SMS_spam_classifier.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/anshu2308/java-car-parking-system/blob/master/SMS_spam_classifier.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 160,
      "metadata": {
        "id": "zFKH37LPOYeY"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 161,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74,
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "ok": true,
              "status": 200,
              "status_text": ""
            }
          }
        },
        "id": "dVACU8xOO45t",
        "outputId": "7d77136b-d739-46b5-92c5-40f686d92ccf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-5f851bd0-3db2-4779-ab3e-e73ce1834ddd\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-5f851bd0-3db2-4779-ab3e-e73ce1834ddd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving mail_data.csv to mail_data (1).csv\n"
          ]
        }
      ],
      "source": [
        "#load the data\n",
        "from google.colab import files\n",
        "uploaded= files.upload()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 162,
      "metadata": {
        "id": "qVVZriguP3_W"
      },
      "outputs": [],
      "source": [
        "df =pd.read_csv('mail_data.csv')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 163,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "OmEB4HntP-Bd",
        "outputId": "5b83f180-e3b8-4f0a-d072-9804ae1c7340"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Category                                            Message\n",
              "895       ham  Doesn't g have class early tomorrow and thus s...\n",
              "744       ham                 Ok. There may be a free gym about.\n",
              "2029      ham             Kallis is ready for bat in 2nd innings\n",
              "5188      ham                                               Okie\n",
              "2120      ham              I hope you know I'm still mad at you.\n",
              "3595     spam  Do you want a New Nokia 3510i Colour Phone Del...\n",
              "300       ham  Need a coffee run tomo?Can't believe it's that...\n",
              "1196     spam  You have 1 new voicemail. Please call 08719181503\n",
              "3317      ham  I know girls always safe and selfish know i go...\n",
              "2101      ham    Oh Howda gud gud.. Mathe en samachara chikku:-)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cbe7c856-b324-418b-bc5f-1b118fa0dc2a\">\n",
              "    <div 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>Category</th>\n",
              "      <th>Message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>895</th>\n",
              "      <td>ham</td>\n",
              "      <td>Doesn't g have class early tomorrow and thus s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>744</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok. There may be a free gym about.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2029</th>\n",
              "      <td>ham</td>\n",
              "      <td>Kallis is ready for bat in 2nd innings</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5188</th>\n",
              "      <td>ham</td>\n",
              "      <td>Okie</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2120</th>\n",
              "      <td>ham</td>\n",
              "      <td>I hope you know I'm still mad at you.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3595</th>\n",
              "      <td>spam</td>\n",
              "      <td>Do you want a New Nokia 3510i Colour Phone Del...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300</th>\n",
              "      <td>ham</td>\n",
              "      <td>Need a coffee run tomo?Can't believe it's that...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1196</th>\n",
              "      <td>spam</td>\n",
              "      <td>You have 1 new voicemail. Please call 08719181503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3317</th>\n",
              "      <td>ham</td>\n",
              "      <td>I know girls always safe and selfish know i go...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2101</th>\n",
              "      <td>ham</td>\n",
              "      <td>Oh Howda gud gud.. Mathe en samachara chikku:-)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cbe7c856-b324-418b-bc5f-1b118fa0dc2a')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-cbe7c856-b324-418b-bc5f-1b118fa0dc2a 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-cbe7c856-b324-418b-bc5f-1b118fa0dc2a');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ],
      "source": [
        "df.sample(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ibwEHSCaQBzd",
        "outputId": "b077f6d3-aaa1-4b22-daba-5d7757d3c781"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5572, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 165,
      "metadata": {
        "id": "y1BfOBt8QIn6"
      },
      "outputs": [],
      "source": [
        "#1. Data Cleaning\n",
        "#2. EDA\n",
        "#3. Text Processing\n",
        "#4. Model building\n",
        "#5. Evaluation\n",
        "#6. Improvement\n",
        "#7. Website\n",
        "#8. Deploy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "udJ4KNGXQJ-L"
      },
      "outputs": [],
      "source": [
        "## DATA CLEANING"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 166,
      "metadata": {
        "id": "9JLgYQZgQMk8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "81d8b49b-a874-4cd0-f4a5-b79868a17a91"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 5572 entries, 0 to 5571\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   Category  5572 non-null   object\n",
            " 1   Message   5572 non-null   object\n",
            "dtypes: object(2)\n",
            "memory usage: 87.2+ KB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3zQlYif-QUVM"
      },
      "outputs": [],
      "source": [
        "#Drop columns\n",
        "#df.drop(columns=['Unnamed: 0','label_num'],inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 167,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "T6j8TJmMQXG1",
        "outputId": "6881e38d-9d13-441a-8bf7-6505c0d545e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     target                                               text\n",
              "5490    ham                            K. I will sent it again\n",
              "5352    ham           Good morning princess! Have a great day!\n",
              "3936    ham                           You need to get up. Now.\n",
              "513     ham                            Lol ok your forgiven :)\n",
              "576    spam  You have won ?1,000 cash or a ?2,000 prize! To..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52bec0fe-3d0a-4f69-ab86-d13178a28aae\">\n",
              "    <div 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>target</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5490</th>\n",
              "      <td>ham</td>\n",
              "      <td>K. I will sent it again</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5352</th>\n",
              "      <td>ham</td>\n",
              "      <td>Good morning princess! Have a great day!</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3936</th>\n",
              "      <td>ham</td>\n",
              "      <td>You need to get up. Now.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>513</th>\n",
              "      <td>ham</td>\n",
              "      <td>Lol ok your forgiven :)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>576</th>\n",
              "      <td>spam</td>\n",
              "      <td>You have won ?1,000 cash or a ?2,000 prize! To...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52bec0fe-3d0a-4f69-ab86-d13178a28aae')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-52bec0fe-3d0a-4f69-ab86-d13178a28aae 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-52bec0fe-3d0a-4f69-ab86-d13178a28aae');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ],
      "source": [
        "#Renaming the column\n",
        "df.rename(columns={'Category':'target','Message':'text'},inplace=True)\n",
        "df.sample(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 168,
      "metadata": {
        "id": "fFTg5cVTQfas"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "encoder=LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 169,
      "metadata": {
        "id": "b8z5yz2NQnJm"
      },
      "outputs": [],
      "source": [
        "df['target']=encoder.fit_transform(df['target'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 170,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VF7Ah-60QqMQ",
        "outputId": "8dadf483-a5bd-4d10-fd62-077fae90902d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text\n",
              "0       0  Go until jurong point, crazy.. Available only ...\n",
              "1       0                      Ok lar... Joking wif u oni...\n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3       0  U dun say so early hor... U c already then say...\n",
              "4       0  Nah I don't think he goes to usf, he lives aro..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ef85fa5-7368-4dae-97ac-71e653ea0e21\">\n",
              "    <div 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>target</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ef85fa5-7368-4dae-97ac-71e653ea0e21')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-3ef85fa5-7368-4dae-97ac-71e653ea0e21 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-3ef85fa5-7368-4dae-97ac-71e653ea0e21');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsMSdPEFQuR9",
        "outputId": "8b50edf2-bb4e-402a-a741-d19b440850e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "target    0\n",
              "text      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 171
        }
      ],
      "source": [
        "#missing values\n",
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eEUXO79QyZg",
        "outputId": "dac72e7f-6aa5-4a72-8c62-e9ee195611be"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "415"
            ]
          },
          "metadata": {},
          "execution_count": 172
        }
      ],
      "source": [
        "#Check for duplicates values\n",
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "id": "VzJ2IaXmQ2aa"
      },
      "outputs": [],
      "source": [
        "#Remove duplicates\n",
        "df=df.drop_duplicates(keep='first')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdV3-l9LRBI8",
        "outputId": "58fbe868-170c-4be2-8761-efffc2cea558"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ],
      "source": [
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 176,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3fQ4o3t6RKOw",
        "outputId": "ff978359-d3a4-4e30-9836-ea79d001ecf6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5157, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QZNJsUNbROnT"
      },
      "outputs": [],
      "source": [
        "##EDA(Explolatory Data Analysis)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 177,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8ZYIkwTQRPx8",
        "outputId": "af9c419e-a553-457d-8c11-a8c3410d1839"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text\n",
              "0       0  Go until jurong point, crazy.. Available only ...\n",
              "1       0                      Ok lar... Joking wif u oni...\n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3       0  U dun say so early hor... U c already then say...\n",
              "4       0  Nah I don't think he goes to usf, he lives aro..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6a1954b-0426-4a37-bd32-bb5871508a62\">\n",
              "    <div 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>target</th>\n",
              "      <th>text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b6a1954b-0426-4a37-bd32-bb5871508a62')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-b6a1954b-0426-4a37-bd32-bb5871508a62 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-b6a1954b-0426-4a37-bd32-bb5871508a62');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ],
      "source": [
        "df.head()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 178,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LHnrgd3CRUP9",
        "outputId": "ee403e3d-aec1-40c1-b722-c47db94cc346"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    4516\n",
              "1     641\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ],
      "source": [
        "df['target'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 179,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "Dxs8mzFaRYLw",
        "outputId": "29c9330b-c1f5-499c-8f6f-118cc1e115c1"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.pie(df['target'].value_counts(), labels=['ham','spam'], autopct=\"%0.2f\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e9lbU0zbRc6B"
      },
      "outputs": [],
      "source": [
        "#Data is imbalanced"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 180,
      "metadata": {
        "id": "FBQxs3YyReG1"
      },
      "outputs": [],
      "source": [
        "import nltk"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install nltk"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3wxX3Zveo62Q",
        "outputId": "6ac79ab2-2b26-4b2d-dbe5-9a1b130535ca"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 182,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59VKdjP7RhfG",
        "outputId": "04a7cc66-eec7-450d-869d-a5ded6c0adf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ],
      "source": [
        "nltk.download('punkt')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 183,
      "metadata": {
        "id": "EeHphUFARkjv"
      },
      "outputs": [],
      "source": [
        "df['num_characters']= df['text'].apply(len)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 184,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "S1PRDNDDRnwO",
        "outputId": "996e957c-968c-4990-910a-9b561cfc5103"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text  num_characters\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111\n",
              "1       0                      Ok lar... Joking wif u oni...              29\n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155\n",
              "3       0  U dun say so early hor... U c already then say...              49\n",
              "4       0  Nah I don't think he goes to usf, he lives aro...              61"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-224a16a7-7c01-4782-b6db-46360078bb3f\">\n",
              "    <div 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>target</th>\n",
              "      <th>text</th>\n",
              "      <th>num_characters</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>61</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-224a16a7-7c01-4782-b6db-46360078bb3f')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-224a16a7-7c01-4782-b6db-46360078bb3f 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-224a16a7-7c01-4782-b6db-46360078bb3f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 184
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 185,
      "metadata": {
        "id": "DLOTd9mtTAX0"
      },
      "outputs": [],
      "source": [
        "  #num of words\n",
        "df['num_words']=df['text'].apply(lambda x:len(nltk.word_tokenize(x)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 186,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gT4reCULTI6T",
        "outputId": "dc427b46-0dc7-44d2-9839-f606d47d2b81"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text  num_characters  \\\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
              "1       0                      Ok lar... Joking wif u oni...              29   \n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
              "3       0  U dun say so early hor... U c already then say...              49   \n",
              "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
              "\n",
              "   num_words  \n",
              "0         23  \n",
              "1          8  \n",
              "2         37  \n",
              "3         13  \n",
              "4         15  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ec8a409d-ad7a-4ad0-bd24-201394041d9c\">\n",
              "    <div 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>target</th>\n",
              "      <th>text</th>\n",
              "      <th>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>111</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>49</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>61</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec8a409d-ad7a-4ad0-bd24-201394041d9c')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-ec8a409d-ad7a-4ad0-bd24-201394041d9c 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-ec8a409d-ad7a-4ad0-bd24-201394041d9c');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 187,
      "metadata": {
        "id": "wSXYH8jiRuJy"
      },
      "outputs": [],
      "source": [
        "#num of sentences\n",
        "df['num_sentences']=df['text'].apply(lambda x:len(nltk.sent_tokenize(x)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 188,
      "metadata": {
        "id": "xP7SCAmxRxyZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b82e58d5-0f24-47d4-bda9-ddfad67d55fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text  num_characters  \\\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
              "1       0                      Ok lar... Joking wif u oni...              29   \n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
              "3       0  U dun say so early hor... U c already then say...              49   \n",
              "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
              "\n",
              "   num_words  num_sentences  \n",
              "0         23              2  \n",
              "1          8              2  \n",
              "2         37              2  \n",
              "3         13              1  \n",
              "4         15              1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0ee3d013-570d-409b-8e48-a2b867fefa33\">\n",
              "    <div 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>target</th>\n",
              "      <th>text</th>\n",
              "      <th>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>111</td>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "      <td>37</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>49</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>61</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ee3d013-570d-409b-8e48-a2b867fefa33')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-0ee3d013-570d-409b-8e48-a2b867fefa33 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-0ee3d013-570d-409b-8e48-a2b867fefa33');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 188
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 189,
      "metadata": {
        "id": "HDww3ULYR3ip",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "7eac6e05-50d6-46cb-d0b1-1a660dade364"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_characters    num_words  num_sentences\n",
              "count     5157.000000  5157.000000    5157.000000\n",
              "mean        79.103936    18.390537       1.965290\n",
              "std         58.382922    13.307527       1.439549\n",
              "min          2.000000     1.000000       1.000000\n",
              "25%         36.000000     9.000000       1.000000\n",
              "50%         61.000000    15.000000       1.000000\n",
              "75%        118.000000    26.000000       2.000000\n",
              "max        910.000000   219.000000      38.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-230ae0d5-bcb5-4afa-bb17-8c38662a229d\">\n",
              "    <div 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>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5157.000000</td>\n",
              "      <td>5157.000000</td>\n",
              "      <td>5157.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>79.103936</td>\n",
              "      <td>18.390537</td>\n",
              "      <td>1.965290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>58.382922</td>\n",
              "      <td>13.307527</td>\n",
              "      <td>1.439549</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>61.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>118.000000</td>\n",
              "      <td>26.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>910.000000</td>\n",
              "      <td>219.000000</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-230ae0d5-bcb5-4afa-bb17-8c38662a229d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-230ae0d5-bcb5-4afa-bb17-8c38662a229d 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-230ae0d5-bcb5-4afa-bb17-8c38662a229d');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 189
        }
      ],
      "source": [
        "df[['num_characters','num_words','num_sentences']].describe() "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 190,
      "metadata": {
        "id": "ywFZVRsUdO3U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "1c9df5f2-b453-4761-a523-0c3035438050"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_characters    num_words  num_sentences\n",
              "count     4516.000000  4516.000000    4516.000000\n",
              "mean        70.869353    17.101417       1.822852\n",
              "std         56.708301    13.488402       1.374848\n",
              "min          2.000000     1.000000       1.000000\n",
              "25%         34.000000     8.000000       1.000000\n",
              "50%         53.000000    13.000000       1.000000\n",
              "75%         91.000000    22.000000       2.000000\n",
              "max        910.000000   219.000000      38.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-40045e71-41df-42bc-b5dc-d59122af2ba4\">\n",
              "    <div 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>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4516.000000</td>\n",
              "      <td>4516.000000</td>\n",
              "      <td>4516.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>70.869353</td>\n",
              "      <td>17.101417</td>\n",
              "      <td>1.822852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>56.708301</td>\n",
              "      <td>13.488402</td>\n",
              "      <td>1.374848</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>34.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>53.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>91.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>910.000000</td>\n",
              "      <td>219.000000</td>\n",
              "      <td>38.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-40045e71-41df-42bc-b5dc-d59122af2ba4')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-40045e71-41df-42bc-b5dc-d59122af2ba4 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-40045e71-41df-42bc-b5dc-d59122af2ba4');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 190
        }
      ],
      "source": [
        "#ham messages\n",
        "df[df['target']==0][['num_characters','num_words','num_sentences']].describe()  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 191,
      "metadata": {
        "id": "aGv1SWcTdRr5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "0694ac66-d835-4592-ff4f-68fe0a801d21"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       num_characters   num_words  num_sentences\n",
              "count      641.000000  641.000000     641.000000\n",
              "mean       137.118565   27.472699       2.968799\n",
              "std         30.399707    6.988134       1.486069\n",
              "min          7.000000    2.000000       1.000000\n",
              "25%        130.000000   25.000000       2.000000\n",
              "50%        148.000000   29.000000       3.000000\n",
              "75%        157.000000   32.000000       4.000000\n",
              "max        223.000000   44.000000       9.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-17f7e632-bb16-4ca4-948b-a31a57ec67c0\">\n",
              "    <div 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>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "      <td>641.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>137.118565</td>\n",
              "      <td>27.472699</td>\n",
              "      <td>2.968799</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>30.399707</td>\n",
              "      <td>6.988134</td>\n",
              "      <td>1.486069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>130.000000</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>148.000000</td>\n",
              "      <td>29.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>157.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>223.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>9.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-17f7e632-bb16-4ca4-948b-a31a57ec67c0')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-17f7e632-bb16-4ca4-948b-a31a57ec67c0 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-17f7e632-bb16-4ca4-948b-a31a57ec67c0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 191
        }
      ],
      "source": [
        "#spam messages\n",
        "df[df['target']==1][['num_characters','num_words','num_sentences']].describe()  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 192,
      "metadata": {
        "id": "kaqWPuZjdYRI"
      },
      "outputs": [],
      "source": [
        "#making histograph\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 290,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KxKMkAHydYXh",
        "outputId": "0f08190f-e3e9-4b0d-e71e-e7a731465210"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ee62b8050>"
            ]
          },
          "metadata": {},
          "execution_count": 290
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x2880 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,40))\n",
        "sns.histplot(df[df['target']==0]['num_characters'])\n",
        "sns.histplot(df[df['target']==1]['num_characters'],color='red')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "metadata": {
        "id": "F_R7YCrIdh3_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 567
        },
        "outputId": "ef61cf8b-9c2a-4ffb-efb6-fd4e29350f1f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f5ef2a4e590>"
            ]
          },
          "metadata": {},
          "execution_count": 194
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 582.375x540 with 12 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.pairplot(df,hue='target')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 195,
      "metadata": {
        "id": "K0tgKs6QdmOX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "9db95633-5cad-442f-dd67-5a632bb9c492"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ee69e5410>"
            ]
          },
          "metadata": {},
          "execution_count": 195
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.heatmap(df.corr(),annot=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9RVEAIpId4pK"
      },
      "outputs": [],
      "source": [
        "##3.Data Preprocessing\n",
        "#a.Lowercase\n",
        "#b.Tokenization\n",
        "#c.Removing special characters\n",
        "#d.Removing stop words and punctuation\n",
        "#e.Stemming"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IVGDIRBd7Ok",
        "outputId": "ee560c90-48aa-4000-dabe-a2aa5b8207d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['i',\n",
              " 'me',\n",
              " 'my',\n",
              " 'myself',\n",
              " 'we',\n",
              " 'our',\n",
              " 'ours',\n",
              " 'ourselves',\n",
              " 'you',\n",
              " \"you're\",\n",
              " \"you've\",\n",
              " \"you'll\",\n",
              " \"you'd\",\n",
              " 'your',\n",
              " 'yours',\n",
              " 'yourself',\n",
              " 'yourselves',\n",
              " 'he',\n",
              " 'him',\n",
              " 'his',\n",
              " 'himself',\n",
              " 'she',\n",
              " \"she's\",\n",
              " 'her',\n",
              " 'hers',\n",
              " 'herself',\n",
              " 'it',\n",
              " \"it's\",\n",
              " 'its',\n",
              " 'itself',\n",
              " 'they',\n",
              " 'them',\n",
              " 'their',\n",
              " 'theirs',\n",
              " 'themselves',\n",
              " 'what',\n",
              " 'which',\n",
              " 'who',\n",
              " 'whom',\n",
              " 'this',\n",
              " 'that',\n",
              " \"that'll\",\n",
              " 'these',\n",
              " 'those',\n",
              " 'am',\n",
              " 'is',\n",
              " 'are',\n",
              " 'was',\n",
              " 'were',\n",
              " 'be',\n",
              " 'been',\n",
              " 'being',\n",
              " 'have',\n",
              " 'has',\n",
              " 'had',\n",
              " 'having',\n",
              " 'do',\n",
              " 'does',\n",
              " 'did',\n",
              " 'doing',\n",
              " 'a',\n",
              " 'an',\n",
              " 'the',\n",
              " 'and',\n",
              " 'but',\n",
              " 'if',\n",
              " 'or',\n",
              " 'because',\n",
              " 'as',\n",
              " 'until',\n",
              " 'while',\n",
              " 'of',\n",
              " 'at',\n",
              " 'by',\n",
              " 'for',\n",
              " 'with',\n",
              " 'about',\n",
              " 'against',\n",
              " 'between',\n",
              " 'into',\n",
              " 'through',\n",
              " 'during',\n",
              " 'before',\n",
              " 'after',\n",
              " 'above',\n",
              " 'below',\n",
              " 'to',\n",
              " 'from',\n",
              " 'up',\n",
              " 'down',\n",
              " 'in',\n",
              " 'out',\n",
              " 'on',\n",
              " 'off',\n",
              " 'over',\n",
              " 'under',\n",
              " 'again',\n",
              " 'further',\n",
              " 'then',\n",
              " 'once',\n",
              " 'here',\n",
              " 'there',\n",
              " 'when',\n",
              " 'where',\n",
              " 'why',\n",
              " 'how',\n",
              " 'all',\n",
              " 'any',\n",
              " 'both',\n",
              " 'each',\n",
              " 'few',\n",
              " 'more',\n",
              " 'most',\n",
              " 'other',\n",
              " 'some',\n",
              " 'such',\n",
              " 'no',\n",
              " 'nor',\n",
              " 'not',\n",
              " 'only',\n",
              " 'own',\n",
              " 'same',\n",
              " 'so',\n",
              " 'than',\n",
              " 'too',\n",
              " 'very',\n",
              " 's',\n",
              " 't',\n",
              " 'can',\n",
              " 'will',\n",
              " 'just',\n",
              " 'don',\n",
              " \"don't\",\n",
              " 'should',\n",
              " \"should've\",\n",
              " 'now',\n",
              " 'd',\n",
              " 'll',\n",
              " 'm',\n",
              " 'o',\n",
              " 're',\n",
              " 've',\n",
              " 'y',\n",
              " 'ain',\n",
              " 'aren',\n",
              " \"aren't\",\n",
              " 'couldn',\n",
              " \"couldn't\",\n",
              " 'didn',\n",
              " \"didn't\",\n",
              " 'doesn',\n",
              " \"doesn't\",\n",
              " 'hadn',\n",
              " \"hadn't\",\n",
              " 'hasn',\n",
              " \"hasn't\",\n",
              " 'haven',\n",
              " \"haven't\",\n",
              " 'isn',\n",
              " \"isn't\",\n",
              " 'ma',\n",
              " 'mightn',\n",
              " \"mightn't\",\n",
              " 'mustn',\n",
              " \"mustn't\",\n",
              " 'needn',\n",
              " \"needn't\",\n",
              " 'shan',\n",
              " \"shan't\",\n",
              " 'shouldn',\n",
              " \"shouldn't\",\n",
              " 'wasn',\n",
              " \"wasn't\",\n",
              " 'weren',\n",
              " \"weren't\",\n",
              " 'won',\n",
              " \"won't\",\n",
              " 'wouldn',\n",
              " \"wouldn't\"]"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ],
      "source": [
        "# python -m nltk.downloader stopwords\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "stopwords.words('english')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 197,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "XuXYhI00eAeZ",
        "outputId": "5dee73b3-c093-4005-a0a6-37224c857963"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 197
        }
      ],
      "source": [
        "import string\n",
        "string.punctuation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "oiT5BCxQeFRV",
        "outputId": "b2a6b1f0-f551-4f73-9b94-2bcf03ce9768"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'danc'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 198
        }
      ],
      "source": [
        "from nltk.stem.porter import PorterStemmer\n",
        "ps=PorterStemmer()\n",
        "ps.stem('dancing')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {
        "id": "CIdacicPeOkb"
      },
      "outputs": [],
      "source": [
        "def transform_text(text):\n",
        "    text=text.lower()\n",
        "    text=nltk.word_tokenize(text)\n",
        "    \n",
        "    y=[]\n",
        "    for i in text:\n",
        "        if i.isalnum():\n",
        "             y.append(i)\n",
        "    text=y[:]  \n",
        "    y.clear()\n",
        "    \n",
        "    for i in text:\n",
        "        if i not in stopwords.words('english')and i not in string.punctuation:\n",
        "            y.append(i)\n",
        "                \n",
        "    text=y[:]\n",
        "    y.clear()\n",
        "    \n",
        "    for i in text:\n",
        "        y.append(ps.stem(i))\n",
        "                \n",
        "    return \" \".join(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "n_dMVpHMeROm",
        "outputId": "4af17d6b-cefb-4841-df20-d04d8b55a688"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'got 90 exam'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 200
        }
      ],
      "source": [
        "transform_text('I got 90% in exams!!!')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 202,
      "metadata": {
        "id": "M8HwzOS6eXHL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "0b3839fb-a445-4fb8-ee5f-0ddbf5bf31f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'like present ml'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 202
        }
      ],
      "source": [
        "transform_text('Did you like my presentation on ML?')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 203,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "m-wfCSIYeZJg",
        "outputId": "9355e504-830e-4f88-9f78-8c92a0c158fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'love youtub lectur machin learn'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 203
        }
      ],
      "source": [
        "transform_text('I loved the Youtube lectures on Machine Learning .How about you?')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 204,
      "metadata": {
        "id": "2FQcMz_qedPT"
      },
      "outputs": [],
      "source": [
        "df['transformed_text']=df['text'].apply(transform_text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 205,
      "metadata": {
        "id": "83z5U4hRek5v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "8df7f7e0-b54e-44e4-bd21-e04f51f6c50b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   target                                               text  num_characters  \\\n",
              "0       0  Go until jurong point, crazy.. Available only ...             111   \n",
              "1       0                      Ok lar... Joking wif u oni...              29   \n",
              "2       1  Free entry in 2 a wkly comp to win FA Cup fina...             155   \n",
              "3       0  U dun say so early hor... U c already then say...              49   \n",
              "4       0  Nah I don't think he goes to usf, he lives aro...              61   \n",
              "\n",
              "   num_words  num_sentences                                   transformed_text  \n",
              "0         23              2  go jurong point avail bugi n great world la e ...  \n",
              "1          8              2                              ok lar joke wif u oni  \n",
              "2         37              2  free entri 2 wkli comp win fa cup final tkt 21...  \n",
              "3         13              1                u dun say earli hor u c alreadi say  \n",
              "4         15              1               nah think goe usf live around though  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e60c6e53-a28d-40b2-854a-00ef85fec7d0\">\n",
              "    <div 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>target</th>\n",
              "      <th>text</th>\n",
              "      <th>num_characters</th>\n",
              "      <th>num_words</th>\n",
              "      <th>num_sentences</th>\n",
              "      <th>transformed_text</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "      <td>111</td>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>go jurong point avail bugi n great world la e ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "      <td>8</td>\n",
              "      <td>2</td>\n",
              "      <td>ok lar joke wif u oni</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>Free entry in 2 a wkly comp to win FA Cup fina...</td>\n",
              "      <td>155</td>\n",
              "      <td>37</td>\n",
              "      <td>2</td>\n",
              "      <td>free entri 2 wkli comp win fa cup final tkt 21...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>U dun say so early hor... U c already then say...</td>\n",
              "      <td>49</td>\n",
              "      <td>13</td>\n",
              "      <td>1</td>\n",
              "      <td>u dun say earli hor u c alreadi say</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "      <td>61</td>\n",
              "      <td>15</td>\n",
              "      <td>1</td>\n",
              "      <td>nah think goe usf live around though</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e60c6e53-a28d-40b2-854a-00ef85fec7d0')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-e60c6e53-a28d-40b2-854a-00ef85fec7d0 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-e60c6e53-a28d-40b2-854a-00ef85fec7d0');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 205
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 206,
      "metadata": {
        "id": "qPUNRPovelIh"
      },
      "outputs": [],
      "source": [
        "from wordcloud import WordCloud\n",
        "wc=WordCloud(width=500,height=500,min_font_size=10,background_color='white')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 207,
      "metadata": {
        "id": "B3cp2rawelSl"
      },
      "outputs": [],
      "source": [
        "spam_wc=wc.generate(df[df['target']==1]['transformed_text'].str.cat(sep=\" \"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 208,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "bePDfwR0elYf",
        "outputId": "89c40526-fe96-4ef5-adab-66d3b288d180"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5ee65640d0>"
            ]
          },
          "metadata": {},
          "execution_count": 208
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(15,6))\n",
        "plt.imshow(spam_wc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 209,
      "metadata": {
        "id": "WgpCKerzelZt"
      },
      "outputs": [],
      "source": [
        "ham_wc=wc.generate(df[df['target']==0]['transformed_text'].str.cat(sep=\" \"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 210,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "E_4Vzw7Qelf9",
        "outputId": "256982a9-89c7-4660-b6fa-2e2a040b1cf0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5ee65b4490>"
            ]
          },
          "metadata": {},
          "execution_count": 210
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3960x432 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.figure(figsize=(55,6))\n",
        "plt.imshow(ham_wc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 211,
      "metadata": {
        "id": "dJpwzv3lelhB"
      },
      "outputs": [],
      "source": [
        "spam_corpus=[]\n",
        "for msg in df[df['target']==1]['transformed_text'].tolist():\n",
        "  for word in msg.split():\n",
        "    spam_corpus.append(word)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 212,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1gxJsqrIhEBk",
        "outputId": "39067bc5-2bdd-4c22-b4ec-7a581cebe8d3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9726"
            ]
          },
          "metadata": {},
          "execution_count": 212
        }
      ],
      "source": [
        "len(spam_corpus) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        },
        "id": "KrRxPOffh_gv",
        "outputId": "4d53430e-8b1f-42da-8a81-e52896600350"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from collections import Counter\n",
        "sns.barplot(pd.DataFrame(Counter(spam_corpus).most_common(30))[0],pd.DataFrame(Counter(spam_corpus).most_common(30))[1])\n",
        "plt.xticks(rotation='vertical') \n",
        "plt.show()  \n",
        "                                                                                                             "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 213,
      "metadata": {
        "id": "e14uOp18j0Mc"
      },
      "outputs": [],
      "source": [
        "ham_corpus=[]\n",
        "for msg in df[df['target']==0]['transformed_text'].tolist():\n",
        "  for word in msg.split():\n",
        "    ham_corpus.append(word)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 214,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlm8ZvCPj0Yp",
        "outputId": "f09b7340-3b3a-4a14-a4ee-04f0b58dd1b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "35290"
            ]
          },
          "metadata": {},
          "execution_count": 214
        }
      ],
      "source": [
        "len(ham_corpus)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 215,
      "metadata": {
        "id": "BGeNajifj0g7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        },
        "outputId": "4fa1ec86-c641-40a1-e729-889d7e666745"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "from collections import Counter\n",
        "sns.barplot(pd.DataFrame(Counter(ham_corpus).most_common(30))[0],pd.DataFrame(Counter(ham_corpus).most_common(30))[1])\n",
        "plt.xticks(rotation='vertical') \n",
        "plt.show()  \n",
        "             "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "APZEhJDVj0iP"
      },
      "outputs": [],
      "source": [
        " ##4.Model Building"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 251,
      "metadata": {
        "id": "7DZrxSUmj0nw"
      },
      "outputs": [],
      "source": [
        "#Text Vextorizaztion using Bag of words\n",
        "from sklearn.feature_extraction.text import CountVectorizer,TfidfVectorizer\n",
        "#cv=CountVectorizer()\n",
        "tfidf=TfidfVectorizer(max_features=5000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 252,
      "metadata": {
        "id": "Qbb3Kdjzj0sW"
      },
      "outputs": [],
      "source": [
        "#X=cv.fit_transform(df['transformed_text']).toarray()\n",
        "X=tfidf.fit_transform(df['transformed_text']).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X  = np.hstack((X,df['num_characters'].values.reshape(-1,1)))"
      ],
      "metadata": {
        "id": "ABNt1ztQ1U8f"
      },
      "execution_count": 253,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 254,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "th09-MDqj005",
        "outputId": "9bf1d64c-3429-4934-aa8d-014c4410163a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5157, 5001)"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 255,
      "metadata": {
        "id": "ukjv_tfRltpM"
      },
      "outputs": [],
      "source": [
        "y=df['target'].values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 256,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KX-2panjl0FB",
        "outputId": "bf7d4c2e-88ca-4fbb-bc5b-cc20ed0fb752"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 1, ..., 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ],
      "source": [
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 257,
      "metadata": {
        "id": "9I3qXS2Rl0Li"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 258,
      "metadata": {
        "id": "W63aK2BLl0M1"
      },
      "outputs": [],
      "source": [
        "X_train,X_test,y_train,y_test= train_test_split(X,y,test_size=0.2,random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 259,
      "metadata": {
        "id": "AwhHlLn7l0ZE"
      },
      "outputs": [],
      "source": [
        "from sklearn.naive_bayes import GaussianNB,MultinomialNB,BernoulliNB\n",
        "from sklearn.metrics import accuracy_score,confusion_matrix,precision_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 260,
      "metadata": {
        "id": "jvTPplwil0aZ"
      },
      "outputs": [],
      "source": [
        "gnb=GaussianNB()\n",
        "mnb=MultinomialNB()\n",
        "bnb=BernoulliNB()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 261,
      "metadata": {
        "id": "hHl1e8Honb-7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed41bbe8-50e8-410b-f655-f2fe06ceee99"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.875\n",
            "[[791 114]\n",
            " [ 15 112]]\n",
            "0.49557522123893805\n"
          ]
        }
      ],
      "source": [
        "gnb.fit(X_train,y_train)\n",
        "y_pred1=gnb.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred1))\n",
        "print(confusion_matrix(y_test,y_pred1))\n",
        "print(precision_score(y_test,y_pred1))\n",
        " \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 262,
      "metadata": {
        "id": "Y-ZnH5ZencAL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ecb76af0-f501-4231-fe03-a6fde55a43fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8885658914728682\n",
            "[[905   0]\n",
            " [115  12]]\n",
            "1.0\n",
            "[1 0 0 ... 0 0 0]\n"
          ]
        }
      ],
      "source": [
        "mnb.fit(X_train,y_train)\n",
        "y_pred2=mnb.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred2))\n",
        "print(confusion_matrix(y_test,y_pred2))\n",
        "print(precision_score(y_test,y_pred2))\n",
        "print(mnb.predict(X_train))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 263,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "070bU6PMncEO",
        "outputId": "04b58d38-6e7c-42db-c9be-a406e42398c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9757751937984496\n",
            "[[903   2]\n",
            " [ 23 104]]\n",
            "0.9811320754716981\n"
          ]
        }
      ],
      "source": [
        "bnb.fit(X_train,y_train)\n",
        "y_pred3=bnb.predict(X_test)\n",
        "print(accuracy_score(y_test,y_pred3))\n",
        "print(confusion_matrix(y_test,y_pred3))\n",
        "print(precision_score(y_test,y_pred3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 264,
      "metadata": {
        "id": "gqlHII6wncF1"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 265,
      "metadata": {
        "id": "OuGsAbSRncJQ"
      },
      "outputs": [],
      "source": [
        "svc=SVC(kernel='sigmoid', gamma=1.0)\n",
        "knc=KNeighborsClassifier()\n",
        "mnb=MultinomialNB()\n",
        "dtc=DecisionTreeClassifier(max_depth=5)\n",
        "lrc=LogisticRegression(solver='liblinear',penalty='l1')\n",
        "rfc=RandomForestClassifier(n_estimators=50,random_state=2)\n",
        "abc=AdaBoostClassifier(n_estimators=50,random_state=2)\n",
        "bc=BaggingClassifier(n_estimators=50,random_state=2)\n",
        "etc=ExtraTreesClassifier(n_estimators=50,random_state=2)\n",
        "gbdt=GradientBoostingClassifier(n_estimators=50,random_state=2)\n",
        "xgb=XGBClassifier(n_estimators=50,random_state=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 266,
      "metadata": {
        "id": "Aa3LWid3ncK1"
      },
      "outputs": [],
      "source": [
        "clfs={\n",
        "    'SVC':svc,\n",
        "    'KN': knc,\n",
        "    'NB': mnb,\n",
        "    'DT': dtc,\n",
        "    'LR': lrc,\n",
        "    'RF': rfc,\n",
        "    'AdaBoost': abc,\n",
        "    'BgC': bc,\n",
        "    'ETC': etc,\n",
        "    'GBDT': gbdt,\n",
        "    'xgb': xgb\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 267,
      "metadata": {
        "id": "_mMm_QI1ncOX"
      },
      "outputs": [],
      "source": [
        "def train_classifier(clf,X_train,y_train,X_test,y_test):\n",
        "  clf.fit(X_train,y_train)\n",
        "  y_pred=clf.predict(X_test)\n",
        "  accuracy=accuracy_score(y_test,y_pred)\n",
        "  precision=precision_score(y_test,y_pred)\n",
        "                            \n",
        "  return accuracy,precision"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 268,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N0Gxn6xYncQ7",
        "outputId": "315885ec-d1af-4e88-ed65-2b19d6a27f9f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.876937984496124, 0.0)"
            ]
          },
          "metadata": {},
          "execution_count": 268
        }
      ],
      "source": [
        "train_classifier(svc,X_train,y_train,X_test,y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 269,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNSeUrupncTZ",
        "outputId": "e64cf616-db92-4fd8-9e07-1a094cf4036b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For SVC\n",
            "Accuracy - 0.876937984496124\n",
            "Precision- 0.0\n",
            "For KN\n",
            "Accuracy - 0.936046511627907\n",
            "Precision- 0.7479674796747967\n",
            "For NB\n",
            "Accuracy - 0.8885658914728682\n",
            "Precision- 1.0\n",
            "For DT\n",
            "Accuracy - 0.9554263565891473\n",
            "Precision- 0.9354838709677419\n",
            "For LR\n",
            "Accuracy - 0.9622093023255814\n",
            "Precision- 0.9583333333333334\n",
            "For RF\n",
            "Accuracy - 0.9738372093023255\n",
            "Precision- 1.0\n",
            "For AdaBoost\n",
            "Accuracy - 0.9660852713178295\n",
            "Precision- 0.9107142857142857\n",
            "For BgC\n",
            "Accuracy - 0.9699612403100775\n",
            "Precision- 0.9363636363636364\n",
            "For ETC\n",
            "Accuracy - 0.9786821705426356\n",
            "Precision- 1.0\n",
            "For GBDT\n",
            "Accuracy - 0.9554263565891473\n",
            "Precision- 0.9354838709677419\n",
            "For xgb\n",
            "Accuracy - 0.9544573643410853\n",
            "Precision- 0.9347826086956522\n"
          ]
        }
      ],
      "source": [
        "\n",
        "accuracy_scores=[]\n",
        "precision_scores=[]\n",
        "\n",
        "for name,clf in clfs.items():\n",
        "  current_accuracy,current_precision=train_classifier(clf,X_train,y_train,X_test,y_test)\n",
        "  print(\"For\",name)\n",
        "  print(\"Accuracy -\",current_accuracy)\n",
        "  print(\"Precision-\",current_precision)\n",
        "  \n",
        "  accuracy_scores.append(current_accuracy)\n",
        "  precision_scores.append(current_precision)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 272,
      "metadata": {
        "id": "tHw8Yil1ncVl"
      },
      "outputs": [],
      "source": [
        "performance_df=pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy':accuracy_scores,'Precision':precision_scores}).sort_values('Accuracy',ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "metadata": {
        "id": "mEPNRqG6ncZo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "3de926de-c866-4ceb-8161-b8aff8c097ac"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Algorithm  Accuracy  Precision\n",
              "8        ETC  0.978682   1.000000\n",
              "5         RF  0.973837   1.000000\n",
              "7        BgC  0.969961   0.936364\n",
              "6   AdaBoost  0.966085   0.910714\n",
              "4         LR  0.962209   0.958333\n",
              "3         DT  0.955426   0.935484\n",
              "9       GBDT  0.955426   0.935484\n",
              "10       xgb  0.954457   0.934783\n",
              "1         KN  0.936047   0.747967\n",
              "2         NB  0.888566   1.000000\n",
              "0        SVC  0.876938   0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5f3f3d49-b3cc-4001-bde2-6f6fa9dd44af\">\n",
              "    <div 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>Algorithm</th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>ETC</td>\n",
              "      <td>0.978682</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RF</td>\n",
              "      <td>0.973837</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>BgC</td>\n",
              "      <td>0.969961</td>\n",
              "      <td>0.936364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>0.966085</td>\n",
              "      <td>0.910714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LR</td>\n",
              "      <td>0.962209</td>\n",
              "      <td>0.958333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>DT</td>\n",
              "      <td>0.955426</td>\n",
              "      <td>0.935484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>GBDT</td>\n",
              "      <td>0.955426</td>\n",
              "      <td>0.935484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>xgb</td>\n",
              "      <td>0.954457</td>\n",
              "      <td>0.934783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>KN</td>\n",
              "      <td>0.936047</td>\n",
              "      <td>0.747967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NB</td>\n",
              "      <td>0.888566</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>SVC</td>\n",
              "      <td>0.876938</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f3f3d49-b3cc-4001-bde2-6f6fa9dd44af')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-5f3f3d49-b3cc-4001-bde2-6f6fa9dd44af 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-5f3f3d49-b3cc-4001-bde2-6f6fa9dd44af');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 273
        }
      ],
      "source": [
        "performance_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "metadata": {
        "id": "FRWhEU3J4P4_"
      },
      "outputs": [],
      "source": [
        "performance_df1=pd.melt(performance_df,id_vars=\"Algorithm\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "metadata": {
        "id": "yBAryHd54P9Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "outputId": "8b812cd8-8761-49e8-9b67-6c8a4d3239ca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Algorithm   variable     value\n",
              "0        ETC   Accuracy  0.978682\n",
              "1         RF   Accuracy  0.973837\n",
              "2        BgC   Accuracy  0.969961\n",
              "3   AdaBoost   Accuracy  0.966085\n",
              "4         LR   Accuracy  0.962209\n",
              "5         DT   Accuracy  0.955426\n",
              "6       GBDT   Accuracy  0.955426\n",
              "7        xgb   Accuracy  0.954457\n",
              "8         KN   Accuracy  0.936047\n",
              "9         NB   Accuracy  0.888566\n",
              "10       SVC   Accuracy  0.876938\n",
              "11       ETC  Precision  1.000000\n",
              "12        RF  Precision  1.000000\n",
              "13       BgC  Precision  0.936364\n",
              "14  AdaBoost  Precision  0.910714\n",
              "15        LR  Precision  0.958333\n",
              "16        DT  Precision  0.935484\n",
              "17      GBDT  Precision  0.935484\n",
              "18       xgb  Precision  0.934783\n",
              "19        KN  Precision  0.747967\n",
              "20        NB  Precision  1.000000\n",
              "21       SVC  Precision  0.000000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aadef6bf-441f-4cda-aaec-ec89b94486c7\">\n",
              "    <div 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>Algorithm</th>\n",
              "      <th>variable</th>\n",
              "      <th>value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ETC</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.978682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RF</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.973837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BgC</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.969961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.966085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LR</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.962209</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>DT</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.955426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>GBDT</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.955426</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>xgb</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.954457</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>KN</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.936047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>NB</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.888566</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>SVC</td>\n",
              "      <td>Accuracy</td>\n",
              "      <td>0.876938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ETC</td>\n",
              "      <td>Precision</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>RF</td>\n",
              "      <td>Precision</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>BgC</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.936364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>AdaBoost</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.910714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LR</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.958333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>DT</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.935484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>GBDT</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.935484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>xgb</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.934783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>KN</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.747967</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>NB</td>\n",
              "      <td>Precision</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>SVC</td>\n",
              "      <td>Precision</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aadef6bf-441f-4cda-aaec-ec89b94486c7')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-aadef6bf-441f-4cda-aaec-ec89b94486c7 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-aadef6bf-441f-4cda-aaec-ec89b94486c7');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 276
        }
      ],
      "source": [
        "performance_df1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 277,
      "metadata": {
        "id": "VRyg0aGm4P-0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "outputId": "0b9f3747-4d46-47e7-97d8-ba2ea17e9daf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 441.75x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "sns.catplot(x='Algorithm',y='value',\n",
        "            hue='variable',data=performance_df1,kind='bar',height=5)\n",
        "plt.ylim(0.5,1.0)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zRwrSSnB4QDv"
      },
      "outputs": [],
      "source": [
        "#improve model\n",
        "# 1. Change the max_features parameter of Tfidf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "bFzySbZ14QFT"
      },
      "outputs": [],
      "source": [
        "#temp_df=pd.DataFrame({'Algorithm':clfs.keys(),'Accuracy_max_ft_3000':accuracy_scores,'Precision_max_ft':precision_scores '})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 104,
      "metadata": {
        "id": "2Dk-AvNJXgXU"
      },
      "outputs": [],
      "source": [
        "#perfomance_df.merge(temp_df,on='Algorithm')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 278,
      "metadata": {
        "id": "Vd-jLT1Y4QJk"
      },
      "outputs": [],
      "source": [
        " #Voting Classifer\n",
        "svc=SVC(kernel='sigmoid', gamma=1.0,probability=True)\n",
        "mnb=MultinomialNB()\n",
        "etc=ExtraTreesClassifier(n_estimators=50,random_state=2)\n",
        "\n",
        "from sklearn.ensemble import VotingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 279,
      "metadata": {
        "id": "92qjsO2a4QMG"
      },
      "outputs": [],
      "source": [
        "voting=VotingClassifier(estimators=[('svm',svc),('nb',mnb),('et',etc)],voting='soft')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 280,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "floik-x84QPb",
        "outputId": "3a3c98d6-dbc4-47b6-9a11-91a37326bb40"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "VotingClassifier(estimators=[('svm',\n",
              "                              SVC(gamma=1.0, kernel='sigmoid',\n",
              "                                  probability=True)),\n",
              "                             ('nb', MultinomialNB()),\n",
              "                             ('et',\n",
              "                              ExtraTreesClassifier(n_estimators=50,\n",
              "                                                   random_state=2))],\n",
              "                 voting='soft')"
            ]
          },
          "metadata": {},
          "execution_count": 280
        }
      ],
      "source": [
        "voting.fit(X_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 281,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzlCdUoS4QV5",
        "outputId": "eb5b0e7d-e636-4b0a-8c81-d145b1b3121c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.8924418604651163\n",
            "Precision 1.0\n"
          ]
        }
      ],
      "source": [
        "y_pred=voting.predict(X_test)\n",
        "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
        "print(\"Precision\",precision_score(y_test,y_pred))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 282,
      "metadata": {
        "id": "fAkNDuX2Im_1"
      },
      "outputs": [],
      "source": [
        "# APPLYINNG STACKING\n",
        "estimators=[('svm',svc),('nb',mnb),('et',etc)]\n",
        "final_estimator=RandomForestClassifier()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 283,
      "metadata": {
        "id": "3vptRWE5JkGu"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import StackingClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 284,
      "metadata": {
        "id": "7ZRAARqtJxpP"
      },
      "outputs": [],
      "source": [
        "clf=StackingClassifier(estimators=estimators,final_estimator=final_estimator)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 285,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhHZCFpuKGXS",
        "outputId": "affbfb86-cb85-4408-d7f8-5c84915c43bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy 0.9767441860465116\n",
            "Precision 0.9256198347107438\n"
          ]
        }
      ],
      "source": [
        "clf.fit(X_train,y_train)\n",
        "y_pred=clf.predict(X_test)\n",
        "print(\"Accuracy\",accuracy_score(y_test,y_pred))\n",
        "print(\"Precision\",precision_score(y_test,y_pred))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 288,
      "metadata": {
        "id": "jVf8xJAGLWvT"
      },
      "outputs": [],
      "source": [
        "import pickle\n",
        "# pickle.dump(tfidf,open('vectorizer.pkl', 'wb')  )\n",
        "pickle.dump(voting,open('model.pkl', 'wb'))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pickle.dump(tfidf,open('vectorize.pkl', 'wb'))"
      ],
      "metadata": {
        "id": "-uYCtS6gggdB"
      },
      "execution_count": 289,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "SMS_spam_classifier.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNL1xRIj2DE0WLQjeSHrQW7",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}