Spam_filter_with_NLTK.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Spam filter with NLTK.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPehLXxw6nC6pPBHk22mIfP",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/goldenfingaz/Machine_Learning_with_Python/blob/main/Spam_filter_with_NLTK.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_FbjO1yVEufn"
      },
      "source": [
        "import nltk"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y-Ws-sodHeLK"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "Ly8gQ29weXJpZ2h0IDIwMTcgR29vZ2xlIExMQwovLwovLyBMaWNlbnNlZCB1bmRlciB0aGUgQXBhY2hlIExpY2Vuc2UsIFZlcnNpb24gMi4wICh0aGUgIkxpY2Vuc2UiKTsKLy8geW91IG1heSBub3QgdXNlIHRoaXMgZmlsZSBleGNlcHQgaW4gY29tcGxpYW5jZSB3aXRoIHRoZSBMaWNlbnNlLgovLyBZb3UgbWF5IG9idGFpbiBhIGNvcHkgb2YgdGhlIExpY2Vuc2UgYXQKLy8KLy8gICAgICBodHRwOi8vd3d3LmFwYWNoZS5vcmcvbGljZW5zZXMvTElDRU5TRS0yLjAKLy8KLy8gVW5sZXNzIHJlcXVpcmVkIGJ5IGFwcGxpY2FibGUgbGF3IG9yIGFncmVlZCB0byBpbiB3cml0aW5nLCBzb2Z0d2FyZQovLyBkaXN0cmlidXRlZCB1bmRlciB0aGUgTGljZW5zZSBpcyBkaXN0cmlidXRlZCBvbiBhbiAiQVMgSVMiIEJBU0lTLAovLyBXSVRIT1VUIFdBUlJBTlRJRVMgT1IgQ09ORElUSU9OUyBPRiBBTlkgS0lORCwgZWl0aGVyIGV4cHJlc3Mgb3IgaW1wbGllZC4KLy8gU2VlIHRoZSBMaWNlbnNlIGZvciB0aGUgc3BlY2lmaWMgbGFuZ3VhZ2UgZ292ZXJuaW5nIHBlcm1pc3Npb25zIGFuZAovLyBsaW1pdGF0aW9ucyB1bmRlciB0aGUgTGljZW5zZS4KCi8qKgogKiBAZmlsZW92ZXJ2aWV3IEhlbHBlcnMgZm9yIGdvb2dsZS5jb2xhYiBQeXRob24gbW9kdWxlLgogKi8KKGZ1bmN0aW9uKHNjb3BlKSB7CmZ1bmN0aW9uIHNwYW4odGV4dCwgc3R5bGVBdHRyaWJ1dGVzID0ge30pIHsKICBjb25zdCBlbGVtZW50ID0gZG9jdW1lbnQuY3JlYXRlRWxlbWVudCgnc3BhbicpOwogIGVsZW1lbnQudGV4dENvbnRlbnQgPSB0ZXh0OwogIGZvciAoY29uc3Qga2V5IG9mIE9iamVjdC5rZXlzKHN0eWxlQXR0cmlidXRlcykpIHsKICAgIGVsZW1lbnQuc3R5bGVba2V5XSA9IHN0eWxlQXR0cmlidXRlc1trZXldOwogIH0KICByZXR1cm4gZWxlbWVudDsKfQoKLy8gTWF4IG51bWJlciBvZiBieXRlcyB3aGljaCB3aWxsIGJlIHVwbG9hZGVkIGF0IGEgdGltZS4KY29uc3QgTUFYX1BBWUxPQURfU0laRSA9IDEwMCAqIDEwMjQ7CgpmdW5jdGlvbiBfdXBsb2FkRmlsZXMoaW5wdXRJZCwgb3V0cHV0SWQpIHsKICBjb25zdCBzdGVwcyA9IHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCk7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICAvLyBDYWNoZSBzdGVwcyBvbiB0aGUgb3V0cHV0RWxlbWVudCB0byBtYWtlIGl0IGF2YWlsYWJsZSBmb3IgdGhlIG5leHQgY2FsbAogIC8vIHRvIHVwbG9hZEZpbGVzQ29udGludWUgZnJvbSBQeXRob24uCiAgb3V0cHV0RWxlbWVudC5zdGVwcyA9IHN0ZXBzOwoKICByZXR1cm4gX3VwbG9hZEZpbGVzQ29udGludWUob3V0cHV0SWQpOwp9CgovLyBUaGlzIGlzIHJvdWdobHkgYW4gYXN5bmMgZ2VuZXJhdG9yIChub3Qgc3VwcG9ydGVkIGluIHRoZSBicm93c2VyIHlldCksCi8vIHdoZXJlIHRoZXJlIGFyZSBtdWx0aXBsZSBhc3luY2hyb25vdXMgc3RlcHMgYW5kIHRoZSBQeXRob24gc2lkZSBpcyBnb2luZwovLyB0byBwb2xsIGZvciBjb21wbGV0aW9uIG9mIGVhY2ggc3RlcC4KLy8gVGhpcyB1c2VzIGEgUHJvbWlzZSB0byBibG9jayB0aGUgcHl0aG9uIHNpZGUgb24gY29tcGxldGlvbiBvZiBlYWNoIHN0ZXAsCi8vIHRoZW4gcGFzc2VzIHRoZSByZXN1bHQgb2YgdGhlIHByZXZpb3VzIHN0ZXAgYXMgdGhlIGlucHV0IHRvIHRoZSBuZXh0IHN0ZXAuCmZ1bmN0aW9uIF91cGxvYWRGaWxlc0NvbnRpbnVlKG91dHB1dElkKSB7CiAgY29uc3Qgb3V0cHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKG91dHB1dElkKTsKICBjb25zdCBzdGVwcyA9IG91dHB1dEVsZW1lbnQuc3RlcHM7CgogIGNvbnN0IG5leHQgPSBzdGVwcy5uZXh0KG91dHB1dEVsZW1lbnQubGFzdFByb21pc2VWYWx1ZSk7CiAgcmV0dXJuIFByb21pc2UucmVzb2x2ZShuZXh0LnZhbHVlLnByb21pc2UpLnRoZW4oKHZhbHVlKSA9PiB7CiAgICAvLyBDYWNoZSB0aGUgbGFzdCBwcm9taXNlIHZhbHVlIHRvIG1ha2UgaXQgYXZhaWxhYmxlIHRvIHRoZSBuZXh0CiAgICAvLyBzdGVwIG9mIHRoZSBnZW5lcmF0b3IuCiAgICBvdXRwdXRFbGVtZW50Lmxhc3RQcm9taXNlVmFsdWUgPSB2YWx1ZTsKICAgIHJldHVybiBuZXh0LnZhbHVlLnJlc3BvbnNlOwogIH0pOwp9CgovKioKICogR2VuZXJhdG9yIGZ1bmN0aW9uIHdoaWNoIGlzIGNhbGxlZCBiZXR3ZWVuIGVhY2ggYXN5bmMgc3RlcCBvZiB0aGUgdXBsb2FkCiAqIHByb2Nlc3MuCiAqIEBwYXJhbSB7c3RyaW5nfSBpbnB1dElkIEVsZW1lbnQgSUQgb2YgdGhlIGlucHV0IGZpbGUgcGlja2VyIGVsZW1lbnQuCiAqIEBwYXJhbSB7c3RyaW5nfSBvdXRwdXRJZCBFbGVtZW50IElEIG9mIHRoZSBvdXRwdXQgZGlzcGxheS4KICogQHJldHVybiB7IUl0ZXJhYmxlPCFPYmplY3Q+fSBJdGVyYWJsZSBvZiBuZXh0IHN0ZXBzLgogKi8KZnVuY3Rpb24qIHVwbG9hZEZpbGVzU3RlcChpbnB1dElkLCBvdXRwdXRJZCkgewogIGNvbnN0IGlucHV0RWxlbWVudCA9IGRvY3VtZW50LmdldEVsZW1lbnRCeUlkKGlucHV0SWQpOwogIGlucHV0RWxlbWVudC5kaXNhYmxlZCA9IGZhbHNlOwoKICBjb25zdCBvdXRwdXRFbGVtZW50ID0gZG9jdW1lbnQuZ2V0RWxlbWVudEJ5SWQob3V0cHV0SWQpOwogIG91dHB1dEVsZW1lbnQuaW5uZXJIVE1MID0gJyc7CgogIGNvbnN0IHBpY2tlZFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgaW5wdXRFbGVtZW50LmFkZEV2ZW50TGlzdGVuZXIoJ2NoYW5nZScsIChlKSA9PiB7CiAgICAgIHJlc29sdmUoZS50YXJnZXQuZmlsZXMpOwogICAgfSk7CiAgfSk7CgogIGNvbnN0IGNhbmNlbCA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2J1dHRvbicpOwogIGlucHV0RWxlbWVudC5wYXJlbnRFbGVtZW50LmFwcGVuZENoaWxkKGNhbmNlbCk7CiAgY2FuY2VsLnRleHRDb250ZW50ID0gJ0NhbmNlbCB1cGxvYWQnOwogIGNvbnN0IGNhbmNlbFByb21pc2UgPSBuZXcgUHJvbWlzZSgocmVzb2x2ZSkgPT4gewogICAgY2FuY2VsLm9uY2xpY2sgPSAoKSA9PiB7CiAgICAgIHJlc29sdmUobnVsbCk7CiAgICB9OwogIH0pOwoKICAvLyBXYWl0IGZvciB0aGUgdXNlciB0byBwaWNrIHRoZSBmaWxlcy4KICBjb25zdCBmaWxlcyA9IHlpZWxkIHsKICAgIHByb21pc2U6IFByb21pc2UucmFjZShbcGlja2VkUHJvbWlzZSwgY2FuY2VsUHJvbWlzZV0pLAogICAgcmVzcG9uc2U6IHsKICAgICAgYWN0aW9uOiAnc3RhcnRpbmcnLAogICAgfQogIH07CgogIGNhbmNlbC5yZW1vdmUoKTsKCiAgLy8gRGlzYWJsZSB0aGUgaW5wdXQgZWxlbWVudCBzaW5jZSBmdXJ0aGVyIHBpY2tzIGFyZSBub3QgYWxsb3dlZC4KICBpbnB1dEVsZW1lbnQuZGlzYWJsZWQgPSB0cnVlOwoKICBpZiAoIWZpbGVzKSB7CiAgICByZXR1cm4gewogICAgICByZXNwb25zZTogewogICAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgICAgfQogICAgfTsKICB9CgogIGZvciAoY29uc3QgZmlsZSBvZiBmaWxlcykgewogICAgY29uc3QgbGkgPSBkb2N1bWVudC5jcmVhdGVFbGVtZW50KCdsaScpOwogICAgbGkuYXBwZW5kKHNwYW4oZmlsZS5uYW1lLCB7Zm9udFdlaWdodDogJ2JvbGQnfSkpOwogICAgbGkuYXBwZW5kKHNwYW4oCiAgICAgICAgYCgke2ZpbGUudHlwZSB8fCAnbi9hJ30pIC0gJHtmaWxlLnNpemV9IGJ5dGVzLCBgICsKICAgICAgICBgbGFzdCBtb2RpZmllZDogJHsKICAgICAgICAgICAgZmlsZS5sYXN0TW9kaWZpZWREYXRlID8gZmlsZS5sYXN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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "FtpbcSBlIJQv",
        "outputId": "721c754a-87dc-43cc-edfc-62f3d0696aac"
      },
      "source": [
        "uploaded = files.upload()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-feeb330a-a84a-4da8-8671-6fc3bf19d3b7\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-feeb330a-a84a-4da8-8671-6fc3bf19d3b7\">\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> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving SMSSpamCollection to SMSSpamCollection\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGHUo_yOImXw"
      },
      "source": [
        "SMSSpamCollection = uploaded"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aD8PZKxVJnv8"
      },
      "source": [
        "SMSSpamCollection = uploaded['SMSSpamCollection'].decode(\"utf-8\").split(\"\\r\\n\")"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "67DBPqbGE61_"
      },
      "source": [
        "messages = [line.rstrip() for line in open('SMSSpamCollection')]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "e4rd-ECjFDsr",
        "outputId": "4f89423d-a3fd-4ae8-9be4-026df582e8b4"
      },
      "source": [
        "messages[50]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'ham\\tWhat you thinked about me. First time you saw me in class.'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iHRpsF4qKOFL",
        "outputId": "3dab2486-4e17-444b-a50b-dc35fb1dd25f"
      },
      "source": [
        "for mess_no,message in enumerate (messages[0:10]):\n",
        "  print(mess_no,message)\n",
        "  print('\\n')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 ham\tGo until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...\n",
            "\n",
            "\n",
            "1 ham\tOk lar... Joking wif u oni...\n",
            "\n",
            "\n",
            "2 spam\tFree entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\n",
            "\n",
            "\n",
            "3 ham\tU dun say so early hor... U c already then say...\n",
            "\n",
            "\n",
            "4 ham\tNah I don't think he goes to usf, he lives around here though\n",
            "\n",
            "\n",
            "5 spam\tFreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv\n",
            "\n",
            "\n",
            "6 ham\tEven my brother is not like to speak with me. They treat me like aids patent.\n",
            "\n",
            "\n",
            "7 ham\tAs per your request 'Melle Melle (Oru Minnaminunginte Nurungu Vettam)' has been set as your callertune for all Callers. Press *9 to copy your friends Callertune\n",
            "\n",
            "\n",
            "8 spam\tWINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.\n",
            "\n",
            "\n",
            "9 spam\tHad your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on 08002986030\n",
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pN7bbt7xKsLA"
      },
      "source": [
        "import pandas as pd\n",
        "import io"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "icySQuL4OwWG",
        "outputId": "f5678f69-44b0-4e63-c161-e938fc347891"
      },
      "source": [
        "io.StringIO(uploaded['SMSSpamCollection'].decode(\"utf-8\"))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<_io.StringIO at 0x7f133565f7d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "23RllcxcP4Y2"
      },
      "source": [
        "messages = pd.read_csv(io.StringIO(uploaded['SMSSpamCollection'].decode(\"utf-8\")),sep='\\t',names=['label','message'])"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "rftDcdbDPHP5",
        "outputId": "90b7ea4c-351a-468e-f085-fca24a53e4b3"
      },
      "source": [
        "messages.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>label</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</td>\n",
              "      <td>Go until jurong point, crazy.. Available only ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</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>ham</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>ham</td>\n",
              "      <td>Nah I don't think he goes to usf, he lives aro...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  label                                            message\n",
              "0   ham  Go until jurong point, crazy.. Available only ...\n",
              "1   ham                      Ok lar... Joking wif u oni...\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...\n",
              "3   ham  U dun say so early hor... U c already then say...\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro..."
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "k8KMaZiKR2vD",
        "outputId": "829aeb74-4e6f-40b9-ddc2-f2a3518dbaab"
      },
      "source": [
        "messages.describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>label</th>\n",
              "      <th>message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>5572</td>\n",
              "      <td>5572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2</td>\n",
              "      <td>5169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>ham</td>\n",
              "      <td>Sorry, I'll call later</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>4825</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       label                 message\n",
              "count   5572                    5572\n",
              "unique     2                    5169\n",
              "top      ham  Sorry, I'll call later\n",
              "freq    4825                      30"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "gYCiywl_R5NK",
        "outputId": "9c3a8af8-ed25-408c-c2fb-f8cf175c9fb6"
      },
      "source": [
        "messages.groupby('label').describe()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th colspan=\"4\" halign=\"left\">message</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>label</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ham</th>\n",
              "      <td>4825</td>\n",
              "      <td>4516</td>\n",
              "      <td>Sorry, I'll call later</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spam</th>\n",
              "      <td>747</td>\n",
              "      <td>653</td>\n",
              "      <td>Please call our customer service representativ...</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      message                                                               \n",
              "        count unique                                                top freq\n",
              "label                                                                       \n",
              "ham      4825   4516                             Sorry, I'll call later   30\n",
              "spam      747    653  Please call our customer service representativ...    4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhSN2HKRSBDg"
      },
      "source": [
        "messages['length'] = messages['message'].apply(len)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "nDk_0gfKSV34",
        "outputId": "53610659-6358-400e-c99d-7ad9ea58b2bb"
      },
      "source": [
        "messages.head()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>label</th>\n",
              "      <th>message</th>\n",
              "      <th>length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</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>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</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>ham</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>ham</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>"
            ],
            "text/plain": [
              "  label                                            message  length\n",
              "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
              "1   ham                      Ok lar... Joking wif u oni...      29\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
              "3   ham  U dun say so early hor... U c already then say...      49\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5pbZKetESYPy"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "T9FZsWusSdxI",
        "outputId": "8e48060e-1035-4d3b-9ee0-6c3c9089db90"
      },
      "source": [
        "messages['length'].plot.hist(bins=150)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f131c1a22d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ifShWrXBStEU",
        "outputId": "36124b4e-7d2b-4949-b7c8-667f12a0d56e"
      },
      "source": [
        "messages['length'].describe()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    5572.000000\n",
              "mean       80.489950\n",
              "std        59.942907\n",
              "min         2.000000\n",
              "25%        36.000000\n",
              "50%        62.000000\n",
              "75%       122.000000\n",
              "max       910.000000\n",
              "Name: length, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 120
        },
        "id": "dMs-RuJpS9WK",
        "outputId": "00110332-7eb4-4075-d51c-75327377129c"
      },
      "source": [
        "messages[messages['length']==910]['message'].iloc[0]"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"For me the love should start with attraction.i should feel that I need her every time around me.she should be the first thing which comes in my thoughts.I would start the day and end it with her.she should be there every time I dream.love will be then when my every breath has her name.my life should happen around her.my life will be named to her.I would cry for her.will give all my happiness and take all her sorrows.I will be ready to fight with anyone for her.I will be in love when I will be doing the craziest things for her.love will be when I don't have to proove anyone that my girl is the most beautiful lady on the whole planet.I will always be singing praises for her.love will be when I start up making chicken curry and end up makiing sambar.life will be the most beautiful then.will get every morning and thank god for the day because she is with me.I would like to say a lot..will tell later..\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "0QnU44TGTEl5",
        "outputId": "a140055b-6cc9-4bff-e8dc-e810e8dd900b"
      },
      "source": [
        "messages.hist(column='length',by='label',bins=60,figsize=(12,4))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f131b2ff710>,\n",
              "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f131b2b0fd0>],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sSaNjCJcktWEFcALwnqp6MfDPzBp60swnUXPtXFWbqmq6qqanpqZGXqwkjZohXJLUhu3A9qq6qbl9NYNQvivJkQDNzwc6qk+SWmUIlySNXFXtBLYlOb5pWgfcCVwLrG/a1gPXdFCeJLVuUdPWS5K0D34aeH+S/YF7gHMYdAZdleRc4D7gzA7rk6TWGMIlSa2oqluB6TlWrWu7FknqmsNRJEmSpJYZwiVJkqSWGcIlSZKklhnCJUmSpJYZwiVJkqSWGcIlSZKklhnCJUmSpJYZwiVJkqSWOVnPt7Dmgg8/pe3ejad1UIkkSZL6xJ5wSZIkqWWGcEmSJKllhnBJkiSpZYZwSZIkqWWGcEmSJKllhnBJkiSpZYZwSZIkqWWGcEmSJKllhnBJkiSpZc6YKUmSNAbmmslb42vRPeFJ9kvy6SR/1txem+SmJFuTXJlk/6b9gOb21mb9msU+tiRJkjSOlmI4ys8Cdw3dfidwUVUdCzwEnNu0nws81LRf1GwnSZIkTZxFhfAkq4HTgEua2wFeClzdbLIZOKNZPr25TbN+XbO9JEmSNFEW2xP+O8Bbgceb24cDD1fVY83t7cCqZnkVsA2gWf9Is70kSZI0URZ8YmaSVwMPVNXNSU5ZqoKSbAA2ABxzzDFLdbeSJEnLliddTp7F9ISfDPxIknuBKxgMQ7kYOCTJ7nC/GtjRLO8AjgZo1h8MfGX2nVbVpqqarqrpqampRZQnSZIkLU8LDuFV9baqWl1Va4CzgI9V1Y8Dfwm8ttlsPXBNs3xtc5tm/ceqqhb6+JIkSdK4GsVkPecDb0mylcGY70ub9kuBw5v2twAXjOCxJUmSpGVvSSbrqaqPAx9vlu8BTpxjm38FXrcUjydJGk9J9gO2ADuq6tVJ1jIY0ng4cDPwhqr6epc1SlIbnLZektSm+c4tIUm9ZgiXJLViH+eWkKReM4RLktqyL3NLPEmSDUm2JNkyMzMz+kolacQM4ZKkkRueW2Ih+3v5Wkl9syQnZkqStBe755Z4FfAM4NkMzS3R9IYPzy0hSb1mT7gkaeQWMLeEJPWaPeH7aPa0svduPK2jSiSpF84Hrkjya8CneWJuCUnqNUO4JKlV85lbQpL6zhAuSZK0RGZ/Yw5+a665OSZckiRJapkhXJIkSWqZIVySJElqmSFckiRJapkhXJIkSWqZIVySJElqmSFckiRJapkhXJIkSWqZk/VIkiSNkBP4aC6GcEmSpJbNFcw1WRyOIkmSJLXMEC5JkiS1zBAuSZIktcwQLkmSJLXMEC5JkiS1zBAuSZIktcwQLkmSJLXMEC5JkiS1zBAuSZIktcwQLkmSJLXMEC5JkiS1bEXXBYy7NRd8+Clt9248rYNKJEmSNC4WHMKTHA28D1gJFLCpqi5OchhwJbAGuBc4s6oeShLgYuBVwKPAG6vqlsWVvzzNDuaGckmSJA1bzHCUx4BfqKrnAycB5yV5PnABcENVHQfc0NwGeCVwXPNvA/CeRTy2JEmSNLYW3BNeVfcD9zfLX01yF7AKOB04pdlsM/Bx4Pym/X1VVcCNSQ5JcmRzPxPHYSySJEmTa0nGhCdZA7wYuAlYORSsdzIYrgKDgL5taLftTduTQniSDQx6yjnmmGOWoryx4TAWSX21r0MYu6pTktqy6KujJHkW8IfAz1XVPw6va3q9a1/ur6o2VdV0VU1PTU0ttjxJ0vKwr0MYJanXFtUTnuTpDAL4+6vqj5rmXbuHmSQ5Enigad8BHD20++qmrffmGnoiSZNkAUMYJanXFtwT3lzt5FLgrqr67aFV1wLrm+X1wDVD7Wdn4CTgkUkdDy5Jk2yeQxglqdcW0xN+MvAG4DNJbm3afhHYCFyV5FzgPuDMZt11DC5PuJXBJQrPWcRjS5LG0OwhjIP+nIGqqiRzDmGc5POFJPXTYq6O8tdA9rB63RzbF3DeQh9PkjTe9nEI45NU1SZgE8D09PQ+nWskScuR09ZLkkZuAUMYJanXnLZektSGfR3CKEm9ZgiXJI3cvg5hlKS+cziKJEmS1DJDuCRJktQyh6NIkqSJN9fEevduPK2DSjQp7AmXJEmSWmYIlyRJklpmCJckSZJa1ssx4XON65IkSZKWC3vCJUmSpJb1sidckiRNhvlc1WSprnziN+1aSvaES5IkSS0zhEuSJEktM4RLkiRJLTOES5IkSS3zxExJkgQ89cTDSZ+23RMxNUr2hEuSJEktsydckiSNhfn2TNuDrXFgCJckSZogS3XddC2Ow1EkSZKkltkTLknSMmWPpdRf9oRLkiRJLbMnXJIkzWk+PfGj7K33BEv1mT3hkiRJUsvsCZckSUtqIZP+2OutSWMIX8Y8IUeSJKmfDOGSJPXccuzU6brnu+vHHwcL+UZD82cIlyRpERYacA2BWk7m8/vY9Ym6feOJmZIkSVLLWu8JT3IqcDGwH3BJVW1su4ZxNp9Pqn7ilDROPC50Y6E98QvZz15/6alaDeFJ9gP+N/CfgO3Ap5JcW1V3tlnHpFmqr0oN95KWmscFSZOq7Z7wE4GtVXUPQJIrgNMB/9guoYWM61ro+MW59jO8S9oHrR0XFvJNYpu9xQu97/n8HZbGTddjy9t4/LZD+Cpg29Dt7cBLWq5BcxjlgWahJ3LsbZ/5GochPF3/sZE65HFB0kRKVbX3YMlrgVOr6k3N7TcAL6mqNw9tswHY0Nw8HviHfXyYI4AvL0G542TSnrPPt9/G+fk+t6qmui5inMznuNC0L/bY0Afj/H9jqfga+BrA+L0Gcx4b2u4J3wEcPXR7ddP2b6pqE7BpoQ+QZEtVTS90/3E0ac/Z59tvk/Z8tffjAiz+2NAH/t/wNQBfA+jPa9D2JQo/BRyXZG2S/YGzgGtbrkGStHx4XJA0kVrtCa+qx5K8Gfgog0tRXVZVd7RZgyRp+fC4IGlStX6d8Kq6DrhuhA8xiV9XTtpz9vn226Q934nXwnGhL/y/4WsAvgbQk9eg1RMzJUmSJDltvSRJktQ6Q7gkSZLUstbHhC+1JM9jMLvaqqZpB3BtVd3VXVWSJEnSno31mPAk5wOvB65gMMsaDK4xexZwRVVt7Kq2UUqykqEPHVW1q8t62pDkMICqerDrWtrgeyxJ0hP6eFwc9xD+OeC7quobs9r3B+6oquO6qWw0krwI+D/AwTwxmcVq4GHgp6rqlq5qG4UkxwC/Aaxj8BwDPBv4GHBBVd3bXXWj4Xvc//dYmo8kBwNvA84AngMU8ABwDbCxqh7usLxW9TF87YskAU7kyd/4f7LGOcDtgz4fF8d9OMrjwFHAfbPaj2zW9c17gZ+sqpuGG5OcBFwOvLCLokboSuB3gB+vqm8CJNkPeB2Dbz9O6rC2UXkvvsd9f4+l+biKwYfRU6pqJ0CSfwesb9a9vMPaWrGn8JVk7MPXfCV5OfBu4G6eHECPTfJTVfUXnRXXnvfS0+PiuPeEnwr8LoNfzm1N8zHAscCbq+ojXdU2Cknu3lPvfpKtVXVs2zWN0l6e7x7XjTPf4/mtk/ouyT9U1fH7uq5PktzKnsPX71XV2Iav+UpyF/DK2d8KJlkLXFdV39lJYS3q83FxrHvCq+ojSb6Dp35N86ndvWo98+dJPgy8jyc+dBwNnA306gNH4+Yk7wY28+Tnux74dGdVjZbvcf/fY2k+7kvyVmDz7uEXzbCMN/LE/5W+O2h2AAeoqhuTHNRFQR1YwRPnvA3bATy95Vq60tvj4lj3hE+iJK9k7qvB9G62uWZs/7nM8XyBS6vqa13VNkq+x/1/j6W9SXIocAGD/xsrGYwJ38Xg/8Y7J+EE5iTvAr6ducPXF6rqzV3V1pYkbwPOZDA8b/g1OAu4qqr+Z1e1tamvx0VDuCRJy1ySH2Dwre9nJmQcMNDf8LUvknwnc78Gd3ZXlZaCIXyMDJ0tP9wz0tuz5ZOsYNBLegZP/uNzDYNe0m/sad9x5XsM9Pw9luYjySer6sRm+U3AecCfMDgh80/7egleabY+HxedMXO8XAU8BPxwVR1WVYcDP8zgMj1XdVrZaPxf4EXA24FXNf/ezuBM6D/osK5R8j3u/3sszcfweN+fBF5eVW9nEMJ/vJuS2pXk4CQbk9yV5MEkX2mWNyY5pOv62tBcgGL38sFJLklyW5IPNOcITILeHhftCR8jk3a2fJLPVdV37Ou6ceZ7PL91Ut8l+XvgFAadZR+tqumhdZ+uqhd3VVtbknyUwWUaN8+6TOMbgZdW1SRcpvGWqjqhWb4E2An8PvAa4Ieq6owu62tDn4+L9oSPl/uSvHX402+Slc3MoX08W/7BJK9L8m+/p0meluTHGHwq7iPf4/6/x9J8HAzcDGwBDktyJECSZzGY1GoSrKmqd+4O4ABVtbMZivPcDuvqynRV/XJV3VdVFwFrui6oJb09LhrCx8uPAYcDf5XkoSQPAh8HDmNw9nTfnAW8FtiV5HNJ7mbQC/CaZl0fTep7vLN5jz9H/99jaa+qak1V/fuqWtv8vL9Z9Tjwo13W1qLehq998Jwkb0nyC8Czkwx/AJuUDNfb46LDUcZMkucxmC3rxqr6p6H2U/s2OdGwJIc3ixdX1U90WswIJXkJ8NmqeiTJgQwuUXYCcAfw61X1SKcFLrHmEoWvB74E3AKcCpzM4Plu8sRMaXLNukzjc5rm3Zdp3FhVvf+2LMmFs5reXVUzzbCc36iqs7uoq219zT6G8DGS5GcYnCF/F4OT2X62qq5p1v3buLG+SHLtHM0vZTBGkKr6kXYrGr0kdwAvrKrHkmwC/hn4Q2Bd0/6aTgtcYknez2AyimcCjwAHAX/M4PmmqtZ3WJ6kZSrJOVV1edd1dGlSXoM+Z5+xnjFzAv1X4Hur6p+SrAGuTrKmqi6mn2MEVwN3ApcwuCRRgO8DfqvLokbsaVX1WLM8PfTH5a8zmMK5b767qr6nuVThDuCoqvpmkj8A/r7j2iQtX28Heh9A92JSXoPeZh9D+Hh52u6vYarq3iSnMPhlfC5j/ou4B9PAzwK/BPz3qro1yb9U1V91XNco3T7Uu/H3SaarakuS7wD6ODTjac2QlIOAAxmcjPYgcACTMyWzpDkkuW1PqxhcL7r3fA2AHmcfQ/h42ZXkRVV1K0DzqfDVwGXAd3db2tKrqseBi5J8qPm5i/7/zr4JuDjJLwNfBv4uyTYGJyG9qdPKRuNS4LPAfgw+bH0oyT3ASQymaZY0uVYCr+CpV0oK8Lftl9MJX4MeZx/HhI+RJKuBx4Yv1zS07uSq+psOympNktOAk6vqF7uuZdSSPBtYy+BDx/aq2tVxSSOT5CiAqvpSMwHHy4AvVtUnu61MUpeSXApcXlV/Pce6D1TVf+6grFb5GvQ7+xjCJUmSpJZNyjUmJUmSpGXDEC5JkiS1zBAuSZIktcwQLkmSJLXMEC5JkiS17P8DXlQlCpU6lK0AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tbcj3Dl2TcCD"
      },
      "source": [
        "import string"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqLOF14JUSaz"
      },
      "source": [
        "mess = 'Sample message! Notice: it has punctuation'"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "sXn88QbHUZgQ",
        "outputId": "f1a5276d-4be3-43f7-ec1f-5f3cfd56a53c"
      },
      "source": [
        "string.punctuation"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'!\"#$%&\\'()*+,-./:;<=>?@[\\\\]^_`{|}~'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eynBS0-5UbZr"
      },
      "source": [
        "no_punc = [c for c in mess if c not in string.punctuation]"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EBZZf5JxUnRO",
        "outputId": "ad4cb2bf-19f1-4c07-bc09-8a4ce82af038"
      },
      "source": [
        "print(no_punc)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['S', 'a', 'm', 'p', 'l', 'e', ' ', 'm', 'e', 's', 's', 'a', 'g', 'e', ' ', 'N', 'o', 't', 'i', 'c', 'e', ' ', 'i', 't', ' ', 'h', 'a', 's', ' ', 'p', 'u', 'n', 'c', 't', 'u', 'a', 't', 'i', 'o', 'n']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YZhbiWTdgs3",
        "outputId": "c0d0aaf4-423b-44fa-ccc7-c55f7d9b030f"
      },
      "source": [
        "nltk.download('stopwords')"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBhQQLUgUsVX"
      },
      "source": [
        "from nltk.corpus import stopwords"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dQ-_84wHU8FO",
        "outputId": "30230144-e650-4100-d1f2-ac8f4ed725d9"
      },
      "source": [
        "print(stopwords.words('english'))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', \"you're\", \"you've\", \"you'll\", \"you'd\", 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', \"she's\", 'her', 'hers', 'herself', 'it', \"it's\", 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', \"that'll\", 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', \"don't\", 'should', \"should've\", 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', \"aren't\", 'couldn', \"couldn't\", 'didn', \"didn't\", 'doesn', \"doesn't\", 'hadn', \"hadn't\", 'hasn', \"hasn't\", 'haven', \"haven't\", 'isn', \"isn't\", 'ma', 'mightn', \"mightn't\", 'mustn', \"mustn't\", 'needn', \"needn't\", 'shan', \"shan't\", 'shouldn', \"shouldn't\", 'wasn', \"wasn't\", 'weren', \"weren't\", 'won', \"won't\", 'wouldn', \"wouldn't\"]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1ySS7kvkU-8C"
      },
      "source": [
        "no_punc = \"\".join(no_punc)"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kKW8hK8kVQtf",
        "outputId": "431c9390-a26c-4010-fec8-5dcb18b4117a"
      },
      "source": [
        "print(no_punc)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sample message Notice it has punctuation\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gsj2pe0jVSXL",
        "outputId": "b9322bc3-a986-45e8-dbd1-e9fae838e454"
      },
      "source": [
        "no_punc.split()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sample', 'message', 'Notice', 'it', 'has', 'punctuation']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJKYbPCMVtgJ"
      },
      "source": [
        "clean_mess = [word for word in no_punc.split() if word.lower() not in stopwords.words('english')]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6qZ4vfrV40v",
        "outputId": "44474660-75ec-488b-9d87-75f3b751a3a6"
      },
      "source": [
        "clean_mess"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Sample', 'message', 'Notice', 'punctuation']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WapaP_FBV_7R"
      },
      "source": [
        "def text_process(mess):\n",
        "  \"\"\"\n",
        "  1. remove punctuation\n",
        "  2. remove stop words\n",
        "  3. return list of clean text words\n",
        "  \"\"\"\n",
        "  no_punc = [char for char in mess if char not in string.punctuation]\n",
        "  no_punc = \"\".join(no_punc)\n",
        "  \n",
        "  return [word for word in no_punc.split() if word.lower() not in stopwords.words('english')]"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "tTwrzArKXnJH",
        "outputId": "b1935212-9df8-4e64-d33a-35778af2b194"
      },
      "source": [
        "messages.head()"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>label</th>\n",
              "      <th>message</th>\n",
              "      <th>length</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ham</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>ham</td>\n",
              "      <td>Ok lar... Joking wif u oni...</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>spam</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>ham</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>ham</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>"
            ],
            "text/plain": [
              "  label                                            message  length\n",
              "0   ham  Go until jurong point, crazy.. Available only ...     111\n",
              "1   ham                      Ok lar... Joking wif u oni...      29\n",
              "2  spam  Free entry in 2 a wkly comp to win FA Cup fina...     155\n",
              "3   ham  U dun say so early hor... U c already then say...      49\n",
              "4   ham  Nah I don't think he goes to usf, he lives aro...      61"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlH71tTeXu2k",
        "outputId": "b1c99700-cdab-435b-c000-0aba1c1a6353"
      },
      "source": [
        "messages['message'].head(5).apply(text_process)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    [Go, jurong, point, crazy, Available, bugis, n...\n",
              "1                       [Ok, lar, Joking, wif, u, oni]\n",
              "2    [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n",
              "3        [U, dun, say, early, hor, U, c, already, say]\n",
              "4    [Nah, dont, think, goes, usf, lives, around, t...\n",
              "Name: message, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "urmZCBTvX3XO"
      },
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cf0O_YoSZGc_"
      },
      "source": [
        "bow_transformer = CountVectorizer(analyzer = text_process).fit(messages['message'])"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kdNZ6XFZUUx",
        "outputId": "6f2b9a53-a794-46d7-e8a3-13cda3ba5d89"
      },
      "source": [
        "print(len(bow_transformer.vocabulary_))"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "11425\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PN_whzJQZbZ_"
      },
      "source": [
        "mess4 = messages['message'][3]"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tfi1ULi1ZiEL",
        "outputId": "0ba5e338-ff3b-45af-c27a-d4462f00f3f1"
      },
      "source": [
        "print(mess4)"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "U dun say so early hor... U c already then say...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0oYyowefZjVk"
      },
      "source": [
        "bow4 = bow_transformer.transform([mess4])"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7DbK4uoZp4M",
        "outputId": "6c01a69e-0ce1-46e9-f769-f0432ec456ed"
      },
      "source": [
        "print(bow4)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  (0, 4068)\t2\n",
            "  (0, 4629)\t1\n",
            "  (0, 5261)\t1\n",
            "  (0, 6204)\t1\n",
            "  (0, 6222)\t1\n",
            "  (0, 7186)\t1\n",
            "  (0, 9554)\t2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqVXN4TFZrU1",
        "outputId": "4f9cbbbf-e272-4d5a-9792-3c0578526982"
      },
      "source": [
        "print(bow4.shape)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 11425)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "r75sinP5Z6yc",
        "outputId": "6989b571-c0c8-4da2-cee9-1961aa410698"
      },
      "source": [
        "bow_transformer.get_feature_names()[9554]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'say'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BBbrLHqHZ_S8"
      },
      "source": [
        "messages_bow = bow_transformer.transform(messages['message'])"
      ],
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xCsjq0QK89Kv",
        "outputId": "bb501050-9603-49b8-ce96-4f56327b7dad"
      },
      "source": [
        "print(\"Shape of Sparse Matrix: \", messages_bow.shape)"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Shape of Sparse Matrix:  (5572, 11425)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKNrB8mEeC6L",
        "outputId": "45e7ec27-fe11-407b-b200-f7b7a98931c0"
      },
      "source": [
        "messages_bow.nnz"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50548"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vxKL5wHTeJaY",
        "outputId": "148135ea-6196-4f57-ab54-8853d8977f89"
      },
      "source": [
        "sparsity = (100 * messages_bow.nnz / (messages_bow.shape[0] * messages_bow.shape[1]))\n",
        "print(f\"Sparcity: {sparsity}\")"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Sparcity: 0.07940295412668218\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QCfqLDobe3q1"
      },
      "source": [
        "from sklearn.feature_extraction.text import TfidfTransformer"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ivC_oCsyfT4g"
      },
      "source": [
        "tfidf_transformer = TfidfTransformer().fit(messages_bow)"
      ],
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ca22gykgfblt"
      },
      "source": [
        "tfidf4 = tfidf_transformer.transform(bow4)"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iwx_pqTBfifJ",
        "outputId": "e99f72ed-76ca-4b6f-f658-8606fa227324"
      },
      "source": [
        "print(tfidf4)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  (0, 9554)\t0.5385626262927564\n",
            "  (0, 7186)\t0.4389365653379857\n",
            "  (0, 6222)\t0.3187216892949149\n",
            "  (0, 6204)\t0.29953799723697416\n",
            "  (0, 5261)\t0.29729957405868723\n",
            "  (0, 4629)\t0.26619801906087187\n",
            "  (0, 4068)\t0.40832589933384067\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrz4WUBgflud",
        "outputId": "08134c50-ecc4-413d-81b6-b3157797b75c"
      },
      "source": [
        "tfidf_transformer.idf_[bow_transformer.vocabulary_['university']]"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.527076498901426"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EU0gqVyf5HR"
      },
      "source": [
        "messages_tfidf = tfidf_transformer.transform(messages_bow)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PezDqYiJgHNB"
      },
      "source": [
        "from sklearn.naive_bayes import MultinomialNB"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PUcQd2CzgWF0"
      },
      "source": [
        "spam_detect_model = MultinomialNB().fit(messages_tfidf, messages['label'])"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "7-2j11ICgmIs",
        "outputId": "c3abebfd-d6a9-4a11-9027-f73d02c8c503"
      },
      "source": [
        "spam_detect_model.predict(tfidf4)[0]"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'ham'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Y31cnYTpgrKa",
        "outputId": "296293e0-dcbd-478f-a6ad-492eb26ab530"
      },
      "source": [
        "messages['label'][3]"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'ham'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9w0o0vtwg4WY"
      },
      "source": [
        "all_pred = spam_detect_model.predict(messages_tfidf)"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_oHjZrDGhRGz",
        "outputId": "3b33e17c-fbea-44d5-b960-0198c737c08f"
      },
      "source": [
        "all_pred"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ham', 'ham', 'spam', ..., 'ham', 'ham', 'ham'], dtype='<U4')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9IWa1dVphSlV"
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-HxyjFKhcQH"
      },
      "source": [
        "msg_train, msg_test, label_train, label_test = train_test_split(messages['message'],messages['label'],test_size=0.3)"
      ],
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TO4bNVEvhvnP"
      },
      "source": [
        "from sklearn.pipeline import Pipeline"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p8l1cfqOiJRk"
      },
      "source": [
        "pipeline = Pipeline([\n",
        "                     ('bow',CountVectorizer(analyzer=text_process)),\n",
        "                     ('tfidf',TfidfTransformer()),\n",
        "                     ('classifier',MultinomialNB()),\n",
        "                  \n",
        "                    ])"
      ],
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAykyAICixqF",
        "outputId": "ded466ac-e411-4f29-b175-0baa061369ee"
      },
      "source": [
        "pipeline.fit(msg_train,label_train)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('bow',\n",
              "                 CountVectorizer(analyzer=<function text_process at 0x7f131acb2f80>,\n",
              "                                 binary=False, decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=None, min_df=1,\n",
              "                                 ngram_range=(1, 1), preprocessor=None,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
              "                                 tokenizer=None, vocabulary=None)),\n",
              "                ('tfidf',\n",
              "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
              "                                  sublinear_tf=False, use_idf=True)),\n",
              "                ('classifier',\n",
              "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jujMc44li6q_"
      },
      "source": [
        "predictions = pipeline.predict(msg_test)"
      ],
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qWj8CM4jDk5"
      },
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 81,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "k-UxOMNzjHk3",
        "outputId": "80bae378-0638-4da5-e59a-3403b9ecdbe4"
      },
      "source": [
        "print(classification_report(label_test,predictions))"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.96      1.00      0.98      1457\n",
            "        spam       1.00      0.73      0.84       215\n",
            "\n",
            "    accuracy                           0.96      1672\n",
            "   macro avg       0.98      0.86      0.91      1672\n",
            "weighted avg       0.97      0.96      0.96      1672\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pgPs8aJJjkaa"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nm-CRAmPjQSv"
      },
      "source": [
        "pipeline_rf = Pipeline([\n",
        "                     ('bow',CountVectorizer(analyzer=text_process)),\n",
        "                     ('tfidf',TfidfTransformer()),\n",
        "                     ('classifier',RandomForestClassifier()),\n",
        "                  \n",
        "                    ])"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wWbg5Rljjs7i",
        "outputId": "e5f9b812-f349-4b08-e924-43e48fd4f620"
      },
      "source": [
        "pipeline_rf.fit(msg_train, label_train)"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(memory=None,\n",
              "         steps=[('bow',\n",
              "                 CountVectorizer(analyzer=<function text_process at 0x7f131acb2f80>,\n",
              "                                 binary=False, decode_error='strict',\n",
              "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
              "                                 input='content', lowercase=True, max_df=1.0,\n",
              "                                 max_features=None, min_df=1,\n",
              "                                 ngram_range=(1, 1), preprocessor=None,\n",
              "                                 stop_words=None, strip_accents=None,\n",
              "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b...\n",
              "                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,\n",
              "                                        class_weight=None, criterion='gini',\n",
              "                                        max_depth=None, max_features='auto',\n",
              "                                        max_leaf_nodes=None, max_samples=None,\n",
              "                                        min_impurity_decrease=0.0,\n",
              "                                        min_impurity_split=None,\n",
              "                                        min_samples_leaf=1, min_samples_split=2,\n",
              "                                        min_weight_fraction_leaf=0.0,\n",
              "                                        n_estimators=100, n_jobs=None,\n",
              "                                        oob_score=False, random_state=None,\n",
              "                                        verbose=0, warm_start=False))],\n",
              "         verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXitSrF9kfUZ"
      },
      "source": [
        "rf_predictions = pipeline_rf.predict(msg_test)"
      ],
      "execution_count": 90,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9Qv__zxj8al",
        "outputId": "3641851a-590c-4a99-ea83-c5348c97313f"
      },
      "source": [
        "print(classification_report(label_test, rf_predictions))"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         ham       0.97      1.00      0.99      1457\n",
            "        spam       0.99      0.80      0.89       215\n",
            "\n",
            "    accuracy                           0.97      1672\n",
            "   macro avg       0.98      0.90      0.94      1672\n",
            "weighted avg       0.97      0.97      0.97      1672\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PlJuM03SkEiz"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}