Automated Resume Screening System using Machine Learning (With Dataset)

A multi-agent recruitment assistant that leverages Microsoft AutoGen framework to streamline hiring processes. The system employs specialized AI agents working in concert to automate resume screening, candidate evaluation, and interview preparation.

A command-line Application Tracking System (ATS) that automates resume screening by analyzing job descriptions and ranking candidate resumes using NLP and machine learning techniques.

Automated Resume Screening System (With Dataset) A web app to help employers by analysing resumes and CVs, surfacing candidates that best match the position and filtering out those who don't. Description Used recommendation engine techniques such as Collaborative , Content-Based filtering for fuzzy matching job description with multiple resumes. Prerequisites Software textract==1.6.3 requests==2.22.0 Flask==1.1.1 gensim==3.8.0 sklearn==0.0 PyPDF2==1.26.0 autocorrect==0.4.4 nltk==3.4.5 contractions==0.0.21 textsearch==0.0.17 inflect==2.1.0 numpy==1.17.2 pdfminer.six==20181108 Python 3.6.0 |Anaconda 4.3.0 (64-bit)| Dataset Link1 : https://s3.ap-south-1.amazonaws.com/codebyte-bucket/Resume%26Job_Descriptions.zip Mirror : https://drive.google.com/open?id=17M9oDPip5JFFFNJhDCBQKy8BMqoyxajU Running localhost Run pip install -r requirements.txt to install dependencies Simply run this command from root directory. Login Username :admin Password : whitetigers2018 python app.py Running using Docker Create the docker image running: docker build -t arss . Run the container: docker run -it -p 5000:5000 arss

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Automate resume screening with this AI-powered system, leveraging NLP to match candidates with job descriptions efficiently. Streamlit app for easy resume upload and ranking.

AI Recruiter Agency - An AI-powered resume analysis and job matching system that automates candidate screening using LLaMA 3.2 and coordinated AI agents.

Automated Resume Screening System

This project is a web application that analyzes and scores resumes for compatibility with job descriptions using ATS (Applicant Tracking System) criteria. It provides actionable suggestions to help users improve their resumes and increase their chances of passing automated screening systems.

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An automated Resume Screening and Ranking System using NLP and Machine Learning. Extracts text from PDFs/DOCX, classifies candidates using SVM, and ranks them against job descriptions via Cosine Similarity. Built with Python, Flask, and Scikit-Learn.

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Automated Resume Screening System: Enhancing Recruitment Efficiency

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An AI-powered resume screening system that analyzes candidate profiles, matches them with job criteria, and provides structured justifications. Automates hiring decisions with intelligent ranking and insights. 🚀🤖

The AI Resume Screening & Candidate Ranking System is a Streamlit-based web app that automates resume evaluation by ranking candidates based on job description relevance. Using NLP and Machine Learning, it extracts text from PDFs, converts it into numerical vectors via TF-IDF, and computes Cosine Similarity to identify the best matches.

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Developed an AI-powered system to automate the process of resume screening and shortlisting based on job descriptions.

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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.

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an AI-powered Resume Analyser that automates the process of resume screening and job-role matching. the system parses resumes, extracts key candidate information such as skills, experience, and qualifications, and provide job matches relevant to the details provided

This project aims to develop a fair, accurate, and interpretable AI-based resume screening system that minimizes biases in automated hiring processes. It employs cutting-edge NLP techniques like BERT embeddings, Bias Detection and Mitigation strategies, and Explainable AI (XAI) methods to ensure ethical and transparent decision-making.

This project automates the recruitment process by analyzing and ranking resumes based on job requirements. Using NLP, it extracts key information such as skills, experience, and education, then ranks candidates accordingly.

Automates resume screening by matching candidates to job descriptions. This web application extracts text from .pdf, .docx, and .txt files, preprocesses content, and uses NLP to rank resumes by relevance, streamlining recruitment.

An automated Applicant Tracking System (ATS) that evaluates resumes against job descriptions using keyword-based scoring and LLM-assisted recruiter insights.

The ATS resume analyzing system revolutionizes recruitment by automating resume screening. Leveraging advanced technologies like Natural Language Processing and machine learning, it streamlines candidate evaluation, ensuring efficiency and reducing biases.

A automated resume screening and ranking system

AI Resume Screening System using Flask with automated email notification and HR summary.