Fake_News_Classifier_using_Machine_Learning.ipynb

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        "import numpy as np\n",
        "import pandas as pd"
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      "source": [
        "data = pd.read_csv(\"/content/drive/My Drive/fake-news/train.csv\")\n",
        "data.head()"
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        "data.dropna(inplace=True)"
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      "source": [
        "X = data['text'].values\n",
        "y = data['label'].values"
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      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KgyC4meXif8v"
      },
      "source": [
        "import nltk\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "nltk.download(\"stopwords\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "kEBokQx2ipS_"
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      "source": [
        "ps = PorterStemmer()\n",
        "corpus = []\n",
        "for i in range(len(X)):\n",
        "  news = re.sub('[^a-zA-Z]', ' ', X[i])\n",
        "  news = news.lower()\n",
        "  news = news.split()\n",
        "\n",
        "  news = [ps.stem(word) for word in news if word not in stopwords.words('english')]\n",
        "  news = ' '.join(news)\n",
        "  corpus.append(news)"
      ],
      "execution_count": 12,
      "outputs": []
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    {
      "cell_type": "code",
      "metadata": {
        "id": "gbrivQhkjNfu",
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        "X[0], corpus[0]"
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              "('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',\n",
              " 'hous dem aid even see comey letter jason chaffetz tweet darrel lucu octob subscrib jason chaffetz stump american fork utah imag courtesi michael jolley avail creativ common licens apolog keith olbermann doubt worst person world week fbi director jame comey accord hous democrat aid look like also know second worst person well turn comey sent infam letter announc fbi look email may relat hillari clinton email server rank democrat relev committe hear comey found via tweet one republican committe chairmen know comey notifi republican chairmen democrat rank member hous intellig judiciari oversight committe agenc review email recent discov order see contain classifi inform long letter went oversight committe chairman jason chaffetz set polit world ablaz tweet fbi dir inform fbi learn exist email appear pertin investig case reopen jason chaffetz jasoninthehous octob cours know case comey actual say review email light unrel case know anthoni weiner sext teenag appar littl thing fact matter chaffetz utah republican alreadi vow initi raft investig hillari win least two year worth possibl entir term worth appar chaffetz thought fbi alreadi work result tweet briefli roil nation cooler head realiz dud accord senior hous democrat aid misread letter may least chaffetz sin aid told shareblu boss democrat even know comey letter time found check twitter democrat rank member relev committe receiv comey letter republican chairmen fact democrat rank member receiv chairman oversight govern reform committe jason chaffetz tweet made public let see got right fbi director tell chaffetz gop committe chairmen major develop potenti polit explos investig neither chaffetz colleagu courtesi let democrat counterpart know instead accord aid made find twitter alreadi talk daili ko comey provid advanc notic letter chaffetz republican give time turn spin machin may make good theater noth far even suggest case noth far suggest comey anyth grossli incompet tone deaf suggest howev chaffetz act way make dan burton darrel issa look like model respons bipartisanship even decenc notifi rank member elijah cum someth explos trampl basic standard fair know grant like chaffetz answer sit ridicul republican district anchor provo orem cook partisan vote index r gave mitt romney punish percent vote moreov republican hous leadership given full support chaffetz plan fish expedit mean turn hot light textbook exampl hous becom republican control also second worst person world darrel lucu darrel someth graduat univers north carolina consid journalist old school attempt turn member religi right colleg succeed turn religi right worst nightmar charismat christian unapologet liber desir stand scare silenc increas surviv abus three year marriag may know daili ko christian dem nc follow twitter darrelllucu connect facebook click buy darrel mello yello connect')"
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edJ-ud6wrbkP"
      },
      "source": [
        "from sklearn.feature_extraction.text import  TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4EvR8OmTskG3"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(np.asarray(corpus), y, test_size = 0.2, random_state = 24)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "-cmjgW5gswHP",
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          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape, X_test.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((14628,), (3657,))"
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          "execution_count": 16
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        "id": "MrToWZfls0hn",
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      "source": [
        "X_train[0], y_train[0]"
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          "output_type": "execute_result",
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            "text/plain": [
              "('nfl certain sport network would like believ elect reason seriou downtick viewer particip yet data realli support premis fair nfl bounc rate occur elect end earli novemb fact rate averag game report aw announc increas repres biggest jump sinc rate show increas elect number look quit impress howev compar viewership number year elect last year nfl season number period time per game viewership declin approxim percent million viewer year million quick arithmet calcul nfl lost viewer sinc last year period mani specul colin kaepernick other kneel nation anthem may contribut fan decid clean garag take kid park rather tune game accord poll percent nfl fan turn tv watch someth els game time moreov four ten tune blame kaepernick protest reason final analysi elect effect viewership impact nfl would like believ',\n",
              " 0)"
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          "data": {
            "text/plain": [
              "('video amnesti intl western back syrian rebel must end unlaw attack w aleppo peopl includ children kill civilian area govern control western aleppo sinc offens began accord syrian observatori human right sociabl share screenshot show syrian rebel use american made bgm tow missil fierc offens western aleppo citi launch arm opposit group octob mark indiscrimin attack civilian area cannot justifi way break relentless sieg spark humanitarian crisi eastern aleppo amnesti intern said peopl includ children kill civilian area govern control western aleppo sinc offens began accord syrian observatori human right goal break sieg eastern aleppo give arm opposit group licens flout rule intern humanitarian law bombard civilian neighbourhood govern held area without distinct samah hadid deputi director campaign amnesti intern beirut region offic arm opposit group display shock disregard civilian live video footag show use imprecis explos weapon includ mortar katyusha rocket whose use vicin dens popul civilian area flagrantli violat intern humanitarian law arm opposit group must end attack fail distinguish militari target civilian octob alleg toxic ga attack took place al hamdaniyeh al assad area western aleppo caus dozen injuri accord syrian state news agenc sana chemic weapon intern ban use war crime weapon caus immens suffer health damag use never justifi regardless behind attack parti conflict must halt use prohibit weapon war said samah hadid sociabl share',\n",
              " 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NV3YzZ5ytz6u"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sqUGEpiYuWwr"
      },
      "source": [
        "from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZ_hW_sztIHU"
      },
      "source": [
        "tfidf = TfidfVectorizer()\n",
        "model = RandomForestClassifier()"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYjg9sABxe5j"
      },
      "source": [
        "x_train = tfidf.fit_transform(X_train)\n",
        "x_test = tfidf.transform(X_test)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xyqED9Umxe9N"
      },
      "source": [
        "# from sklearn.model_selection import RandomizedSearchCV\n",
        "# param_dist={'max_depth':[3,5,7,10,None],\n",
        "#               'n_estimators':[10,50,100,150,200,250,300,400,500],\n",
        "#                'criterion':['gini','entropy'],\n",
        "#                'bootstrap':[True,False],\n",
        "#               }\n",
        "# rf_model = RandomForestClassifier(class_weight='balanced', random_state = 42)\n",
        "# search_clfr = RandomizedSearchCV(rf_model, param_distributions = param_dist, n_jobs=-1, n_iter = 10, cv = 3, verbose =1)\n",
        "# search_clfr.fit(x_train, y_train)\n",
        "# search_clfr.best_estimator_"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c3oV5Y4ixfCg"
      },
      "source": [
        "model = RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight='balanced',\n",
        "                       criterion='gini', max_depth=None, max_features='auto',\n",
        "                       max_leaf_nodes=None, max_samples=None,\n",
        "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
        "                       min_samples_leaf=1, min_samples_split=2,\n",
        "                       min_weight_fraction_leaf=0.0, n_estimators=250,\n",
        "                       n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
        "                       warm_start=False)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "advlX0aZuAkA"
      },
      "source": [
        "pipe_line = Pipeline([\n",
        "                      ('tfidf', tfidf),\n",
        "                      ('model', model)\n",
        "])"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y6bRisLyuR1p",
        "outputId": "306ae466-188a-4476-8c87-5337a976de75",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        }
      },
      "source": [
        "pipe_line.fit(X_train, y_train)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('tfidf',\n",
              "                 TfidfVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.float64'>,\n",
              "                                 encoding='utf-8', input='content',\n",
              "                                 lowercase=True, max_df=1.0, max_features=None,\n",
              "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
              "                                 preprocessor=None, smooth_idf=True,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 sublinear_tf=False,\n",
              "                                 token_pattern='...\n",
              "                 RandomForestClassifier(bootstrap=False, ccp_alpha=0.0,\n",
              "                                        class_weight='balanced',\n",
              "                                        criterion='gini', max_depth=None,\n",
              "                                        max_features='auto',\n",
              "                                        max_leaf_nodes=None, max_samples=None,\n",
              "                                        min_impurity_decrease=0.0,\n",
              "                                        min_impurity_split=None,\n",
              "                                        min_samples_leaf=1, min_samples_split=2,\n",
              "                                        min_weight_fraction_leaf=0.0,\n",
              "                                        n_estimators=250, n_jobs=None,\n",
              "                                        oob_score=False, random_state=42,\n",
              "                                        verbose=0, warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BV1Gpuiz0hKX",
        "outputId": "a7f11568-3aaf-4870-973e-4b23ffa97e85",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "pred = pipe_line.predict(X_test)\n",
        "print(accuracy_score(y_test, pred))\n",
        "print(precision_score(y_test, pred))\n",
        "print(recall_score(y_test, pred))\n",
        "print(confusion_matrix(y_test, pred))"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9209734755263878\n",
            "0.966878083157153\n",
            "0.8500619578686494\n",
            "[[1996   47]\n",
            " [ 242 1372]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B43D76fM1CLh"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L-GfQ8YzugPN"
      },
      "source": [
        "# pickle.dump(pipe_line, open(\"fake_news_predictor.pkl\", 'wb'))"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C1O30PeVXxCr",
        "outputId": "493c5796-5021-4715-ced3-5c6e9c0a2ebd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67
        }
      },
      "source": [
        "!pip install xgboost==1.1.1"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost==1.1.1 in /usr/local/lib/python3.6/dist-packages (1.1.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost==1.1.1) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost==1.1.1) (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1qiusb4Nuvqw"
      },
      "source": [
        "import xgboost\n",
        "from xgboost import XGBClassifier"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DjOxnwdh2PjV"
      },
      "source": [
        "xgb_model = XGBClassifier()\n",
        "xgb_pipe_line = Pipeline([\n",
        "                      ('tfidf', tfidf),\n",
        "                      ('model', xgb_model)\n",
        "])"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O6-r16c32YN8",
        "outputId": "c3d78f2c-082f-4367-e209-e543b4b273b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        }
      },
      "source": [
        "xgb_pipe_line.fit(X_train, y_train)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('tfidf',\n",
              "                 TfidfVectorizer(analyzer='word', binary=False,\n",
              "                                 decode_error='strict',\n",
              "                                 dtype=<class 'numpy.float64'>,\n",
              "                                 encoding='utf-8', input='content',\n",
              "                                 lowercase=True, max_df=1.0, max_features=None,\n",
              "                                 min_df=1, ngram_range=(1, 1), norm='l2',\n",
              "                                 preprocessor=None, smooth_idf=True,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 sublinear_tf=False,\n",
              "                                 token_pattern='...\n",
              "                               interaction_constraints='',\n",
              "                               learning_rate=0.300000012, max_delta_step=0,\n",
              "                               max_depth=6, min_child_weight=1, missing=nan,\n",
              "                               monotone_constraints='()', n_estimators=100,\n",
              "                               n_jobs=0, num_parallel_tree=1,\n",
              "                               objective='binary:logistic', random_state=0,\n",
              "                               reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
              "                               subsample=1, tree_method='exact',\n",
              "                               validate_parameters=1, verbosity=None))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "da1eXKHc2bm5",
        "outputId": "4fe0f2a3-d046-48b0-ba31-a385b809e50c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 101
        }
      },
      "source": [
        "pred = xgb_pipe_line.predict(X_test)\n",
        "print(accuracy_score(y_test, pred))\n",
        "print(precision_score(y_test, pred))\n",
        "print(recall_score(y_test, pred))\n",
        "print(confusion_matrix(y_test, pred))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.9625375991249658\n",
            "0.9612742036227357\n",
            "0.9535315985130112\n",
            "[[1981   62]\n",
            " [  75 1539]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6z9pOS8fDdA3"
      },
      "source": [
        "pickle.dump(xgb_pipe_line, open(\"xgb_fake_news_predictor.pkl\", 'wb'))"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wEad6HPiYT-H",
        "outputId": "62332ce3-1f8d-4e09-a4ee-0ce04d5ccd70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "xgboost.__version__"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.1.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    }
  ]
}