Fake_News_Prediction.ipynb

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    "colab": {
      "name": "Fake News Prediction.ipynb",
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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount(\"/content/gdrive\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VikMkgwi2yIh",
        "outputId": "77194e81-e09e-46fb-a37f-5de7bf3191b9"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "news_dataset = pd.read_csv('/content/gdrive/My Drive/train.csv')"
      ],
      "metadata": {
        "id": "NE63Hz9wCwjS"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the Dependencies"
      ],
      "metadata": {
        "id": "cP7i4P3rGilY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import nltk\n",
        "import re\n",
        "import io\n",
        "import string\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.linear_model import PassiveAggressiveClassifier\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_curve, auc\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "ffpY09NNDE4s"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBxgcL15DGQk",
        "outputId": "6c7a708e-ce4d-44a4-a9ba-df663bc656f9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "True"
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          "metadata": {},
          "execution_count": 4
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    },
    {
      "cell_type": "code",
      "source": [
        "# printing the stopwords in English\n",
        "print(stopwords.words('english'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MGXaxYmVJ869",
        "outputId": "18f53862-1fd4-46b4-a941-1860e3eff27e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Punctuations Removal"
      ],
      "metadata": {
        "id": "6RFLhBydJ5rl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def cleanpunc(sentence):\n",
        "    cleaned = re.sub(r'[?|!|\\'|\"|#]',r'',sentence)\n",
        "    cleaned = re.sub(r'[.|,|)|(|\\|/]',r' ',cleaned)\n",
        "    return  cleaned"
      ],
      "metadata": {
        "id": "G5nz3gcqJusa"
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      "execution_count": 6,
      "outputs": []
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    {
      "cell_type": "markdown",
      "source": [
        "# Data Pre-Processing"
      ],
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      "cell_type": "code",
      "source": [
        "news_dataset.describe()"
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      "execution_count": 7,
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              "                 id         label\n",
              "count  20800.000000  20800.000000\n",
              "mean   10399.500000      0.500625\n",
              "std     6004.587135      0.500012\n",
              "min        0.000000      0.000000\n",
              "25%     5199.750000      0.000000\n",
              "50%    10399.500000      1.000000\n",
              "75%    15599.250000      1.000000\n",
              "max    20799.000000      1.000000"
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      "cell_type": "code",
      "source": [
        "news_dataset.shape"
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              "(20800, 5)"
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        "# print the first 5 rows of the dataframe\n",
        "news_dataset.head()"
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              "   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",
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              "                                                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  "
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e22c7dee-bdef-4e3c-af28-109b8e57e7fd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e22c7dee-bdef-4e3c-af28-109b8e57e7fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# counting the number of missing values in the dataset\n",
        "news_dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XXZAu85GDXoi",
        "outputId": "b602d947-d644-4e8a-8a6a-713a0948be2b"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id           0\n",
              "title      558\n",
              "author    1957\n",
              "text        39\n",
              "label        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "news_dataset['label'].size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zzKWi_DVMfgc",
        "outputId": "42d2538c-12e4-493d-8152-7d6fbbd400cf"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20800"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "news_dataset['label_num'] = news_dataset['label'].map(lambda x: 1 if x == 1 else 0)"
      ],
      "metadata": {
        "id": "bUJMFTEkMioF"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# replacing the null values with empty string\n",
        "news_dataset = news_dataset.fillna('')"
      ],
      "metadata": {
        "id": "delr9LflDj1T"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# merging the author name and news title\n",
        "news_dataset['content'] = news_dataset['author']+' '+news_dataset['title']"
      ],
      "metadata": {
        "id": "dGRvkPEkDmBL"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(news_dataset['content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPyf983GDrmq",
        "outputId": "c7183618-5a04-4860-8c73-f1a46d3c9e75"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        Darrell Lucus House Dem Aide: We Didn’t Even S...\n",
            "1        Daniel J. Flynn FLYNN: Hillary Clinton, Big Wo...\n",
            "2        Consortiumnews.com Why the Truth Might Get You...\n",
            "3        Jessica Purkiss 15 Civilians Killed In Single ...\n",
            "4        Howard Portnoy Iranian woman jailed for fictio...\n",
            "                               ...                        \n",
            "20795    Jerome Hudson Rapper T.I.: Trump a ’Poster Chi...\n",
            "20796    Benjamin Hoffman N.F.L. Playoffs: Schedule, Ma...\n",
            "20797    Michael J. de la Merced and Rachel Abrams Macy...\n",
            "20798    Alex Ansary NATO, Russia To Hold Parallel Exer...\n",
            "20799              David Swanson What Keeps the F-35 Alive\n",
            "Name: content, Length: 20800, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# separating the data & label\n",
        "X = news_dataset.drop(columns='label', axis=1)\n",
        "Y = news_dataset['label']"
      ],
      "metadata": {
        "id": "s9_iUj0uDu-U"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCfrIK_JDw5k",
        "outputId": "224dd79a-61e4-4fb3-835b-c5f1ebffb1a0"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          id                                              title  \\\n",
            "0          0  House Dem Aide: We Didn’t Even See Comey’s Let...   \n",
            "1          1  FLYNN: Hillary Clinton, Big Woman on Campus - ...   \n",
            "2          2                  Why the Truth Might Get You Fired   \n",
            "3          3  15 Civilians Killed In Single US Airstrike Hav...   \n",
            "4          4  Iranian woman jailed for fictional unpublished...   \n",
            "...      ...                                                ...   \n",
            "20795  20795  Rapper T.I.: Trump a ’Poster Child For White S...   \n",
            "20796  20796  N.F.L. Playoffs: Schedule, Matchups and Odds -...   \n",
            "20797  20797  Macy’s Is Said to Receive Takeover Approach by...   \n",
            "20798  20798  NATO, Russia To Hold Parallel Exercises In Bal...   \n",
            "20799  20799                          What Keeps the F-35 Alive   \n",
            "\n",
            "                                          author  \\\n",
            "0                                  Darrell Lucus   \n",
            "1                                Daniel J. Flynn   \n",
            "2                             Consortiumnews.com   \n",
            "3                                Jessica Purkiss   \n",
            "4                                 Howard Portnoy   \n",
            "...                                          ...   \n",
            "20795                              Jerome Hudson   \n",
            "20796                           Benjamin Hoffman   \n",
            "20797  Michael J. de la Merced and Rachel Abrams   \n",
            "20798                                Alex Ansary   \n",
            "20799                              David Swanson   \n",
            "\n",
            "                                                    text  label_num  \\\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",
            "...                                                  ...        ...   \n",
            "20795  Rapper T. I. unloaded on black celebrities who...          0   \n",
            "20796  When the Green Bay Packers lost to the Washing...          0   \n",
            "20797  The Macy’s of today grew from the union of sev...          0   \n",
            "20798  NATO, Russia To Hold Parallel Exercises In Bal...          1   \n",
            "20799    David Swanson is an author, activist, journa...          1   \n",
            "\n",
            "                                                 content  \n",
            "0      Darrell Lucus House Dem Aide: We Didn’t Even S...  \n",
            "1      Daniel J. Flynn FLYNN: Hillary Clinton, Big Wo...  \n",
            "2      Consortiumnews.com Why the Truth Might Get You...  \n",
            "3      Jessica Purkiss 15 Civilians Killed In Single ...  \n",
            "4      Howard Portnoy Iranian woman jailed for fictio...  \n",
            "...                                                  ...  \n",
            "20795  Jerome Hudson Rapper T.I.: Trump a ’Poster Chi...  \n",
            "20796  Benjamin Hoffman N.F.L. Playoffs: Schedule, Ma...  \n",
            "20797  Michael J. de la Merced and Rachel Abrams Macy...  \n",
            "20798  Alex Ansary NATO, Russia To Hold Parallel Exer...  \n",
            "20799            David Swanson What Keeps the F-35 Alive  \n",
            "\n",
            "[20800 rows x 6 columns]\n",
            "0        1\n",
            "1        0\n",
            "2        1\n",
            "3        1\n",
            "4        1\n",
            "        ..\n",
            "20795    0\n",
            "20796    0\n",
            "20797    0\n",
            "20798    1\n",
            "20799    1\n",
            "Name: label, Length: 20800, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Cleaning"
      ],
      "metadata": {
        "id": "4w1w91I9OFZQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "news_dataset.duplicated(subset={\"title\"}).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_MydqguOFuz",
        "outputId": "7b3b85b1-b2ba-46e0-a625-e7c5b1b82aae"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    19804\n",
              "True       996\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Deleting the duplicates\n",
        "news_dataset_1 =  news_dataset.drop_duplicates(subset={\"title\"},keep=\"first\")"
      ],
      "metadata": {
        "id": "LHL8kvEwOGAg"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news_dataset_1.duplicated(subset={\"title\"}).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdHNCIPmObUJ",
        "outputId": "bf82a15b-d6da-4c6f-b6a2-294743f59e3b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    19804\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Stemming** : is the process of reducing a word to its Root word\n",
        "\n",
        "Example: actor, actress, acting --> act"
      ],
      "metadata": {
        "id": "0pfJuR2gHPIH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "port_stem = PorterStemmer()"
      ],
      "metadata": {
        "id": "ysHZ5tqLD4FS"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stemming(content):\n",
        "    stemmed_content = re.sub('[^a-zA-Z]',' ',content)\n",
        "    stemmed_content = stemmed_content.lower()\n",
        "    stemmed_content = stemmed_content.split()\n",
        "    stemmed_content = [port_stem.stem(word) for word in stemmed_content if not word in stopwords.words('english')]\n",
        "    stemmed_content = ' '.join(stemmed_content)\n",
        "    return stemmed_content"
      ],
      "metadata": {
        "id": "JNVndz0JD6Vy"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news_dataset['content'] = news_dataset['content'].apply(stemming)"
      ],
      "metadata": {
        "id": "NAbK_76JD8G7"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(news_dataset['content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qBY5J0NND_Lz",
        "outputId": "af88085f-3633-4a81-dd30-100d615b703f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0        darrel lucu hous dem aid even see comey letter...\n",
            "1        daniel j flynn flynn hillari clinton big woman...\n",
            "2                   consortiumnew com truth might get fire\n",
            "3        jessica purkiss civilian kill singl us airstri...\n",
            "4        howard portnoy iranian woman jail fiction unpu...\n",
            "                               ...                        \n",
            "20795    jerom hudson rapper trump poster child white s...\n",
            "20796    benjamin hoffman n f l playoff schedul matchup...\n",
            "20797    michael j de la merc rachel abram maci said re...\n",
            "20798    alex ansari nato russia hold parallel exercis ...\n",
            "20799                            david swanson keep f aliv\n",
            "Name: content, Length: 20800, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Excluding important words for Sentiment Analysis"
      ],
      "metadata": {
        "id": "PwGz3zVxYl13"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.stem.wordnet import WordNetLemmatizer\n",
        "\n",
        "stop = stopwords.words('english') #All the stopwords in English language\n",
        "\n",
        "#excluding some useful words from stop words list as we doing sentiment analysis\n",
        "excluding = ['against','not','don', \"don't\",'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\",\n",
        "             'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", \n",
        "             'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\",'shouldn', \"shouldn't\", 'wasn',\n",
        "             \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n",
        "stop = [words for words in stop if words not in excluding]\n",
        "print(stop)"
      ],
      "metadata": {
        "id": "BsN8-hFuSYwg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a49e042-a600-40f1-86b3-de13979372a8"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ma', 'shan', \"shan't\"]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        " #initialising the Porter Stemmer\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "porter = PorterStemmer()"
      ],
      "metadata": {
        "id": "fWjc_vhWRYjf"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preprocessing on the Dataset"
      ],
      "metadata": {
        "id": "RKlhgcxeOs1I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "i = 0\n",
        "string1 = ' '\n",
        "final_string = []\n",
        "fake_words = []                \n",
        "real_words = []\n",
        "s = ''\n",
        "\n",
        "for sent in news_dataset_1['text'].values:\n",
        "    filtered_sentence = []\n",
        "\n",
        "    for w in sent.split():\n",
        "        if((w.isalpha()) and (len(w)>2)):  \n",
        "            if(w.lower() not in stop):    # If it is a stopword\n",
        "                s = (port_stem.stem(w.lower())).encode('utf8')\n",
        "                filtered_sentence.append(s)\n",
        "                if(news_dataset_1['label'].values)[i] == 0:\n",
        "                    real_words.append(s)\n",
        "                if(news_dataset_1['label'].values)[i] == 1:\n",
        "                    fake_words.append(s)\n",
        "            else:\n",
        "                continue\n",
        "        else:\n",
        "            continue \n",
        "    string1 = b\" \".join(filtered_sentence) \n",
        "    final_string.append(string1)\n",
        "    i += 1"
      ],
      "metadata": {
        "id": "0vZQLDzkOsBz"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Counting Fake and Real Words in Text"
      ],
      "metadata": {
        "id": "OO4WFPvoSiil"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "print(\"Number of Real words: \", len(real_words))\n",
        "print(\"Number of Fake words: \", len(fake_words))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DoiJR88KOsJX",
        "outputId": "cb6c0396-9fb4-4e0e-8383-af8f7e91f67c"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Real words:  3835240\n",
            "Number of Fake words:  2736499\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Separating Data and Labels"
      ],
      "metadata": {
        "id": "LDFMgIFWSmuS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#separating the data and label\n",
        "X = news_dataset['content'].values\n",
        "Y = news_dataset['label'].values"
      ],
      "metadata": {
        "id": "8jPzKONeENKL"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4B6L2NINEPh7",
        "outputId": "6600e2d0-86c8-470a-bf00-7a819e35bd94"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['darrel lucu hous dem aid even see comey letter jason chaffetz tweet'\n",
            " 'daniel j flynn flynn hillari clinton big woman campu breitbart'\n",
            " 'consortiumnew com truth might get fire' ...\n",
            " 'michael j de la merc rachel abram maci said receiv takeov approach hudson bay new york time'\n",
            " 'alex ansari nato russia hold parallel exercis balkan'\n",
            " 'david swanson keep f aliv']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPObki2nEQBL",
        "outputId": "5e557b63-92d7-4b90-82f3-f6b9ec838325"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 ... 0 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHgnt7kBEVGS",
        "outputId": "d933bfeb-c01e-4406-8edf-6b93651d6335"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20800,)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# converting the textual data to numerical data\n",
        "vectorizer = TfidfVectorizer()\n",
        "vectorizer.fit(X)\n",
        "\n",
        "X = vectorizer.transform(X)"
      ],
      "metadata": {
        "id": "bwP7PXwfEVLu"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PEazbtIzEZ-L",
        "outputId": "bf7615ac-e574-489b-93f5-23295c38f38d"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 15686)\t0.28485063562728646\n",
            "  (0, 13473)\t0.2565896679337957\n",
            "  (0, 8909)\t0.3635963806326075\n",
            "  (0, 8630)\t0.29212514087043684\n",
            "  (0, 7692)\t0.24785219520671603\n",
            "  (0, 7005)\t0.21874169089359144\n",
            "  (0, 4973)\t0.233316966909351\n",
            "  (0, 3792)\t0.2705332480845492\n",
            "  (0, 3600)\t0.3598939188262559\n",
            "  (0, 2959)\t0.2468450128533713\n",
            "  (0, 2483)\t0.3676519686797209\n",
            "  (0, 267)\t0.27010124977708766\n",
            "  (1, 16799)\t0.30071745655510157\n",
            "  (1, 6816)\t0.1904660198296849\n",
            "  (1, 5503)\t0.7143299355715573\n",
            "  (1, 3568)\t0.26373768806048464\n",
            "  (1, 2813)\t0.19094574062359204\n",
            "  (1, 2223)\t0.3827320386859759\n",
            "  (1, 1894)\t0.15521974226349364\n",
            "  (1, 1497)\t0.2939891562094648\n",
            "  (2, 15611)\t0.41544962664721613\n",
            "  (2, 9620)\t0.49351492943649944\n",
            "  (2, 5968)\t0.3474613386728292\n",
            "  (2, 5389)\t0.3866530551182615\n",
            "  (2, 3103)\t0.46097489583229645\n",
            "  :\t:\n",
            "  (20797, 13122)\t0.2482526352197606\n",
            "  (20797, 12344)\t0.27263457663336677\n",
            "  (20797, 12138)\t0.24778257724396507\n",
            "  (20797, 10306)\t0.08038079000566466\n",
            "  (20797, 9588)\t0.174553480255222\n",
            "  (20797, 9518)\t0.2954204003420313\n",
            "  (20797, 8988)\t0.36160868928090795\n",
            "  (20797, 8364)\t0.22322585870464118\n",
            "  (20797, 7042)\t0.21799048897828688\n",
            "  (20797, 3643)\t0.21155500613623743\n",
            "  (20797, 1287)\t0.33538056804139865\n",
            "  (20797, 699)\t0.30685846079762347\n",
            "  (20797, 43)\t0.29710241860700626\n",
            "  (20798, 13046)\t0.22363267488270608\n",
            "  (20798, 11052)\t0.4460515589182236\n",
            "  (20798, 10177)\t0.3192496370187028\n",
            "  (20798, 6889)\t0.32496285694299426\n",
            "  (20798, 5032)\t0.4083701450239529\n",
            "  (20798, 1125)\t0.4460515589182236\n",
            "  (20798, 588)\t0.3112141524638974\n",
            "  (20798, 350)\t0.28446937819072576\n",
            "  (20799, 14852)\t0.5677577267055112\n",
            "  (20799, 8036)\t0.45983893273780013\n",
            "  (20799, 3623)\t0.37927626273066584\n",
            "  (20799, 377)\t0.5677577267055112\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Splitting the dataset to training and test data"
      ],
      "metadata": {
        "id": "PNEmxK05HiO6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, stratify=Y, random_state=2)"
      ],
      "metadata": {
        "id": "iCH21kPtEiQr"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Logistic Regression"
      ],
      "metadata": {
        "id": "yJ9O7rGsHvjJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_1 = LogisticRegression()\n",
        "model_1.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "eoLhHAKLEiii",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b5d9dbc7-f962-4226-fe54-763987c7105e"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Passive Aggressive Classifier"
      ],
      "metadata": {
        "id": "ICuONi6JFSQf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_2 = PassiveAggressiveClassifier()\n",
        "model_2.fit(X_train, Y_train)"
      ],
      "metadata": {
        "id": "9CtUAxYDFShu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3938a1c6-e06e-49f3-c16f-de439902b4e3"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassiveAggressiveClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Naive Bayes"
      ],
      "metadata": {
        "id": "5x3TdiNduPCS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_3 = MultinomialNB()\n",
        "model_3.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n6MFd8hTuPKA",
        "outputId": "d24a4ea1-3436-4d5d-a172-e4402f1ded76"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MultinomialNB()"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Tree Classifier"
      ],
      "metadata": {
        "id": "fXjAj4jxvS_Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_4 = DecisionTreeClassifier()\n",
        "model_4.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ru_DJqLvTG7",
        "outputId": "efd300ab-79fa-450d-ba9b-fa1c11d57561"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN"
      ],
      "metadata": {
        "id": "75HDi2GlwHof"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_5 = KNeighborsClassifier(n_neighbors=3)\n",
        "model_5.fit(X_train, Y_train)\n",
        "pred_knn = model_5.predict(X_test)"
      ],
      "metadata": {
        "id": "FkBQgY4lwHwO"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "zfOlxXqFH6vz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " Model 1 : Logistic Regression"
      ],
      "metadata": {
        "id": "J5WqbW8pIDL-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy score on the Training and Test Data\n",
        "X_train_prediction = model_1.predict(X_train)\n",
        "X_test_prediction = model_1.predict(X_test)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "_4au6fX2EmlS"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)\n",
        "print('Accuracy score of the testing  data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQP9FbsbEs-S",
        "outputId": "ed55072b-1eed-48a5-82c2-1c201fc13074"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.9865985576923076\n",
            "Accuracy score of the testing  data :  0.9790865384615385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Model 2 : Passive Aggressive Classifier"
      ],
      "metadata": {
        "id": "1SvDpY8sIKuv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy score on the Training and Test Data\n",
        "X_train_prediction = model_2.predict(X_train)\n",
        "X_test_prediction = model_2.predict(X_test)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "fKjRLkheEtja"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)\n",
        "print('Accuracy score of the testing  data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wca-V6fkEtnh",
        "outputId": "563d0799-3ab8-4f3a-d6aa-05b1906b7dc1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  1.0\n",
            "Accuracy score of the testing  data :  0.9920673076923077\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Model 3 : Naive Bayes"
      ],
      "metadata": {
        "id": "W4IaItVpF2OD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy score on the Training and Test Data\n",
        "X_train_prediction = model_3.predict(X_train)\n",
        "X_test_prediction = model_3.predict(X_test)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "iOKXdfDRt-hg"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)\n",
        "print('Accuracy score of the testing  data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kH82De-st-pH",
        "outputId": "4569c86f-3b20-4755-8739-6607ade5cd50"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.9782451923076924\n",
            "Accuracy score of the testing  data :  0.9550480769230769\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model 4 : Decision Tree Classifier"
      ],
      "metadata": {
        "id": "M3ZTiOd1vqSb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy score on the Training and Test Data\n",
        "X_train_prediction = model_4.predict(X_train)\n",
        "X_test_prediction = model_4.predict(X_test)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "_X2mhltZvqfn"
      },
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)\n",
        "print('Accuracy score of the testing  data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8MDQl9WtvqrO",
        "outputId": "5ea009b7-005f-4926-e84d-09e6f5131c4d"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  1.0\n",
            "Accuracy score of the testing  data :  0.9915865384615384\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model 5 : KNN"
      ],
      "metadata": {
        "id": "_vgRZc7jwhtA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy score on the Training and Test Data\n",
        "X_train_prediction = model_5.predict(X_train)\n",
        "X_test_prediction = model_5.predict(X_test)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)"
      ],
      "metadata": {
        "id": "bXV_OgFdwhzW"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)\n",
        "print('Accuracy score of the testing  data : ', test_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQSD9-IRwh4P",
        "outputId": "198609e6-7eac-41fc-b562-a162e4ef1e39"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.5865985576923077\n",
            "Accuracy score of the testing  data :  0.5360576923076923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predictive System"
      ],
      "metadata": {
        "id": "1cw1seEJIUuS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Case 1 : Testing for Real News"
      ],
      "metadata": {
        "id": "1nYx-XqhIUyX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X_test[1]\n",
        "\n",
        "prediction = model_1.predict(X_new)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0]==0):\n",
        "  print('The news is Real')\n",
        "else:\n",
        "  print('The news is Fake')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CppgehqVE5C8",
        "outputId": "683e3d41-df1e-4078-9265-9af4efd5fbcd"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "The news is Real\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_test[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vPg4Wyf3E690",
        "outputId": "9df831ab-505c-4948-a176-12a541306f64"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test Case 2 : Testing for Fake News"
      ],
      "metadata": {
        "id": "A5-G4KiHId8v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X_test[2]\n",
        "\n",
        "prediction = model_1.predict(X_new)\n",
        "print(prediction)\n",
        "\n",
        "if (prediction[0]==0):\n",
        "  print('The news is Real')\n",
        "else:\n",
        "  print('The news is Fake')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHAXGisXFIQx",
        "outputId": "2bd81017-297e-47fb-8d05-12fbff01bc55"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "The news is Fake\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_test[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O16tJzCrFNLM",
        "outputId": "7f062b15-2194-48f9-c408-039c359cebb7"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Graphical Analysis for Training Dataset"
      ],
      "metadata": {
        "id": "oXG-CoJXyFZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_accuracy = {\n",
        "    'Logistic Regression': 0.986,\n",
        "    'Passive Aggressive Classifier': 1.0,\n",
        "    'Naive Bayes': 0.978,\n",
        "    'Decision Tree Classifier': 1.0,\n",
        "    'KNN': 0.586\n",
        "}\n",
        "\n",
        "pd.Series(model_accuracy).plot(kind='pie',label=\"\")\n",
        "plt.title('Accuracy Score on Training Dataset',fontsize=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "wT3y8OfWyFfe",
        "outputId": "e9fdbce7-04dc-42cd-d362-d4ebbc030a4b"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score on Training Dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 56
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Graphical Analysis for Testing Dataset"
      ],
      "metadata": {
        "id": "m5etWBY1zlzV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_accuracy = {\n",
        "    'Logistic Regression': 0.979,\n",
        "    'Passive Aggressive Classifier': 0.9918,\n",
        "    'Naive Bayes': 0.955,\n",
        "    'Decision Tree Classifier': 0.991,\n",
        "    'KNN': 0.536\n",
        "}\n",
        "\n",
        "pd.Series(model_accuracy).plot(kind='pie',label=\"\")\n",
        "plt.title('Accuracy Score on Testing Dataset',fontsize=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "wPUpOP0rzl7j",
        "outputId": "2753062d-a382-4537-e00c-bfff265d94a7"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Accuracy Score on Testing Dataset')"
            ]
          },
          "metadata": {},
          "execution_count": 57
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}