Project Fake News Prediction.ipynb

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  "metadata": {
    "colab": {
      "name": "Project 5. Fake News Prediction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FaIBmnXCknPl"
      },
      "source": [
        "About the Dataset:\n",
        "\n",
        "1. id: unique id for a news article\n",
        "2. title: the title of a news article\n",
        "3. author: author of the news article\n",
        "4. text: the text of the article; could be incomplete\n",
        "5. label: a label that marks whether the news article is real or fake:\n",
        "           1: Fake news\n",
        "           0: real News\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teiEZCF8Lnlw"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ur6GL4jOLpNO",
        "outputId": "d50c7531-f618-473e-bbdb-5e8009b227f6"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k399dHafvL5N"
      },
      "source": [
        "Importing the Dependencies"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fetC5yqkPVe"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1AC1YpmGwIDw",
        "outputId": "efa7602b-d03b-4805-c7cd-e91cec8031e6"
      },
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dxIOt3DowpUR",
        "outputId": "da27d117-8f64-4c64-86f6-fd80c237edce"
      },
      "source": [
        "# printing the stopwords in English\n",
        "print(stopwords.words('english'))"
      ],
      "execution_count": 4,
      "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": "code",
      "metadata": {
        "id": "cuarnd4AL0wD"
      },
      "source": [
        "file = '/content/drive/MyDrive/train.csv'"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NjeGd1CLw_6R"
      },
      "source": [
        "Data Pre-processing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCGcpu_1wzLw"
      },
      "source": [
        "# loading the dataset to a pandas DataFrame\n",
        "news_dataset = pd.read_csv(file)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aRgmbYSbxV4-",
        "outputId": "58215064-fb78-4e50-d547-e16e2f552105"
      },
      "source": [
        "news_dataset.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20800, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "jjJ1eB6RxZaS",
        "outputId": "4494271f-8715-4619-e64e-2d3c7a256487"
      },
      "source": [
        "# print the first 5 rows of the dataframe\n",
        "news_dataset.head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>title</th>\n",
              "      <th>author</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
              "      <td>Darrell Lucus</td>\n",
              "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>FLYNN: Hillary Clinton, Big Woman on Campus - ...</td>\n",
              "      <td>Daniel J. Flynn</td>\n",
              "      <td>Ever get the feeling your life circles the rou...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Why the Truth Might Get You Fired</td>\n",
              "      <td>Consortiumnews.com</td>\n",
              "      <td>Why the Truth Might Get You Fired October 29, ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>15 Civilians Killed In Single US Airstrike Hav...</td>\n",
              "      <td>Jessica Purkiss</td>\n",
              "      <td>Videos 15 Civilians Killed In Single US Airstr...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
              "      <td>Howard Portnoy</td>\n",
              "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id  ... label\n",
              "0   0  ...     1\n",
              "1   1  ...     0\n",
              "2   2  ...     1\n",
              "3   3  ...     1\n",
              "4   4  ...     1\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QYkDi4SwxlKi",
        "outputId": "b449b1a3-04c7-417a-c4f1-83dbb11b0bb0"
      },
      "source": [
        "# counting the number of missing values in the dataset\n",
        "news_dataset.isnull().sum()"
      ],
      "execution_count": 9,
      "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": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "S2DtCbjzbqOp",
        "outputId": "b2d16fe7-9d2e-471e-8033-47dd82c60c6e"
      },
      "source": [
        "news_dataset['label'].value_counts().plot(kind='pie')\n",
        "plt.title('Count of fake/real News')\n",
        "plt.xlabel('Label')\n",
        "plt.ylabel('Count')\n",
        "#addlabels('Label', 'Count')\n",
        "plt.show()\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 292
        },
        "id": "6kdWMG-hcCMF",
        "outputId": "8ce919ca-794e-4f84-f650-7723e445a4da"
      },
      "source": [
        "news_dataset['label'].value_counts().plot(kind='bar')\n",
        "plt.title('Count of fake/real News')\n",
        "plt.xlabel('Label')\n",
        "plt.ylabel('Count')\n",
        "#addlabels('Label', 'Count')\n",
        "plt.show()\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EqSDQPysb0MJ",
        "outputId": "3ad2626a-f88b-4543-a9ba-479237ca7bd8"
      },
      "source": [
        "print(news_dataset['label'].value_counts())"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    10413\n",
            "0    10387\n",
            "Name: label, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mc04lQrhx57m"
      },
      "source": [
        "# replacing the null values with empty string\n",
        "news_dataset = news_dataset.fillna('')"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zp1jD5J7UbOk",
        "outputId": "f3acf30e-fb23-43de-8006-fe91d2846d75"
      },
      "source": [
        "news_dataset.isnull().sum()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id        0\n",
              "title     0\n",
              "author    0\n",
              "text      0\n",
              "label     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H7TZgHszygxj"
      },
      "source": [
        "# merging the author name and news title\n",
        "news_dataset['content'] = news_dataset['author']+' '+news_dataset['title']"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cbF6GBBpzBey",
        "outputId": "f1aaae78-d20b-49b9-d348-2e99bf816588"
      },
      "source": [
        "print(news_dataset['content'])"
      ],
      "execution_count": 16,
      "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",
      "metadata": {
        "id": "LfBtAvLtzEo6"
      },
      "source": [
        "# separating the data & label\n",
        "X = news_dataset.drop(columns='label', axis=1)\n",
        "Y = news_dataset['label']"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHPBr540zl1h",
        "outputId": "49acb881-aaea-4790-f756-eacf24524d2a"
      },
      "source": [
        "print(X)\n",
        "print(Y)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "          id  ...                                            content\n",
            "0          0  ...  Darrell Lucus House Dem Aide: We Didn’t Even S...\n",
            "1          1  ...  Daniel J. Flynn FLYNN: Hillary Clinton, Big Wo...\n",
            "2          2  ...  Consortiumnews.com Why the Truth Might Get You...\n",
            "3          3  ...  Jessica Purkiss 15 Civilians Killed In Single ...\n",
            "4          4  ...  Howard Portnoy Iranian woman jailed for fictio...\n",
            "...      ...  ...                                                ...\n",
            "20795  20795  ...  Jerome Hudson Rapper T.I.: Trump a ’Poster Chi...\n",
            "20796  20796  ...  Benjamin Hoffman N.F.L. Playoffs: Schedule, Ma...\n",
            "20797  20797  ...  Michael J. de la Merced and Rachel Abrams Macy...\n",
            "20798  20798  ...  Alex Ansary NATO, Russia To Hold Parallel Exer...\n",
            "20799  20799  ...            David Swanson What Keeps the F-35 Alive\n",
            "\n",
            "[20800 rows x 5 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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "zifDZd7HayDe",
        "outputId": "d851a1d9-eba7-4e60-c09e-dcdde01a3322"
      },
      "source": [
        "news_dataset['label'].value_counts().plot(kind='pie')\n",
        "plt.title('Count of fake/real News')\n",
        "plt.xlabel('Label')\n",
        "plt.ylabel('Count')\n",
        "#addlabels('Label', 'Count')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NwFcpqcz37a"
      },
      "source": [
        "Stemming:\n",
        "\n",
        "Stemming is the process of reducing a word to its Root word\n",
        "\n",
        "example:\n",
        "actor, actress, acting --> act"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ga_DaZxhzoWM"
      },
      "source": [
        "port_stem = PorterStemmer()"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zY-n0dCh0e-y"
      },
      "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"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MBUIk4c94yTL"
      },
      "source": [
        "news_dataset['content'] = news_dataset['content'].apply(stemming)"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xmwK-zyO5Stg",
        "outputId": "d14b3d80-6a2d-41f1-e3ff-cced5399e330"
      },
      "source": [
        "print(news_dataset['content'])"
      ],
      "execution_count": 23,
      "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": "code",
      "metadata": {
        "id": "5ZIidnta5k5h"
      },
      "source": [
        "#separating the data and label\n",
        "X = news_dataset['content'].values\n",
        "Y = news_dataset['label'].values"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3nA_SBZX6BeH",
        "outputId": "c9cfa4c6-103f-4b57-bc9a-4d5e5c2ce4fb"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 25,
      "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",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NgkFGXkg6HS4",
        "outputId": "5c033e73-f619-4691-f28e-cb4480be6d33"
      },
      "source": [
        "print(Y)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 ... 0 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iu2ZEBkL6QTm",
        "outputId": "aa9f73f5-77d5-43e3-82ac-520cab45740d"
      },
      "source": [
        "Y.shape"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20800,)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMfepsQZ6TES"
      },
      "source": [
        "# converting the textual data to numerical data\n",
        "vectorizer = TfidfVectorizer()\n",
        "vectorizer.fit(X)\n",
        "\n",
        "X = vectorizer.transform(X)"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJj5esbs7Nzy",
        "outputId": "901f3a8c-c506-4c98-bd18-b9ec5b8a3ab9"
      },
      "source": [
        "print(X)"
      ],
      "execution_count": 29,
      "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",
      "metadata": {
        "id": "mKBRGiSQ7YCZ"
      },
      "source": [
        "Splitting the dataset to training & test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjMYwmBo7Pbx"
      },
      "source": [
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, stratify=Y, random_state=2)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rxDsQvgO8Oln"
      },
      "source": [
        "Training the Model: Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrSItcqc7qAy"
      },
      "source": [
        "model = LogisticRegression()"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdVJ839l8Vgx",
        "outputId": "0a467d8a-9df5-4cfb-8bc9-eb6e87ceb718"
      },
      "source": [
        "model.fit(X_train, Y_train)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sbPKIFT89W1C"
      },
      "source": [
        "Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YG6gqVty9ZDB"
      },
      "source": [
        "accuracy score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgwtWZY59PBw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c315bd4c-6854-4921-ff67-f4790fe87085"
      },
      "source": [
        "# accuracy score on the training data\n",
        "X_train_prediction = model.predict(X_train)\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)\n",
        "print('Accuracy score of the training data : ', training_data_accuracy)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.9865985576923076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4L-r5mld-BFn",
        "outputId": "e431c654-fb58-47b9-c7f3-7da1258515cb"
      },
      "source": [
        "print('Accuracy score of the training data : ', training_data_accuracy)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data :  0.9865985576923076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Kgcn13oO-H6e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a85b3b9c-0527-486b-b46e-148ecce950a2"
      },
      "source": [
        "# accuracy score on the test data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "test_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
        "print('Accuracy score of the test data : ', test_data_accuracy)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the test data :  0.9790865384615385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9TG0Yof1-vg2",
        "outputId": "d427c65b-8a7c-4d12-9e55-1fa473ca7683"
      },
      "source": [
        "print('Accuracy score of the test data : ', test_data_accuracy)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the test data :  0.9790865384615385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yun4seaE-6tV"
      },
      "source": [
        "Making a Predictive System"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPjssDL_-zo8",
        "outputId": "7f828e8e-e6d3-4336-d4b3-0910831cb019"
      },
      "source": [
        "X_new = X_test[3]\n",
        "\n",
        "prediction = model.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')"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\n",
            "The news is Real\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8KaWdvDI_eUk",
        "outputId": "ab1c7c21-0a93-4dbb-a50c-93a13100ac47"
      },
      "source": [
        "print(Y_test[3])"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JBbWkLGr_lb_"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def plot_confusion_matrix(cm, classes,\n",
        "                          normalize=False,\n",
        "                          title='Confusion matrix',\n",
        "                          cmap=plt.cm.Blues):\n",
        "    \"\"\"\n",
        "    See full source and example: \n",
        "    http://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html\n",
        "    \n",
        "    This function prints and plots the confusion matrix.\n",
        "    Normalization can be applied by setting `normalize=True`.\n",
        "    \"\"\"\n",
        "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
        "    plt.title(title)\n",
        "    plt.colorbar()\n",
        "    tick_marks = np.arange(len(classes))\n",
        "    plt.xticks(tick_marks, classes, rotation=45)\n",
        "    plt.yticks(tick_marks, classes)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "        print(\"Normalized confusion matrix\")\n",
        "    else:\n",
        "        print('Confusion matrix, without normalization')\n",
        "\n",
        "    thresh = cm.max() / 2.\n",
        "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
        "        plt.text(j, i, cm[i, j],\n",
        "                 horizontalalignment=\"center\",\n",
        "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.ylabel('True label')\n",
        "    plt.xlabel('Predicted label')"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "vBkhyURCdWb2",
        "outputId": "c6a0136a-088a-407d-d238-f87216ad7c27"
      },
      "source": [
        "import itertools\n",
        "from sklearn import metrics\n",
        "\n",
        "model.fit(X_train, Y_train)\n",
        "pred = model.predict(X_test)\n",
        "score = metrics.accuracy_score(Y_test, pred)\n",
        "print(\"accuracy:   %0.3f\" % score)\n",
        "cm = metrics.confusion_matrix(Y_test, pred)\n",
        "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy:   0.979\n",
            "Confusion matrix, without normalization\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgEafskSd-DT",
        "outputId": "031a7e40-aa93-429e-bc93-574392069577"
      },
      "source": [
        "import itertools\n",
        "from sklearn import metrics\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "clf = KNeighborsClassifier(n_neighbors=3)\n",
        "clf.fit(X_train, Y_train)\n",
        "\n",
        "X_train_prediction = clf.predict(X_train)                               # Predicting the accuracy on X_train data and store the labels in X_train_prediction\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)    # Comparing our predicted data with the actual labels (from dataset)\n",
        "\n",
        "print('Accuracy score of the training data:', training_data_accuracy)\n",
        "\n",
        "        # accuracy score on the training data\n",
        "\n",
        "X_test_prediction = clf.predict(X_test)                                 # Predicting the accuracy on X_train data and store the labels in X_train_prediction\n",
        "test_data_accuracy_CLF = accuracy_score(X_test_prediction, Y_test)          # Comparing our predicted data with the actual labels (from dataset)\n",
        "\n",
        "print('Accuracy score of the testing data:', test_data_accuracy_CLF)\n",
        "\n"
      ],
      "execution_count": 41,
      "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": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 346
        },
        "id": "49Hjn9rOe4TD",
        "outputId": "678a9ded-4ae9-4ba8-c696-1330371f2cc3"
      },
      "source": [
        "\n",
        "clf.fit(X_train, Y_train)\n",
        "pred = clf.predict(X_test)\n",
        "score = metrics.accuracy_score(Y_test, pred)\n",
        "print(\"accuracy:   %0.3f\" % score)\n",
        "cm = metrics.confusion_matrix(Y_test, pred)\n",
        "plot_confusion_matrix(cm, classes=['FAKE', 'REAL'])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy:   0.536\n",
            "Confusion matrix, without normalization\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfDWUD29ltGj",
        "outputId": "a0e6b354-4488-49d5-eaf6-b0c71f821c6c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "clf = KNeighborsClassifier(n_neighbors=3)\n",
        "clf.fit(X_train, Y_train)\n",
        "\n",
        "X_train_prediction = clf.predict(X_train)                               # Predicting the accuracy on X_train data and store the labels in X_train_prediction\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)    # Comparing our predicted data with the actual labels (from dataset)\n",
        "\n",
        "print('Accuracy score of the training data:', training_data_accuracy)"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the training data: 0.5865985576923077\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8t2GYeAgltux",
        "outputId": "7eca4a92-c78c-4109-d55a-dd6d8697dcc7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "X_test_prediction = clf.predict(X_test)                                 # Predicting the accuracy on X_train data and store the labels in X_train_prediction\n",
        "test_data_accuracy_CLF = accuracy_score(X_test_prediction, Y_test)          # Comparing our predicted data with the actual labels (from dataset)\n",
        "\n",
        "print('Accuracy score of the testing data:', test_data_accuracy_CLF)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score of the testing data: 0.5360576923076923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASHwmEltl4Ve",
        "outputId": "a3316de3-2149-4bf3-c4f0-06bdf3e75577",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "clf = KNeighborsClassifier(n_neighbors=3)\n",
        "clf.fit(X_train, Y_train)\n",
        "X_train_prediction = clf.predict(X_train)                               # Predicting the accuracy on X_train data and store the labels in X_train_prediction\n",
        "training_data_accuracy = accuracy_score(X_train_prediction, Y_train)    # Comparing our predicted data with the actual labels (from dataset)\n",
        "\n",
        "print('Accuracy score of the training data:', training_data_accuracy)\n",
        "X_test_prediction = clf.predict(X_test)                                 # Predicting the accuracy on X_train data and store the labels in X_train_prediction\n",
        "test_data_accuracy_CLF = accuracy_score(X_test_prediction, Y_test)          # Comparing our predicted data with the actual labels (from dataset)\n",
        "\n",
        "print('Accuracy score of the testing data:', test_data_accuracy_CLF)"
      ],
      "execution_count": 52,
      "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": "code",
      "metadata": {
        "id": "RvnquGy2m8JU"
      },
      "source": [
        ""
      ],
      "execution_count": 45,
      "outputs": []
    }
  ]
}