Detecting_Fake_News_with_Python_and_Machine_Learning.ipynb

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      "name": "Detecting Fake News with Python and Machine Learning",
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      "cell_type": "markdown",
      "source": [
        "# Detecting Fake News with Python and Machine Learning"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###What is Fake News?\n",
        "\n",
        "A type of yellow journalism, fake news encapsulates pieces of news that may be hoaxes and is generally spread through social media and other online media. This is often done to further or impose certain ideas and is often achieved with political agendas. Such news items may contain false and/or exaggerated claims, and may end up being viralized by algorithms, and users may end up in a filter bubble.\n",
        "\n",
        "###What is a TfidfVectorizer?\n",
        "\n",
        "**TF (Term Frequency):** The number of times a word appears in a document is its Term Frequency. A higher value means a term appears more often than others, and so, the document is a good match when the term is part of the search terms.\n",
        "\n",
        "**IDF (Inverse Document Frequency):** Words that occur many times a document, but also occur many times in many others, may be irrelevant. IDF is a measure of how significant a term is in the entire corpus.\n",
        "\n",
        "The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.\n",
        "\n",
        "###What is a PassiveAggressiveClassifier?\n",
        "\n",
        "Passive Aggressive algorithms are online learning algorithms. Such an algorithm remains passive for a correct classification outcome, and turns aggressive in the event of a miscalculation, updating and adjusting. Unlike most other algorithms, it does not converge. Its purpose is to make updates that correct the loss, causing very little change in the norm of the weight vector.\n",
        "\n",
        "###Detecting Fake News with Python\n",
        "To build a model to accurately classify a piece of news as REAL or FAKE.\n",
        "\n",
        "###About Detecting Fake News with Python\n",
        "\n",
        "This advanced python project of detecting fake news deals with fake and real news. Using sklearn, we build a TfidfVectorizer on our dataset. Then, we initialize a PassiveAggressive Classifier and fit the model. In the end, the accuracy score and the confusion matrix tell us how well our model fares.\n",
        "\n",
        "###The fake news Dataset\n",
        "\n",
        "The dataset we’ll use for this python project- we’ll call it news.csv. This dataset has a shape of **7796×4.** The first column identifies the news, the second and third are the title and text, and the fourth column has labels denoting whether the news is REAL or FAKE. **The dataset takes up 29.2MB of space**"
      ],
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          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.19.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (1.1.5)\n",
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            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from sklearn) (1.0.2)\n",
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            "Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->sklearn) (1.4.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install numpy pandas sklearn"
      ]
    },
    {
      "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": "oVvCm50i9PPG"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let’s read the data into a DataFrame, and get the shape of the data and the first 5 records."
      ],
      "metadata": {
        "id": "8zWG3CfZ_He_"
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    },
    {
      "cell_type": "code",
      "source": [
        "#Read the data\n",
        "df=pd.read_csv('/content/drive/MyDrive/Data Sets/news.csv')\n",
        "#Get shape and head\n",
        "df.shape\n",
        "df.head()"
      ],
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          "height": 206
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        "id": "ieaS2X2E9U4I",
        "outputId": "54e4d2cd-b4c0-4169-b848-7d53fd30b3c4"
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              "      <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",
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              "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
              "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
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              "      <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",
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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": {},
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      ]
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    {
      "cell_type": "markdown",
      "source": [
        "And get the labels from the DataFrame."
      ],
      "metadata": {
        "id": "JjF-U0-T_KXr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Get the labels\n",
        "labels=df.label\n",
        "labels.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKRxJH_M93cJ",
        "outputId": "195a8f5c-1691-45dd-865b-659aa9e6ce1d"
      },
      "execution_count": 4,
      "outputs": [
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          "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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Split the dataset into training and testing sets."
      ],
      "metadata": {
        "id": "fUFR0pMn_NwH"
      }
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    {
      "cell_type": "code",
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)"
      ],
      "metadata": {
        "id": "L6PWk6AM932A"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s initialize 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). Stop words are the most common words in a language that are to be filtered out before processing the natural language data. And a TfidfVectorizer turns a collection of raw documents into a matrix of TF-IDF features.\n",
        "\n",
        "Now, fit and transform the vectorizer on the train set, and transform the vectorizer on the test set."
      ],
      "metadata": {
        "id": "HeE1p5Xu_Qyy"
      }
    },
    {
      "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": "ccXZDvJN-HAk"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we’ll initialize a PassiveAggressiveClassifier. This is. We’ll fit this on tfidf_train and y_train."
      ],
      "metadata": {
        "id": "7rjVowph_cDr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#DataFlair - Initialize a PassiveAggressiveClassifier\n",
        "pac=PassiveAggressiveClassifier(max_iter=50)\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*100,2)}%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RKPF9t9f-Hkd",
        "outputId": "9fc2d271-c451-449c-c65c-0ece8140da59"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 92.34%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We got an accuracy of 92.82% 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": "wFSOZzII_fiJ"
      }
    },
    {
      "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": "S_k1qKQS-OCN",
        "outputId": "5c1c5bba-d5d4-4250-bf93-8308588f3ea5"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[587,  51],\n",
              "       [ 46, 583]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So with this model, we have 589 true positives, 587 true negatives, 42 false positives, and 49 false negatives."
      ],
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}