CNN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbd6fa66",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('job_recommendations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "34afa915",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Job Opening Title</th>\n",
       "      <th>Description</th>\n",
       "      <th>Skills Needed</th>\n",
       "      <th>Level</th>\n",
       "      <th>Hiring Manager</th>\n",
       "      <th>Company Review</th>\n",
       "      <th>Estimated Salary (INR)</th>\n",
       "      <th>Job Location</th>\n",
       "      <th>Employment Type</th>\n",
       "      <th>Experience Required</th>\n",
       "      <th>Education Required</th>\n",
       "      <th>Application Deadline</th>\n",
       "      <th>Job Posting Date</th>\n",
       "      <th>Company Size</th>\n",
       "      <th>Remote Option</th>\n",
       "      <th>Contact Information</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mahindra &amp; Mahindra</td>\n",
       "      <td>Software Engineer</td>\n",
       "      <td>As a Software Engineer, you will be responsibl...</td>\n",
       "      <td>Java, Python, Algorithms</td>\n",
       "      <td>Entry-Level</td>\n",
       "      <td>Manoj Kumar</td>\n",
       "      <td>3.683055</td>\n",
       "      <td>940000</td>\n",
       "      <td>Bengaluru</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>Entry-Level</td>\n",
       "      <td>MSc in Data Science</td>\n",
       "      <td>2024-03-25</td>\n",
       "      <td>2024-02-10</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>maanas.narain@mahindra&amp;mahindra.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adani Group</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "      <td>Statistics, Python, Machine Learning, Data Vis...</td>\n",
       "      <td>Mid-Level</td>\n",
       "      <td>Ravi Patel</td>\n",
       "      <td>4.346144</td>\n",
       "      <td>765000</td>\n",
       "      <td>Indore</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>Mid-Level</td>\n",
       "      <td>BTech in CS</td>\n",
       "      <td>2024-04-15</td>\n",
       "      <td>2024-05-24</td>\n",
       "      <td>Large</td>\n",
       "      <td>Yes</td>\n",
       "      <td>oni.bhatnagar@adanigroup.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ICICI Bank</td>\n",
       "      <td>Technical Writer</td>\n",
       "      <td>As a Technical Writer, you will be responsible...</td>\n",
       "      <td>Content Creation, User Guides, Software Docume...</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Ritika Sharma</td>\n",
       "      <td>4.441064</td>\n",
       "      <td>640000</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>Contract</td>\n",
       "      <td>Senior</td>\n",
       "      <td>MSc in Data Science</td>\n",
       "      <td>2024-02-05</td>\n",
       "      <td>2024-01-22</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>sarthak.trivedi@icicibank.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Larsen &amp; Toubro</td>\n",
       "      <td>System Analyst</td>\n",
       "      <td>As a System Analyst, you will be responsible f...</td>\n",
       "      <td>Documentation, Business Analysis, Requirements...</td>\n",
       "      <td>Entry-Level</td>\n",
       "      <td>Neha Mehta</td>\n",
       "      <td>4.176766</td>\n",
       "      <td>680000</td>\n",
       "      <td>Surat</td>\n",
       "      <td>Contract</td>\n",
       "      <td>Entry-Level</td>\n",
       "      <td>MBA</td>\n",
       "      <td>2024-04-09</td>\n",
       "      <td>2024-07-30</td>\n",
       "      <td>Medium</td>\n",
       "      <td>No</td>\n",
       "      <td>janya.chandra@larsen&amp;toubro.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wipro</td>\n",
       "      <td>Cloud Engineer</td>\n",
       "      <td>As a Cloud Engineer, you will be responsible f...</td>\n",
       "      <td>GCP, Cloud Security, Azure, DevOps, AWS</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Amitabh Bachchan</td>\n",
       "      <td>3.133351</td>\n",
       "      <td>945000</td>\n",
       "      <td>Noida</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>Senior</td>\n",
       "      <td>BTech in IT</td>\n",
       "      <td>2024-07-02</td>\n",
       "      <td>2024-03-27</td>\n",
       "      <td>Large</td>\n",
       "      <td>No</td>\n",
       "      <td>neel.mand@wipro.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Company Name  Job Opening Title  \\\n",
       "0  Mahindra & Mahindra  Software Engineer   \n",
       "1          Adani Group     Data Scientist   \n",
       "2           ICICI Bank   Technical Writer   \n",
       "3      Larsen & Toubro     System Analyst   \n",
       "4                Wipro     Cloud Engineer   \n",
       "\n",
       "                                         Description  \\\n",
       "0  As a Software Engineer, you will be responsibl...   \n",
       "1  As a Data Scientist, you will play a pivotal r...   \n",
       "2  As a Technical Writer, you will be responsible...   \n",
       "3  As a System Analyst, you will be responsible f...   \n",
       "4  As a Cloud Engineer, you will be responsible f...   \n",
       "\n",
       "                                       Skills Needed        Level  \\\n",
       "0                           Java, Python, Algorithms  Entry-Level   \n",
       "1  Statistics, Python, Machine Learning, Data Vis...    Mid-Level   \n",
       "2  Content Creation, User Guides, Software Docume...       Senior   \n",
       "3  Documentation, Business Analysis, Requirements...  Entry-Level   \n",
       "4            GCP, Cloud Security, Azure, DevOps, AWS       Senior   \n",
       "\n",
       "     Hiring Manager  Company Review  Estimated Salary (INR) Job Location  \\\n",
       "0       Manoj Kumar        3.683055                  940000    Bengaluru   \n",
       "1        Ravi Patel        4.346144                  765000       Indore   \n",
       "2     Ritika Sharma        4.441064                  640000    Hyderabad   \n",
       "3        Neha Mehta        4.176766                  680000        Surat   \n",
       "4  Amitabh Bachchan        3.133351                  945000        Noida   \n",
       "\n",
       "  Employment Type Experience Required   Education Required  \\\n",
       "0       Part-Time         Entry-Level  MSc in Data Science   \n",
       "1       Full-Time           Mid-Level          BTech in CS   \n",
       "2        Contract              Senior  MSc in Data Science   \n",
       "3        Contract         Entry-Level                  MBA   \n",
       "4       Part-Time              Senior          BTech in IT   \n",
       "\n",
       "  Application Deadline Job Posting Date Company Size Remote Option  \\\n",
       "0           2024-03-25       2024-02-10       Medium            No   \n",
       "1           2024-04-15       2024-05-24        Large           Yes   \n",
       "2           2024-02-05       2024-01-22       Medium            No   \n",
       "3           2024-04-09       2024-07-30       Medium            No   \n",
       "4           2024-07-02       2024-03-27        Large            No   \n",
       "\n",
       "                   Contact Information  \n",
       "0  maanas.narain@mahindra&mahindra.com  \n",
       "1         oni.bhatnagar@adanigroup.com  \n",
       "2        sarthak.trivedi@icicibank.com  \n",
       "3      janya.chandra@larsen&toubro.com  \n",
       "4                  neel.mand@wipro.com  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "84c46efa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 17 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   Company Name            1000 non-null   object \n",
      " 1   Job Opening Title       1000 non-null   object \n",
      " 2   Description             1000 non-null   object \n",
      " 3   Skills Needed           1000 non-null   object \n",
      " 4   Level                   1000 non-null   object \n",
      " 5   Hiring Manager          1000 non-null   object \n",
      " 6   Company Review          1000 non-null   float64\n",
      " 7   Estimated Salary (INR)  1000 non-null   int64  \n",
      " 8   Job Location            1000 non-null   object \n",
      " 9   Employment Type         1000 non-null   object \n",
      " 10  Experience Required     1000 non-null   object \n",
      " 11  Education Required      1000 non-null   object \n",
      " 12  Application Deadline    1000 non-null   object \n",
      " 13  Job Posting Date        1000 non-null   object \n",
      " 14  Company Size            1000 non-null   object \n",
      " 15  Remote Option           1000 non-null   object \n",
      " 16  Contact Information     1000 non-null   object \n",
      "dtypes: float64(1), int64(1), object(15)\n",
      "memory usage: 132.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9c43c90d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/imvk45/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to /home/imvk45/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /home/imvk45/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                         Description  \\\n",
      "0  As a Software Engineer, you will be responsibl...   \n",
      "1  As a Data Scientist, you will play a pivotal r...   \n",
      "2  As a Technical Writer, you will be responsible...   \n",
      "3  As a System Analyst, you will be responsible f...   \n",
      "4  As a Cloud Engineer, you will be responsible f...   \n",
      "\n",
      "                               Processed Description  \n",
      "0  software engineer responsible designing develo...  \n",
      "1  data scientist play pivotal role harnessing po...  \n",
      "2  technical writer responsible creating maintain...  \n",
      "3  system analyst responsible evaluating improvin...  \n",
      "4  cloud engineer responsible designing deploying...  \n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from string import punctuation\n",
    "\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "\n",
    "def preprocess_text(text):\n",
    "    word_sent = word_tokenize(text.lower().replace(\"\\n\", \"\"))\n",
    "    stop_words = set(stopwords.words('english') + list(punctuation))\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    processed_text = [lemmatizer.lemmatize(word) for word in word_sent if word not in stop_words]\n",
    "    return ' '.join(processed_text)\n",
    "\n",
    "df['Processed Description'] = df['Description'].apply(preprocess_text)\n",
    "\n",
    "print(df[['Description', 'Processed Description']].head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "27fa8264",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1000, 782)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidf_vectorizer = TfidfVectorizer()\n",
    "tfidf_matrix = tfidf_vectorizer.fit_transform(df['Processed Description'])\n",
    "print(tfidf_matrix.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eae0e7e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "cv_text = \"\"\"\n",
    "vikas kumar cid 131 91 9559631515 vikas kumar1 plaksha edu cid 239 vikas kumar thisisvk45 cid 252 imvk45 education btech data science economics business plaksha university mohali 8 10 10 09 2021 05 2025 12th cbse vidyagyan school sitapur 95 2 04 2020 07 2021 10th cbse vidyagyan school sitapur 94 6 04 2018 05 2019 experience isb research intern 06 2024 08 2024 boosted data efficiency 30 implementing pipeline integrating postgresql mongodb data via api operation automated tag assignment creation via openmetadata api enhancing data accessibility consumer stakeholder created streamlit prototype enhancing data ownership analysis management user organization omdena machine learning intern 02 2024 04 2024 designed deployed gpt 3 based chatbot boosting user engagement 40 real time mental health support engineered nlp algorithm bert gpt 3 delivering 30 accurate personalized advice resource user implemented continuous learning mechanism enhancing chatbot response 25 based user interaction feedback airtel machine learning intern 05 2023 08 2023 integrated data processing streamlined pipeline achieving 20 boost python clustering analysis optimizedwebscrapingefficiencywithbeautifulsoupusingmultithreadinganddelays achievinga30 reductioninretrievaltime executed spark script gpu architecture boosting computational speed 20 enhancing large scale data handling project song recognition system using dl python inceptionv3 resnet50 01 2024 05 2024 transformed audio spectrogram applied advanced preprocessing increasing model training efficiency 15 reduced size spectrogram 30 using grayscale image patch significantly improving model processing speed achieved 90 5 validation accuracy inception v3 utilizing angular margin loss capture intricate audio pattern ai powered database query analysis tool python mysql ai 01 2024 03 2024 increased schema analysis efficiency 40 automation delivering actionable insight supply chain optimization accelerated sql query generation 50 using ai enhancing data retrieval time improved decision making created streamlit app natural language query auto generate sql simplifying data retrieval table field evaluation esg impact financial performance python selenium refinitiv 07 2023 12 2023 collected data 300 u company using refinitiv web scraping focusing esg score product strategy used python excel identify 4 performance boost high esg firm enhancing supply chain analytics analyzed sector specific esg impact delivering insight stakeholder sustainable finance jobmate advanced job search talent matching platform python nlp ocr data analysis 07 2023 12 2023 builtarecommendationsystemusingocrandnlp processing20 137jobpostsand54cvstomatchjobseekersandrecruiters enhancedaccuracyby25 withdatapreprocessingtechniqueslikecountvectorizerandtf idfforimprovedsimilarityanalysis developed user friendly streamlit interface display tailored job recommendation interactive dashboard technical skill language python sql html cs jss framework pytorch tensorflow kera library hugging face transformer spacy nltk scikit learn xgboost spark git selenium beautifulsoup opencv tool git docker openai api competition achievement awarded bharati scholarship worth 35 lakh secured india rank 2nd 5th 12th nrti ipmat dujat entrance exam respectively gold medalist caseify consulting based case study competition going 12 team gold medalist psip creating wind turbine prototype 8 higher energy efficiency competing 20 team gold medalist sp dutta innovative wind turbine 8 energy output competing 40 team winner captain consecutive intra university cricket volleyball tournament competing 10 team certificate british airway data science virtual experince programme kpmg data analytics virtual internship bcg introduction strategy consulting job simulation\"\"\"\n",
    "\n",
    "processed_cv_text = preprocess_text(cv_text)\n",
    "\n",
    "cv_tfidf = tfidf_vectorizer.transform([processed_cv_text])\n",
    "\n",
    "cosine_similarities = cosine_similarity(cv_tfidf, tfidf_matrix).flatten()\n",
    "\n",
    "top_indices = cosine_similarities.argsort()[-5:][::-1]\n",
    "\n",
    "recommended_jobs = df.iloc[top_indices][['Job Opening Title', 'Company Name', 'Job Location', 'Estimated Salary (INR)', 'Description']]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5069be6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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",
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       "        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>Job Opening Title</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Job Location</th>\n",
       "      <th>Estimated Salary (INR)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Adani Group</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>880000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>HCL Technologies</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>900000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Infosys</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>1155000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Larsen &amp; Toubro</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>1100000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Larsen &amp; Toubro</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>1210000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Job Opening Title      Company Name Job Location  Estimated Salary (INR)  \\\n",
       "538    Data Scientist       Adani Group   Chandigarh                  880000   \n",
       "757    Data Scientist  HCL Technologies    Ahmedabad                  900000   \n",
       "669    Data Scientist           Infosys    Hyderabad                 1155000   \n",
       "675    Data Scientist   Larsen & Toubro    Ahmedabad                 1100000   \n",
       "330    Data Scientist   Larsen & Toubro       Jaipur                 1210000   \n",
       "\n",
       "                                           Description  \n",
       "538  As a Data Scientist, you will play a pivotal r...  \n",
       "757  As a Data Scientist, you will play a pivotal r...  \n",
       "669  As a Data Scientist, you will play a pivotal r...  \n",
       "675  As a Data Scientist, you will play a pivotal r...  \n",
       "330  As a Data Scientist, you will play a pivotal r...  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_jobs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2662ceb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Job Opening Title      Company Name Job Location  Estimated Salary (INR)  \\\n",
      "538    Data Scientist       Adani Group   Chandigarh                  880000   \n",
      "757    Data Scientist  HCL Technologies    Ahmedabad                  900000   \n",
      "669    Data Scientist           Infosys    Hyderabad                 1155000   \n",
      "675    Data Scientist   Larsen & Toubro    Ahmedabad                 1100000   \n",
      "330    Data Scientist   Larsen & Toubro       Jaipur                 1210000   \n",
      "\n",
      "                                           Description  \n",
      "538  As a Data Scientist, you will play a pivotal r...  \n",
      "757  As a Data Scientist, you will play a pivotal r...  \n",
      "669  As a Data Scientist, you will play a pivotal r...  \n",
      "675  As a Data Scientist, you will play a pivotal r...  \n",
      "330  As a Data Scientist, you will play a pivotal r...  \n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vectorizer = CountVectorizer()\n",
    "\n",
    "count_matrix = count_vectorizer.fit_transform(df['Processed Description'])\n",
    "\n",
    "cv_count = count_vectorizer.transform([processed_cv_text])\n",
    "\n",
    "cosine_similarities_count = cosine_similarity(cv_count, count_matrix).flatten()\n",
    "\n",
    "top_indices_count = cosine_similarities_count.argsort()[-5:][::-1]\n",
    "\n",
    "recommended_jobs_count = df.iloc[top_indices_count][['Job Opening Title', 'Company Name', 'Job Location', 'Estimated Salary (INR)', 'Description']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ee5c439d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Job Opening Title</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Job Location</th>\n",
       "      <th>Estimated Salary (INR)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Adani Group</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>880000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>757</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>HCL Technologies</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>900000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Infosys</td>\n",
       "      <td>Hyderabad</td>\n",
       "      <td>1155000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Larsen &amp; Toubro</td>\n",
       "      <td>Ahmedabad</td>\n",
       "      <td>1100000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Larsen &amp; Toubro</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>1210000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Job Opening Title      Company Name Job Location  Estimated Salary (INR)  \\\n",
       "538    Data Scientist       Adani Group   Chandigarh                  880000   \n",
       "757    Data Scientist  HCL Technologies    Ahmedabad                  900000   \n",
       "669    Data Scientist           Infosys    Hyderabad                 1155000   \n",
       "675    Data Scientist   Larsen & Toubro    Ahmedabad                 1100000   \n",
       "330    Data Scientist   Larsen & Toubro       Jaipur                 1210000   \n",
       "\n",
       "                                           Description  \n",
       "538  As a Data Scientist, you will play a pivotal r...  \n",
       "757  As a Data Scientist, you will play a pivotal r...  \n",
       "669  As a Data Scientist, you will play a pivotal r...  \n",
       "675  As a Data Scientist, you will play a pivotal r...  \n",
       "330  As a Data Scientist, you will play a pivotal r...  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_jobs_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "901d8dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import NearestNeighbors\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "knn_tfidf_vectorizer = TfidfVectorizer(stop_words='english')\n",
    "knn_matrix = knn_tfidf_vectorizer.fit_transform(df['Processed Description'])\n",
    "\n",
    "knn_model = NearestNeighbors(n_neighbors=5, algorithm='auto').fit(knn_matrix)\n",
    "distances, indices = knn_model.kneighbors(knn_tfidf_vectorizer.transform([processed_cv_text]))\n",
    "\n",
    "recommended_jobs_knn = df.iloc[indices[0]][['Job Opening Title', 'Company Name', 'Job Location', 'Estimated Salary (INR)', 'Description']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b8d8810a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
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       "        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>Job Opening Title</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Job Location</th>\n",
       "      <th>Estimated Salary (INR)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Reliance Industries</td>\n",
       "      <td>Surat</td>\n",
       "      <td>1200000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Adani Group</td>\n",
       "      <td>Indore</td>\n",
       "      <td>765000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>HCL Technologies</td>\n",
       "      <td>Gurgaon</td>\n",
       "      <td>900000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Larsen &amp; Toubro</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1200000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Adani Group</td>\n",
       "      <td>Mumbai</td>\n",
       "      <td>945000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Job Opening Title         Company Name Job Location  \\\n",
       "67     Data Scientist  Reliance Industries        Surat   \n",
       "1      Data Scientist          Adani Group       Indore   \n",
       "49     Data Scientist     HCL Technologies      Gurgaon   \n",
       "160    Data Scientist      Larsen & Toubro        Delhi   \n",
       "140    Data Scientist          Adani Group       Mumbai   \n",
       "\n",
       "     Estimated Salary (INR)                                        Description  \n",
       "67                  1200000  As a Data Scientist, you will play a pivotal r...  \n",
       "1                    765000  As a Data Scientist, you will play a pivotal r...  \n",
       "49                   900000  As a Data Scientist, you will play a pivotal r...  \n",
       "160                 1200000  As a Data Scientist, you will play a pivotal r...  \n",
       "140                  945000  As a Data Scientist, you will play a pivotal r...  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recommended_jobs_knn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f2cf3a51",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "df_tfidf = pd.DataFrame(cosine_similarities, columns=['TF-IDF'])\n",
    "df_count = pd.DataFrame(cosine_similarities_count, columns=['CountVectorizer'])\n",
    "df_knn = pd.DataFrame(np.zeros(len(df)), columns=['KNN'])\n",
    "df_knn.iloc[indices[0], 0] = 1 - distances[0]\n",
    "\n",
    "combined_df = pd.concat([df_tfidf, df_count, df_knn], axis=1)\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "combined_df[['TF-IDF', 'CountVectorizer', 'KNN']] = scaler.fit_transform(combined_df[['TF-IDF', 'CountVectorizer', 'KNN']])\n",
    "\n",
    "weights = {'TF-IDF': 0.3, 'CountVectorizer': 0.3, 'KNN': 0.4}\n",
    "combined_df['FinalScore'] = (combined_df['TF-IDF'] * weights['TF-IDF'] +\n",
    "                             combined_df['CountVectorizer'] * weights['CountVectorizer'] +\n",
    "                             combined_df['KNN'] * weights['KNN'])\n",
    "\n",
    "top_indices_final = combined_df['FinalScore'].argsort()[-5:][::-1]\n",
    "\n",
    "final_recommendations = df.iloc[top_indices_final][['Job Opening Title', 'Company Name', 'Job Location', 'Estimated Salary (INR)', 'Description']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "53d497b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Job Opening Title</th>\n",
       "      <th>Company Name</th>\n",
       "      <th>Job Location</th>\n",
       "      <th>Estimated Salary (INR)</th>\n",
       "      <th>Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>538</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Adani Group</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>880000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>639</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Reliance Industries</td>\n",
       "      <td>Jaipur</td>\n",
       "      <td>1155000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Adani Group</td>\n",
       "      <td>Chandigarh</td>\n",
       "      <td>1140000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Wipro</td>\n",
       "      <td>Kolkata</td>\n",
       "      <td>1140000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>Reliance Industries</td>\n",
       "      <td>Delhi</td>\n",
       "      <td>1045000</td>\n",
       "      <td>As a Data Scientist, you will play a pivotal r...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Job Opening Title         Company Name Job Location  \\\n",
       "538    Data Scientist          Adani Group   Chandigarh   \n",
       "639    Data Scientist  Reliance Industries       Jaipur   \n",
       "222    Data Scientist          Adani Group   Chandigarh   \n",
       "368    Data Scientist                Wipro      Kolkata   \n",
       "225    Data Scientist  Reliance Industries        Delhi   \n",
       "\n",
       "     Estimated Salary (INR)                                        Description  \n",
       "538                  880000  As a Data Scientist, you will play a pivotal r...  \n",
       "639                 1155000  As a Data Scientist, you will play a pivotal r...  \n",
       "222                 1140000  As a Data Scientist, you will play a pivotal r...  \n",
       "368                 1140000  As a Data Scientist, you will play a pivotal r...  \n",
       "225                 1045000  As a Data Scientist, you will play a pivotal r...  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_recommendations.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89b8d45f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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