Fake_news_detector_1.ipynb

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    "colab": {
      "name": "Fake news detector.ipynb",
      "provenance": [],
      "toc_visible": true,
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMhvLk7qgoVAG6PCDQ6/USd",
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/vkusnezh/Projects-Portfolio/blob/main/Fake_news_detector_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The goal of this project is to create a sand-box to play with building a models to classify a news as REAL or FAKE."
      ],
      "metadata": {
        "id": "zvc1ervCeTs-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Project"
      ],
      "metadata": {
        "id": "SFubgisIf0jm"
      }
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "xY-5TftvgIqx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# New Section"
      ],
      "metadata": {
        "id": "ef-M1PVdTHQu"
      }
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      "cell_type": "code",
      "source": [
        "#Read the data from google drive\n",
        "url='https://drive.google.com/file/d/1g1DS0TbDYWKVORIbC3XJJ6Do73KrkQ9b/view?usp=sharing'\n",
        "url_c='https://drive.google.com/uc?export=download&id='+url.split('/')[-2]\n",
        "df=pd.read_csv(url_c)\n",
        "\n",
        "#Get head\n",
        "\n",
        "#df.shape\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "KjyXLwdh_4lB",
        "outputId": "63398ae7-462a-4927-b514-d1087d9b623f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<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",
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            "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]"
            ]
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          "metadata": {},
          "execution_count": 4
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    {
      "cell_type": "code",
      "source": [
        "#DataFlair - get the labels from the DataFrame\n",
        "labels=df.label\n",
        "labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fCwRCDCwXEeE",
        "outputId": "508240c2-ee44-4605-bb3b-07a23b11016b"
      },
      "execution_count": null,
      "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": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Split the dataset into training and testing sets\n",
        "x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.3, random_state=10)"
      ],
      "metadata": {
        "id": "lgsBqWznXQa5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initialization of a TfidfVectorizer with stop words from the English language and a maximum document frequency of 0.7 (terms with a higher document frequency will be discarded). \n",
        "TfidfVectorizer turns a collection of raw documents into a matrix of TF-IDF features.\n",
        "\n",
        "Fiting and transformation of the vectorizer on the train set, and transformation of the vectorizer on the test set."
      ],
      "metadata": {
        "id": "naY2syb-Xip9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Initialize a TfidfVectorizer\n",
        "tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)\n",
        "\n",
        "#DataFlair - 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)"
      ],
      "metadata": {
        "id": "BiCkfQGcX7TJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initialization of a PassiveAggressiveClassifier, and fit it on tfidf_train and y_train.\n",
        "\n",
        "Then, we’ll predict on the test set from the TfidfVectorizer and calculate the accuracy with accuracy_score() from sklearn.metrics."
      ],
      "metadata": {
        "id": "biVyeG2qYNYS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Initialize a PassiveAggressiveClassifier\n",
        "pac=PassiveAggressiveClassifier(max_iter=30)\n",
        "pac.fit(tfidf_train,y_train)\n",
        "#DataFlair - 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*100,3)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hxU8YYB1ddEL",
        "outputId": "a0ff8dfc-90e0-4840-9586-48538fb0e9ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 94.214%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Accuracy is 94.2% with this model. Finally, let’s print out a confusion matrix to gain insight into the number of false and true negatives and positives."
      ],
      "metadata": {
        "id": "ErBw7j3ZdpCR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Build confusion matrix\n",
        "confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LS8mU_H2dzDI",
        "outputId": "9dc23547-7d20-4543-a98a-a12f5cba2c1b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[874,  46],\n",
              "       [ 64, 917]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So with this model, we have 874 true positives, 917 true negatives, 46 false positives, and 64 false negatives."
      ],
      "metadata": {
        "id": "UXY5hLVQd-AQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusions"
      ],
      "metadata": {
        "id": "VsYxldCggHk9"
      }
    }
  ]
}