datasets

This project explores the integration of <b>Geographic Information Systems (GIS)</b> and <b>Natural Language Processing (NLP)</b> to improve job–candidate matching in recruitment. Traditional AI-based e-recruitment systems often ignore geographic constraints. Our hybrid model addresses this gap by incorporating both textual similarity and spatial relevance in matching candidates to job postings.Data UsedCandidate Data (CVs)<b>Source</b>: Scraped from emploi.ma<b>Size</b>: 1000 CVs after cleaning<b>Content</b>: Candidate names (anonymized), skills, experiences, locations (coordinates), availability, etc.Job Descriptions<b>Source</b>: Publicly available dataset from Kaggle<b>Size</b>: we took 1000 job postings using category :Morocco<b>Content</b>: Titles, descriptions, required skills, sector labels, and office locations...All datasets have been cleaned and anonymized for privacy and research ethics compliance.