Web- Based Smart Skills and Job Role Mapping System using Data Science and Machine Learning

Author(s)	Purvi Sahu, Unzila Sheikh, Poornima Pawar, Prof.Bhavesh khasdev
Country	India
Abstract	This paper presents a Web-Based Smart Skills and Job Role Mapping System that employs TF-IDF vectorization and Cosine Similarity to compute high-dimensional similarity scores between user skill vectors and job-role embeddings. A hybrid recommendation strategy integrates rule-based filtering for constraint-aware refinement. The backend architecture, implemented using Flask APIs, interfaces with a Streamlit-based frontend for real-time inference and visualization. The system additionally performs gap detection, dynamically generating task-oriented learning roadmaps backed by structured datasets. A dashboard module facilitates longitudinal progress tracking through analytical metrics. Experimental evaluation demonstrates low-latency inference and scalable deployment potential for AI-driven career guidance platforms.
Keywords	Skill Embedding, Cosine Similarity, Hybrid Recommendation, Flask API, Web-Based Career Guidance.
Field	Engineering
Published In	Volume 6, Issue 11, November 2025
Published On	2025-11-14
Cite This	Web- Based Smart Skills and Job Role Mapping System using Data Science and Machine Learning - Purvi Sahu, Unzila Sheikh, Poornima Pawar, Prof.Bhavesh khasdev - IJLRP Volume 6, Issue 11, November 2025. DOI 10.70528/IJLRP.v6.i11.1833
DOI	https://doi.org/10.70528/IJLRP.v6.i11.1833
Short DOI	https://doi.org/hbb4jc

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