A simple job recommendation system project (using Python) for my final year!

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

This repository implements a Job Recommendation System using Machine Learning (ML) and Natural Language Processing (NLP). Designed to recommend job offers based on skill similarity, the system is developed in Python and offers a user-friendly interface through a Flask web application.

Job Recommendation System - AI-powered platform matching candidates with ideal jobs using NLP and ML. Features automated job classification, skill extraction, and personalized recommendations via FastAPI. Built with Python, scikit-learn, and MLflow. Perfect for HR and career services.

Automated Recommendation System for Job Search Analysis Using Stochastic Gradient Descent Model with Enhanced Keyword Search Platform using Python and R.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library. Collaborative filtering is based on the fact that relationships exist between products and people's interests. Many recommendation systems use collaborative filtering to find these relationships and to give an accurate recommendation of a product that the user might like or be interested in. Collaborative filtering has basically two approaches: user-based and item-based. User-based collaborative filtering is based on the user similarity or neighborhood. Item-based collaborative filtering is based on similarity among items. Let's first look at the intuition behind the user-based approach. In user-based collaborative filtering, we have an active user for whom the recommendation is aimed. The collaborative filtering engine first looks for users who are similar. That is users who share the active users rating patterns. Collaborative filtering basis this similarity on things like history, preference, and choices that users make when buying, watching, or enjoying something.

Job Recommendation System using Dynamic Weightage for implicit skill extraction

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This Job recommendation system is Content based recommendation system, where Users/ Candidate get the recommendation of jobs based of what job they applied.

A job recommendation system using Python

Job recommendation system using python and machine learning

Skill-Based Job Recommendation System using Python that analyzes user skills and suggests suitable IT job roles with skill match percentage visualization.

🧠 AI-powered job recommendation system that intelligently matches candidates with opportunities using NLP and web scraping 🚀 Built with Python, Scrapy, spaCy, and machine learning to deliver personalized job suggestions based on skills analysis

Resume Parser and Job Recommendation System – A Python-Flask web app that uses NLP to extract skills, experience, and education from resumes, then recommends suitable job roles using machine learning. Built for efficient, accurate, and automated recruitment.

SkillSync – AI Skill Matching & Career Recommendation System SkillSync is a Python-based multi-agent AI project that analyzes user skills, resumes, and job descriptions to generate job matches and personalized career recommendations. The goal of this project was to explore real-world applications of Generative AI by automating tasks skill.

User-Based Film Recommendation System (Python, TensorFlow): Employ TensorFlow for user skill similarity calculations. Utilizes JSON data, constructs a skill similarity model, and identifies the top-K users most similar to a given skill set. A foundation for skill-based recommendation systems with applications in job matching and social networks.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders.Here we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.

This is a job recommendation system called JobMatch AI.I has a Flask backend in Python, a frontend in HTML/CSS/Bootstrap and an NLP/ML layer that uses TF-IDF + cosine similarity. Flow is: user logs in→fills profile / uploads resume→convert skills to vectors→compute similarity with all jobs→then shows ranked recommendations and stats on dashboard.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore recommendation systems based on Collaborative Filtering and implement simple version of one using Python and the Pandas library.

Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library

Full-stack AI-driven career development system built with Python and modern frontend technologies. Integrates NLP-based resume parsing, machine learning recommendation engines, and an interactive UI to deliver job matching, skill gap analysis, and personalized learning roadmaps.

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A Flask-based AI job recommendation system using Gemini API and text similarity

Job-Recommendation-System-Backend-Python

Job Recommendation System Using Python

Job-Recommendation-System-Backend Python

making a job recommendation system using python

Job Recommendation System Experiment Platform - A reusable Python framework for job recommendation experiments