Fake_News_Detection.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Project Name : ✨ Fake News Detection Analysis ✨**\n",
        "\n",
        "AUTHOR : **Jamshed Butt** from **Data Science**"
      ],
      "metadata": {
        "id": "PL8rwNodN0p-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Dataset Description**\n",
        "\n",
        "**train.csv:** A full training dataset with the following\n",
        "\n",
        "**attributes:**\n",
        "\n",
        "**id:** unique id for a news article\n",
        "\n",
        "**title:** the title of a news article\n",
        "\n",
        "**author:** author of the news article\n",
        "\n",
        "**text:** the text of the article; could be incomplete\n",
        "\n",
        "**label:** a label that marks the article as potentially unreliable\n",
        "1: unreliable\n",
        "0: reliable"
      ],
      "metadata": {
        "id": "WQ8qZQHBPHA0"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tI1W_TuLTYSh"
      },
      "source": [
        "# **Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "T-comAUJTDFS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import re\n",
        "\n",
        "from sklearn.model_selection import train_test_split,cross_val_score\n",
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,classification_report,roc_curve, auc"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m4WHtevSU5Vk"
      },
      "source": [
        "# **Load Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "id": "PaOQvSG8U4b5",
        "outputId": "ac7d6a2a-b7fe-40e4-89b4-c4313b9f55ef"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-723fb2d5-4f24-45a2-99dc-4987bc5e644a\" class=\"colab-df-container\">\n",
              "    <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>id</th>\n",
              "      <th>title</th>\n",
              "      <th>author</th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
              "      <td>Darrell Lucus</td>\n",
              "      <td>House Dem Aide: We Didn’t Even See Comey’s Let...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>FLYNN: Hillary Clinton, Big Woman on Campus - ...</td>\n",
              "      <td>Daniel J. Flynn</td>\n",
              "      <td>Ever get the feeling your life circles the rou...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>Why the Truth Might Get You Fired</td>\n",
              "      <td>Consortiumnews.com</td>\n",
              "      <td>Why the Truth Might Get You Fired October 29, ...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>15 Civilians Killed In Single US Airstrike Hav...</td>\n",
              "      <td>Jessica Purkiss</td>\n",
              "      <td>Videos 15 Civilians Killed In Single US Airstr...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>Iranian woman jailed for fictional unpublished...</td>\n",
              "      <td>Howard Portnoy</td>\n",
              "      <td>Print \\nAn Iranian woman has been sentenced to...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-723fb2d5-4f24-45a2-99dc-4987bc5e644a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-723fb2d5-4f24-45a2-99dc-4987bc5e644a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-723fb2d5-4f24-45a2-99dc-4987bc5e644a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-6c1c1556-e830-448f-b228-58d01705d6ac\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c1c1556-e830-448f-b228-58d01705d6ac')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6c1c1556-e830-448f-b228-58d01705d6ac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "df = pd.read_csv(\"/content/drive/MyDrive/Datasets/Fake News Detection/train.csv.zip\")\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDk1_VwZWi-N",
        "outputId": "7ca32971-d078-441c-dbaf-ae8dda1425e3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20800, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yloWoUywWtar",
        "outputId": "25ac4aba-20ad-4949-e678-6934f9732095"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 20800 entries, 0 to 20799\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   id      20800 non-null  int64 \n",
            " 1   title   20242 non-null  object\n",
            " 2   author  18843 non-null  object\n",
            " 3   text    20761 non-null  object\n",
            " 4   label   20800 non-null  int64 \n",
            "dtypes: int64(2), object(3)\n",
            "memory usage: 812.6+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZonEgVhW3-i"
      },
      "source": [
        "# **Data Preprocessing**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yxlinn4UXl4g"
      },
      "source": [
        "#### **Remove Duplicate**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udLaTMvJXrjT",
        "outputId": "3b7c4f25-6c07-4767-a75e-125eb51f64c9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n"
          ]
        }
      ],
      "source": [
        "duplicate = df.duplicated()\n",
        "print(duplicate.sum())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaEYSr8KXU9u"
      },
      "source": [
        "#### **Check and Remove NaN Values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SikdO5IAW0_S",
        "outputId": "93c3cd0b-30a5-4c08-8d1e-56f760dbd1c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id           0\n",
              "title      558\n",
              "author    1957\n",
              "text        39\n",
              "label        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rbV6zrRUXg7Z"
      },
      "outputs": [],
      "source": [
        "df.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgdQj0rpYTYr",
        "outputId": "1544a7d5-ecf8-4d22-9c50-530333ae9aee"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id        0\n",
              "title     0\n",
              "author    0\n",
              "text      0\n",
              "label     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "N8DsSQK9ZnuT"
      },
      "source": [
        "#### **Preprocess Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "E-2PVRBJZufl"
      },
      "outputs": [],
      "source": [
        "def preprocessing_text(text):\n",
        "  text = text.lower()\n",
        "  text = re.sub(r\"[^\\s\\w]\",\"\",text)\n",
        "  return text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JK7JV-0GZ-f7"
      },
      "outputs": [],
      "source": [
        "df['preprocessed_text'] = df['text'].apply(preprocessing_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5JHMqpLeYfXB"
      },
      "source": [
        "#### **Remove Unuse Columns**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CCUQgV0kYWBJ"
      },
      "outputs": [],
      "source": [
        "X = df[\"preprocessed_text\"]\n",
        "Y = df[\"label\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6L_RL5luZhRc",
        "outputId": "87794bcb-a7b8-427f-911f-2a0b09dea394"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        house dem aide we didnt even see comeys letter...\n",
              "1        ever get the feeling your life circles the rou...\n",
              "2        why the truth might get you fired october 29 2...\n",
              "3        videos 15 civilians killed in single us airstr...\n",
              "4        print \\nan iranian woman has been sentenced to...\n",
              "                               ...                        \n",
              "20795    rapper t i unloaded on black celebrities who m...\n",
              "20796    when the green bay packers lost to the washing...\n",
              "20797    the macys of today grew from the union of seve...\n",
              "20798    nato russia to hold parallel exercises in balk...\n",
              "20799      david swanson is an author activist journali...\n",
              "Name: preprocessed_text, Length: 18285, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3xTtRkvmcE6H"
      },
      "source": [
        "#### **Feature Extraction**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ja4LTfMyb2vV"
      },
      "outputs": [],
      "source": [
        "tfidf = TfidfVectorizer()\n",
        "X = tfidf.fit_transform(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4qQ4xwMRc-0J",
        "outputId": "5306643f-eeda-4d59-eff1-08f0532a0ba4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X :  (18285, 189470)\n"
          ]
        }
      ],
      "source": [
        "print(\"X : \",X.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jRgvi7Ara4yF"
      },
      "source": [
        "# **Spliting the Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uzYDX6-KaxlI"
      },
      "outputs": [],
      "source": [
        "train_data, test_data, train_label, test_label = train_test_split(X, Y, test_size=0.3, random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fa-mpUdrbxwf",
        "outputId": "c921f211-6e21-4dd2-a44d-cc95ca54bf7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_data :  (12799, 189470)\n",
            "train_label :  (12799,)\n",
            "test_data :  (5486, 189470)\n",
            "test_label :  (5486,)\n"
          ]
        }
      ],
      "source": [
        "print(\"train_data : \",train_data.shape)\n",
        "print(\"train_label : \",train_label.shape)\n",
        "print(\"test_data : \",test_data.shape)\n",
        "print(\"test_label : \",test_label.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dh1sjkcpdoof"
      },
      "source": [
        "# **Model**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "he4P2dW9d-63"
      },
      "source": [
        "#### **Logistic Regression**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eJyP1ma-dVXj"
      },
      "outputs": [],
      "source": [
        "model_lr = LogisticRegression().fit(train_data,train_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wELJ4j5beoZA"
      },
      "outputs": [],
      "source": [
        "y_pred = model_lr.predict(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HExV59sAe5T2",
        "outputId": "af02b123-a43a-46a9-bac9-a25f2c10f614"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "y_pred"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTY2nnnHfwx0",
        "outputId": "f7a61843-d0f6-4f23-b3ac-53b1325d02c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.949690120306234"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "model_lr.score(test_data,test_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LLCFpDorf7E4",
        "outputId": "6d8bdcf8-64c3-45b7-e6d7-4d6b784de222"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2971,  163],\n",
              "       [ 113, 2239]])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "confusion_matrix(y_pred,test_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_8rot7DJggPE",
        "outputId": "2fc66de8-1946-4984-abd0-cadf31bf4c32"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score :  0.949690120306234\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy Score : \",accuracy_score(y_pred,test_label))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9N63c6MEgmrt",
        "outputId": "ccff98bf-cf08-4e47-9164-d4dfee80c381"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.96      0.95      0.96      3134\n",
            "           1       0.93      0.95      0.94      2352\n",
            "\n",
            "    accuracy                           0.95      5486\n",
            "   macro avg       0.95      0.95      0.95      5486\n",
            "weighted avg       0.95      0.95      0.95      5486\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_pred,test_label))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DOw7N197nP_Z",
        "outputId": "d795b379-b03e-4419-8127-dfebc9d287f1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corss _Val_Score Train Data :  0.9425734234564282\n",
            "Corss _Val_Score Test Data :  0.9276330711511607\n"
          ]
        }
      ],
      "source": [
        "print(\"Corss _Val_Score Train Data : \",cross_val_score(model_lr,train_data,train_label,cv=5).mean())\n",
        "print(\"Corss _Val_Score Test Data : \",cross_val_score(model_lr,test_data,test_label,cv=5).mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "O1sOYX6Rha6_",
        "outputId": "e1c40cfc-5259-402c-dcc5-fb33adfbd442"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Compute ROC curve and ROC area\n",
        "fpr, tpr, _ = roc_curve(test_label, y_pred)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot the ROC curve\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC)')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bS9TfLTHo-gp"
      },
      "source": [
        "#### **Random Forest Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zNg40_66pEm6"
      },
      "outputs": [],
      "source": [
        "model_rf = RandomForestClassifier().fit(train_data,train_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kiPs2a2LqrhM"
      },
      "outputs": [],
      "source": [
        "y_pred_2 = model_rf.predict(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aMUPomSmrLvx",
        "outputId": "caf568ae-de78-4134-c70c-06a10693055f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 1, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "y_pred_2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QVyTTpbHrq9J",
        "outputId": "1ce759b6-69bd-4f1f-d4ae-f93c195d518a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy Score :  0.8946409041195771\n"
          ]
        }
      ],
      "source": [
        "print(\"Accuracy Score : \",accuracy_score(y_pred_2,test_label))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0MCkE8-7qzto",
        "outputId": "140aa595-4c17-4e23-f45a-88f92badcdaa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3007,  501],\n",
              "       [  77, 1901]])"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "confusion_matrix(y_pred_2,test_label)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4qWKVmfrgt7",
        "outputId": "21209333-ce80-4ba0-dde1-1d402001c265"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.98      0.86      0.91      3508\n",
            "           1       0.79      0.96      0.87      1978\n",
            "\n",
            "    accuracy                           0.89      5486\n",
            "   macro avg       0.88      0.91      0.89      5486\n",
            "weighted avg       0.91      0.89      0.90      5486\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(classification_report(y_pred_2,test_label))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9oUs5DE6rg5z",
        "outputId": "9d2da526-e980-415b-c635-0cb2bbf49337"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Corss _Val_Score Train Data :  0.8897576873290347\n",
            "Corss _Val_Score Test Data :  0.8716755250700287\n"
          ]
        }
      ],
      "source": [
        "print(\"Corss _Val_Score Train Data : \",cross_val_score(model_rf,train_data,train_label,cv=5).mean())\n",
        "print(\"Corss _Val_Score Test Data : \",cross_val_score(model_rf,test_data,test_label,cv=5).mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "ifLFuE3nsG50",
        "outputId": "845f8394-fe28-4da9-b440-924b63ea9774"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Compute ROC curve and ROC area\n",
        "fpr, tpr, _ = roc_curve(test_label, y_pred_2)\n",
        "roc_auc = auc(fpr, tpr)\n",
        "\n",
        "# Plot the ROC curve\n",
        "plt.figure()\n",
        "lw = 2\n",
        "plt.plot(fpr, tpr, color='darkorange',\n",
        "         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\n",
        "plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
        "plt.xlim([0.0, 1.0])\n",
        "plt.ylim([0.0, 1.05])\n",
        "plt.xlabel('False Positive Rate')\n",
        "plt.ylabel('True Positive Rate')\n",
        "plt.title('Receiver Operating Characteristic (ROC)')\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "toc_visible": true,
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}