fake_news_detect.ipynb

{
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  "metadata": {
    "colab": {
      "name": "fake-news-detect.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zs-qephq_2PQ",
        "colab_type": "text"
      },
      "source": [
        "###1. Make Necessary imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61_RsuHD-V-Q",
        "colab_type": "code",
        "colab": {}
      },
      "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": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ph14U2gIABMW",
        "colab_type": "text"
      },
      "source": [
        "###2. Load the data into a dataframe and get the shape of the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DB5_bar3_HpZ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "outputId": "0be12602-585f-4968-9955-ea79c3cf84e3"
      },
      "source": [
        "#Read the data\n",
        "df=pd.read_csv('news.csv')\n",
        "#Get shape and head\n",
        "df.shape\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2u-BQIcAakH",
        "colab_type": "text"
      },
      "source": [
        "###3. Get the labels from the dataframe"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmh-2yyhAkN8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "43a3a4ce-8320-4b5d-e735-839fca2e2a58"
      },
      "source": [
        "labels=df.label\n",
        "labels.head()"
      ],
      "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": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k-NJsDQfAswl",
        "colab_type": "text"
      },
      "source": [
        "###4. Split the dataset into training and testing sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "clYevE9fAxln",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x_train,x_test,y_train,y_test=train_test_split(df['text'], labels, test_size=0.2, random_state=7)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_f8Hg7HgBBx_",
        "colab_type": "text"
      },
      "source": [
        "###5. 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."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xigIHSWZBNCG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfidf_vectorizer=TfidfVectorizer(stop_words='english', max_df=0.7)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9_SWAWF9BXze",
        "colab_type": "text"
      },
      "source": [
        "###6.  Fit and transform the vectorizer on the train set, and transform the vectorizer on the test set."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NzEsLU4nBcUH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tfidf_train=tfidf_vectorizer.fit_transform(x_train) \n",
        "tfidf_test=tfidf_vectorizer.transform(x_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aq_ZTxQ8BtPB",
        "colab_type": "text"
      },
      "source": [
        "###7. Initialize a PassiveAggressiveClassifier which we will fit on on tfidf_train and y_train."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XlTrw0zOCOO-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "3b5961ae-7605-45b4-d1a8-7feb281a36d5"
      },
      "source": [
        "pac=PassiveAggressiveClassifier(max_iter=50)\n",
        "pac.fit(tfidf_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PassiveAggressiveClassifier(C=1.0, average=False, class_weight=None,\n",
              "                            early_stopping=False, fit_intercept=True,\n",
              "                            loss='hinge', max_iter=50, n_iter_no_change=5,\n",
              "                            n_jobs=None, random_state=None, shuffle=True,\n",
              "                            tol=0.001, validation_fraction=0.1, verbose=0,\n",
              "                            warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FeoBapD6CJC9",
        "colab_type": "text"
      },
      "source": [
        "###8. Predict on the test set from the TfidfVectorizer and calculate the accuracy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4TzJzwHCO3T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "beeb0a0e-770b-4238-c693-4ef786dd62a7"
      },
      "source": [
        "y_pred=pac.predict(tfidf_test)\n",
        "score=accuracy_score(y_test,y_pred)\n",
        "print(f'Accuracy: {round(score*100,2)}%')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy: 92.66%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-Wa8_N4Cij_",
        "colab_type": "text"
      },
      "source": [
        "###9. Print out a confusion matrix to gain insight into the number of false and true negatives and positives."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UpP_Ay_nCllK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "47585a18-97e3-4ae6-d67c-af9603f2335b"
      },
      "source": [
        "confusion_matrix(y_test,y_pred, labels=['FAKE','REAL'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[588,  50],\n",
              "       [ 43, 586]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SPQQxs_7C0MH",
        "colab_type": "text"
      },
      "source": [
        "## Thus with this model, we have **589** true positives, **587** true negatives, **42** false positives, and **49** false negatives."
      ]
    }
  ]
}