Fake_News_Prediction.ipynb

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    "colab": {
      "name": "Fake News Prediction.ipynb",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Dataset Features:\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 denotes whether the article is fake of real\n",
        "\n",
        "1 : Fake News\n",
        "\n",
        "0 : Real News\n"
      ],
      "metadata": {
        "id": "Tf22SsWXuTUH"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "Import the Libraries"
      ],
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        "id": "BNuMhL_4vpht"
      }
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      "cell_type": "code",
      "execution_count": 43,
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import re # will be used to remove regular expressions in a text\n",
        "from nltk.corpus import stopwords # corpus is the content of the text, nltk = natural language toolkit\n",
        "from nltk.stem.porter import PorterStemmer # stemming takes a word and removes prefix and suffix and return root word\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer # used to convert text data into numerical data \n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Downloading the stopwords"
      ],
      "metadata": {
        "id": "4Hs4uG7HxcYA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('stopwords')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g7FNw5oaw119",
        "outputId": "d5d1e581-618e-4a9f-cd56-132bcd1b4cf2"
      },
      "execution_count": 4,
      "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": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Printing the stopwords in English Language"
      ],
      "metadata": {
        "id": "hVTn5a8uxmM5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(stopwords.words('english'))"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "VP8EqjoJxaXQ",
        "outputId": "2c228535-019e-4eb9-b7b1-56b7dcb87407"
      },
      "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": [
        "Data Preprocessing"
      ],
      "metadata": {
        "id": "yJ2fDvSax0wp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load Dataset into a pandas DataFrame\n",
        "news_dataset = pd.read_csv('/content/train.csv')"
      ],
      "metadata": {
        "id": "bMxE_kynxk1W"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "news_dataset.shape"
      ],
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        },
        "id": "rLLO146ayHwH",
        "outputId": "8b8fc801-2678-4449-e683-7dc3f0bb8895"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "(20800, 5)"
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          "metadata": {},
          "execution_count": 7
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    },
    {
      "cell_type": "code",
      "source": [
        "# Print the first 5 Rows of our dataset\n",
        "news_dataset.head()"
      ],
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            "text/plain": [
              "   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",
              "\n",
              "                                                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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    {
      "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": "lLL9Voa4yY_q",
        "outputId": "3e692149-d1ff-44d8-e1cf-41a8ae9bf087"
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      "execution_count": 10,
      "outputs": [
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          "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": [
        "# Replacing the missing values with empty strings\n",
        "news_dataset = news_dataset.fillna('')"
      ],
      "metadata": {
        "id": "6fb84xvyy_IC"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Merging the Author Name and News Title\n",
        "news_dataset['content'] = news_dataset['author'] + ' ' +news_dataset['title'] "
      ],
      "metadata": {
        "id": "8_ktb6jCzW_z"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(news_dataset['content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CLP_TLmlzxsc",
        "outputId": "70520f2e-0f40-4260-d65f-df93ff81414b"
      },
      "execution_count": 14,
      "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": "markdown",
      "source": [
        "Stemming \n",
        "\n",
        "Stemming is the process of reducing a word to its root word by removing its prefixes and suffixes\n",
        "\n",
        "example:-\n",
        "actor, actress, acting -> root word = act"
      ],
      "metadata": {
        "id": "9iSAIYxr0u87"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "port_stem = PorterStemmer()"
      ],
      "metadata": {
        "id": "jAQuyL7n0iB5"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stemming(content):\n",
        "  # remove all number and punctuations from the content and replace by ' ' blank space\n",
        "  stemmed_content =  re.sub('[^a-zA-Z]',' ', content)\n",
        "  # convert everything to lower case\n",
        "  stemmed_content = stemmed_content.lower()\n",
        "  # convert everything in the content into a list\n",
        "  stemmed_content = stemmed_content.split()\n",
        "  # stemming each word in list if it isn't a stopword\n",
        "  stemmed_content = [port_stem.stem(word) for word in stemmed_content if not word in stopwords.words('english')]\n",
        "  # we are joining all the words back into a string from the list\n",
        "  stemmed_content = ' '.join(stemmed_content)\n",
        "  # return stemmed string\n",
        "  return stemmed_content"
      ],
      "metadata": {
        "id": "BrwHF2W21NuG"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# apply takes a function and applies it to all values of pandas series.\n",
        "news_dataset['content'] = news_dataset['content'].apply(stemming)"
      ],
      "metadata": {
        "id": "fjVi2AX-35KI"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Stemmed content"
      ],
      "metadata": {
        "id": "nWwhL4FM4nYK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(news_dataset['content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3N9oGiV4S5g",
        "outputId": "12989ca5-9acd-42b2-a8a5-aa81e7513595"
      },
      "execution_count": 20,
      "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",
      "source": [
        "# Seperating the data and label\n",
        "X = news_dataset['content'].values\n",
        "Y = news_dataset['label'].values"
      ],
      "metadata": {
        "id": "FOTcf8Ax4jGQ"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "bMaxjfMr5EXo",
        "outputId": "901f38df-b479-4fd3-9382-402cd3121c0b"
      },
      "execution_count": 24,
      "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)\n",
        "#  1-> fake\n",
        "#  0-> Real"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "17xmVuT45Fgg",
        "outputId": "28ac01e8-9722-4cf8-8b31-5d69fe1e6b18"
      },
      "execution_count": 26,
      "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": "L2PIJNlH5FKg",
        "outputId": "eb2fa43b-2ae2-4615-fa0a-73b2d33377dd"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20800,)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Converting the textual data to Numerical Data"
      ],
      "metadata": {
        "id": "5ajDQWKY5aNc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# tf = term frequency idf = inverse document frequency\n",
        "vectorizer = TfidfVectorizer()\n",
        "vectorizer.fit(X)\n",
        "X = vectorizer.transform(X)"
      ],
      "metadata": {
        "id": "N1KlWczL5YfY"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X)"
      ],
      "metadata": {
        "id": "7I-fdamU6Fc8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Splitting Dataset into training and test data"
      ],
      "metadata": {
        "id": "YgmQqZJd6NRZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# here stratify=Y is used to distribute the data equally among real and fake ones into both train and the test data\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, stratify=Y, random_state=2)"
      ],
      "metadata": {
        "id": "slNtOS_Q6Gz_"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Training the Logistic Regression Model"
      ],
      "metadata": {
        "id": "7Dt0xRhr67NT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()"
      ],
      "metadata": {
        "id": "uBkKOFTm6unB"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qS1hixHi7CSd",
        "outputId": "d81fd4ca-d1bd-4c91-8179-2f6d663e747a"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model Evaluation using Accuracy Score"
      ],
      "metadata": {
        "id": "qIirDA9574re"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy Score for 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 for training data =\",training_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VFfjI4xd7lcT",
        "outputId": "f711ffa6-0895-4a78-e269-866f5e7a0352"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score for training data = 0.9865985576923076\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Accuracy Score for testing data\n",
        "X_test_prediction = model.predict(X_test)\n",
        "testing_data_accuracy = accuracy_score(X_test_prediction, Y_test)\n",
        "print(\"Accuracy Score for testing data =\",testing_data_accuracy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LgEdJ0OL8WLM",
        "outputId": "c9967744-8274-4508-9e23-0af376a7feb5"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score for testing data = 0.9790865384615385\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making a Predictive System for our model"
      ],
      "metadata": {
        "id": "cdprXXPq9QIA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_new = X_test[0]\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\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rfsLuKk18-wM",
        "outputId": "be26c84d-5e8d-47c0-ef83-08d716c84e9e"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1]\n",
            "The News is Fake\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(Y_test[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B03EuPxI91LP",
        "outputId": "df41c5b4-2674-450c-d7b1-24e523916659"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    }
  ]
}