Resume_Parser.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NER based Resume parser and summarizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vtjgVlJ3Es9l",
    "outputId": "bbd35489-787d-40c8-b2cc-6f8f7791320f"
   },
   "outputs": [],
   "source": [
    "#!python -m spacy info\n",
    "#!pip install fitz\n",
    "#!pip install PyMuPDF\n",
    "#!pip install wordcloud\n",
    "#!pip install nltk\n",
    "#!pip install PyMuPDF==1.16.14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "3YDswYTMEs9n",
    "outputId": "337357fb-7396-4f55-ca28-cd8fdab2fd80"
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "import pickle\n",
    "import random\n",
    "import nltk\n",
    "# nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "import numpy as np\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "from PIL import Image\n",
    "from nltk.stem import PorterStemmer\n",
    "nlp_spacy = spacy.load('en_core_web_sm')\n",
    "stpwords = stopwords.words('english')\n",
    "import sys,fitz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "OgItpjHiEs9o"
   },
   "outputs": [],
   "source": [
    "train_data = pickle.load(open('train_data.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "deF4AywmEs9o"
   },
   "outputs": [],
   "source": [
    "nlp = spacy.blank('en')\n",
    "\n",
    "def train_model(train_data):\n",
    "    if 'ner' not in nlp.pipe_names:\n",
    "        ner = nlp.create_pipe('ner')\n",
    "        nlp.add_pipe(ner, last = True)\n",
    "        \n",
    "    for _, annotation in train_data:\n",
    "        for ent in annotation['entities']:\n",
    "            ner.add_label(ent[2])\n",
    "            \n",
    "    other_pipes = [pipe for pipe in nlp.pipe_names if pipe != 'ner']\n",
    "    with nlp.disable_pipes(*other_pipes):\n",
    "        optimizer = nlp.begin_training()\n",
    "        for itn in range(10):\n",
    "            print('Starting iteration ' + str(itn))\n",
    "            random.shuffle(train_data)\n",
    "            losses = {}\n",
    "            index = 0\n",
    "            for text, annotations in train_data:\n",
    "                try:\n",
    "                    nlp.update(\n",
    "                        [text],\n",
    "                        [annotations],\n",
    "                        drop=0.2,\n",
    "                        sgd = optimizer,\n",
    "                        losses = losses)\n",
    "                except Exception as e:\n",
    "                    pass\n",
    "            print(losses)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "74TBbl7zEs9p",
    "outputId": "c99f3214-cd45-4e0d-fb1f-49f87e3e96e2"
   },
   "outputs": [],
   "source": [
    "train_model(train_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "2osEgqvkEs9p"
   },
   "outputs": [],
   "source": [
    "nlp.to_disk('nlp_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading the train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "M3UpHnVfEs9p"
   },
   "outputs": [],
   "source": [
    "nlp_model = spacy.load('nlp_model')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PDF to text convert function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2_XHGRBREs9p",
    "outputId": "acedd8db-565d-4112-dba0-6f6ed1e09d90"
   },
   "outputs": [],
   "source": [
    "def pdf2text(input_resume):\n",
    "    doc = fitz.open(input_resume)\n",
    "    text = \"\"\n",
    "    for page in doc:\n",
    "        text = text+ str(page.getText())\n",
    "    tx = \" \".join(text.split('\\n'))\n",
    "    return tx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing Skills and skill score function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def candidateOnlyUniMatching(required_skills,updated_tokens_list):\n",
    "    list_skills = required_skills.lower().split(\",\")\n",
    "    found_skills_each_resume = []\n",
    "    score = 0\n",
    "    for x in list_skills:\n",
    "        l = [w for w in x.split()]\n",
    "        li = []\n",
    "        t = tuple(l)\n",
    "        li.append(t)\n",
    "        if x in updated_tokens_list:\n",
    "            score += 1\n",
    "            found_skills_each_resume.append(x)\n",
    "\n",
    "    req_skills_len = len(list_skills)\n",
    "    match = round(score / req_skills_len * 100, 1)\n",
    "    \n",
    "    return found_skills_each_resume,match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def candidateMatching(required_skills,updated_tokens_list,bigrams):\n",
    "    list_skills = required_skills.lower().split(\",\")\n",
    "    found_skills_each_resume = []\n",
    "    score = 0\n",
    "    for x in list_skills:\n",
    "        if len(x.split()) > 1:\n",
    "            l = [w for w in x.split()]\n",
    "            li = []\n",
    "            t = tuple(l)\n",
    "            li.append(t)\n",
    "            if li[0] in bigrams:\n",
    "                score+=1\n",
    "                found_skills_each_resume.append(' '.join(l))\n",
    "        else:\n",
    "            if x in updated_tokens_list:\n",
    "                score += 1\n",
    "                found_skills_each_resume.append(x)\n",
    "\n",
    "    req_skills_len = len(list_skills)\n",
    "    match = round(score / req_skills_len * 100, 1)\n",
    "    \n",
    "    return found_skills_each_resume,match"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bi-grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bigramGenerator(text):\n",
    "    list = []\n",
    "    list.append(text.lower())\n",
    "    bigrams = [b for l in list for b in zip(l.split(\" \")[:-1], l.split(\" \")[1:])]\n",
    "    return bigrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Porter stemmer function for stemming "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def stemming_tokenizer(tokens_list):\n",
    "    rootWord = []\n",
    "    tokens = [x.lower() for xs in tokens_list for x in xs.split(',')]\n",
    "    ps = PorterStemmer()\n",
    "    for w in tokens_list:\n",
    "        if w not in stpwords:\n",
    "            rootWord.append(ps.stem(w))\n",
    "    return rootWord"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lemmatization function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemma_tokenizer(tokens_list):\n",
    "    rootWord = []\n",
    "    for token in tokens_list:\n",
    "        if token.lemma_ not in stpwords:\n",
    "            rootWord.append(token.lemma_)\n",
    "    return [x.lower() for xs in rootWord for x in xs.split(',')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entity extraction function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def entity_extraction(resume, required_skills):\n",
    "    test=pdf2text(resume)\n",
    "    doc = nlp_model(test)\n",
    "    l=[]\n",
    "    s=[]\n",
    "    x={}\n",
    "    y={}\n",
    "    for ent in doc.ents:\n",
    "        if ent.label_.upper() not in l:\n",
    "            l.append(ent.label_.upper())\n",
    "            print(f'{ent.label_.upper():{30}}- {ent.text}')\n",
    "            x[ent.label_.upper()]=ent.text\n",
    "            y[ent.label_.upper()]=ent.text\n",
    "        text_list=[test]\n",
    "        tokens_list=' '.join(text_list).split()\n",
    "        \n",
    "#     updated_tokens_list= [x.lower() for xs in tokens_list for x in xs.split(',')]\n",
    "#     updated_tokens_list= stemming_tokenizer(tokens_list)\n",
    "    updated_tokens_list=lemma_tokenizer(doc)\n",
    "    final_token_list.extend(updated_tokens_list)\n",
    "    bigrams.extend(bigramGenerator(test))\n",
    "    x['Skills'],x['Match']=candidateMatching(required_skills,updated_tokens_list,bigrams)\n",
    "    y['Skills'],y['Match']=candidateOnlyUniMatching(required_skills,updated_tokens_list)\n",
    "    extractedDetails[i]=x\n",
    "    extractedDetailsUni[i]=y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "extractedDetails={} \n",
    "extractedDetailsUni={} \n",
    "final_token_list=[]\n",
    "found_skills = {}\n",
    "bigrams=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding resume documents path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['E:/Book 3/SEMESTER 5/CSE4022 - NATURAL LANGUAGE PROCESSING/J COMP/resumes\\\\Alice Clark CV.pdf', 'E:/Book 3/SEMESTER 5/CSE4022 - NATURAL LANGUAGE PROCESSING/J COMP/resumes\\\\ara.pdf', 'E:/Book 3/SEMESTER 5/CSE4022 - NATURAL LANGUAGE PROCESSING/J COMP/resumes\\\\dd.pdf', 'E:/Book 3/SEMESTER 5/CSE4022 - NATURAL LANGUAGE PROCESSING/J COMP/resumes\\\\Hash.pdf', 'E:/Book 3/SEMESTER 5/CSE4022 - NATURAL LANGUAGE PROCESSING/J COMP/resumes\\\\Narendra.pdf']\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "path = \"E:/Book 3/SEMESTER 5/CSE4022 - NATURAL LANGUAGE PROCESSING/J COMP/resumes\"\n",
    "  \n",
    "os.chdir(path)\n",
    "file_path=[]\n",
    "for file in os.listdir():\n",
    "    file_path.append(f\"{path}\\{file}\")\n",
    "print(file_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining requirement skills and cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "skill_set='engineering,python,NLP,API,HTML,express,Machine learning'\n",
    "cutoff = 25.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calling the entity extraction function "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME                          - Alice Clark\n",
      "LOCATION                      - Delhi\n",
      "DESIGNATION                   - Software Engineer\n",
      "COMPANIES WORKED AT           - Microsoft\n",
      "DEGREE                        - Indian Institute of Technology – Mumbai\n",
      "NAME                          - B ARAVINDA\n",
      "DEGREE                        - B. Tech Student I'm currently pursuing, B. Tech in Computer Science at VIT. Interested in the ﬁeld of Computer Science and Technologies. Enthusiastic Learner in the ﬁeld of Data Science, Web and App Development. aravinda992@gmail.com +919840280874 Chennai\n",
      "NAME                          - Divya shree\n",
      "DESIGNATION                   - Score B.Tech\n",
      "NAME                          - Hashwanth S\n",
      "DESIGNATION                   - +91-8248336430 | hashwanth2002@gmail.com | https://www.linkedin.com/in/stykerhash/ |\n",
      "DEGREE                        - Institute of Technology\n",
      "NAME                          - Narendra G\n",
      "COLLEGE NAME                  - Vellore Institute of Technology, Chennai\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(file_path)):\n",
    "    entity_extraction(file_path[i],skill_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bi gram chunks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('alice', 'clark'),\n",
       " ('clark', ''),\n",
       " ('', 'ai'),\n",
       " ('ai', '/'),\n",
       " ('/', 'machine'),\n",
       " ('machine', 'learning'),\n",
       " ('learning', ''),\n",
       " ('', ''),\n",
       " ('', ''),\n",
       " ('', 'delhi,'),\n",
       " ('delhi,', 'india'),\n",
       " ('india', 'email'),\n",
       " ('email', 'me'),\n",
       " ('me', 'on'),\n",
       " ('on', 'indeed'),\n",
       " ('indeed', ''),\n",
       " ('', '•'),\n",
       " ('•', ''),\n",
       " ('', '20+'),\n",
       " ('20+', 'years'),\n",
       " ('years', 'of'),\n",
       " ('of', 'experience'),\n",
       " ('experience', 'in'),\n",
       " ('in', 'data'),\n",
       " ('data', 'handling,'),\n",
       " ('handling,', 'design,'),\n",
       " ('design,', 'and'),\n",
       " ('and', 'development'),\n",
       " ('development', ''),\n",
       " ('', '•'),\n",
       " ('•', ''),\n",
       " ('', 'data'),\n",
       " ('data', 'warehouse:'),\n",
       " ('warehouse:', 'data'),\n",
       " ('data', 'analysis,'),\n",
       " ('analysis,', 'star/snow'),\n",
       " ('star/snow', 'flake'),\n",
       " ('flake', 'scema'),\n",
       " ('scema', 'data'),\n",
       " ('data', 'modelling'),\n",
       " ('modelling', 'and'),\n",
       " ('and', 'design'),\n",
       " ('design', 'specific'),\n",
       " ('specific', 'to'),\n",
       " ('to', ''),\n",
       " ('', 'data'),\n",
       " ('data', 'warehousing'),\n",
       " ('warehousing', 'and'),\n",
       " ('and', 'business'),\n",
       " ('business', 'intelligence'),\n",
       " ('intelligence', ''),\n",
       " ('', '•'),\n",
       " ('•', ''),\n",
       " ('', 'database:'),\n",
       " ('database:', 'experience'),\n",
       " ('experience', 'in'),\n",
       " ('in', 'database'),\n",
       " ('database', 'designing,'),\n",
       " ('designing,', 'scalability,'),\n",
       " ('scalability,', 'back-up'),\n",
       " ('back-up', 'and'),\n",
       " ('and', 'recovery,'),\n",
       " ('recovery,', 'writing'),\n",
       " ('writing', 'and'),\n",
       " ('and', ''),\n",
       " ('', 'optimizing'),\n",
       " ('optimizing', 'sql'),\n",
       " ('sql', 'code'),\n",
       " ('code', 'and'),\n",
       " ('and', 'stored'),\n",
       " ('stored', 'procedures,'),\n",
       " ('procedures,', 'creating'),\n",
       " ('creating', 'functions,'),\n",
       " ('functions,', 'views,'),\n",
       " ('views,', 'triggers'),\n",
       " ('triggers', 'and'),\n",
       " ('and', 'indexes.'),\n",
       " ('indexes.', ''),\n",
       " ('', 'cloud'),\n",
       " ('cloud', 'platform:'),\n",
       " ('platform:', 'worked'),\n",
       " ('worked', 'on'),\n",
       " ('on', 'microsoft'),\n",
       " ('microsoft', 'azure'),\n",
       " ('azure', 'cloud'),\n",
       " ('cloud', 'services'),\n",
       " ('services', 'like'),\n",
       " ('like', 'document'),\n",
       " ('document', 'db,'),\n",
       " ('db,', 'sql'),\n",
       " ('sql', 'azure,'),\n",
       " ('azure,', ''),\n",
       " ('', 'stream'),\n",
       " ('stream', 'analytics,'),\n",
       " ('analytics,', 'event'),\n",
       " ('event', 'hub,'),\n",
       " ('hub,', 'power'),\n",
       " ('power', 'bi,'),\n",
       " ('bi,', 'web'),\n",
       " ('web', 'job,'),\n",
       " ('job,', 'web'),\n",
       " ('web', 'app,'),\n",
       " ('app,', 'power'),\n",
       " ('power', 'bi,'),\n",
       " ('bi,', 'azure'),\n",
       " ('azure', 'data'),\n",
       " ('data', 'lake'),\n",
       " ('lake', ''),\n",
       " ('', 'analytics(u-sql)'),\n",
       " ('analytics(u-sql)', ''),\n",
       " ('', 'willing'),\n",
       " ('willing', 'to'),\n",
       " ('to', 'relocate'),\n",
       " ('relocate', 'anywhere'),\n",
       " ('anywhere', ''),\n",
       " ('', ''),\n",
       " ('', ''),\n",
       " ('', 'work'),\n",
       " ('work', 'experience'),\n",
       " ('experience', ''),\n",
       " ('', 'software'),\n",
       " ('software', 'engineer'),\n",
       " ('engineer', ''),\n",
       " ('', 'microsoft'),\n",
       " ('microsoft', '–'),\n",
       " ('–', 'bangalore,'),\n",
       " ('bangalore,', 'karnataka'),\n",
       " ('karnataka', ''),\n",
       " ('', 'january'),\n",
       " ('january', '2000'),\n",
       " ('2000', 'to'),\n",
       " ('to', 'present'),\n",
       " ('present', ''),\n",
       " ('', '1.'),\n",
       " ('1.', 'microsoft'),\n",
       " ('microsoft', 'rewards'),\n",
       " ('rewards', 'live'),\n",
       " ('live', 'dashboards:'),\n",
       " ('dashboards:', ''),\n",
       " ('', 'description:'),\n",
       " ('description:', '-'),\n",
       " ('-', 'microsoft'),\n",
       " ('microsoft', 'rewards'),\n",
       " ('rewards', 'is'),\n",
       " ('is', 'loyalty'),\n",
       " ('loyalty', 'program'),\n",
       " ('program', 'that'),\n",
       " ('that', 'rewards'),\n",
       " ('rewards', 'users'),\n",
       " ('users', 'for'),\n",
       " ('for', 'browsing'),\n",
       " ('browsing', 'and'),\n",
       " ('and', 'shopping'),\n",
       " ('shopping', ''),\n",
       " ('', 'online.'),\n",
       " ('online.', 'microsoft'),\n",
       " ('microsoft', 'rewards'),\n",
       " ('rewards', 'members'),\n",
       " ('members', 'can'),\n",
       " ('can', 'earn'),\n",
       " ('earn', 'points'),\n",
       " ('points', 'when'),\n",
       " ('when', 'searching'),\n",
       " ('searching', 'with'),\n",
       " ('with', 'bing,'),\n",
       " ('bing,', 'browsing'),\n",
       " ('browsing', 'with'),\n",
       " ('with', ''),\n",
       " ('', 'microsoft'),\n",
       " ('microsoft', 'edge'),\n",
       " ('edge', 'and'),\n",
       " ('and', 'making'),\n",
       " ('making', 'purchases'),\n",
       " ('purchases', 'at'),\n",
       " ('at', 'the'),\n",
       " ('the', 'xbox'),\n",
       " ('xbox', 'store,'),\n",
       " ('store,', 'the'),\n",
       " ('the', 'windows'),\n",
       " ('windows', 'store'),\n",
       " ('store', 'and'),\n",
       " ('and', 'the'),\n",
       " ('the', 'microsoft'),\n",
       " ('microsoft', ''),\n",
       " ('', 'store.'),\n",
       " ('store.', 'plus,'),\n",
       " ('plus,', 'user'),\n",
       " ('user', 'can'),\n",
       " ('can', 'pick'),\n",
       " ('pick', 'up'),\n",
       " ('up', 'bonus'),\n",
       " ('bonus', 'points'),\n",
       " ('points', 'for'),\n",
       " ('for', 'taking'),\n",
       " ('taking', 'daily'),\n",
       " ('daily', 'quizzes'),\n",
       " ('quizzes', 'and'),\n",
       " ('and', 'tours'),\n",
       " ('tours', 'on'),\n",
       " ('on', 'the'),\n",
       " ('the', 'microsoft'),\n",
       " ('microsoft', ''),\n",
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       " ('certificates', '-'),\n",
       " ('-', 'artiﬁcial'),\n",
       " ('artiﬁcial', 'intelligence'),\n",
       " ('intelligence', 'foundation-'),\n",
       " ('foundation-', 'nasscom'),\n",
       " ('nasscom', '(08/2021)'),\n",
       " ('(08/2021)', '-'),\n",
       " ('-', 'using'),\n",
       " ('using', 'python'),\n",
       " ('python', 'to'),\n",
       " ('to', 'access'),\n",
       " ('access', 'web'),\n",
       " ('web', 'data-'),\n",
       " ('data-', 'coursera'),\n",
       " ('coursera', '(07/2020)'),\n",
       " ('(07/2020)', '-'),\n",
       " ('-', 'machine'),\n",
       " ('machine', 'learning'),\n",
       " ('learning', 'using'),\n",
       " ('using', 'python'),\n",
       " ('python', '-'),\n",
       " ('-', 'vitol'),\n",
       " ('vitol', 'course'),\n",
       " ('course', '(06/2020)'),\n",
       " ('(06/2020)', '-'),\n",
       " ('-', 'introduction'),\n",
       " ('introduction', 'to'),\n",
       " ('to', 'statistics'),\n",
       " ('statistics', '&'),\n",
       " ('&', 'data'),\n",
       " ('data', 'analysis'),\n",
       " ('analysis', 'in'),\n",
       " ('in', 'public'),\n",
       " ('public', 'health'),\n",
       " ('health', ''),\n",
       " ('', '(06/2020)'),\n",
       " ('(06/2020)', '-'),\n",
       " ('-', 'using'),\n",
       " ('using', 'databases'),\n",
       " ('databases', 'with'),\n",
       " ('with', 'python-'),\n",
       " ('python-', 'coursera'),\n",
       " ('coursera', '(05/2020)'),\n",
       " ('(05/2020)', 'achievements'),\n",
       " ('achievements', '-'),\n",
       " ('-', 'won'),\n",
       " ('won', 'the'),\n",
       " ('the', 'arch-a-thon'),\n",
       " ('arch-a-thon', 'event'),\n",
       " ('event', 'conducted'),\n",
       " ('conducted', 'by'),\n",
       " ('by', 'the'),\n",
       " ('the', 'vit'),\n",
       " ('vit', 'chennai'),\n",
       " ('chennai', 'for'),\n",
       " ('for', 'the'),\n",
       " ('the', 'category'),\n",
       " ('category', '“most'),\n",
       " ('“most', 'nominated”'),\n",
       " ('nominated”', '(09/2020)'),\n",
       " ('(09/2020)', '-'),\n",
       " ('-', 'overall'),\n",
       " ('overall', 'school'),\n",
       " ('school', '3rd'),\n",
       " ('3rd', 'in'),\n",
       " ('in', 'international'),\n",
       " ('international', 'science'),\n",
       " ('science', 'olympiad'),\n",
       " ('olympiad', ''),\n",
       " ('', '(01/2018)'),\n",
       " ('(01/2018)', 'languages'),\n",
       " ('languages', 'english'),\n",
       " ('english', 'professional'),\n",
       " ('professional', 'working'),\n",
       " ('working', 'proﬁciency'),\n",
       " ('proﬁciency', 'telugu'),\n",
       " ('telugu', 'native'),\n",
       " ('native', 'or'),\n",
       " ('or', 'bilingual'),\n",
       " ('bilingual', 'proﬁciency'),\n",
       " ('proﬁciency', 'tamil'),\n",
       " ('tamil', 'full'),\n",
       " ('full', 'professional'),\n",
       " ('professional', 'proﬁciency'),\n",
       " ('proﬁciency', 'hindi'),\n",
       " ('hindi', 'professional'),\n",
       " ('professional', 'working'),\n",
       " ('working', 'proﬁciency'),\n",
       " ('proﬁciency', 'courses'),\n",
       " ('courses', 'achievements/tasks'),\n",
       " ('achievements/tasks', ''),\n",
       " ('divya', 'shree'),\n",
       " ('shree', 's'),\n",
       " ('s', 'women'),\n",
       " ('women', 'of'),\n",
       " ('of', 'action,'),\n",
       " ('action,', 'innovative'),\n",
       " ('innovative', 'thinker,'),\n",
       " ('thinker,', 'veracious'),\n",
       " ('veracious', 'person,'),\n",
       " ('person,', 'smart'),\n",
       " ('smart', 'worker'),\n",
       " ('worker', '169'),\n",
       " ('169', 'kambar'),\n",
       " ('kambar', 'street'),\n",
       " ('street', 'chennai,'),\n",
       " ('chennai,', 'tn'),\n",
       " ('tn', '600072'),\n",
       " ('600072', 'https://www.linkedin.com/in/'),\n",
       " ('https://www.linkedin.com/in/', 'divyashree-s-463187191/'),\n",
       " ('divyashree-s-463187191/', 'education'),\n",
       " ('education', 'degree/grade'),\n",
       " ('degree/grade', 'institution'),\n",
       " ('institution', 'score'),\n",
       " ('score', 'b.tech'),\n",
       " ('b.tech', 'cse'),\n",
       " ('cse', 'vellore'),\n",
       " ('vellore', 'institute'),\n",
       " ('institute', 'of'),\n",
       " ('of', 'technology-'),\n",
       " ('technology-', 'chennai'),\n",
       " ('chennai', '8.56/10'),\n",
       " ('8.56/10', 'cgpa'),\n",
       " ('cgpa', '(2021)'),\n",
       " ('(2021)', 'higher'),\n",
       " ('higher', 'secondary'),\n",
       " ('secondary', 'school'),\n",
       " ('school', 'd.a.v'),\n",
       " ('d.a.v', 'matriculation'),\n",
       " ('matriculation', 'higher'),\n",
       " ('higher', 'secondary'),\n",
       " ('secondary', 'school(state'),\n",
       " ('school(state', 'board)'),\n",
       " ('board)', '84%'),\n",
       " ('84%', 'secondary'),\n",
       " ('secondary', 'school'),\n",
       " ('school', 'immanuel'),\n",
       " ('immanuel', 'matriculation'),\n",
       " ('matriculation', 'higher'),\n",
       " ('higher', 'secondary'),\n",
       " ('secondary', 'school(state'),\n",
       " ('school(state', 'board)'),\n",
       " ('board)', '97%'),\n",
       " ('97%', 'projects'),\n",
       " ('projects', 'health'),\n",
       " ('health', 'and'),\n",
       " ('and', 'diet'),\n",
       " ('diet', 'analysis—'),\n",
       " ('analysis—', 'the'),\n",
       " ('the', 'project'),\n",
       " ('project', 'is'),\n",
       " ('is', 'about'),\n",
       " ('about', 'analyzing'),\n",
       " ('analyzing', 'the'),\n",
       " ('the', 'health'),\n",
       " ('health', 'condition'),\n",
       " ('condition', 'of'),\n",
       " ('of', 'people'),\n",
       " ('people', 'and'),\n",
       " ('and', 'to'),\n",
       " ('to', 'create'),\n",
       " ('create', 'awareness'),\n",
       " ('awareness', 'about'),\n",
       " ('about', 'ﬁtness.'),\n",
       " ('ﬁtness.', 'this'),\n",
       " ('this', 'also'),\n",
       " ('also', 'helps'),\n",
       " ('helps', 'to'),\n",
       " ('to', 'predict'),\n",
       " ('predict', 'the'),\n",
       " ('the', 'possibility'),\n",
       " ('possibility', 'of'),\n",
       " ('of', 'getting'),\n",
       " ('getting', 'cardiac'),\n",
       " ('cardiac', 'and'),\n",
       " ('and', 'diabetes'),\n",
       " ('diabetes', 'disease.'),\n",
       " ('disease.', 'vitmeet(video'),\n",
       " ('vitmeet(video', 'conferencing'),\n",
       " ('conferencing', 'app)-'),\n",
       " ('app)-', 'it’s'),\n",
       " ('it’s', 'an'),\n",
       " ('an', 'online'),\n",
       " ('online', 'learning'),\n",
       " ('learning', 'platform'),\n",
       " ('platform', 'which'),\n",
       " ('which', 'helps'),\n",
       " ('helps', 'people'),\n",
       " ('people', 'to'),\n",
       " ('to', 'attend'),\n",
       " ('attend', 'their'),\n",
       " ('their', 'meetings'),\n",
       " ('meetings', 'and'),\n",
       " ('and', 'share'),\n",
       " ('share', 'their'),\n",
       " ('their', 'ﬁles'),\n",
       " ('ﬁles', 'and'),\n",
       " ('and', 'documents.'),\n",
       " ('documents.', 'online'),\n",
       " ('online', 'music'),\n",
       " ('music', 'system-'),\n",
       " ('system-', 'the'),\n",
       " ('the', 'objective'),\n",
       " ('objective', 'of'),\n",
       " ('of', 'this'),\n",
       " ('this', 'project'),\n",
       " ('project', 'is'),\n",
       " ('is', 'to'),\n",
       " ('to', 'create'),\n",
       " ('create', 'music'),\n",
       " ('music', 'apps'),\n",
       " ('apps', 'like'),\n",
       " ('like', 'spotify,'),\n",
       " ('spotify,', 'jiosaavan'),\n",
       " ('jiosaavan', 'etc.'),\n",
       " ('etc.', ''),\n",
       " ('', 'the'),\n",
       " ('the', 'project'),\n",
       " ('project', 'includes'),\n",
       " ('includes', 'playlists'),\n",
       " ('playlists', 'and'),\n",
       " ('and', 'albums'),\n",
       " ...]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigrams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracted entities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'NAME': 'Alice Clark',\n",
       "  'LOCATION': 'Delhi',\n",
       "  'DESIGNATION': 'Software Engineer',\n",
       "  'COMPANIES WORKED AT': 'Microsoft',\n",
       "  'DEGREE': 'Indian Institute of Technology – Mumbai',\n",
       "  'Skills': ['machine learning'],\n",
       "  'Match': 14.3},\n",
       " 1: {'NAME': 'B ARAVINDA',\n",
       "  'DEGREE': \"B. Tech Student I'm currently pursuing, B. Tech in Computer Science at VIT. Interested in the ﬁeld of Computer Science and Technologies. Enthusiastic Learner in the ﬁeld of Data Science, Web and App Development. aravinda992@gmail.com +919840280874 Chennai\",\n",
       "  'Skills': ['engineering', 'python', 'html', 'machine learning'],\n",
       "  'Match': 57.1},\n",
       " 2: {'NAME': 'Divya shree',\n",
       "  'DESIGNATION': 'Score B.Tech',\n",
       "  'Skills': ['python', 'html', 'machine learning'],\n",
       "  'Match': 42.9},\n",
       " 3: {'NAME': 'Hashwanth S',\n",
       "  'DESIGNATION': '+91-8248336430 | hashwanth2002@gmail.com | https://www.linkedin.com/in/stykerhash/ |',\n",
       "  'DEGREE': 'Institute of Technology',\n",
       "  'Skills': ['engineering',\n",
       "   'python',\n",
       "   'api',\n",
       "   'html',\n",
       "   'express',\n",
       "   'machine learning'],\n",
       "  'Match': 85.7},\n",
       " 4: {'NAME': 'Narendra G',\n",
       "  'COLLEGE NAME': 'Vellore Institute of Technology, Chennai',\n",
       "  'Skills': ['engineering', 'python', 'html', 'machine learning'],\n",
       "  'Match': 57.1}}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extractedDetails"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: {'NAME': 'Alice Clark',\n",
       "  'LOCATION': 'Delhi',\n",
       "  'DESIGNATION': 'Software Engineer',\n",
       "  'COMPANIES WORKED AT': 'Microsoft',\n",
       "  'DEGREE': 'Indian Institute of Technology – Mumbai',\n",
       "  'Skills': [],\n",
       "  'Match': 0.0},\n",
       " 1: {'NAME': 'B ARAVINDA',\n",
       "  'DEGREE': \"B. Tech Student I'm currently pursuing, B. Tech in Computer Science at VIT. Interested in the ﬁeld of Computer Science and Technologies. Enthusiastic Learner in the ﬁeld of Data Science, Web and App Development. aravinda992@gmail.com +919840280874 Chennai\",\n",
       "  'Skills': ['engineering', 'python', 'html'],\n",
       "  'Match': 42.9},\n",
       " 2: {'NAME': 'Divya shree',\n",
       "  'DESIGNATION': 'Score B.Tech',\n",
       "  'Skills': ['python', 'html'],\n",
       "  'Match': 28.6},\n",
       " 3: {'NAME': 'Hashwanth S',\n",
       "  'DESIGNATION': '+91-8248336430 | hashwanth2002@gmail.com | https://www.linkedin.com/in/stykerhash/ |',\n",
       "  'DEGREE': 'Institute of Technology',\n",
       "  'Skills': ['engineering', 'python', 'api', 'html', 'express'],\n",
       "  'Match': 71.4},\n",
       " 4: {'NAME': 'Narendra G',\n",
       "  'COLLEGE NAME': 'Vellore Institute of Technology, Chennai',\n",
       "  'Skills': ['engineering', 'python', 'html'],\n",
       "  'Match': 42.9}}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extractedDetailsUni"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cutoff checker and ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4o8te45EHL8V",
    "outputId": "7d781396-4b80-4092-cc97-bf30f049d9a9"
   },
   "outputs": [],
   "source": [
    "def cutoff_checker(extractedDetails, cutoff):\n",
    "    for i in range(len(extractedDetails)):\n",
    "        name = extractedDetails.get(i, {}).get('NAME')\n",
    "        match = extractedDetails.get(i, {}).get('Match')\n",
    "        if match>=cutoff:\n",
    "            ranking[name]=match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cutoff_checker_uni(extractedDetailsUni, cutoff):\n",
    "    for i in range(len(extractedDetailsUni)):\n",
    "        name = extractedDetailsUni.get(i, {}).get('NAME')\n",
    "        match = extractedDetailsUni.get(i, {}).get('Match')\n",
    "        if match>=cutoff:\n",
    "            rankingUni[name]=match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "ranking = {}\n",
    "rankingUni = {}\n",
    "cutoff_checker(extractedDetails, cutoff)\n",
    "cutoff_checker_uni(extractedDetailsUni, cutoff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B ARAVINDA': 57.1,\n",
       " 'Divya shree': 42.9,\n",
       " 'Hashwanth S': 85.7,\n",
       " 'Narendra G': 57.1}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'B ARAVINDA': 42.9,\n",
       " 'Divya shree': 28.6,\n",
       " 'Hashwanth S': 71.4,\n",
       " 'Narendra G': 42.9}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rankingUni"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### N grams Performance Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>Candidates</th>\n",
       "      <th>bi-gram skill score</th>\n",
       "      <th>skill score</th>\n",
       "      <th>% increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B ARAVINDA</td>\n",
       "      <td>57.1</td>\n",
       "      <td>42.9</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Divya shree</td>\n",
       "      <td>42.9</td>\n",
       "      <td>28.6</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hashwanth S</td>\n",
       "      <td>85.7</td>\n",
       "      <td>71.4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Narendra G</td>\n",
       "      <td>57.1</td>\n",
       "      <td>42.9</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Candidates  bi-gram skill score  skill score % increase\n",
       "0   B ARAVINDA                 57.1         42.9         33\n",
       "1  Divya shree                 42.9         28.6         49\n",
       "2  Hashwanth S                 85.7         71.4         20\n",
       "3   Narendra G                 57.1         42.9         33"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(list(ranking.items()))\n",
    "df.columns = ['Candidates', 'bi-gram skill score']\n",
    "df['skill score'] = list(rankingUni.values())\n",
    "df['% increase'] = ((df['bi-gram skill score'] - df['skill score']) /df['skill score'])*100\n",
    "df['% increase'] = df['% increase'].astype(int)\n",
    "df['% increase'] = df['% increase'].astype(str)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "On an average, 34 % increase in overall skill matching performance\n"
     ]
    }
   ],
   "source": [
    "Average_per_increase = statistics.mean(df['% increase'].astype(int))\n",
    "print(\"On an average, \" + str(math.ceil(Average_per_increase)) + \" % increase in overall skill matching performance\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Final shortlisted resume and score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B ARAVINDA - 57.1\n",
      "Divya shree - 42.9\n",
      "Hashwanth S - 85.7\n",
      "Narendra G - 57.1\n"
     ]
    }
   ],
   "source": [
    "for i in ranking:\n",
    "    print(i,'-',ranking.get(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "w8arseXBEs9u"
   },
   "outputs": [],
   "source": [
    "String = ' '.join([str(elem) for elem in final_token_list])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### word cloud for tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "XOhO9La6Es9u"
   },
   "outputs": [],
   "source": [
    "def plot_wordcloud(text,max_words=400, max_font_size=120, figure_size=(6.0,4.0), \n",
    "                   title = None, title_size=40, image_color=False):\n",
    "\n",
    "    wordcloud = WordCloud(background_color='white',\n",
    "                    stopwords = stpwords,\n",
    "                    max_words = max_words,\n",
    "                    max_font_size = max_font_size, \n",
    "                    random_state = 42)\n",
    "    wordcloud.generate(text)\n",
    "    \n",
    "    plt.figure(figsize=figure_size)\n",
    "    plt.imshow(wordcloud);\n",
    "    plt.title(title, fontdict={'size': title_size, 'color': 'green', \n",
    "                                  'verticalalignment': 'bottom'})\n",
    "    plt.axis('off');\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 257
    },
    "id": "CgbW7BoCEs9v",
    "outputId": "c39798e7-7d9f-471a-b3ff-3b3beb970c1b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "common_text = String\n",
    "plot_wordcloud(common_text,max_words=80, max_font_size=100, \n",
    "               title = 'TOKENS', title_size=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### visualizing the resume ranking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "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>Candidates</th>\n",
       "      <th>skill score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B ARAVINDA</td>\n",
       "      <td>57.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Divya shree</td>\n",
       "      <td>42.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Hashwanth S</td>\n",
       "      <td>85.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Narendra G</td>\n",
       "      <td>57.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Candidates  skill score\n",
       "0   B ARAVINDA         57.1\n",
       "1  Divya shree         42.9\n",
       "2  Hashwanth S         85.7\n",
       "3   Narendra G         57.1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(list(ranking.items()))\n",
    "df.columns = ['Candidates', 'skill score']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\SOFTWARE_INSTALLED\\ANACONDA\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot('skill score','Candidates',data = df, palette=\"viridis\")\n",
    "ax.set(xlabel='Score', ylabel='Candidates')\n",
    "plt.title(\"Candidates vs Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(df['skill score'],labels=df['Candidates'],autopct=\"%.1f%%\",explode=[0.02]*len(df))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapper = {}\n",
    "max_entity = 0\n",
    "for i in range(len(extractedDetails)):\n",
    "    entity = len(extractedDetails[i])-1\n",
    "    if entity > max_entity:\n",
    "        max_entity = entity\n",
    "    mapper[extractedDetails[i]['NAME']] = entity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "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>Candidates</th>\n",
       "      <th>Entities Found</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice Clark</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B ARAVINDA</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Divya shree</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hashwanth S</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Narendra G</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Candidates  Entities Found\n",
       "0  Alice Clark               6\n",
       "1   B ARAVINDA               3\n",
       "2  Divya shree               3\n",
       "3  Hashwanth S               4\n",
       "4   Narendra G               3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Entities_df = pd.DataFrame(list(mapper.items()))\n",
    "Entities_df.columns = ['Candidates', 'Entities Found']\n",
    "Entities_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max entities found :  6\n"
     ]
    }
   ],
   "source": [
    "print(\"Max entities found : \",max_entity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "Avg_entities = sum(Entities_df['Entities Found'])/len(df)\n",
    "math.floor(Avg_entities) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The model was able to find maximum 6 entities and an average of 4 entities per resume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarizer and skills dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alice Clark': ['Alice Clark',\n",
       "  'Delhi',\n",
       "  'Software Engineer',\n",
       "  'Microsoft',\n",
       "  'Indian Institute of Technology – Mumbai',\n",
       "  ['machine learning']],\n",
       " 'B ARAVINDA': ['B ARAVINDA',\n",
       "  \"B. Tech Student I'm currently pursuing, B. Tech in Computer Science at VIT. Interested in the ﬁeld of Computer Science and Technologies. Enthusiastic Learner in the ﬁeld of Data Science, Web and App Development. aravinda992@gmail.com +919840280874 Chennai\",\n",
       "  ['engineering', 'python', 'html', 'machine learning']],\n",
       " 'Divya shree': ['Divya shree',\n",
       "  'Score B.Tech',\n",
       "  ['python', 'html', 'machine learning']],\n",
       " 'Hashwanth S': ['Hashwanth S',\n",
       "  '+91-8248336430 | hashwanth2002@gmail.com | https://www.linkedin.com/in/stykerhash/ |',\n",
       "  'Institute of Technology',\n",
       "  ['engineering', 'python', 'api', 'html', 'express', 'machine learning']],\n",
       " 'Narendra G': ['Narendra G',\n",
       "  'Vellore Institute of Technology, Chennai',\n",
       "  ['engineering', 'python', 'html', 'machine learning']]}"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summarized = {}\n",
    "for i in range(len(extractedDetails)):\n",
    "    each_resume = []\n",
    "    for j in extractedDetails[i]:\n",
    "        if j!='Match':\n",
    "            each_resume.append(extractedDetails[i][j])\n",
    "    summarized[extractedDetails[i]['NAME']] = each_resume\n",
    "summarized"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataframe of candidates and their skills matched with requirement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "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>Candidates</th>\n",
       "      <th>Matched Skills</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alice Clark</td>\n",
       "      <td>machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B ARAVINDA</td>\n",
       "      <td>engineering,python,html,machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Divya shree</td>\n",
       "      <td>python,html,machine learning</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hashwanth S</td>\n",
       "      <td>engineering,python,api,html,express,machine le...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Narendra G</td>\n",
       "      <td>engineering,python,html,machine learning</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Candidates                                     Matched Skills\n",
       "0  Alice Clark                                   machine learning\n",
       "1   B ARAVINDA           engineering,python,html,machine learning\n",
       "2  Divya shree                       python,html,machine learning\n",
       "3  Hashwanth S  engineering,python,api,html,express,machine le...\n",
       "4   Narendra G           engineering,python,html,machine learning"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candidate_skills = {}\n",
    "for i in summarized:\n",
    "    skills = summarized[i][-1]\n",
    "    if skills != []:\n",
    "        candidate_skills[i] = \",\".join(summarized[i][-1])\n",
    "    else:\n",
    "        candidate_skills[i] = 'None'\n",
    "candidate_skills = pd.DataFrame(list(candidate_skills.items()))\n",
    "candidate_skills.columns = ['Candidates', 'Matched Skills']\n",
    "candidate_skills"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summarized resume of input candidate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B ARAVINDA\n",
      "B ARAVINDA,B. Tech Student I'm currently pursuing, B. Tech in Computer Science at VIT. Interested in the ﬁeld of Computer Science and Technologies. Enthusiastic Learner in the ﬁeld of Data Science, Web and App Development. aravinda992@gmail.com +919840280874 Chennai,['engineering', 'python', 'html', 'machine learning'],"
     ]
    }
   ],
   "source": [
    "candidate_name = input()\n",
    "length = len(summarized[candidate_name])\n",
    "candidate_details = []\n",
    "for i in range(length):\n",
    "    candidate_details.append(summarized[candidate_name][i])\n",
    "for text in candidate_details:\n",
    "    print(text, end=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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