Given a job description, the model uses POS and Classifier to determine the skills therein.

Extract ESCO skills and ISCO occupations from texts such as job descriptions or CVs

NLP tool for optimizing a resume for a job description, computing similarity, and extracting skills

A web app that lets users upload a resume (PDF), extracts text, analyzes skills and matches it against a job description to produce an ATS-like score and improvement suggestions.

A CV is a representation of a candidate’s profile, skill sets and achievements. Matching CV across a job description to identify potential candidates to interview is a tedious job. In this project we will be solving this problem by designing and implementing resume parser and analyzer which will help recruiters to process resumes by extracting data in a meaningful way given a certain job description. The application is aimed for recruiters to efficiently manage electronic resume documents sent via the internet.

Problem Statement Introduction So far, in this course, you have learned about the Hadoop Framework, RDBMS design, and Hive Querying. You have understood how to work with an EMR cluster and write optimised queries on Hive. This assignment aims at testing your skills in Hive, and Hadoop concepts learned throughout this course. Similar to Big Data Analysts, you will be required to extract the data, load them into Hive tables, and gather insights from the dataset. Problem Statement With online sales gaining popularity, tech companies are exploring ways to improve their sales by analysing customer behaviour and gaining insights about product trends. Furthermore, the websites make it easier for customers to find the products they require without much scavenging. Needless to say, the role of big data analysts is among the most sought-after job profiles of this decade. Therefore, as part of this assignment, we will be challenging you, as a big data analyst, to extract data and gather insights from a real-life data set of an e-commerce company. In the next video, you will learn the various stages in collecting and processing the e-commerce website data. Play Video2079378 One of the most popular use cases of Big Data is in eCommerce companies such as Amazon or Flipkart. So before we get into the details of the dataset, let us understand how eCommerce companies make use of these concepts to give customers product recommendations. This is done by tracking your clicks on their website and searching for patterns within them. This kind of data is called a clickstream data. Let us understand how it works in detail. The clickstream data contains all the logs as to how you navigated through the website. It also contains other details such as time spent on every page, etc. From this, they make use of data ingesting frameworks such as Apache Kafka or AWS Kinesis in order to store it in frameworks such as Hadoop. From there, machine learning engineers or business analysts use this data to derive valuable insights. In the next video, Kautuk will give you a brief idea on the data that is used in this case study and the kind of analysis you can perform with the same. Play Video2079378 For this assignment, you will be working with a public clickstream dataset of a cosmetics store. Using this dataset, your job is to extract valuable insights which generally data engineers come up within an e-retail company. So now, let us understand the dataset in detail in the next video. Play Video2079378 You will find the data in the link given below. https://e-commerce-events-ml.s3.amazonaws.com/2019-Oct.csv https://e-commerce-events-ml.s3.amazonaws.com/2019-Nov.csv You can find the description of the attributes in the dataset given below. In the next video, you will learn about the various implementation stages involved in this case study. Attribute Description Download Play Video2079378 The implementation phase can be divided into the following parts: Copying the data set into the HDFS: Launch an EMR cluster that utilizes the Hive services, and Move the data from the S3 bucket into the HDFS Creating the database and launching Hive queries on your EMR cluster: Create the structure of your database, Use optimized techniques to run your queries as efficiently as possible Show the improvement of the performance after using optimization on any single query. Run Hive queries to answer the questions given below. Cleaning up Drop your database, and Terminate your cluster You are required to provide answers to the questions given below. Find the total revenue generated due to purchases made in October. Write a query to yield the total sum of purchases per month in a single output. Write a query to find the change in revenue generated due to purchases from October to November. Find distinct categories of products. Categories with null category code can be ignored. Find the total number of products available under each category. Which brand had the maximum sales in October and November combined? Which brands increased their sales from October to November? Your company wants to reward the top 10 users of its website with a Golden Customer plan. Write a query to generate a list of top 10 users who spend the most. Note: To write your queries, please make necessary optimizations, such as selecting the appropriate table format and using partitioned/bucketed tables. You will be awarded marks for enhancing the performance of your queries. Each question should have one query only. Use a 2-node EMR cluster with both the master and core nodes as M4.large. Make sure you terminate the cluster when you are done working with it. Since EMR can only be terminated and cannot be stopped, always have a copy of your queries in a text editor so that you can copy-paste them every time you launch a new cluster. Do not leave PuTTY idle for so long. Do some activity like pressing the space bar at regular intervals. If the terminal becomes inactive, you don't have to start a new cluster. You can reconnect to the master node by opening the puTTY terminal again, giving the host address and loading .ppk key file. For your information, if you are using emr-6.x release, certain queries might take a longer time, we would suggest you use emr-5.29.0 release for this case study. There are different options for storing the data in an EMR cluster. You can briefly explore them in this link. In your previous module on hive querying, you copied the data to the local file system, i.e., to the master node's file system and performed the queries. Since the size of the dataset is large here in this case study, it is a good practice to load the data into the HDFS and not into the local file system. You can revisit the segment on 'Working with HDFS' from the earlier module on 'Introduction to Big data and Cloud'. You may have to use CSVSerde with the default properties value for loading the dataset into a Hive table. You can refer to this link for more details on using CSVSerde. Also, you may want to skip the column names from getting inserted into the Hive table. You can refer to this link on how to skip the headers.

A Python tool to extract key skills and terms from job descriptions, optimizing resumes and LinkedIn profiles for ATS and recruiters.

**Smart Resume Analyzer** is an AI-powered **ATS (Applicant Tracking System) Resume Checker** built with Django and Python. It allows users to upload resumes and match them against job descriptions to: - Extract **skills** and experience from resumes etc..

The Smart Resume Analyzer is an AI-based tool designed to automate the resume screening process. It parses resumes, extracts key information (skills, experience, education), and compares them to job descriptions using Natural Language Processing (NLP). The system scores and ranks candidates based on job fit and provides feedback for improvement.

An intelligent resume screening tool that uses machine learning to automatically analyze and rank candidates based on job descriptions. This project extracts key skills and experience from resumes, compares them with role requirements, and generates match scores to help recruiters find the right candidates faster.

🛠 Extract skills from job descriptions and match candidates with relevant resumes using this NLP-powered recommendation engine.

This project automates the process of generating cold emails tailored to specific job roles by extracting relevant skills and experience from a portfolio and matching them to job descriptions.

AI-powered Resume Analyzer that extracts skills, compares resumes with job descriptions using NLP (TF-IDF & Cosine Similarity), and generates ATS-style match scores with missing skill insights.

AI-powered resume generator that helps tailor your resume to specific job descriptions. This Chrome extension analyzes job postings, extracts key requirements, and highlights your relevant skills and experiences to increase your chances of getting interviews. Originally developed as a project to help fellow students navigate the challenging job

A Flask-based web app that extracts skills from resumes (PDF/DOCX) and matches them against job descriptions using NLP (TF-IDF + cosine similarity). Generates a match score, highlights missing skills, and provides an ATS-style evaluation. Includes a simple HTML upload interface for testing.

Python code to extract information like Company Name, Skills and Work experience from Job Description

Repo for Resume Matching with Job Descriptions by extracting details from CVs in PDF format, and matching them against the fetched job descriptions based on skills and education.

AI-powered text analysis agent, designed to help job seekers by analysing job descriptions and resumes. The primary function is to extract keywords and skills from job descriptions and compare them with the information present in a resume.

Job Scraper with Resume Matching is a web app that helps job seekers find LinkedIn jobs tailored to their resumes. Using NLP, it dynamically extracts skills and experience, matches them with job descriptions, and ranks jobs based on similarity scores.

"Job Recommendation System with Streamlit: Upload CVs, extract skills, and get job recommendations using TF-IDF and Jooble API. Recruiters can paste job descriptions to find the best-matched CVs with similarity scoring. Features PDF parsing,NLP for skill extraction, real-time job fetch, and downloadable reports.

Job Recommendation System using Streamlit: Upload CVs, extract skills, and receive personalized job recommendations using TF-IDF and real-time job APIs like Jooble. For recruiters, paste job descriptions to find the best-matched CVs with similarity scoring. Features PDF parsing, skill extraction, and downloadable reports.

A Python tool that helps job seekers see how well their resume matches a job description — just like an ATS (Applicant Tracking System). 🎯 What it does Takes: 📄 Resume text 🧾 Job description text Extracts important keywords Calculates match percentage Shows missing & matched skills Fully offline, no API.

Analyze your resume, GitHub profile, and a job description together. Extract skills from each source, compare them, and get insights on skill gaps, overlaps, and match scores to improve your resume and public profile.

Automated AI Resume Screening is a web application that allows users to upload resumes and job descriptions in PDF format. The system processes these documents using AI and NLP techniques to extract skills, education, experience, and match resumes with job descriptions based on similarity scores.

An AI-powered ATS Resume Analyzer built with Python and Streamlit that evaluates resumes against job descriptions using NLP and SBERT. It extracts keywords, calculates ATS scores, highlights missing skills, and provides actionable insights for improving resume-job relevance

🧠 Project: AI-Powered Resume Scanner This is a Python-based tool that reads a resume PDF, extracts key details like name, email, phone number, skills, and education using Regex and NLP logic, then compares the skills with a job description to calculate a match percentage.

This script scrapes job data from CareerPlanner, extracting descriptions, duties, activities, skills, abilities, and knowledge. It uses `requests` for HTTP requests and `BeautifulSoup` for HTML parsing. Processing the first ten job titles, it stores data in dictionaries, converts them to a pandas DataFrame, and exports to Excel and CSV files.

This project is an AI-based system that uses NLP and machine learning to analyze resumes and match them with job descriptions. It extracts skills, applies TF-IDF for feature representation, and uses multiple models for classification. The system generates a match score and highlights skill gaps through a Streamlit interface.

This project automates resume shortlisting by matching resumes with job descriptions using rule-based logic, TF-IDF, and BERT. It extracts skills, education, and experience from PDFs, ranks candidates, and compares different models to improve hiring decisions with both speed and context-aware accuracy.

A Resume Screening System that applies Natural Language Processing (NLP) techniques to extract and compare skills from resumes against job descriptions. It uses methods like TF-IDF and BERT embeddings for similarity matching, and provides an interactive interface built with Streamlit and deployed on Hugging Face Spaces.