pdf_extraction (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "irZftTHgGitF",
    "outputId": "ded67d00-8389-4545-c1ba-6554fe0e436a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: PyPDF2 in /usr/local/lib/python3.6/dist-packages (1.26.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install PyPDF2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "TBln7LXRGltX",
    "outputId": "fa4672a3-9929-4095-899e-4f26be9e2aa2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (3.2.5)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk) (1.10.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DQD9OHG0Glph"
   },
   "outputs": [],
   "source": [
    "import PyPDF2 \n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": ""
      }
     }
    },
    "colab_type": "code",
    "id": "UIefQHXoGlmh",
    "outputId": "088f78cb-cbed-432d-ad31-8c9dd02f6a7a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "     <input type=\"file\" id=\"files-b9831c71-21ec-4420-b58c-a5dd70fd5696\" name=\"files[]\" multiple disabled />\n",
       "     <output id=\"result-b9831c71-21ec-4420-b58c-a5dd70fd5696\">\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": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving resume.pdf to resume.pdf\n"
     ]
    }
   ],
   "source": [
    "from google.colab import files\n",
    "uploaded = files.upload()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wymKfu_ZGljL"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "datafile=\"resume.pdf\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SVCkjRw1GlgJ"
   },
   "outputs": [],
   "source": [
    "pdffile = open(datafile,'rb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "OrBfWSpgGldo",
    "outputId": "8c837675-2906-4add-f55a-fbd21133b6bd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PyPDF2.pdf.PdfFileReader at 0x7fbe1f940cc0>"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdfread = PyPDF2.PdfFileReader(pdffile)\n",
    "pdfread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "i1tsj8CeGlbe"
   },
   "outputs": [],
   "source": [
    "# seeing the number of pages will allow us to see through all #the pages\n",
    "num_pages = pdfread.numPages\n",
    "count = 0\n",
    "text = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PZWMvGpNGlZH"
   },
   "outputs": [],
   "source": [
    "num_pages = pdfread.numPages\n",
    "count = 0\n",
    "text = \"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tUOWyayxGlXJ"
   },
   "outputs": [],
   "source": [
    "#The while loop will read each page\n",
    "while count < num_pages:\n",
    "    page = pdfread.getPage(count)\n",
    "    count +=1\n",
    "    text += page.extractText()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xxSFryFbGlUa"
   },
   "outputs": [],
   "source": [
    "#This if statement exists to check if the above library returned words. It's done because PyPDF2 cannot read scanned files.\n",
    "if text != \"\":\n",
    "   text = text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "muDnUYw0HlNQ",
    "outputId": "8599c388-75dc-473f-bde1-e7a2768ab259"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "_YA2ipnGHnXM",
    "outputId": "bf810f16-4d1c-4bed-d72f-8062bbf52077"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Professional',\n",
       " 'Summary',\n",
       " 'Pursing',\n",
       " 'Post',\n",
       " 'Graduation',\n",
       " 'diploma',\n",
       " 'in',\n",
       " 'Data',\n",
       " 'science',\n",
       " 'Having',\n",
       " '2',\n",
       " 'years',\n",
       " 'of',\n",
       " 'experience',\n",
       " 'in',\n",
       " 'Software',\n",
       " 'Industry',\n",
       " '.',\n",
       " 'Worked',\n",
       " 'as',\n",
       " 'an',\n",
       " 'Application',\n",
       " 'Developer',\n",
       " 'in',\n",
       " 'Tata',\n",
       " 'Consultancy',\n",
       " 'Services',\n",
       " ',',\n",
       " 'Kolkata',\n",
       " '.',\n",
       " 'Participated',\n",
       " 'in',\n",
       " 'the',\n",
       " 'International',\n",
       " 'Global',\n",
       " 'Datathon',\n",
       " '(',\n",
       " '2019',\n",
       " ')',\n",
       " 's',\n",
       " 'ext',\n",
       " 'Analytics',\n",
       " 'Participated',\n",
       " 'in',\n",
       " 'the',\n",
       " 'Kaggle',\n",
       " 'competitons',\n",
       " 'Titatinc',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " 'from',\n",
       " 'Disaster',\n",
       " 'Professional',\n",
       " 'Experience',\n",
       " '2',\n",
       " 'Years',\n",
       " 'of',\n",
       " 'IT',\n",
       " 'experience',\n",
       " 'in',\n",
       " 'Data',\n",
       " 'ware',\n",
       " 'housing',\n",
       " '.',\n",
       " 'Good',\n",
       " 'Knowledge',\n",
       " 'in',\n",
       " 'Machine',\n",
       " 'Learn',\n",
       " 'ing',\n",
       " ',',\n",
       " 'Data',\n",
       " 'Mining',\n",
       " ',',\n",
       " 'Statistics',\n",
       " 'Hands',\n",
       " 'on',\n",
       " 'experience',\n",
       " 'with',\n",
       " 'Agile',\n",
       " 'Development',\n",
       " 'Model',\n",
       " 'and',\n",
       " 'Waterfall',\n",
       " 'Model',\n",
       " '.',\n",
       " 'Having',\n",
       " 'good',\n",
       " 'knowledge',\n",
       " 'in',\n",
       " 'programming',\n",
       " 'in',\n",
       " 'various',\n",
       " 'languages',\n",
       " ',',\n",
       " 'communication',\n",
       " 'and',\n",
       " 'interpersonal',\n",
       " 'skills',\n",
       " ',',\n",
       " 'active',\n",
       " 'team',\n",
       " 'worker',\n",
       " 'and',\n",
       " 'full',\n",
       " 'of',\n",
       " 'enterprise',\n",
       " 'initiatives',\n",
       " '.',\n",
       " 'Technical',\n",
       " 'Skills',\n",
       " 'Set',\n",
       " 'Platforms',\n",
       " ':',\n",
       " 'Google',\n",
       " 'Clou',\n",
       " 'd',\n",
       " 'Platf',\n",
       " 'orm',\n",
       " ',',\n",
       " 'Unix',\n",
       " ',',\n",
       " 'Windows',\n",
       " '.',\n",
       " 'Languages',\n",
       " ':',\n",
       " 'Python',\n",
       " ',',\n",
       " 'R',\n",
       " ',',\n",
       " 'SQL',\n",
       " 'Tools',\n",
       " ':',\n",
       " 'Tableau',\n",
       " 'Database',\n",
       " ':',\n",
       " 'O',\n",
       " 'racle',\n",
       " 'database',\n",
       " '11g',\n",
       " ',',\n",
       " 'MySQl',\n",
       " 'Others',\n",
       " ':',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " ',',\n",
       " 'statistics',\n",
       " ',',\n",
       " 'Data',\n",
       " 'Mining',\n",
       " 'Domain',\n",
       " ':',\n",
       " 'Retail',\n",
       " 'Academic',\n",
       " 'Projects',\n",
       " ':',\n",
       " 'Project',\n",
       " ':',\n",
       " 'Breast',\n",
       " 'Cancer',\n",
       " 'Prediction',\n",
       " 'Technolgy',\n",
       " 'Used',\n",
       " ':',\n",
       " 'Python',\n",
       " '(',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " 'Algorithms',\n",
       " ')',\n",
       " 'P',\n",
       " 'roject',\n",
       " 'Description',\n",
       " ':',\n",
       " 'Built',\n",
       " 'a',\n",
       " 'model',\n",
       " 'with',\n",
       " 'good',\n",
       " 'accuracy',\n",
       " 'score',\n",
       " ',',\n",
       " 'whether',\n",
       " 'a',\n",
       " 'person',\n",
       " 'is',\n",
       " 'effected',\n",
       " 'with',\n",
       " 'cancer',\n",
       " 'or',\n",
       " 'not',\n",
       " 'Project',\n",
       " ':',\n",
       " 'IMDB',\n",
       " 'Recommendation',\n",
       " 'System',\n",
       " 'Technolgy',\n",
       " 'Used',\n",
       " ':',\n",
       " 'Python',\n",
       " '(',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " 'Algorithms',\n",
       " ')',\n",
       " 'P',\n",
       " 'roject',\n",
       " 'Description',\n",
       " ':',\n",
       " 'Scrapped',\n",
       " 'top',\n",
       " '250',\n",
       " 'IMDB',\n",
       " 'movies',\n",
       " 'and',\n",
       " 'build',\n",
       " 'a',\n",
       " 'recommendation',\n",
       " 'sytem',\n",
       " 'based',\n",
       " 'on',\n",
       " 'the',\n",
       " 'plot',\n",
       " 'of',\n",
       " 'the',\n",
       " 'movie',\n",
       " '.',\n",
       " 'Project',\n",
       " ':',\n",
       " 'Dashboard',\n",
       " 'Creation',\n",
       " 'IPL',\n",
       " 'dataset',\n",
       " '(',\n",
       " '2008',\n",
       " '-',\n",
       " '2016',\n",
       " ')',\n",
       " 'T',\n",
       " 'echnolgy',\n",
       " 'Used',\n",
       " ':',\n",
       " 'Tableau',\n",
       " 'P',\n",
       " 'roject',\n",
       " 'Description',\n",
       " ':',\n",
       " 'Built',\n",
       " 'a',\n",
       " 'dashboard',\n",
       " 'on',\n",
       " 'IPL',\n",
       " 'data',\n",
       " 'set',\n",
       " 'with',\n",
       " 'he',\n",
       " 'help',\n",
       " 'of',\n",
       " 'KPI',\n",
       " 'boxes',\n",
       " ',',\n",
       " 'bar',\n",
       " 'graph',\n",
       " 'Phanikrishna',\n",
       " 'Velicheti',\n",
       " '401',\n",
       " ',',\n",
       " 'G',\n",
       " '-',\n",
       " 'block',\n",
       " ',',\n",
       " 'Brigade',\n",
       " 'Metropolis',\n",
       " ',',\n",
       " 'Garudachar',\n",
       " 'Palya',\n",
       " ',',\n",
       " 'Mahadevapura',\n",
       " ',',\n",
       " 'Bengaluru',\n",
       " ',',\n",
       " 'Karnataka',\n",
       " '-',\n",
       " '560048',\n",
       " 'PHONE',\n",
       " ':',\n",
       " '+91',\n",
       " '9959676104',\n",
       " 'Mail',\n",
       " ':',\n",
       " 'velicheti2phani',\n",
       " '@',\n",
       " 'gmail.com',\n",
       " 'Work',\n",
       " 'History',\n",
       " 'Tata',\n",
       " 'Consultancy',\n",
       " 'Services',\n",
       " '(',\n",
       " 'O',\n",
       " 'ct2016',\n",
       " 'Oct2018',\n",
       " ')',\n",
       " 'Project',\n",
       " ':',\n",
       " 'Morrisons',\n",
       " '(',\n",
       " 'Business',\n",
       " 'I',\n",
       " 'ntelligence',\n",
       " 'Department',\n",
       " ')',\n",
       " 'Technology',\n",
       " 'Used',\n",
       " ':',\n",
       " 'Google',\n",
       " 'Cloud',\n",
       " 'Platform',\n",
       " ',',\n",
       " 'BigQuery',\n",
       " ',',\n",
       " 'Dataflow',\n",
       " ',',\n",
       " 'Pub/Sub',\n",
       " ',',\n",
       " 'Project',\n",
       " 'Description',\n",
       " ':',\n",
       " 'Morrison',\n",
       " 's',\n",
       " 'is',\n",
       " 'a',\n",
       " 'leading',\n",
       " 'chain',\n",
       " 'of',\n",
       " 'supermarkets',\n",
       " 'in',\n",
       " 'the',\n",
       " 'United',\n",
       " 'Kingdom',\n",
       " 'and',\n",
       " 'also',\n",
       " 'it',\n",
       " 'is',\n",
       " 'trading',\n",
       " 'grocery',\n",
       " 'items',\n",
       " 'via',\n",
       " 'a',\n",
       " 'web',\n",
       " 'shop',\n",
       " 'through',\n",
       " 'Ocado',\n",
       " 'M.Com',\n",
       " 'grocery',\n",
       " 'channel',\n",
       " 'since',\n",
       " '2014',\n",
       " '.',\n",
       " 'Roles',\n",
       " 'and',\n",
       " 'Responsibilities',\n",
       " ':',\n",
       " 'Requirement',\n",
       " 'analysis',\n",
       " 'Application',\n",
       " 'd',\n",
       " 'esign',\n",
       " 'and',\n",
       " 'maintaining',\n",
       " 'design',\n",
       " 'specification',\n",
       " 'and',\n",
       " 'test',\n",
       " 'case',\n",
       " 'specification',\n",
       " 'as',\n",
       " 'per',\n",
       " 'the',\n",
       " 'new',\n",
       " 'changes',\n",
       " '.',\n",
       " 'Development',\n",
       " 'and',\n",
       " 'coding',\n",
       " 'for',\n",
       " 'new',\n",
       " 'functionality',\n",
       " 'incorporation',\n",
       " '.',\n",
       " 'Analysis',\n",
       " 'of',\n",
       " 'codes',\n",
       " 'and',\n",
       " 'jobs',\n",
       " 'in',\n",
       " 'AWS',\n",
       " 'and',\n",
       " 'Google',\n",
       " 'Cloud',\n",
       " 'Platform',\n",
       " 'to',\n",
       " 'understand',\n",
       " 'the',\n",
       " 'new',\n",
       " 'application',\n",
       " '.',\n",
       " 'Internship',\n",
       " ':',\n",
       " 'Kopenz',\n",
       " 'Private',\n",
       " 'Limited',\n",
       " ',',\n",
       " 'Bombay',\n",
       " '(',\n",
       " 'Inayo',\n",
       " ')',\n",
       " '(',\n",
       " 'Jan',\n",
       " '2016',\n",
       " 'June',\n",
       " '2016',\n",
       " ')',\n",
       " 'Technology',\n",
       " 'Used',\n",
       " ':',\n",
       " 'Python',\n",
       " ',',\n",
       " 'Django',\n",
       " 'Project',\n",
       " 'Description',\n",
       " ':',\n",
       " 'Distribution',\n",
       " 'of',\n",
       " 'medicines',\n",
       " 'through',\n",
       " 'an',\n",
       " 'app',\n",
       " 'within',\n",
       " '90',\n",
       " 'mins',\n",
       " 'throughout',\n",
       " 'the',\n",
       " 'Bombay',\n",
       " 'city',\n",
       " 'Roles',\n",
       " 'and',\n",
       " 'Responsibilities',\n",
       " ':',\n",
       " 'Worked',\n",
       " 'as',\n",
       " 'python',\n",
       " 'backend',\n",
       " 'developer',\n",
       " 'for',\n",
       " 'structuring',\n",
       " 'the',\n",
       " 'database',\n",
       " 'using',\n",
       " 'mysql',\n",
       " '.',\n",
       " 'Education',\n",
       " 'Post',\n",
       " '-',\n",
       " 'Graduation',\n",
       " ':',\n",
       " 'Pursing',\n",
       " 'Post',\n",
       " 'Graduation',\n",
       " 'diploma',\n",
       " 'in',\n",
       " 'data',\n",
       " 'science',\n",
       " 'form',\n",
       " 'Manipal',\n",
       " 'ProLearn',\n",
       " '-',\n",
       " '9.125',\n",
       " '(',\n",
       " 'Till',\n",
       " 'date',\n",
       " ')',\n",
       " 'Post',\n",
       " '-',\n",
       " 'Graduation',\n",
       " ':',\n",
       " 'Masters',\n",
       " 'of',\n",
       " 'science',\n",
       " 'in',\n",
       " 'IT',\n",
       " 'with',\n",
       " 'CGPA',\n",
       " '8.3',\n",
       " 'from',\n",
       " 'Jawaharlal',\n",
       " 'Nehru',\n",
       " 'Technological',\n",
       " 'University',\n",
       " 'Kakinada',\n",
       " '(',\n",
       " 'JNTUK',\n",
       " ')',\n",
       " 'Graduation',\n",
       " ':',\n",
       " 'B.Tech',\n",
       " 'with',\n",
       " '68',\n",
       " '%',\n",
       " 'from',\n",
       " 'Jawaharlal',\n",
       " 'Nehru',\n",
       " 'Technological',\n",
       " 'University',\n",
       " 'Hyderabad',\n",
       " '(',\n",
       " 'JNTUH',\n",
       " ')',\n",
       " 'XII',\n",
       " 'th',\n",
       " ':',\n",
       " 'Higher',\n",
       " 'Se',\n",
       " 'condary',\n",
       " 'Education',\n",
       " 'Andra',\n",
       " 'Pradesh',\n",
       " '(',\n",
       " '1',\n",
       " '2',\n",
       " 'th',\n",
       " 'score',\n",
       " '83.5',\n",
       " '%',\n",
       " ')',\n",
       " '.',\n",
       " 'X',\n",
       " 'th',\n",
       " ':',\n",
       " 'Secondary',\n",
       " 'Education',\n",
       " '-',\n",
       " 'Andra',\n",
       " 'Pradesh',\n",
       " '(',\n",
       " '10',\n",
       " 'th',\n",
       " 'score',\n",
       " '76',\n",
       " '%',\n",
       " ')',\n",
       " '.']"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The word_tokenize() function will break our text phrases into individual words\n",
    "token = word_tokenize(text)\n",
    "token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3qkNeU-SHnTU"
   },
   "outputs": [],
   "source": [
    "# Now we have a text variable which contains all the text derived #from our PDF file.  print(text) to see what it contains. contains a lot of spaces, possibly junk such as '\\n' etc.\n",
    "# Now, we will clean our text variable, and return it as a list of keywords.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "XA3JK2RmHnPv"
   },
   "outputs": [],
   "source": [
    "#The word_tokenize() function will break our text phrases into individual words\n",
    "token = word_tokenize(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "o3SnoKjQHlJA"
   },
   "outputs": [],
   "source": [
    "#we'll create a new list which contains punctuation  to clean\n",
    "punctuations = ['(',')',';',':','[',']',',']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 68
    },
    "colab_type": "code",
    "id": "W3AIWW7tJLoN",
    "outputId": "048b5420-0c3f-4f7d-de71-9f1c0d770a1e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J2KErDbpJAoU"
   },
   "outputs": [],
   "source": [
    "#We initialize the stopwords variable which has list of words like #\"The\", \"I\", \"and\", etc. that don't have much value as keywords\n",
    "stop_words = stopwords.words('english')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QGEQNUEzJAlp"
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "yM1bNRORJAiK"
   },
   "outputs": [],
   "source": [
    "#We create a list comprehension which only returns a list of words that are NOT IN stop_words and NOT IN punctuations.\n",
    "keywords = [word for word in token if not word in stop_words and not word in punctuations]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "StTCuhn1JAe5",
    "outputId": "96d99b01-01a9-4a74-9838-7b12e8273d76"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Professional',\n",
       " 'Summary',\n",
       " 'Pursing',\n",
       " 'Post',\n",
       " 'Graduation',\n",
       " 'diploma',\n",
       " 'Data',\n",
       " 'science',\n",
       " 'Having',\n",
       " '2',\n",
       " 'years',\n",
       " 'experience',\n",
       " 'Software',\n",
       " 'Industry',\n",
       " '.',\n",
       " 'Worked',\n",
       " 'Application',\n",
       " 'Developer',\n",
       " 'Tata',\n",
       " 'Consultancy',\n",
       " 'Services',\n",
       " 'Kolkata',\n",
       " '.',\n",
       " 'Participated',\n",
       " 'International',\n",
       " 'Global',\n",
       " 'Datathon',\n",
       " '2019',\n",
       " 'ext',\n",
       " 'Analytics',\n",
       " 'Participated',\n",
       " 'Kaggle',\n",
       " 'competitons',\n",
       " 'Titatinc',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " 'Disaster',\n",
       " 'Professional',\n",
       " 'Experience',\n",
       " '2',\n",
       " 'Years',\n",
       " 'IT',\n",
       " 'experience',\n",
       " 'Data',\n",
       " 'ware',\n",
       " 'housing',\n",
       " '.',\n",
       " 'Good',\n",
       " 'Knowledge',\n",
       " 'Machine',\n",
       " 'Learn',\n",
       " 'ing',\n",
       " 'Data',\n",
       " 'Mining',\n",
       " 'Statistics',\n",
       " 'Hands',\n",
       " 'experience',\n",
       " 'Agile',\n",
       " 'Development',\n",
       " 'Model',\n",
       " 'Waterfall',\n",
       " 'Model',\n",
       " '.',\n",
       " 'Having',\n",
       " 'good',\n",
       " 'knowledge',\n",
       " 'programming',\n",
       " 'various',\n",
       " 'languages',\n",
       " 'communication',\n",
       " 'interpersonal',\n",
       " 'skills',\n",
       " 'active',\n",
       " 'team',\n",
       " 'worker',\n",
       " 'full',\n",
       " 'enterprise',\n",
       " 'initiatives',\n",
       " '.',\n",
       " 'Technical',\n",
       " 'Skills',\n",
       " 'Set',\n",
       " 'Platforms',\n",
       " 'Google',\n",
       " 'Clou',\n",
       " 'Platf',\n",
       " 'orm',\n",
       " 'Unix',\n",
       " 'Windows',\n",
       " '.',\n",
       " 'Languages',\n",
       " 'Python',\n",
       " 'R',\n",
       " 'SQL',\n",
       " 'Tools',\n",
       " 'Tableau',\n",
       " 'Database',\n",
       " 'O',\n",
       " 'racle',\n",
       " 'database',\n",
       " '11g',\n",
       " 'MySQl',\n",
       " 'Others',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " 'statistics',\n",
       " 'Data',\n",
       " 'Mining',\n",
       " 'Domain',\n",
       " 'Retail',\n",
       " 'Academic',\n",
       " 'Projects',\n",
       " 'Project',\n",
       " 'Breast',\n",
       " 'Cancer',\n",
       " 'Prediction',\n",
       " 'Technolgy',\n",
       " 'Used',\n",
       " 'Python',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " 'Algorithms',\n",
       " 'P',\n",
       " 'roject',\n",
       " 'Description',\n",
       " 'Built',\n",
       " 'model',\n",
       " 'good',\n",
       " 'accuracy',\n",
       " 'score',\n",
       " 'whether',\n",
       " 'person',\n",
       " 'effected',\n",
       " 'cancer',\n",
       " 'Project',\n",
       " 'IMDB',\n",
       " 'Recommendation',\n",
       " 'System',\n",
       " 'Technolgy',\n",
       " 'Used',\n",
       " 'Python',\n",
       " 'Machine',\n",
       " 'Learning',\n",
       " 'Algorithms',\n",
       " 'P',\n",
       " 'roject',\n",
       " 'Description',\n",
       " 'Scrapped',\n",
       " 'top',\n",
       " '250',\n",
       " 'IMDB',\n",
       " 'movies',\n",
       " 'build',\n",
       " 'recommendation',\n",
       " 'sytem',\n",
       " 'based',\n",
       " 'plot',\n",
       " 'movie',\n",
       " '.',\n",
       " 'Project',\n",
       " 'Dashboard',\n",
       " 'Creation',\n",
       " 'IPL',\n",
       " 'dataset',\n",
       " '2008',\n",
       " '-',\n",
       " '2016',\n",
       " 'T',\n",
       " 'echnolgy',\n",
       " 'Used',\n",
       " 'Tableau',\n",
       " 'P',\n",
       " 'roject',\n",
       " 'Description',\n",
       " 'Built',\n",
       " 'dashboard',\n",
       " 'IPL',\n",
       " 'data',\n",
       " 'set',\n",
       " 'help',\n",
       " 'KPI',\n",
       " 'boxes',\n",
       " 'bar',\n",
       " 'graph',\n",
       " 'Phanikrishna',\n",
       " 'Velicheti',\n",
       " '401',\n",
       " 'G',\n",
       " '-',\n",
       " 'block',\n",
       " 'Brigade',\n",
       " 'Metropolis',\n",
       " 'Garudachar',\n",
       " 'Palya',\n",
       " 'Mahadevapura',\n",
       " 'Bengaluru',\n",
       " 'Karnataka',\n",
       " '-',\n",
       " '560048',\n",
       " 'PHONE',\n",
       " '+91',\n",
       " '9959676104',\n",
       " 'Mail',\n",
       " 'velicheti2phani',\n",
       " '@',\n",
       " 'gmail.com',\n",
       " 'Work',\n",
       " 'History',\n",
       " 'Tata',\n",
       " 'Consultancy',\n",
       " 'Services',\n",
       " 'O',\n",
       " 'ct2016',\n",
       " 'Oct2018',\n",
       " 'Project',\n",
       " 'Morrisons',\n",
       " 'Business',\n",
       " 'I',\n",
       " 'ntelligence',\n",
       " 'Department',\n",
       " 'Technology',\n",
       " 'Used',\n",
       " 'Google',\n",
       " 'Cloud',\n",
       " 'Platform',\n",
       " 'BigQuery',\n",
       " 'Dataflow',\n",
       " 'Pub/Sub',\n",
       " 'Project',\n",
       " 'Description',\n",
       " 'Morrison',\n",
       " 'leading',\n",
       " 'chain',\n",
       " 'supermarkets',\n",
       " 'United',\n",
       " 'Kingdom',\n",
       " 'also',\n",
       " 'trading',\n",
       " 'grocery',\n",
       " 'items',\n",
       " 'via',\n",
       " 'web',\n",
       " 'shop',\n",
       " 'Ocado',\n",
       " 'M.Com',\n",
       " 'grocery',\n",
       " 'channel',\n",
       " 'since',\n",
       " '2014',\n",
       " '.',\n",
       " 'Roles',\n",
       " 'Responsibilities',\n",
       " 'Requirement',\n",
       " 'analysis',\n",
       " 'Application',\n",
       " 'esign',\n",
       " 'maintaining',\n",
       " 'design',\n",
       " 'specification',\n",
       " 'test',\n",
       " 'case',\n",
       " 'specification',\n",
       " 'per',\n",
       " 'new',\n",
       " 'changes',\n",
       " '.',\n",
       " 'Development',\n",
       " 'coding',\n",
       " 'new',\n",
       " 'functionality',\n",
       " 'incorporation',\n",
       " '.',\n",
       " 'Analysis',\n",
       " 'codes',\n",
       " 'jobs',\n",
       " 'AWS',\n",
       " 'Google',\n",
       " 'Cloud',\n",
       " 'Platform',\n",
       " 'understand',\n",
       " 'new',\n",
       " 'application',\n",
       " '.',\n",
       " 'Internship',\n",
       " 'Kopenz',\n",
       " 'Private',\n",
       " 'Limited',\n",
       " 'Bombay',\n",
       " 'Inayo',\n",
       " 'Jan',\n",
       " '2016',\n",
       " 'June',\n",
       " '2016',\n",
       " 'Technology',\n",
       " 'Used',\n",
       " 'Python',\n",
       " 'Django',\n",
       " 'Project',\n",
       " 'Description',\n",
       " 'Distribution',\n",
       " 'medicines',\n",
       " 'app',\n",
       " 'within',\n",
       " '90',\n",
       " 'mins',\n",
       " 'throughout',\n",
       " 'Bombay',\n",
       " 'city',\n",
       " 'Roles',\n",
       " 'Responsibilities',\n",
       " 'Worked',\n",
       " 'python',\n",
       " 'backend',\n",
       " 'developer',\n",
       " 'structuring',\n",
       " 'database',\n",
       " 'using',\n",
       " 'mysql',\n",
       " '.',\n",
       " 'Education',\n",
       " 'Post',\n",
       " '-',\n",
       " 'Graduation',\n",
       " 'Pursing',\n",
       " 'Post',\n",
       " 'Graduation',\n",
       " 'diploma',\n",
       " 'data',\n",
       " 'science',\n",
       " 'form',\n",
       " 'Manipal',\n",
       " 'ProLearn',\n",
       " '-',\n",
       " '9.125',\n",
       " 'Till',\n",
       " 'date',\n",
       " 'Post',\n",
       " '-',\n",
       " 'Graduation',\n",
       " 'Masters',\n",
       " 'science',\n",
       " 'IT',\n",
       " 'CGPA',\n",
       " '8.3',\n",
       " 'Jawaharlal',\n",
       " 'Nehru',\n",
       " 'Technological',\n",
       " 'University',\n",
       " 'Kakinada',\n",
       " 'JNTUK',\n",
       " 'Graduation',\n",
       " 'B.Tech',\n",
       " '68',\n",
       " '%',\n",
       " 'Jawaharlal',\n",
       " 'Nehru',\n",
       " 'Technological',\n",
       " 'University',\n",
       " 'Hyderabad',\n",
       " 'JNTUH',\n",
       " 'XII',\n",
       " 'th',\n",
       " 'Higher',\n",
       " 'Se',\n",
       " 'condary',\n",
       " 'Education',\n",
       " 'Andra',\n",
       " 'Pradesh',\n",
       " '1',\n",
       " '2',\n",
       " 'th',\n",
       " 'score',\n",
       " '83.5',\n",
       " '%',\n",
       " '.',\n",
       " 'X',\n",
       " 'th',\n",
       " 'Secondary',\n",
       " 'Education',\n",
       " '-',\n",
       " 'Andra',\n",
       " 'Pradesh',\n",
       " '10',\n",
       " 'th',\n",
       " 'score',\n",
       " '76',\n",
       " '%',\n",
       " '.']"
      ]
     },
     "execution_count": 24,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jflF1nFuJiWg"
   },
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "d-5mghnJJr3Q"
   },
   "outputs": [],
   "source": [
    "#particularly I want an email from the resume\n",
    "emails = re.findall(r\"[a-z0-9\\.\\-+]+@[a-z0-9\\.\\-+]+\\.[a-z]+\", text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "5HYbeBAYJzrw",
    "outputId": "34ea4fc1-2fe6-49b2-a1b3-b41f522b9d5d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['velicheti2phani@gmail.com']"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gGcXsj-MVXjE"
   },
   "outputs": [],
   "source": [
    "pdfFileObj = open('resume.pdf', 'rb')\n",
    "pdfReader = PyPDF2.PdfFileReader(pdfFileObj)\n",
    "\n",
    "#print(pdfReader.numPages)\n",
    "\n",
    "pageObj1 = pdfReader.getPage(0)\n",
    "pagecontent = pageObj1.extractText()\n",
    "temp = []\n",
    "\n",
    "def between(value, a, b):\n",
    "    # Find and validate before-part.\n",
    "    pos_a = value.find(a)\n",
    "    if pos_a == -1: return \"\"\n",
    "    # Find and validate after part.\n",
    "    pos_b = value.rfind(b)\n",
    "    if pos_b == -1: return \"\"\n",
    "    # Return middle part.\n",
    "    adjusted_pos_a = pos_a + len(a)\n",
    "    if adjusted_pos_a >= pos_b: return \"\"\n",
    "    return value[adjusted_pos_a:pos_b]\n",
    "\n",
    "desired = between(pagecontent,\"initiatives.\",\"Academic\")\n",
    "temp = between(pagecontent,\"initiatives.\",\"Academic\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 54
    },
    "colab_type": "code",
    "id": "UnrGd-_RXzGi",
    "outputId": "be52c628-d592-4183-b203-7ba47b4d6bb4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Technical Skills Set', 'Platforms', ': Google Clou', 'd Platf', 'orm, Unix, Windows', 'Languages ', 'Python,', 'SQL', 'Tools', 'Tableau', 'Database', ': O', 'racle database 11g, MySQl', 'Others: ', 'Machine Learning ,statistics,Data Mining', 'Domain: ', 'Retail']\n"
     ]
    }
   ],
   "source": [
    "temp = [k for k in temp.split('\\n') if len(k)>2]\n",
    "print(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "J8HgZqPZSxAk"
   },
   "outputs": [],
   "source": [
    "#Calculating Weightage\n",
    "#TF: Term Frequency, which measures how frequently a term occurs in a document. Since every document is different in length, it is possible that a term would appear much more times in long documents than shorter ones. Thus, the term frequency is often divided by the document length (aka. the total number of terms in the document) as a way of normalization:\n",
    "#TF(t) = (Number of times term t appears in a document) / (Total number of terms in the document).\n",
    "\n",
    "#IDF: Inverse Document Frequency, which measures how important a term is. While computing TF, all terms are considered equally important. However it is known that certain terms, such as \"is\", \"of\", and \"that\", may appear a lot of times but have little importance. Thus we need to weigh down the frequent terms while scale up the rare ones, by computing the following:\n",
    "#IDF(t) = log_e(Total number of documents / Number of documents with term t in it)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0IfjzjeyJ9qX"
   },
   "outputs": [],
   "source": [
    "from gensim.summarization import keywords\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BepIklfGSIJY"
   },
   "outputs": [],
   "source": [
    "values = keywords(text=text,split='\\n',scores=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "id": "xUG3D8xcSIFm",
    "outputId": "7542f967-0276-4120-9a0a-78e902b2f4d2"
   },
   "outputs": [
    {
     "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>keyword</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>projects</td>\n",
       "      <td>0.241617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>project</td>\n",
       "      <td>0.241617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>data</td>\n",
       "      <td>0.171524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>description</td>\n",
       "      <td>0.169763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>education</td>\n",
       "      <td>0.163987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>graduation</td>\n",
       "      <td>0.145214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>grocery</td>\n",
       "      <td>0.138845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>developer</td>\n",
       "      <td>0.133075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>development</td>\n",
       "      <td>0.133075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>technological</td>\n",
       "      <td>0.127024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          keyword     score\n",
       "0        projects  0.241617\n",
       "1         project  0.241617\n",
       "2            data  0.171524\n",
       "3     description  0.169763\n",
       "4       education  0.163987\n",
       "5      graduation  0.145214\n",
       "6         grocery  0.138845\n",
       "7       developer  0.133075\n",
       "8     development  0.133075\n",
       "10  technological  0.127024"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(values,columns=['keyword','score'])\n",
    "data = data.sort_values('score',ascending=False)\n",
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cahDB_EKSIBy"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Nt5RndR0SH6D"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "pdf_extraction.ipynb",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}