds apart outside the capital\\'s main train station as hundreds of opposition supporters and Kurdish activists gathered for the peace rally organized by Turkey\\'s public workers\\' union and other groups. The protesters planned to call for increased democracy in Turkey and an end to the renewed violence between Kurdish rebels and Turkish security forces.\\n\\nThe attacks Saturday came at a tense time for Turkey, a NATO member that borders war-torn Syria, hosts more refugees than any other nation in the world and has seen renewed fighting with Kurdish rebels that has left hundreds dead in the last few months.\\n\\nMany people at the rally had been anticipating that the rebels of the Kurdistan Workers\\' Party, or PKK, would declare a temporary cease-fire — which it did hours after the bombing — to ensure that Turkey\\'s Nov. 1 election would be held in a safe environment.\\n\\nTelevision footage from Turkey\\'s Dogan news agency showed a line of protesters Saturday near Ankara\\'s train station, chanting and performing a traditional dance with their hands locked when a large explosion went off behind them. An Associated Press photographer saw several bodies covered with bloodied flags and banners that demonstrators had brought for the rally.\\n\\n\"There was a massacre in the middle of Ankara,\" said Lami Ozgen, head of the Confederation of Public Sector Trade Unions, or KESK.\\n\\nThe state-run Anadolu Agency said the attacks were carried out with TNT explosives fortified with metal ball-bearings.\\n\\nTurkey\\'s government late Saturday raised the death toll in the twin bomb blasts to 95 people killed, 248 wounded. It said 48 of the wounded were in serious condition — and a doctor\\'s group said many of them had burns.\\n\\n\"This massacre targeting a pro-Kurdish but mostly Turkish crowd could flame ethnic tensions in Turkey,\" said Soner Cagaptay, an analyst at the Washington Institute.\\n\\nCagaptay said the attack could be the work of groups \"hoping to induce the PKK, or its more radical youth elements, to continue fighting Turkey,\" adding that the Islamic State group would benefit most from the full-blown Turkey-PKK conflict.\\n\\n\"(That) development could make ISIS a secondary concern in the eyes of many Turks to the PKK,\" Cagaptay said in emailed comments, using another acronym for IS militants.\\n\\nSmall anti-government protests broke out at the scene of the explosions and outside Ankara hospitals as Interior Minister Selami Altinok visited the wounded. Some demonstrators chanted \"Murderer Erdogan!\" — referring to President Recep Tayyip Erdogan, whom many accuse of increasing tensions with Kurds to profit at the ballot box in November. Erdogan denies the accusations.\\n\\nLater Saturday, thousands gathered near Istanbul\\'s main square denouncing the attacks and also holding the government responsible.\\n\\nThe Turkish government imposed a temporary news blackout covering images that showed the moment of the blasts, gruesome or bloody pictures or \"images that create a feeling of panic.\" A spokesman warned media organizations they could face a \"full blackout\" if they did not comply.\\n\\nMany people reported being unable to access Twitter and other social media websites for several hours after the blasts. It was not clear if authorities had blocked access to the websites, but Turkey often does impose blackouts following attacks.\\n\\nAt a news conference, Davutoglu declared a three-day official mourning period for the blast victims and said Turkey had been warned about groups aiming to destabilize the country.\\n\\n\"For some time, we have been receiving intelligence information based from some (Kurdish rebel) and Daesh statements that certain suicide attackers would be sent to Turkey... and that through these attackers chaos would be created in Turkey,\" Davutoglu told reporters, using the IS group\\'s Arabic acronym.\\n\\n\"The (Kurdish rebels) or Daesh could emerge (as culprits) of today\\'s terror event,\" Davutoglu said, promising that those behind the attacks would be caught and punished.\\n\\nDavutoglu said authorities had detained at least two suspected would-be suicide bombers in the past three days in Ankara and Istanbul.\\n\\nAuthorities had been on alert after Turkey agreed to take a more active role in the U.S.-led battle against the Islamic State group. Turkey opened up its bases to U.S. aircraft to launch air raids on the extremist group in Syria and carried out a limited number of strikes on the group itself. Russia has also entered the fray on behalf of the Syrian government recently, bombing sites in Syria and reportedly violating Turkish airspace a few times in the past week.\\n\\nOn a separate front, the fighting between Turkish forces and Kurdish rebels flared anew in July, killing at least 150 police and soldiers and hundreds of PKK rebels since then. Turkish jets have also carried out numerous deadly airstrikes on Kurdish rebel targets in northern Iraq.\\n\\nErdogan condemned Saturday\\'s attacks, which he said targeted the country\\'s unity, called for solidarity and canceled a planned visit Monday to Turkmenistan.\\n\\n\"The greatest and most meaningful response to this attack is the solidarity and determination we will show against it,\" Erdogan said.\\n\\nCritics have accused Erdogan of re-igniting the fighting with the Kurds to seek electoral gains — hoping that the turmoil would rally voters back to the ruling Justice and Development Party, or AKP. Electoral gains by the country\\'s pro-Kurdish party caused the AKP, founded by Erdogan, to lose its parliamentary majority in a June election after a decade of single-party rule.\\n\\nThe attacks Saturday, which even surpassed twin Al Qaeda-linked attacks in Istanbul in 2003 that killed some 60 people, also drew widespread condemnation from Turkey\\'s allies.\\n\\nTurkey\\'s state-run news agency said President Obama called Erdogan to extend his condolences. The Anadolu Agency, citing unnamed officials, said Obama told Erdogan the United States would continue to side with Turkey in the fight against terrorism. It quoted Obama as saying the U.S. \"shared Turkey\\'s grief.\"\\n\\nErdogan earlier said the twin bombings aimed to destroy Turkey\\'s \"peace and stability.\" Anadolu said the two leaders agreed to talk more in the coming days.\\n\\nGerman Chancellor Angela Merkel sent her condolences, calling the attacks \"particularly cowardly acts that were aimed directly at civil rights, democracy and peace.\"\\n\\n\"It is an attempt at intimidation and an attempt to spread fear,\" she said. \"I am convinced that the Turkish government and all of Turkish society stands together at this time with a response of unity and democracy.\"\\n\\nNATO Secretary General Jens Stoltenberg said \"there can be no justification for such a horrendous attack on people marching for peace... All NATO allies stand united in the fight against the scourge of terrorism.\"\\n\\nSaturday was the third attack against meetings of Kurdish activists. In July, a suicide bombing blamed on the Islamic State group killed 33 peace activists, including many Kurds, in the town of Suruc near Turkey\\'s border with Syria. Two people were killed in June in a bomb attack at the pro-Kurdish party\\'s election rally.\\n\\n\"This attack (Saturday) resembles and is a continuation of the Diyarbakir and Suruc (attacks),\" said Selahattin Demirtas, leader of the Turkey\\'s pro-Kurdish party. He held Erdogan and Davutoglu\\'s government responsible for the latest attack, saying it was \"carried out by the state against the people.\"\\n\\nIn the aftermath of the Ankara attack, the PKK declared a temporary cease-fire. A rebel statement said Saturday the group is halting hostilities to allow the Nov. 1 election to proceed safely. It said it would not launch attacks but would defend itself.\\n\\nThe government has said there would be no letup in its fight against the Kurdish rebels.\\n\\n\"Our operations (against the PKK) will continue until they lay down arms,\" Davutoglu said late Friday.'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['text'][2168]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "439a451d-5463-49e9-8910-5902d91ef119",
   "metadata": {},
   "outputs": [],
   "source": [
    "lemmatizer=WordNetLemmatizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6c5b35db-ddd7-4adf-bd3a-f7ab32e213d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "stpwrds=list(stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6c75a77-7434-405a-9f88-260e39cf4ca8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['i',\n",
       " 'me',\n",
       " 'my',\n",
       " 'myself',\n",
       " 'we',\n",
       " 'our',\n",
       " 'ours',\n",
       " 'ourselves',\n",
       " 'you',\n",
       " \"you're\",\n",
       " \"you've\",\n",
       " \"you'll\",\n",
       " \"you'd\",\n",
       " 'your',\n",
       " 'yours',\n",
       " 'yourself',\n",
       " 'yourselves',\n",
       " 'he',\n",
       " 'him',\n",
       " 'his',\n",
       " 'himself',\n",
       " 'she',\n",
       " \"she's\",\n",
       " 'her',\n",
       " 'hers',\n",
       " 'herself',\n",
       " 'it',\n",
       " \"it's\",\n",
       " 'its',\n",
       " 'itself',\n",
       " 'they',\n",
       " 'them',\n",
       " 'their',\n",
       " 'theirs',\n",
       " 'themselves',\n",
       " 'what',\n",
       " 'which',\n",
       " 'who',\n",
       " 'whom',\n",
       " 'this',\n",
       " 'that',\n",
       " \"that'll\",\n",
       " 'these',\n",
       " 'those',\n",
       " 'am',\n",
       " 'is',\n",
       " 'are',\n",
       " 'was',\n",
       " 'were',\n",
       " 'be',\n",
       " 'been',\n",
       " 'being',\n",
       " 'have',\n",
       " 'has',\n",
       " 'had',\n",
       " 'having',\n",
       " 'do',\n",
       " 'does',\n",
       " 'did',\n",
       " 'doing',\n",
       " 'a',\n",
       " 'an',\n",
       " 'the',\n",
       " 'and',\n",
       " 'but',\n",
       " 'if',\n",
       " 'or',\n",
       " 'because',\n",
       " 'as',\n",
       " 'until',\n",
       " 'while',\n",
       " 'of',\n",
       " 'at',\n",
       " 'by',\n",
       " 'for',\n",
       " 'with',\n",
       " 'about',\n",
       " 'against',\n",
       " 'between',\n",
       " 'into',\n",
       " 'through',\n",
       " 'during',\n",
       " 'before',\n",
       " 'after',\n",
       " 'above',\n",
       " 'below',\n",
       " 'to',\n",
       " 'from',\n",
       " 'up',\n",
       " 'down',\n",
       " 'in',\n",
       " 'out',\n",
       " 'on',\n",
       " 'off',\n",
       " 'over',\n",
       " 'under',\n",
       " 'again',\n",
       " 'further',\n",
       " 'then',\n",
       " 'once',\n",
       " 'here',\n",
       " 'there',\n",
       " 'when',\n",
       " 'where',\n",
       " 'why',\n",
       " 'how',\n",
       " 'all',\n",
       " 'any',\n",
       " 'both',\n",
       " 'each',\n",
       " 'few',\n",
       " 'more',\n",
       " 'most',\n",
       " 'other',\n",
       " 'some',\n",
       " 'such',\n",
       " 'no',\n",
       " 'nor',\n",
       " 'not',\n",
       " 'only',\n",
       " 'own',\n",
       " 'same',\n",
       " 'so',\n",
       " 'than',\n",
       " 'too',\n",
       " 'very',\n",
       " 's',\n",
       " 't',\n",
       " 'can',\n",
       " 'will',\n",
       " 'just',\n",
       " 'don',\n",
       " \"don't\",\n",
       " 'should',\n",
       " \"should've\",\n",
       " 'now',\n",
       " 'd',\n",
       " 'll',\n",
       " 'm',\n",
       " 'o',\n",
       " 're',\n",
       " 've',\n",
       " 'y',\n",
       " 'ain',\n",
       " 'aren',\n",
       " \"aren't\",\n",
       " 'couldn',\n",
       " \"couldn't\",\n",
       " 'didn',\n",
       " \"didn't\",\n",
       " 'doesn',\n",
       " \"doesn't\",\n",
       " 'hadn',\n",
       " \"hadn't\",\n",
       " 'hasn',\n",
       " \"hasn't\",\n",
       " 'haven',\n",
       " \"haven't\",\n",
       " 'isn',\n",
       " \"isn't\",\n",
       " 'ma',\n",
       " 'mightn',\n",
       " \"mightn't\",\n",
       " 'mustn',\n",
       " \"mustn't\",\n",
       " 'needn',\n",
       " \"needn't\",\n",
       " 'shan',\n",
       " \"shan't\",\n",
       " 'shouldn',\n",
       " \"shouldn't\",\n",
       " 'wasn',\n",
       " \"wasn't\",\n",
       " 'weren',\n",
       " \"weren't\",\n",
       " 'won',\n",
       " \"won't\",\n",
       " 'wouldn',\n",
       " \"wouldn't\"]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stpwrds\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ef0dd925-f80c-4dd0-a670-469c147df80c",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,Y_train,Y_test=train_test_split(df['text'],label_train,test_size=0.3,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7f1bbc67-a1c4-4fb7-ae19-b777a0c62f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_v=TfidfVectorizer()\n",
    "tfidf_X_train=tfidf_v.fit_transform(X_train)\n",
    "tfidf_X_test=tfidf_v.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "181c4ab0-2871-4525-bc6f-c051479df13a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4434, 58207)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_X_train.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "3b495216-b88f-4cac-8eda-1e8056854c9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:94.21%\n"
     ]
    }
   ],
   "source": [
    "pac=PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_X_train,Y_train)\n",
    "y_pred=pac.predict(tfidf_X_test)\n",
    "score=accuracy_score(Y_test,y_pred)\n",
    "print(f'Accuracy:{round(score*100,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4596d2ac-b2a2-470c-a659-7fbe838f3055",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "recall:92.91%\n"
     ]
    }
   ],
   "source": [
    "recall=recall_score(Y_test,y_pred,pos_label=\"FAKE\")\n",
    "print(f'recall:{round(recall*100,2)}%')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ae82d80a-dd53-472d-b96e-58ca7ca46fda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "precision:95.03%\n"
     ]
    }
   ],
   "source": [
    "precision=precision_score(Y_test,y_pred,pos_label='FAKE')\n",
    "print(f'precision:{round(precision*100,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "00711261-6224-48dd-b497-0d7a2bad317d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score:93.95%\n"
     ]
    }
   ],
   "source": [
    "f1score=f1_score(Y_test,y_pred,pos_label='FAKE')\n",
    "print(f'f1 score:{round(f1score*100,2)}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3cd1c576-567d-4dcf-9b54-33e2452ab94d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fake_news_det(news):\n",
    "    input_data=[news]\n",
    "    vectorized_input_data=tfidf_v.transform(input_data)\n",
    "    prediction=pac.predict(vectorized_input_data)\n",
    "    print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a6a9f95e-291b-488f-8a51-c27f739e78be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['REAL']\n"
     ]
    }
   ],
   "source": [
    "fake_news_det(\"Davutoglu said authorities had detained at least two suspected would-be suicide bombers in the past three days in Ankara and Istanbul.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3442cac2-8798-4053-b968-6fb3bb894831",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['FAKE']\n"
     ]
    }
   ],
   "source": [
    "fake_news_det(\"The FBI has wiretaps from the investigation of the Clinton Foundation. It’s finding new emails all the time. And Clintonworld panicked. The spinmeisters of Clintonworld have claimed that the email scandal is just so much smoke without fire\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6cf29b7b-3ec4-408e-a60d-41a629c1fbba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.0%\n",
      "Confusion Matrix:\n",
      "[[920  67]\n",
      " [ 47 867]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x20c276fe970>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score\n",
    "\n",
    "# Assuming pac has already been trained and y_pred is predicted as per your code\n",
    "pac = PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_X_train, Y_train)\n",
    "y_pred = pac.predict(tfidf_X_test)\n",
    "score = accuracy_score(Y_test, y_pred)\n",
    "print(f'Accuracy: {round(score * 100, 2)}%')\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(Y_test, y_pred)\n",
    "\n",
    "# Print the confusion matrix\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n",
    "\n",
    "# Optionally, display the confusion matrix\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "\n",
    "# If you need to visualize it in a Jupyter Notebook, use the following line:\n",
    "# import matplotlib.pyplot as plt\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5c138960-f551-4ece-a491-2560e44678ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 94.06%\n",
      "Confusion Matrix:\n",
      "[[921  66]\n",
      " [ 47 867]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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gIyMDgI8//pgOHTrg4+ND48aNmT59+kW/OHfddReDBg3iqaeeIjw8nJCQEJKSkiguLna0KSwsZOLEiVx55ZX4+fnRuXNn1q9fD5wdaqtXrx7vv/++o327du0IDw93vN64cSPe3t6cOXMGwzB47LHHiIqKwtvbm4iICO6///5L+TLKJajjb6dlx9O8/WwYv2bWwmaDtR/UZU+aH9lZ5ybZRQUWXp0dQc9BJ/ELsJ/njCJVW3hUITfdcZyfD/rwj8TmfLqsPqMfPUTc4BMABIUUU8ffzm1/O8a2lCD+Prw5m1fXZeqifcR0zqvk6MUdpXNw3NlcsXXrVo4dO+bYkpOTAbj11lsBGD9+PJ988gnvvfceKSkpHD16lMGDBzv622w2+vfvT1FREZs3b+b1119n6dKlTJs2zeX3Xmklk+eee46rrrqKl19+ma1bt+Lp6QnA6dOnmTBhAm3btiU/P59p06bx17/+le3bt+Ph4fyFLiwsZNiwYfz0009s2LCBevXqsWHDBoYPH86CBQvo1q0bBw4cYNSoUQA8+uijF4xn3bp1hIeHs27dOvbv38/tt99Ou3btGDlyJABjx45l9+7dLF++nIiICD766CP69OnDzp07adq0Kd27d2f9+vXccsstnDx5kj179uDr68vevXtp0aIFKSkpXHPNNdSpU4f333+f+fPns3z5clq3bk1mZiY7duw4b1yFhYUUFhY6Xufl6ZdNeZj8/CGemRDF/3Vog4enQZOYM/QcdJJ9/6nj1K6kGGb/v4ZgwH2PH6mcYEXcZLHAvp1+LH0qEoADu/1o2Ow3+icc54sP62H576/W1OQgPnotDIAf9/jRqmM+/f/vODu3WCsrdKlm6tWr5/T68ccf56qrrqJHjx7k5uby6quv8vbbb3PDDTcAsGTJElq2bMnXX39Nly5dWLNmDbt37+aLL74gNDSUdu3aMXPmTB566CEee+wxvLy8yhxLpVVwAgMDCQgIwNPTk7CwMMcXZciQIQwePJgmTZrQrl07XnvtNXbu3Mnu3bud+ufn59O/f39OnDjBunXrHP2nT5/Oww8/TGJiIo0bN+bGG29k5syZvPTSSxeNp27duvzzn/+kRYsW3HTTTfTv35+1a9cCZ8tlS5Ys4b333qNbt25cddVVTJw4ka5du7JkyRIAevbs6ajofPXVV7Rv395p3/r16+nRo4fjfGFhYcTFxREVFcVf/vIXRyL1R3PnziUwMNCxRUZGuv7FlnNENCziqQ/38/H+//DWtl08/9k+SoothEf/nkyWJjdZP3sxd/kBVW+k2so+UZuM/b5O+zL2+1AvogiAvJO1KCm2nKeNr6ONVE92LI7nUV3S9t9Jxnl5eU7b//7hfSFFRUW89dZb3HPPPVgsFtLS0iguLiYuLs7RpkWLFkRFRZGamgpAamoqMTExhIaGOtrEx8eTl5fHrl27XHrvVW56/L59+xg2bBiNGzfGarU6hq5Kh59KDRs2jNOnT7NmzRoCAwMd+3fs2MGMGTPw9/d3bCNHjuTYsWOcOXPmgtdt3bq1o4oEEB4ezvHjZ8eod+7cic1mo1mzZk7nTUlJcQyf9ejRg927d3PixAlSUlLo2bOnI8EpLi5m8+bN9OzZEzhbqvvtt99o3LgxI0eO5KOPPrrgENqUKVPIzc11bIcPH3b5ayoX5lPHTkhoCadyPElLsRIbf7ZCVprc/HzQm8ff2Y81WHNvpPravc2fBo1/c9p3ZaMCjv98dqJxSbEHP/zHjwaNC87Tpux/MUvVY7i5gsr4b4ITGRnp9Mf23Llz//TaK1asICcnh7vuuguAzMxMvLy8CAoKcmoXGhpKZmamo83/Jjelx0uPuaLKzeodMGAA0dHRLF68mIiICOx2O23atKGoyPmviH79+vHWW2+RmprqKHXB2crO9OnTncb0Svn4XHhFQO3atZ1eWywW7Ha745yenp6kpaU5JUEA/v5nJ6XGxMQQHBxMSkoKKSkpzJ49m7CwMJ544gm2bt1KcXEx1157LXD2GyU9PZ0vvviC5ORkxowZw5NPPklKSso5cXh7e+PtrXtQlLdt6wMwDIi8qpCfD3rxyswriWxSQO/bf6WkGGaObMT+nb7MeONH7DYL2cfP/qgEBNmo7XX21p7Hj9TmVE4tjv9cG7sNDnx/9q/fiEaF+Pqp2iNVx0evhfHM+3u4fcxRvvo0mOZX59Nv2Ame+3tDR5v3Xw5jyvMH2PlNADtSrXTqkUuXXieZPKxl5QUubiuvp4kfPnwYq/X3ocqyfC69+uqr9O3bl4iIiEu+vjuqVILz66+/kp6ezuLFi+nWrRtwdnLu+YwePZo2bdpw88038+mnnzqGfzp06EB6ejpNmjQpt7jat2+PzWbj+PHjjrj+yGKx0K1bNz7++GN27dpF165dqVOnDoWFhbz00kt06tQJPz8/R3tfX18GDBjAgAEDSEpKokWLFuzcuZMOHTqUW9xyYafzPFkyN5xfjtUmIMjGdf1yuPvhY9SqDZmHvfh6zdmq4JgbWzj1m/f+fq6+Nh+AN54KJ/nd35eIj+nd/Jw2IlXBD//xZ8bfmnD3pCMk3P8zmYe9WTQzinUf/35rjs1rgnn+ERu3jz7K6EcPceRHX2aOacqubQGVGLlUFVar1SnB+TOHDh3iiy++4MMPP3TsCwsLo6ioiJycHKcqTlZWFmFhYY4233zzjdO5SldZlbYpqyqV4NStW5eQkBBefvllwsPDycjI4OGHH75g+/vuuw+bzcZNN93E559/TteuXZk2bRo33XQTUVFR3HLLLXh4eLBjxw6+//57Zs2adUlxNWvWjISEBIYPH87TTz9N+/btOXHiBGvXrqVt27b0798fODsP58EHH6RTp06Oyk737t1ZtmwZkyZNcpxv6dKl2Gw2OnfuTJ06dXjrrbfw9fUlOjr6kuIT1/W4OYceN+ec91hYZBGrj27/03NMfDaDic9m/Gk7kargmy/r8s2XdS/aZs179VjzXr2LtpHqpbLuZLxkyRLq16/v+HwE6NixI7Vr12bt2rUMGTIEgPT0dDIyMoiNjQUgNjaW2bNnc/z4cerXrw9AcnIyVquVVq1auRRDlZqD4+HhwfLly0lLS6NNmzaMHz+eJ5988qJ9xo0bx/Tp0+nXrx+bN28mPj6elStXsmbNGq655hq6dOnC/Pnz3U4elixZwvDhw3nwwQdp3rw5gwYNYuvWrURF/X5TrB49emCz2RxzbeBs0vPHfUFBQSxevJjrrruOtm3b8sUXX/DJJ58QEhLiVowiIiL/y60Jxpc4vGW321myZAmJiYlO97cLDAxkxIgRTJgwgXXr1pGWlsbdd99NbGwsXbp0AaB37960atWKO++8kx07drB69WoeeeQRkpKSXJ6uYTEMoxo/K7TmycvLIzAwkJM/NMYaUKXyU5Fy06dR58oOQaTClBjFrCt8l9zcXJeGfVxR+lkxcM091Pa79InixaeL+Lj3ay7FumbNGuLj40lPT6dZs2ZOxwoKCnjwwQf517/+RWFhIfHx8bzwwgtOw0+HDh1i9OjRrF+/Hj8/PxITE3n88cddvhlwlRqiEhERkfLj7vOkLqVv7969uVDtxMfHh4ULF7Jw4cIL9o+Ojuazzz5z+bp/pARHRETEpMprFVV1pDEOERERMR1VcEREREyqJldwlOCIiIiYVE1OcDREJSIiIqajCo6IiIhJ1eQKjhIcERERkzK4tKXe/9u/ulKCIyIiYlI1uYKjOTgiIiJiOqrgiIiImFRNruAowRERETGpmpzgaIhKRERETEcVHBEREZOqyRUcJTgiIiImZRgWDDeSFHf6VjYNUYmIiIjpqIIjIiJiUnYsbt3oz52+lU0JjoiIiEnV5Dk4GqISERER01EFR0RExKRq8iRjJTgiIiImVZOHqJTgiIiImFRNruBoDo6IiIiYjio4IiIiJmW4OURVnSs4SnBERERMygAMw73+1ZWGqERERMR0VMERERExKTsWLLqTsYiIiJiJVlGJiIiImIgqOCIiIiZlNyxYdKM/ERERMRPDcHMVVTVeRqUhKhERETEdJTgiIiImVTrJ2J3NVT///DN33HEHISEh+Pr6EhMTw7Zt2/4nJoNp06YRHh6Or68vcXFx7Nu3z+kc2dnZJCQkYLVaCQoKYsSIEeTn57sUhxIcERERk7rcCc7Jkye57rrrqF27Np9//jm7d+/m6aefpm7duo428+bNY8GCBSxatIgtW7bg5+dHfHw8BQUFjjYJCQns2rWL5ORkVq5cyVdffcWoUaNcikVzcEREREzqck8yfuKJJ4iMjGTJkiWOfY0aNXL82zAMnn32WR555BEGDhwIwBtvvEFoaCgrVqxg6NCh7Nmzh1WrVrF161Y6deoEwPPPP0+/fv146qmniIiIKFMsquCIiIjIReXl5TlthYWF523373//m06dOnHrrbdSv3592rdvz+LFix3HDx48SGZmJnFxcY59gYGBdO7cmdTUVABSU1MJCgpyJDcAcXFxeHh4sGXLljLHrARHRETEpEpXUbmzAURGRhIYGOjY5s6de97r/fjjj7z44os0bdqU1atXM3r0aO6//35ef/11ADIzMwEIDQ116hcaGuo4lpmZSf369Z2O16pVi+DgYEebstAQlYiIiEmdTVLcuZPx2f8/fPgwVqvVsd/b2/u87e12O506dWLOnDkAtG/fnu+//55FixaRmJh4yXFcClVwRERE5KKsVqvTdqEEJzw8nFatWjnta9myJRkZGQCEhYUBkJWV5dQmKyvLcSwsLIzjx487HS8pKSE7O9vRpiyU4IiIiJjU5V5Fdd1115Genu6074cffiA6Oho4O+E4LCyMtWvXOo7n5eWxZcsWYmNjAYiNjSUnJ4e0tDRHmy+//BK73U7nzp3LHIuGqEREREzK+O/mTn9XjB8/nmuvvZY5c+Zw22238c033/Dyyy/z8ssvA2CxWBg3bhyzZs2iadOmNGrUiKlTpxIREcGgQYOAsxWfPn36MHLkSBYtWkRxcTFjx45l6NChZV5BBUpwREREpJxcc801fPTRR0yZMoUZM2bQqFEjnn32WRISEhxtJk+ezOnTpxk1ahQ5OTl07dqVVatW4ePj42izbNkyxo4dS69evfDw8GDIkCEsWLDApVgshlGdnzRR8+Tl5REYGMjJHxpjDdAIo5hTn0ZlL0OLVDclRjHrCt8lNzfXaeJueSr9rGj8xt/xrOPz5x0uwHamgB+Hz6nQWCuKKjgiIiJmdbnHqKoQJTgiIiJmdYnPk/rf/tWVxjhERETEdFTBERERMan/vRvxpfavrpTgiIiImNSl3Mvmj/2rKw1RiYiIiOmogiMiImJWhsW9icLVuIKjBEdERMSkavIcHA1RiYiIiOmogiMiImJWutGfiIiImE1NXkVVpgTn3//+d5lPePPNN19yMCIiIiLloUwJTukjzP+MxWLBZrO5E4+IiIiUp2o8zOSOMiU4dru9ouMQERGRclaTh6jcWkVVUFBQXnGIiIhIeTPKYaumXE5wbDYbM2fO5Morr8Tf358ff/wRgKlTp/Lqq6+We4AiIiIirnI5wZk9ezZLly5l3rx5eHl5Ofa3adOGV155pVyDExEREXdYymGrnlxOcN544w1efvllEhIS8PT0dOy/+uqr2bt3b7kGJyIiIm7QEFXZ/fzzzzRp0uSc/Xa7neLi4nIJSkRERMQdLic4rVq1YsOGDefsf//992nfvn25BCUiIiLloAZXcFy+k/G0adNITEzk559/xm638+GHH5Kens4bb7zBypUrKyJGERERuRQ1+GniLldwBg4cyCeffMIXX3yBn58f06ZNY8+ePXzyySfceOONFRGjiIiIiEsu6VlU3bp1Izk5ubxjERERkXJkGGc3d/pXV5f8sM1t27axZ88e4Oy8nI4dO5ZbUCIiIlIO9DTxsjty5AjDhg1j06ZNBAUFAZCTk8O1117L8uXLadCgQXnHKCIiIuISl+fg3HvvvRQXF7Nnzx6ys7PJzs5mz5492O127r333oqIUURERC5F6SRjd7ZqyuUKTkpKCps3b6Z58+aOfc2bN+f555+nW7du5RqciIiIXDqLcXZzp3915XKCExkZed4b+tlsNiIiIsolKBERESkHNXgOjstDVE8++ST33Xcf27Ztc+zbtm0bDzzwAE899VS5BiciIiJyKcpUwalbty4Wy+/jcKdPn6Zz587UqnW2e0lJCbVq1eKee+5h0KBBFRKoiIiIuKgG3+ivTAnOs88+W8FhiIiISLmrwUNUZUpwEhMTKzoOERERkXLj8hyc/1VQUEBeXp7TJiIiIlXEZX7Y5mOPPYbFYnHaWrRo4TheUFBAUlISISEh+Pv7M2TIELKyspzOkZGRQf/+/alTpw7169dn0qRJlJSUuPzWXV5Fdfr0aR566CHeffddfv3113OO22w2l4MQERGRClAJQ1StW7fmiy++cLwuna8LMH78eD799FPee+89AgMDGTt2LIMHD2bTpk3A2Ryif//+hIWFsXnzZo4dO8bw4cOpXbs2c+bMcSkOlys4kydP5ssvv+TFF1/E29ubV155henTpxMREcEbb7zh6ulERESkivvjaE1hYeEF29aqVYuwsDDHdsUVVwCQm5vLq6++yjPPPMMNN9xAx44dWbJkCZs3b+brr78GYM2aNezevZu33nqLdu3a0bdvX2bOnMnChQspKipyKWaXE5xPPvmEF154gSFDhlCrVi26devGI488wpw5c1i2bJmrpxMREZGKUk53Mo6MjCQwMNCxzZ0794KX3LdvHxERETRu3JiEhAQyMjIASEtLo7i4mLi4OEfbFi1aEBUVRWpqKgCpqanExMQQGhrqaBMfH09eXh67du1y6a27PESVnZ1N48aNAbBarWRnZwPQtWtXRo8e7erpREREpIKU152MDx8+jNVqdez39vY+b/vOnTuzdOlSmjdvzrFjx5g+fTrdunXj+++/JzMzEy8vL8dzLEuFhoaSmZkJQGZmplNyU3q89JgrXE5wGjduzMGDB4mKiqJFixa8++67/OUvf+GTTz45J2gRERGp/qxWq1OCcyF9+/Z1/Ltt27Z07tyZ6Oho3n33XXx9fSsyxHO4PER19913s2PHDgAefvhhFi5ciI+PD+PHj2fSpEnlHqCIiIhcosu8iuqPgoKCaNasGfv37ycsLIyioiJycnKc2mRlZREWFgZAWFjYOauqSl+XtikrlxOc8ePHc//99wMQFxfH3r17efvtt/nuu+944IEHXD2diIiImFR+fj4HDhwgPDycjh07Urt2bdauXes4np6eTkZGBrGxsQDExsayc+dOjh8/7miTnJyM1WqlVatWLl3b5SGqP4qOjiY6Otrd04iIiEg5s+DmHBwX20+cOJEBAwYQHR3N0aNHefTRR/H09GTYsGEEBgYyYsQIJkyYQHBwMFarlfvuu4/Y2Fi6dOkCQO/evWnVqhV33nkn8+bNIzMzk0ceeYSkpKQLzvu5kDIlOAsWLCjzCUurOyIiIlKzHDlyhGHDhvHrr79Sr149unbtytdff029evUAmD9/Ph4eHgwZMoTCwkLi4+N54YUXHP09PT1ZuXIlo0ePJjY2Fj8/PxITE5kxY4bLsVgMw/jT3K5Ro0ZlO5nFwo8//uhyEFJ2eXl5BAYG0pOB1LLUruxwRCrE84c2VXYIIhUm/5Sda1pnkZubW6aJu5ei9LMi+vHZePj4XPJ57AUFHHr4HxUaa0UpUwXn4MGDFR2HiIiIlLca/LBNt55FJSIiIlIVuT3JWERERKqoGlzBUYIjIiJiUuV1J+PqSENUIiIiYjqq4IiIiJhVDR6iuqQKzoYNG7jjjjuIjY3l559/BuDNN99k48aN5RqciIiIuKGSH9VQmVxOcD744APi4+Px9fXlu+++o7CwEIDc3FzmzJlT7gGKiIiIuMrlBGfWrFksWrSIxYsXU7v27zeau+666/j222/LNTgRERG5dKWTjN3ZqiuX5+Ckp6fTvXv3c/YHBgae84RQERERqUSG5ezmTv9qyuUKTlhYGPv37z9n/8aNG2ncuHG5BCUiIiLlQHNwym7kyJE88MADbNmyBYvFwtGjR1m2bBkTJ05k9OjRFRGjiIiIiEtcHqJ6+OGHsdvt9OrVizNnztC9e3e8vb2ZOHEi9913X0XEKCIiIpegJt/oz+UEx2Kx8I9//INJkyaxf/9+8vPzadWqFf7+/hURn4iIiFyqGnwfnEu+0Z+XlxetWrUqz1hEREREyoXLCc7111+PxXLhWdVffvmlWwGJiIhIOXF3qXdNquC0a9fO6XVxcTHbt2/n+++/JzExsbziEhEREXdpiKrs5s+ff979jz32GPn5+W4HJCIiIuKucnua+B133MFrr71WXqcTERERd9Xg++CU29PEU1NT8fHxKa/TiYiIiJu0TNwFgwcPdnptGAbHjh1j27ZtTJ06tdwCExEREblULic4gYGBTq89PDxo3rw5M2bMoHfv3uUWmIiIiMilcinBsdls3H333cTExFC3bt2KiklERETKQw1eReXSJGNPT0969+6tp4aLiIhUA6VzcNzZqiuXV1G1adOGH3/8sSJiERERESkXLic4s2bNYuLEiaxcuZJjx46Rl5fntImIiEgVUgOXiIMLc3BmzJjBgw8+SL9+/QC4+eabnR7ZYBgGFosFm81W/lGKiIiI62rwHJwyJzjTp0/nb3/7G+vWravIeERERETcVuYExzDOpnE9evSosGBERESk/OhGf2V0saeIi4iISBWjIaqyadas2Z8mOdnZ2W4FJCIiIuIulxKc6dOnn3MnYxEREamaKnOI6vHHH2fKlCk88MADPPvsswAUFBTw4IMPsnz5cgoLC4mPj+eFF14gNDTU0S8jI4PRo0ezbt06/P39SUxMZO7cudSq5drDF1xqPXToUOrXr+/SBURERKSSVNIQ1datW3nppZdo27at0/7x48fz6aef8t577xEYGMjYsWMZPHgwmzZtAs4+MaF///6EhYWxefNmjh07xvDhw6lduzZz5sxxKYYy3wdH829ERERqpj/e866wsPCCbfPz80lISGDx4sVOj3XKzc3l1Vdf5ZlnnuGGG26gY8eOLFmyhM2bN/P1118DsGbNGnbv3s1bb71Fu3bt6Nu3LzNnzmThwoUUFRW5FHOZE5zSVVQiIiJSTbhzk7//qf5ERkYSGBjo2ObOnXvBSyYlJdG/f3/i4uKc9qelpVFcXOy0v0WLFkRFRZGamgpAamoqMTExTkNW8fHx5OXlsWvXLpfeepmHqOx2u0snFhERkcpVXnNwDh8+jNVqdez39vY+b/vly5fz7bffsnXr1nOOZWZm4uXlRVBQkNP+0NBQMjMzHW3+N7kpPV56zBWuzdgRERGR6qOc5uBYrVanBOd8Dh8+zAMPPEBycjI+Pj5uXLR8uPwsKhEREZE/SktL4/jx43To0IFatWpRq1YtUlJSWLBgAbVq1SI0NJSioiJycnKc+mVlZREWFgZAWFgYWVlZ5xwvPeYKJTgiIiJmVU5zcMqiV69e7Ny5k+3btzu2Tp06kZCQ4Ph37dq1Wbt2raNPeno6GRkZxMbGAhAbG8vOnTs5fvy4o01ycjJWq5VWrVq59NY1RCUiImJSl/M+OAEBAbRp08Zpn5+fHyEhIY79I0aMYMKECQQHB2O1WrnvvvuIjY2lS5cuAPTu3ZtWrVpx5513Mm/ePDIzM3nkkUdISkq64LyfC1GCIyIiIpfF/Pnz8fDwYMiQIU43+ivl6enJypUrGT16NLGxsfj5+ZGYmMiMGTNcvpYSHBEREbOq5GdRrV+/3um1j48PCxcuZOHChRfsEx0dzWeffebehVGCIyIiYlo1+WnimmQsIiIipqMKjoiIiFlV8hBVZVKCIyIiYlY1OMHREJWIiIiYjio4IiIiJmX57+ZO/+pKCY6IiIhZ1eAhKiU4IiIiJqVl4iIiIiImogqOiIiIWWmISkREREypGicp7tAQlYiIiJiOKjgiIiImVZMnGSvBERERMasaPAdHQ1QiIiJiOqrgiIiImJSGqERERMR8NEQlIiIiYh6q4IiIiJiUhqhERETEfGrwEJUSHBEREbOqwQmO5uCIiIiI6aiCIyIiYlKagyMiIiLmoyEqEREREfNQBUdERMSkLIaBxbj0Mow7fSubEhwRERGz0hCViIiIiHmogiMiImJSWkUlIiIi5qMhKhERERHzUIIjIiJiUqVDVO5srnjxxRdp27YtVqsVq9VKbGwsn3/+ueN4QUEBSUlJhISE4O/vz5AhQ8jKynI6R0ZGBv3796dOnTrUr1+fSZMmUVJS4vJ7V4IjIiJiVkY5bC5o0KABjz/+OGlpaWzbto0bbriBgQMHsmvXLgDGjx/PJ598wnvvvUdKSgpHjx5l8ODBjv42m43+/ftTVFTE5s2bef3111m6dCnTpk1z+a1bDKMaL3KvgfLy8ggMDKQnA6llqV3Z4YhUiOcPbarsEEQqTP4pO9e0ziI3Nxer1Voh1yj9rOh4+2w8vXwu+Ty2ogLS3vmHW7EGBwfz5JNPcsstt1CvXj3efvttbrnlFgD27t1Ly5YtSU1NpUuXLnz++efcdNNNHD16lNDQUAAWLVrEQw89xIkTJ/Dy8irzdVXBERERkYvKy8tz2goLC/+0j81mY/ny5Zw+fZrY2FjS0tIoLi4mLi7O0aZFixZERUWRmpoKQGpqKjExMY7kBiA+Pp68vDxHFaislOCIiIiYVTkNUUVGRhIYGOjY5s6de8FL7ty5E39/f7y9vfnb3/7GRx99RKtWrcjMzMTLy4ugoCCn9qGhoWRmZgKQmZnplNyUHi895gotExcRETGx8riXzeHDh52GqLy9vS/Ytnnz5mzfvp3c3Fzef/99EhMTSUlJcT8IFynBERERkYsqXRVVFl5eXjRp0gSAjh07snXrVp577jluv/12ioqKyMnJcariZGVlERYWBkBYWBjffPON0/lKV1mVtikrDVGJiIiYlWG4v7nJbrdTWFhIx44dqV27NmvXrnUcS09PJyMjg9jYWABiY2PZuXMnx48fd7RJTk7GarXSqlUrl66rCo6IiIhJXe5HNUyZMoW+ffsSFRXFqVOnePvtt1m/fj2rV68mMDCQESNGMGHCBIKDg7Fardx3333ExsbSpUsXAHr37k2rVq248847mTdvHpmZmTzyyCMkJSVddFjsfJTgiIiISLk4fvw4w4cP59ixYwQGBtK2bVtWr17NjTfeCMD8+fPx8PBgyJAhFBYWEh8fzwsvvODo7+npycqVKxk9ejSxsbH4+fmRmJjIjBkzXI5FCY6IiIhZXeZnUb366qsXPe7j48PChQtZuHDhBdtER0fz2WefuXbh81CCIyIiYlIW+9nNnf7VlSYZi4iIiOmogiPyB7eNzWLE3zP5aPEVLHr0SkIbFPHGN3vO23bWqGg2rAy6vAGK/Am7DT6bH8XWj+px6kRtAkOL6HzLceLvP4LF8nu7zH2+fPx4Q/ZvsWIvsRDW9AwjFu0l+Moifj3szWNdO533/Pe8sJf2/X+9TO9G3HKZh6iqEtMnOD/99BONGjXiu+++o127dpUdjlRxza4+Q/87svlx1+/PbjlxtDZDr3Zentjvjl+5ZfQJtn4ZcLlDFPlTyS82YONbYdzx9D7Cm50h4z/+LJvUFB+rjZ53HwPgxCEf5t8SQ+ztWfQbn4FPgI3MH+pQ2/vsJ1rdiEJmb3W+H8mmf4Wx9qUradXz5GV/T3JpLvcqqqrE9AmOSFn51LHx0D8P8eykBgx7IMux3263cPKE84NNr+2by1efBFFwxvNyhynypw6mBRBzYzZtep1NREIiC0n79xUc2u7vaLPyyShaX3+SQX8/5NhXL7rA8W8PT7DWL3Y6739WBdO+/y94+1XjiRk1jbv3sqnGz+OusnNwioqKKjsEqWHGzvmZb9Za+W7DxasyTWLO0KRNAav/FXyZIhNxTaOOp/hhcyDHfzxbiTyyuw4/brPSqmcOAHY77PoymPqNfmPhna2Y0uEanhrYlh2rL/w9nbHTjyO7/Ym9PeuCbUSqkiqT4PTs2ZOxY8cybtw4rrjiCuLj4wH4/vvv6du3L/7+/oSGhnLnnXfyyy+/OPqtWrWKrl27EhQUREhICDfddBMHDhxw6doNGzZkzpw53HPPPQQEBBAVFcXLL7/s1Obw4cPcdtttBAUFERwczMCBA/npp58cMXp4eHDixAkAsrOz8fDwYOjQoY7+s2bNomvXrgCcPHmShIQE6tWrh6+vL02bNmXJkiXnja2wsPCcp7hK+esx8CRNYn7jtbnhf9q2z7BsDv3gze5tfpchMhHX3TjmCB0G/MKsGzrwwFWxzOvXjp73HOWav579HZX/S20KT3uS/GIDWvbIIenN3bSN/5VX/18L9n19/tvxpy4PJazJGRp3OnU534q4qXSIyp2tuqoyCQ7A66+/jpeXF5s2bWLRokXk5ORwww030L59e7Zt28aqVavIysritttuc/Q5ffo0EyZMYNu2baxduxYPDw/++te/Yre7VkJ9+umn6dSpE9999x1jxoxh9OjRpKenA1BcXEx8fDwBAQFs2LCBTZs24e/vT58+fSgqKqJ169aEhIQ4Hia2YcMGp9cAKSkp9OzZE4CpU6eye/duPv/8c/bs2cOLL77IFVdccd645s6d6/QE18jISJfel/y5ehFFjJ5xlCfGRlFcePEfCS8fO9f/9aSqN1KlfbfyCratqEfigh946NMd3PHMPta+fCVb3q8HgGGcnWkcc2M2N9x7lAatT9N7zM+07nWSjcvOfd5PUYEHaf+uRxdVb6qfcnqaeHVUpebgNG3alHnz5jlez5o1i/bt2zNnzhzHvtdee43IyEh++OEHmjVrxpAhQ5zO8dprr1GvXj12795NmzZtynztfv36MWbMGAAeeugh5s+fz7p162jevDnvvPMOdrudV155Bct/lyAsWbKEoKAg1q9fT+/evenevTvr16/nlltuYf369dx999288sor7N27l6uuuorNmzczefJkADIyMmjfvj2dOp1dodCwYcMLxjVlyhQmTJjgeJ2Xl6ckp5w1afsbdeuVsHD1D459nrUgpstpbr77F25q2Ba7/ex/9279c/D2NfjiPSU4UnWtmNOQG0cfoePNZ6vdES3OkH3EmzUvNKDzLSfwq1uMRy07YU3POPULa3KGA1vPreBs/yyEot88+MuQ4+ccE6mqqlSC07FjR6fXO3bsYN26dfj7+5/T9sCBAzRr1ox9+/Yxbdo0tmzZwi+//OKo3GRkZLiU4LRt29bxb4vFQlhYmONhXzt27GD//v0EBDjPzSgoKHAMh/Xo0cMxrJWSksKcOXP44YcfWL9+PdnZ2RQXF3PdddcBMHr0aIYMGcK3335L7969GTRoENdee+154/L29nb5+Rvimu0b/Bl1fTOnfQ/OP8zh/T68u7CeI7kBiB+WzddrrORmV6kfHREnRb95YPlDMdLD08D47/dyLS+D6Lb5HP/R16nN8YO+BF9ZeM75Ut8JJSYum4CQkgqLWSqGVlFVEX5+znMa8vPzGTBgAE888cQ5bcPDz86VGDBgANHR0SxevJiIiAjsdjtt2rRxeZJy7drOq2QsFosjWcrPz6djx44sW7bsnH716p0t+fbs2ZNx48axb98+du/eTdeuXdm7dy/r16/n5MmTdOrUiTp16gDQt29fDh06xGeffUZycjK9evUiKSmJp556yqWYpXz8dtqTQ+nOv+gLznhw6qTz/oiGhcR0Oc3UOxpd7hBFXNImLps1/2xA3YhCwpud4cguP9a9ciVdbvt9iKnX//uZJWObc1XnPJrF5rJ7fRDffxHM/e/sdDrXiZ98OLDFyt+W7r7cb0PKQw1eRVWlEpw/6tChAx988AENGzakVq1zQ/31119JT09n8eLFdOvWDYCNGzdWSBzvvPMO9evXx2o9/wS8mJgY6taty6xZs2jXrh3+/v707NmTJ554gpMnTzrm35SqV68eiYmJJCYm0q1bNyZNmqQEp4qLH5rNL8dqk5aie99I1Xbr9IN8+nQU705tTP4vZ2/0d93/ZdLngcOONlf3yeb22QdIfqEBHzzaiPpX/caIRXu56hrnScSp79YnKLyIFt1zLvO7EHFPlU5wkpKSWLx4McOGDWPy5MkEBwezf/9+li9fziuvvELdunUJCQnh5ZdfJjw8nIyMDB5++OFyjyMhIYEnn3ySgQMHMmPGDBo0aMChQ4f48MMPmTx5Mg0aNMBisdC9e3eWLVvGxIkTgbPDXoWFhaxdu9ZpHs20adPo2LEjrVu3prCwkJUrV9KyZctyj1su3eRbmpyzb8nj4Sx5/M9XWYlUNh9/G0MePciQRw9etF3s7ceJvf3i82punpzBzZMzyjM8uYxq8hBVlVpF9UcRERFs2rQJm81G7969iYmJYdy4cQQFBeHh4YGHhwfLly8nLS2NNm3aMH78eJ588slyj6NOnTp89dVXREVFMXjwYFq2bMmIESMoKChwquj06NEDm83mqNZ4eHjQvXt3LBaLY/4NgJeXF1OmTKFt27Z0794dT09Pli9fXu5xi4hIDVeDV1FZDKMaD7DVQHl5eQQGBtKTgdSy1P7zDiLV0POHNlV2CCIVJv+UnWtaZ5Gbm3vBaQ/uKv2siO0zg1q1ff68wwWUFBeQumpahcZaUar0EJWIiIhcupo8RKUER0RExKzsxtnNnf7VlBIcERERs3J3Hk31zW+q9iRjERERkUuhCo6IiIhJWXBzDk65RXL5KcERERExqxp8J2MNUYmIiIjpqIIjIiJiUlomLiIiIuajVVQiIiIi5qEKjoiIiElZDAOLGxOF3elb2ZTgiIiImJX9v5s7/aspDVGJiIiI6aiCIyIiYlIaohIRERHzqcGrqJTgiIiImJXuZCwiIiLinrlz53LNNdcQEBBA/fr1GTRoEOnp6U5tCgoKSEpKIiQkBH9/f4YMGUJWVpZTm4yMDPr370+dOnWoX78+kyZNoqSkxKVYlOCIiIiYVOmdjN3ZXJGSkkJSUhJff/01ycnJFBcX07t3b06fPu1oM378eD755BPee+89UlJSOHr0KIMHD3Yct9ls9O/fn6KiIjZv3szrr7/O0qVLmTZtmkuxaIhKRETErC7zENWqVaucXi9dupT69euTlpZG9+7dyc3N5dVXX+Xtt9/mhhtuAGDJkiW0bNmSr7/+mi5durBmzRp2797NF198QWhoKO3atWPmzJk89NBDPPbYY3h5eZUpFlVwRERE5KLy8vKctsLCwjL1y83NBSA4OBiAtLQ0iouLiYuLc7Rp0aIFUVFRpKamApCamkpMTAyhoaGONvHx8eTl5bFr164yx6wER0RExKQsdvc3gMjISAIDAx3b3Llz//TadrudcePGcd1119GmTRsAMjMz8fLyIigoyKltaGgomZmZjjb/m9yUHi89VlYaohIRETGrchqiOnz4MFar1bHb29v7T7smJSXx/fffs3Hjxku/vhtUwREREZGLslqtTtufJThjx45l5cqVrFu3jgYNGjj2h4WFUVRURE5OjlP7rKwswsLCHG3+uKqq9HVpm7JQgiMiImJWRjlsrlzOMBg7diwfffQRX375JY0aNXI63rFjR2rXrs3atWsd+9LT08nIyCA2NhaA2NhYdu7cyfHjxx1tkpOTsVqttGrVqsyxaIhKRETEpC73oxqSkpJ4++23+fjjjwkICHDMmQkMDMTX15fAwEBGjBjBhAkTCA4Oxmq1ct999xEbG0uXLl0A6N27N61ateLOO+9k3rx5ZGZm8sgjj5CUlFSmobFSSnBERESkXLz44osA9OzZ02n/kiVLuOuuuwCYP38+Hh4eDBkyhMLCQuLj43nhhRccbT09PVm5ciWjR48mNjYWPz8/EhMTmTFjhkuxKMERERExq8t8HxyjDO19fHxYuHAhCxcuvGCb6OhoPvvsM5eu/UdKcERERMzKAOxu9q+mlOCIiIiY1OWeg1OVaBWViIiImI4qOCIiImZl4OYcnHKL5LJTgiMiImJWl3mScVWiISoRERExHVVwREREzMoOWNzsX00pwRERETEpraISERERMRFVcERERMyqBk8yVoIjIiJiVjU4wdEQlYiIiJiOKjgiIiJmVYMrOEpwREREzErLxEVERMRstExcRERExERUwRERETErzcERERER07EbYHEjSbFX3wRHQ1QiIiJiOqrgiIiImJWGqERERMR83ExwqL4JjoaoRERExHRUwRERETErDVGJiIiI6dgN3Bpm0ioqERERkapDFRwRERGzMuxnN3f6V1NKcERERMxKc3BERETEdDQHR0RERMQ8VMERERExKw1RiYiIiOkYuJnglFskl52GqERERMR0lOCIiIiYVekQlTubC7766isGDBhAREQEFouFFStW/CEcg2nTphEeHo6vry9xcXHs27fPqU12djYJCQlYrVaCgoIYMWIE+fn5Lr91JTgiIiJmZbe7v7ng9OnTXH311SxcuPC8x+fNm8eCBQtYtGgRW7Zswc/Pj/j4eAoKChxtEhIS2LVrF8nJyaxcuZKvvvqKUaNGufzWNQdHRERELiovL8/ptbe3N97e3ue069u3L3379j3vOQzD4Nlnn+WRRx5h4MCBALzxxhuEhoayYsUKhg4dyp49e1i1ahVbt26lU6dOADz//PP069ePp556ioiIiDLHrAqOiIiIWZXTEFVkZCSBgYGObe7cuS6HcvDgQTIzM4mLi3PsCwwMpHPnzqSmpgKQmppKUFCQI7kBiIuLw8PDgy1btrh0PVVwREREzKqclokfPnwYq9Xq2H2+6s2fyczMBCA0NNRpf2hoqONYZmYm9evXdzpeq1YtgoODHW3KSgmOiIiIXJTVanVKcKoDDVGJiIiYld1wfysnYWFhAGRlZTntz8rKchwLCwvj+PHjTsdLSkrIzs52tCkrJTgiIiImZRh2t7fy0qhRI8LCwli7dq1jX15eHlu2bCE2NhaA2NhYcnJySEtLc7T58ssvsdvtdO7c2aXraYhKRETErAw3qzAuzt/Jz89n//79jtcHDx5k+/btBAcHExUVxbhx45g1axZNmzalUaNGTJ06lYiICAYNGgRAy5Yt6dOnDyNHjmTRokUUFxczduxYhg4d6tIKKlCCIyIiIuVk27ZtXH/99Y7XEyZMACAxMZGlS5cyefJkTp8+zahRo8jJyaFr166sWrUKHx8fR59ly5YxduxYevXqhYeHB0OGDGHBggUux2IxjGr8JK0aKC8vj8DAQHoykFqW2pUdjkiFeP7QpsoOQaTC5J+yc03rLHJzcyts4m7pZ0WvwDupZfG65POUGEWszX2zQmOtKKrgiIiImJXdDhY35tGU4xycy02TjEVERMR0VMERERExK8MALt8k46pECY6IiIhJGXY7hhtDVOW5TPxy0xCViIiImI4qOCIiImalISoRERExHbsBlpqZ4GiISkRERExHFRwRERGzMgzAnfvgVN8KjhIcERERkzLsBoYbQ1TV+WEHSnBERETMyrDjXgVHy8RFREREqgxVcERERExKQ1QiIiJiPjV4iEoJTjVTmk2XUOzWvZtEqrL8U9X3l6rIn8nPP/v9fTmqI+5+VpRQXH7BXGZKcKqZU6dOAbCRzyo5EpGKc03ryo5ApOKdOnWKwMDACjm3l5cXYWFhbMx0/7MiLCwMLy+vcojq8rIY1XmArQay2+0cPXqUgIAALBZLZYdTI+Tl5REZGcnhw4exWq2VHY5IudL39+VnGAanTp0iIiICD4+KW+tTUFBAUVGR2+fx8vLCx8enHCK6vFTBqWY8PDxo0KBBZYdRI1mtVn0AiGnp+/vyqqjKzf/y8fGplolJedEycRERETEdJTgiIiJiOkpwRP6Et7c3jz76KN7e3pUdiki50/e3mJUmGYuIiIjpqIIjIiIipqMER0RERExHCY6IiIiYjhIcqdIMw2DUqFEEBwdjsVjYvn37n/b56aefytxWxMz0syA1mW70J1XaqlWrWLp0KevXr6dx48ZcccUVlR2SiIhUA0pwpEo7cOAA4eHhXHvttZUdishlU1RUVC2f/SNSlWiISqqsu+66i/vuu4+MjAwsFgsNGzYEzlZ1unbtSlBQECEhIdx0000cOHDgguex2Wzcc889tGjRgoyMDAA+/vhjOnTogI+PD40bN2b69OmUlJRcNJZBgwbx1FNPER4eTkhICElJSRQX//6k3cLCQiZOnMiVV16Jn58fnTt3Zv369cDZobZ69erx/vvvO9q3a9eO8PBwx+uNGzfi7e3NmTNnMAyDxx57jKioKLy9vYmIiOD++++/lC+jVAM9e/Zk7NixjBs3jiuuuIL4+HgAvv/+e/r27Yu/vz+hoaHceeed/PLLL45+rv4snE/Dhg2ZM2cO99xzDwEBAURFRfHyyy87tTl8+DC33XYbQUFBBAcHM3DgQH766SdHjB4eHpw4cQKA7OxsPDw8GDp0qKP/rFmz6Nq1KwAnT54kISGBevXq4evrS9OmTVmyZInLXzORP6MER6qs5557jhkzZtCgQQOOHTvG1q1bATh9+jQTJkxg27ZtrF27Fg8PD/76179it9vPOUdhYSG33nor27dvZ8OGDURFRbFhwwaGDx/OAw88wO7du3nppZdYunQps2fPvmg869at48CBA6xbt47XX3+dpUuXsnTpUsfxsWPHkpqayvLly/nPf/7DrbfeSp8+fdi3bx8Wi4Xu3bs7Ep6TJ0+yZ88efvvtN/bu3QtASkoK11xzDXXq1OGDDz5g/vz5vPTSS+zbt48VK1YQExNTPl9YqZJef/11vLy82LRpE4sWLSInJ4cbbriB9u3bs23bNlatWkVWVha33Xabo48rPwsX8/TTT9OpUye+++47xowZw+jRo0lPTweguLiY+Ph4AgIC2LBhA5s2bcLf358+ffpQVFRE69atCQkJISUlBYANGzY4vYaz39s9e/YEYOrUqezevZvPP/+cPXv28OKLL2roWSqGIVKFzZ8/34iOjr5omxMnThiAsXPnTsMwDOPgwYMGYGzYsMHo1auX0bVrVyMnJ8fRvlevXsacOXOczvHmm28a4eHhF7xGYmKiER0dbZSUlDj23Xrrrcbtt99uGIZhHDp0yPD09DR+/vlnp369evUypkyZYhiGYSxYsMBo3bq1YRiGsWLFCqNz587GwIEDjRdffNEwDMOIi4sz/v73vxuGYRhPP/200axZM6OoqOii713MoUePHkb79u2d9s2cOdPo3bu3077Dhw8bgJGenn7e81zoZ+G777674LWjo6ONO+64w/Habrcb9evXd3xfvvnmm0bz5s0Nu93uaFNYWGj4+voaq1evNgzDMAYPHmwkJSUZhmEY48aNMyZNmmTUrVvX2LNnj1FUVGTUqVPHWLNmjWEYhjFgwADj7rvvLsuXRcQtquBItbNv3z6GDRtG48aNsVqtjqGr0uGnUsOGDeP06dOsWbPG6cm9O3bsYMaMGfj7+zu2kSNHcuzYMc6cOXPB67Zu3RpPT0/H6/DwcI4fPw7Azp07sdlsNGvWzOm8KSkpjiGDHj16sHv3bk6cOOH4i7Znz56sX7+e4uJiNm/e7Pgr99Zbb+W3336jcePGjBw5ko8++uiiQ2hS/XXs2NHp9Y4dO1i3bp3T91OLFi0AHN9TZf1Z+DNt27Z1/NtisRAWFub43t6xYwf79+8nICDAEUdwcDAFBQVO39ul1cmUlBRuuOEGR8Vy69atFBcXc9111wEwevRoli9fTrt27Zg8eTKbN2927QslUkaaZCzVzoABA4iOjmbx4sVERERgt9tp06YNRUVFTu369evHW2+9RWpqKjfccINjf35+PtOnT2fw4MHnnNvHx+eC161du7bTa4vF4hgKyM/Px9PTk7S0NKckCMDf3x+AmJgYgoODSUlJISUlhdmzZxMWFsYTTzzh+BAonUwdGRlJeno6X3zxBcnJyYwZM4Ynn3ySlJSUc+IQc/Dz83N6nZ+fz4ABA3jiiSfOaVs6d6usPwt/5s++tzt27MiyZcvO6VevXj3g7ByicePGsW/fPnbv3k3Xrl3Zu3cv69ev5+TJk3Tq1Ik6deoA0LdvXw4dOsRnn31GcnIyvXr1IikpiaeeesqlmEX+jBIcqVZ+/fVX0tPTWbx4Md26dQPOTs49n9GjR9OmTRtuvvlmPv30U3r06AFAhw4dSE9Pp0mTJuUWV/v27bHZbBw/ftwR1x9ZLBa6devGxx9/zK5du+jatSt16tShsLCQl156iU6dOjl9yPn6+jJgwAAGDBhAUlISLVq0YOfOnXTo0KHc4paqq0OHDnzwwQc0bNiQWrXO/VXtys+Cu3G888471K9fH6vVet42MTEx1K1bl1mzZtGuXTv8/f3p2bMnTzzxBCdPnnRUJkvVq1ePxMREEhMT6datG5MmTVKCI+VOQ1RSrdStW5eQkBBefvll9u/fz5dffsmECRMu2P6+++5j1qxZ3HTTTY5f/tOmTeONN95g+vTp7Nq1iz179rB8+XIeeeSRS46rWbNmJCQkMHz4cD788EMOHjzIN998w9y5c/n0008d7Xr27Mm//vUvx4eAh4cH3bt3Z9myZY4EDGDp0qW8+uqrfP/99/z444+89dZb+Pr6Eh0dfckxSvWSlJREdnY2w4YNY+vWrRw4cIDVq1dz9913Y7PZXP5ZuFQJCQlcccUVDBw4kA0bNnDw4EHWr1/P/fffz5EjRwAck+iXLVvmSGbatm1LYWEha9eudfrenjZtGh9//DH79+9n165drFy5kpYtW5Z73CJKcKRa8fDwYPny5aSlpdGmTRvGjx/Pk08+edE+48aNY/r06fTr14/NmzcTHx/PypUrWbNmDddccw1dunRh/vz5bicPS5YsYfjw4Tz44IM0b96cQYMGsXXrVqKiohxtevTogc1mc/qLtmfPnufsCwoKYvHixVx33XW0bduWL774gk8++YSQkBC3YpTqIyIigk2bNmGz2ejduzcxMTGMGzeOoKAgPDw8Luln4VLUqVOHr776iqioKAYPHkzLli0ZMWIEBQUFThWdP35vlybvFovFMf8GwMvLiylTptC2bVu6d++Op6cny5cvL/e4RSyGYRiVHYSIiIhIeVIFR0RERExHCY6IiIiYjhIcERERMR0lOCIiImI6SnBERETEdJTgiIiIiOkowRERERHTUYIjIiIipqMER0RcdtdddzFo0CDH69KHLV5u69evx2KxkJOTc8E2FouFFStWlPmcjz32GO3atXMrrp9++gmLxcL27dvdOo+IXDolOCImcdddd2GxWLBYLHh5edGkSRNmzJhBSUlJhV/7ww8/ZObMmWVqW5akRETEXXqauIiJ9OnThyVLllBYWMhnn31GUlIStWvXZsqUKee0LSoqwsvLq1yuGxwcXC7nEREpL6rgiJiIt7c3YWFhREdHM3r0aOLi4vj3v/8N/D6sNHv2bCIiImjevDkAhw8f5rbbbiMoKIjg4GAGDhzITz/95DinzWZjwoQJBAUFERISwuTJk/njI+z+OERVWFjIQw89RGRkJN7e3jRp0oRXX32Vn376ieuvvx44+2R4i8XCXXfdBYDdbmfu3Lk0atQIX19frr76at5//32n63z22Wc0a9YMX19frr/+eqc4y+qhhx6iWbNm1KlTh8aNGzN16lSKi4vPaffSSy8RGRlJnTp1uO2228jNzXU6/sorr9CyZUt8fHxo0aIFL7zwgsuxiEjFUYIjYmK+vr4UFRU5Xq9du5b09HSSk5NZuXIlxcXFxMfHExAQwIYNG9i0aRP+/v706dPH0e/pp59m6dKlvPbaa2zcuJHs7Gw++uiji153+PDh/Otf/2LBggXs2bOHl156CX9/fyIjI/nggw8ASE9P59ixYzz33HMAzJ07lzfeeINFixaxa9cuxo8fzx133EFKSgpwNhEbPHgwAwYMYPv27dx77708/PDDLn9NAgICWLp0Kbt37+a5555j8eLFzJ8/36nN/v37effdd/nkk09YtWoV3333HWPGjHEcX7ZsGdOmTWP27Nns2bOHOXPmMHXqVF5//XWX4xGRCmKIiCkkJiYaAwcONAzDMOx2u5GcnGx4e3sbEydOdBwPDQ01CgsLHX3efPNNo3nz5obdbnfsKywsNHx9fY3Vq1cbhmEY4eHhxrx58xzHi4uLjQYNGjiuZRiG0aNHD+OBBx4wDMMw0tPTDcBITk4+b5zr1q0zAOPkyZOOfQUFBUadOnWMzZs3O7UdMWKEMWzYMMMwDGPKlClGq1atnI4/9NBD55zrjwDjo48+uuDxJ5980ujYsaPj9aOPPmp4enoaR44ccez7/PPPDQ8PD+PYsWOGYRjGVVddZbz99ttO55k5c6YRGxtrGIZhHDx40ACM77777oLXFZGKpTk4IiaycuVK/P39KS4uxm6383//93889thjjuMxMTFO82527NjB/v37CQgIcDpPQUEBBw4cIDc3l2PHjtG5c2fHsVq1atGpU6dzhqlKbd++HU9PT3r06FHmuPfv38+ZM2e48cYbnfYXFRXRvn17APbs2eMUB0BsbGyZr1HqnXfeYcGCBRw4cID8/HxKSkqwWq1ObaKiorjyyiudrmO320lPTycgIIADBw4wYsQIRo4c6WhTUlJCYGCgy/GISMVQgiNiItdffz0vvvgiXl5eREREUKuW84+4n5+f0+v8/Hw6duzIsmXLzjlXvXr1LikGX19fl/vk5+cD8OmnnzolFnB2XlF5SU1NJSEhgenTpxMfH09gYCDLly/n6aefdjnWxYsXn5NweXp6llusIuIeJTgiJuLn50eTJk3K3L5Dhw6888471K9f/5wqRqnw8HC2bNlC9+7dgbOVirS0NDp06HDe9jExMdjtdlJSUoiLizvneGkFyWazOfa1atUKb29vMjIyLlj5admypWPCdKmvv/76z9/k/9i8eTPR0dH84x//cOw7dOjQOe0yMjI4evQoERERjut4eHjQvHlzQkNDiYiI4McffyQhIcGl64vI5aNJxiI1WEJCAldccQUDBw5kw4YNHDx4kPXr13P//fdz5MgRAB544AEef/xxVqxYwd69exkzZsxF72HTsGFDEhMTueeee1ixYoXjnO+++y4A0dHRWCwWVq5cyYkTJ8jPzycgIICJEycyfvx4Xn/9dQ4cOMC3337L888/75i4+7e//Y19+/YxadIk0tPTefvtt1m6dKlL77dp06ZkZGSwfPlyDhw4wIIFC847YdrHx4fExER27NjBhg0buP/++7ntttsICwsDYPr06cydO5cFCxbwww8/sHPnTpYsWcIzzzzjUjwiUnGU4IjUYHXq1OGrr74iKiqKwYMH07JlS0aMGEFBQYGjovPggw9y5513kpiYSGxsLAEBAfz1r3+96HlffPFFbrnlFsaMGUOLFi0YOXIkp0+fBuDKK69k+vTpPPzww4SGhjJ27FgAZs6cydSpU5k7dy4tW7akT58+fPrppzRq1Ag4Oy/mgw8+YMWKFVx99dUsWrSIOXPmuPR+b775ZsaPH8/YsWNp164dmzdvZurUqee0a9KkCYMHD6Zfv3707t2btm3bOi0Dv/fee3nllVdYsmQJMTEx9OjRg6VLlzpiFZHKZzEuNFNQREREpJpSBUdERERMRwmOiIiImI4SHBERETEdJTgiIiJiOkpwRERExHSU4IiIiIjpKMERERER01GCIyIiIqajBEdERERMRwmOiIiImI4SHBERETGd/w93sylJv06vIQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay, accuracy_score\n",
    "\n",
    "# Assuming pac has already been trained and y_pred is predicted as per your code\n",
    "pac = PassiveAggressiveClassifier(max_iter=50)\n",
    "pac.fit(tfidf_X_train, Y_train)\n",
    "y_pred = pac.predict(tfidf_X_test)\n",
    "score = accuracy_score(Y_test, y_pred)\n",
    "print(f'Accuracy: {round(score * 100, 2)}%')\n",
    "\n",
    "# Determine the unique labels in Y_test to identify the correct label encoding\n",
    "unique_labels = sorted(set(Y_test))\n",
    "\n",
    "# Define the display labels\n",
    "display_labels = ['fake news', 'real news']\n",
    "\n",
    "# Generate the confusion matrix\n",
    "cm = confusion_matrix(Y_test, y_pred, labels=unique_labels)\n",
    "\n",
    "# Print the confusion matrix\n",
    "print('Confusion Matrix:')\n",
    "print(cm)\n",
    "\n",
    "# Display the confusion matrix with custom labels\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=display_labels)\n",
    "disp.plot()\n",
    "\n",
    "# If you need to visualize it in a Jupyter Notebook, use the following line:\n",
    "import matplotlib.pyplot as plt\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "529fa79b-724a-4852-bef6-2a1f8f5b7bb9",
   "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": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}