A Python web scraper using Scrapy to collect job posting information from LinkedIn.
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Updated
Jun 1, 2024 - Python
A Python web scraper using Scrapy to collect job posting information from LinkedIn.
LinkedIn Jobs scraper and Discord bot
A project aiming to leverage text embeddings and Milvus, a high-performance vector search engine, to detect duplicate job postings.
A comprehensive project using Python, APIs, web scraping, and data visualization to identify emerging skills in demand. Includes data collection, wrangling, analysis, visualizations, a Google Looker Studio dashboard, and a final presentation.
A machine learning model is built using PySpark's MLlib library to automatically flag suspicious job postings on Indeed.com. The dataset includes 18,000 job descriptions, out of which about 800 are fake.
A full-featured Job Portal web application built with three key user roles: Admin, User (Job Seeker), and Recruiter (Job Provider). This platform allows job seekers to apply for jobs and recruiters to post and manage job listings, with admin supervision to ensure platform integrity.
This project detects duplicate job postings using SentenceTransformers for semantic embeddings and Milvus for efficient vector similarity search. It includes data preprocessing, evaluation metrics, and a FastAPI service for real-time duplicate detection.
Job posting data scraped from Indeed.com. This data is used in django web for testing purpose.
Scrape job postings from the FedEx career page. The scraper runs at 5-minute intervals, collecting and storing data in both PostgreSQL and MongoDB. Automatic duplicate checking and removal of job postings that are no longer available, leveraging Redis for efficient tracking.
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