Fake_news_Detector/Fake_news_Detector.ipynb

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    "colab": {
      "name": "Fake_news_Detector.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyOUwrzRAj6BaFoLp4RGIeqb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rakshaa2000/30-Days-of-ML/blob/main/Fake_news_Detector.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wlBJDsOJCmMX"
      },
      "source": [
        "**Fake news Detector**\\\n",
        "This notebook helps classifiy fake news from legitimate news with the help of Machine learning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PNQ3Xf_GCcic"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import itertools\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import PassiveAggressiveClassifier\n",
        "from sklearn.metrics import accuracy_score, confusion_matrix"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jbcM9Vo8DMGT",
        "outputId": "58cb2c92-beff-4f73-fec7-2edf0fd8aee9",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        }
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-edf6d607-e997-449e-8a52-d6fefab8aa66\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-edf6d607-e997-449e-8a52-d6fefab8aa66\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving news.csv to news.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YYfiTMSoKAHa",
        "outputId": "76518b73-48fe-4ed1-8050-d21fa7fd1996",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#Read the data\n",
        "df=pd.read_csv('news.csv')\n",
        "\n",
        "#Get shape and head\n",
        "df.shape\n",
        "df.head()"
      ],
      "execution_count": 3,
      "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>Unnamed: 0</th>\n",
              "      <th>title</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8476</td>\n",
              "      <td>You Can Smell Hillary’s Fear</td>\n",
              "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10294</td>\n",
              "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
              "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3608</td>\n",
              "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
              "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
              "      <td>REAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10142</td>\n",
              "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
              "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
              "      <td>FAKE</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>875</td>\n",
              "      <td>The Battle of New York: Why This Primary Matters</td>\n",
              "      <td>It's primary day in New York and front-runners...</td>\n",
              "      <td>REAL</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0  ... label\n",
              "0        8476  ...  FAKE\n",
              "1       10294  ...  FAKE\n",
              "2        3608  ...  REAL\n",
              "3       10142  ...  FAKE\n",
              "4         875  ...  REAL\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9-b8MF2KIJy",
        "outputId": "e647096a-8827-4bcc-e76f-713697df98f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "#Get the labels\n",
        "labels=df.label\n",
        "labels.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    FAKE\n",
              "1    FAKE\n",
              "2    REAL\n",
              "3    FAKE\n",
              "4    REAL\n",
              "Name: label, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ez7PDRbxKU17"
      },
      "source": [
        "#Split the dataset\n",
        "x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4NMcyIMpKbUv"
      },
      "source": [
        "#Initialize a TfidfVectorizer\n",
        "tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)\n",
        "\n",
        "#Fit and transform train set, transform test set\n",
        "tfidf_train=tfidf_vectorizer.fit_transform(x_train) \n",
        "tfidf_test=tfidf_vectorizer.transform(x_test)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idupuLuDKh-6",
        "outputId": "fe45c856-0fd9-4985-ceca-741baacd2706",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Initialize a PassiveAggressiveClassifier\n",
        "pac=PassiveAggressiveClassifier(max_iter=50)\n",
        "pac.fit(tfidf_train,y_train)\n",
        "\n",
        "#Predict on the test set and calculate accuracy\n",
        "y_pred=pac.predict(tfidf_test)\n",
        "score=accuracy_score(y_test,y_pred)\n",
        "print(f'Accuracy: {round(score*100,2)}%')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 93.13%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WGjbWlS_KqTw",
        "outputId": "ca302876-efa6-429e-dcc1-1bade8c46b1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "#Build confusion matrix\n",
        "confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[592,  46],\n",
              "       [ 41, 588]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    }
  ]
}