WEBSCRAPPING_final.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "WEBSCRAPPING.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNQBQev5XlMQEvzfBIAqAfK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/preeti13456/machine-learning-/blob/preeti13456-patch-1/WEBSCRAPPING_final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zSC237nIYxsi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from urllib import request   #for downloading data to url\n",
        "from bs4 import BeautifulSoup   #support\n",
        "import time\n",
        "import re   #importing regular expression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dm47oYj_Z1G0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#pointing to url\n",
        "#url = \"https://www.tensorflow.org/tutorials/structured_data/imbalanced_data\"\n",
        "url1 = \"https://ourworldindata.org/coronavirus-source-data\"\n",
        "htmldata = request.urlopen(url1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yy6CdufVaNQ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#htmldata.read()       # it will download data in html format\n",
        "\n",
        "soupdata = BeautifulSoup(htmldata,'html5lib')\n",
        "#html data, #html parser --\n",
        "# What is html parser : collection of htm tages taht can scape data from particualr tag like h1, html, a, p"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ObzIGo7vcUtI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "a9dd67dd-e717-4da2-9dd9-136ac6c2f930"
      },
      "source": [
        "atagdata = soupdata.find_all('meta')   #all the paragraph start tag come\n",
        "print(atagdata)"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[<meta content=\"width=device-width, initial-scale=1\" name=\"viewport\"/>, <meta content=\"The mission of Our World in Data is to make data and research on the world’s largest problems understandable and accessible.\" name=\"description\"/>, <meta content=\"1149943818390250\" property=\"fb:app_id\"/>, <meta content=\"https://ourworldindata.org/coronavirus-source-data\" property=\"og:url\"/>, <meta content=\"Coronavirus Source Data\" property=\"og:title\"/>, <meta content=\"The mission of Our World in Data is to make data and research on the world’s largest problems understandable and accessible.\" property=\"og:description\"/>, <meta content=\"https://ourworldindata.org/app/uploads/2020/03/Screen-Shot-2020-03-07-at-11.13.45-768x384.png\" property=\"og:image\"/>, <meta content=\"Our World in Data\" property=\"og:site_name\"/>, <meta content=\"summary_large_image\" name=\"twitter:card\"/>, <meta content=\"@OurWorldInData\" name=\"twitter:site\"/>, <meta content=\"@OurWorldInData\" name=\"twitter:creator\"/>, <meta content=\"Coronavirus Source Data\" name=\"twitter:title\"/>, <meta content=\"The mission of Our World in Data is to make data and research on the world’s largest problems understandable and accessible.\" name=\"twitter:description\"/>, <meta content=\"https://ourworldindata.org/app/uploads/2020/03/Screen-Shot-2020-03-07-at-11.13.45-768x384.png\" name=\"twitter:image\"/>]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wOPf1IG7czXW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "58c85e20-9cfd-4cd6-bbd8-d2c6701d97eb"
      },
      "source": [
        "#converting data into html format\n",
        "\n",
        "mydata = \"\"\n",
        "for i in atagdata:\n",
        "  mydata+= i.text\n",
        "print(mydata)  \n",
        " "
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l46QVIJYdTF6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bacb8507-db9c-4f8b-fc86-7e81f9c72cf7"
      },
      "source": [
        "#data cleaning\n",
        "clean_data = re.sub(r'\\[[0-9]* \\ ]','',mydata)  #this will remove 0 or more times  numbers appearing in data\n",
        "clean_data = re.sub(r'\\s+',',',clean_data)              #it will remove a line or more space with single white space\n",
        "clean_data = re.sub(r'[^a-zA-Z]',' ',clean_data)         #it will remove single char from starting of line\n",
        "clean_data = re.sub(r'\\s+',',',clean_data)      \n",
        "clean_data\n",
        "print(type(clean_data))"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'str'>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MttDBu_PvPqA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "newdata = clean_data.split()    #conversion of string to list\n",
        "for words in newdata:\n",
        "  print(words)\n",
        "  time.sleep(1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tmt7L-XAsAW3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "f7915e7c-6535-462e-8736-570518192af6"
      },
      "source": [
        "!pip install nltk   #nltk download\n",
        "import nltk\n",
        "nltk.download('all')  #donload all data required fr nltk"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
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            "[nltk_data]    |   Package book_grammars is already up-to-date!\n",
            "[nltk_data]    | Downloading package sample_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package sample_grammars is already up-to-date!\n",
            "[nltk_data]    | Downloading package spanish_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package spanish_grammars is already up-to-date!\n",
            "[nltk_data]    | Downloading package basque_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package basque_grammars is already up-to-date!\n",
            "[nltk_data]    | Downloading package large_grammars to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package large_grammars is already up-to-date!\n",
            "[nltk_data]    | Downloading package tagsets to /root/nltk_data...\n",
            "[nltk_data]    |   Package tagsets is already up-to-date!\n",
            "[nltk_data]    | Downloading package snowball_data to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package snowball_data is already up-to-date!\n",
            "[nltk_data]    | Downloading package bllip_wsj_no_aux to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package bllip_wsj_no_aux is already up-to-date!\n",
            "[nltk_data]    | Downloading package word2vec_sample to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package word2vec_sample is already up-to-date!\n",
            "[nltk_data]    | Downloading package panlex_swadesh to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package panlex_swadesh is already up-to-date!\n",
            "[nltk_data]    | Downloading package mte_teip5 to /root/nltk_data...\n",
            "[nltk_data]    |   Package mte_teip5 is already up-to-date!\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package averaged_perceptron_tagger is already up-\n",
            "[nltk_data]    |       to-date!\n",
            "[nltk_data]    | Downloading package averaged_perceptron_tagger_ru to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package averaged_perceptron_tagger_ru is already\n",
            "[nltk_data]    |       up-to-date!\n",
            "[nltk_data]    | Downloading package perluniprops to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package perluniprops is already up-to-date!\n",
            "[nltk_data]    | Downloading package nonbreaking_prefixes to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package nonbreaking_prefixes is already up-to-date!\n",
            "[nltk_data]    | Downloading package vader_lexicon to\n",
            "[nltk_data]    |     /root/nltk_data...\n",
            "[nltk_data]    |   Package vader_lexicon is already up-to-date!\n",
            "[nltk_data]    | Downloading package porter_test to /root/nltk_data...\n",
            "[nltk_data]    |   Package porter_test is already up-to-date!\n",
            "[nltk_data]    | Downloading package wmt15_eval to /root/nltk_data...\n",
            "[nltk_data]    |   Package wmt15_eval is already up-to-date!\n",
            "[nltk_data]    | Downloading package mwa_ppdb to /root/nltk_data...\n",
            "[nltk_data]    |   Package mwa_ppdb is already up-to-date!\n",
            "[nltk_data]    | \n",
            "[nltk_data]  Done downloading collection all\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVs9hDB83Cv9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "freq_data = nltk.FreqDist(newdata)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FxR9QPQU46fj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "outputId": "34d18828-ff74-41b7-8a92-1d5145557a27"
      },
      "source": [
        "freq_data.plot(10)  #top 10 having most count"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/Ascn+U6SDyb5/a7//VV1clU9G3gC/VXKbo9UVQ/4MHA98Cbg2cDrkhzVzXkm8MGqehbw\nY+CNgyftVjpvB06vqucDs8CF3fNfBZxUVc8FLh/De5Z+iQEiLVBVPQTMABuAHwHXJnkd8NIktya5\nA3gZcNLA0zZ1j3cAW6vq3m4FcxdwfDe2o6q+2h3/I/DioVP/LrAW+GqS24H1wNOBB4GfAlclOQf4\nyQF7s9JeuAciNaiqnwGbgc1dYPw58FygV1U7klwKPH7gKQ93jz8fON7d3v33cPiu3uF2gBur6vzh\nepKcApwGnAtcQD/ApLFyBSItUJJnJlkz0PU84M7u+L5uX+Lchpd+WrdBD/Aa4N+Gxr8GvCjJiV0d\nv5bkN7vzHVZVXwTeAvx2w7mlBXMFIi3cocD7khwOPApsp38567+BbwDfB7Y0vO6dwJuSfBT4JvCh\nwcGq+lF3qeyTSQ7put8O/A9wfZLH01+lXNhwbmnB/CwsaQlIMg18vtuAl5YFL2FJkpq4ApEkNXEF\nIklqYoBIkpoYIJKkJgaIJKmJASJJavJ/hIKb3vzk3+IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f_nZT_p86AQI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "50d89b30-7c85-4f19-d77f-467173ca2543"
      },
      "source": [
        "#stopwords are unwanted words taht stop you to classufy text category can be removed by tokenization\n",
        "#stopwords\n",
        "mypuredata = []\n",
        "from nltk.corpus import stopwords\n",
        "for i in newdata:\n",
        "  if i.lower() not in stopwords.words('english'):      #because all stopwords in nltk is lower case\n",
        "     mypuredata.append(i)\n",
        "\n",
        "print(mypuredata)  \n",
        "freq_data = nltk.FreqDist(newdata)\n",
        "freq_data.plot(10) "
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/Ascn+U6SDyb5/a7//VV1clU9G3gC/VXKbo9UVQ/4MHA98Cbg2cDrkhzVzXkm8MGqehbw\nY+CNgyftVjpvB06vqucDs8CF3fNfBZxUVc8FLh/De5Z+iQEiLVBVPQTMABuAHwHXJnkd8NIktya5\nA3gZcNLA0zZ1j3cAW6vq3m4FcxdwfDe2o6q+2h3/I/DioVP/LrAW+GqS24H1wNOBB4GfAlclOQf4\nyQF7s9JeuAciNaiqnwGbgc1dYPw58FygV1U7klwKPH7gKQ93jz8fON7d3v33cPiu3uF2gBur6vzh\nepKcApwGnAtcQD/ApLFyBSItUJJnJlkz0PU84M7u+L5uX+Lchpd+WrdBD/Aa4N+Gxr8GvCjJiV0d\nv5bkN7vzHVZVXwTeAvx2w7mlBXMFIi3cocD7khwOPApsp38567+BbwDfB7Y0vO6dwJuSfBT4JvCh\nwcGq+lF3qeyTSQ7put8O/A9wfZLH01+lXNhwbmnB/CwsaQlIMg18vtuAl5YFL2FJkpq4ApEkNXEF\nIklqYoBIkpoYIJKkJgaIJKmJASJJavJ/hIKb3vzk3+IAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}