Resume Screening App With Python and Machine Learning

Smart Resume Analyzer is an AI-powered resume screening tool built using Python and Streamlit. It allows users to upload their resume and analyze how well it aligns with the skill requirements of a selected domain such as Data Science, Web Development, Machine Learning, and more.

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

Resume_Screening Script Python file with source code

AI-powered Resume Screening System that analyzes and ranks resumes based on similarity to a job description using NLP and TF-IDF. Built with Python, Scikit-learn, and Streamlit, it extracts text from PDF/DOCX files and calculates cosine similarity to help recruiters shortlist candidates efficiently.

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.

Resume Screening App With Python and Machine Learning

AI/ML enthusiast | B.E. in AI/ML @ CMRIT | Building innovative solutions with Python, React, TensorFlow, and AWS. Passionate about resume screening, web apps, and ethical tech.

Applicant Tracking System (ATS) is a full-stack web app for streamlining recruitment. It supports job posting, candidate applications, and AI-based resume screening. Built with React.js, NestJS, and Python, it offers role-based access, interview management, and candidate tracking.

Hiring the right talent is very challenging now a days. My machine learning algorithm help recruiters pick right CV from a lot of Junk.

AI-powered resume screening & ATS matching with Python & Streamlit

NLP-powered resume screening app — parses PDFs, ranks candidates by JD match using TF-IDF + cosine similarity, detects skill gaps, scores resumes. Built with Python + Streamlit + Django.

A full-stack, production-ready web application for automated resume screening and intelligent candidate ranking, built with React, Tailwind CSS, Python FastAPI, RAG (Retrieval-Augmented Generation), and LangChain.

Intelligent talent screening tool using Machine Learning and NLP. Automatically analyzes resumes, extracts key information, scores candidates (0-100), and generates actionable recommendations. Built with Python - no external ML libraries required.

What is Resume Screening? Hiring the right talent is a challenge for all businesses. This challenge is magnified by the high volume of applicants if the business is labour-intensive, growing, and facing high attrition rates. What is Resume Screening? Hiring the right talent is a challenge for all businesses. This challenge is magnified by the high volume of applicants if the business is labour-intensive, growing, and facing high attrition rates. Machine Learning Project on Resume Screening with Python In this section, I will take you through a Machine Learning project on Resume Screening with Python programming language. I will start this task by importing the necessary Python libraries and the dataset:

No description available

No description available

This repository contains a Python code for screening a candidate's resume and analyzing his/her background.

No description available

No description available

In this , I used TfidfVectorizer for the word embedding and then the knn model for the train the model

I created a program which can shortlist suitable candidates among all candidates present in a given list of interested profiles. This program use ML models and save a lot of time in resume shortlisting process.

Resume Screening App With Python and Machine Learning

Resume Screening App With Python, Machine Learning and streamlit

AI-based Resume Screening System, with python data science and machine learning

Intelligent Resume Screening System built with Python, Streamlit, and NLP to automate resume-job description matching and skill gap analysis.

AI-powered hiring dashboard with resume screening and analysis using React frontend and Python FastAPI backend with Azure OpenAI integration

AI‑powered resume screening app built with Python and Streamlit. Automates parsing, JD matching, candidate ranking, and transparent scoring.

AI‑powered resume screening app built with Python and Streamlit. Automates parsing, JD matching, candidate ranking, and transparent scoring.

AI-powered resume screening system with Python Flask, Machine Learning (scikit-learn), PDF parsing, skill matching, and gap analysis - featuring resume scoring, job matching, and comprehensive analytics