CodePrep – AIML-Powered Skill Extraction and Skill Building Platform

Abstract

CodePrep is an innovative platform powered by Artificial Intelligence and Machine Learning (AIML), designed to help individuals enhance their career growth through skill extraction, skill development, and intelligent job matching. In today’s fast-changing job market, continuous upskilling has become essential for career success. CodePrep addresses this requirement by utilizing advanced resume parsing techniques to extract relevant skills and competencies from user resumes. The extracted information is then analyzed to detect skill gaps that may limit employment opportunities. Based on this analysis, the platform provides personalized recommendations to help users improve their skills and bridge the gap between their current abilities and industry requirements. In addition, CodePrep incorporates a machine learning-based job matching system that aligns user profiles with suitable job opportunities according to their skills, experience, and career goals. This ensures a more efficient and targeted job search process for users.

Introduction

CodePrep is an AI and Machine Learning (AIML)-powered Skill Extraction and Skill Building Platform designed to help individuals improve their career prospects by identifying their skills, detecting skill gaps, and recommending personalized learning paths. The platform focuses on three main areas: skill extraction, skill enhancement, and job matching, aiming to bridge the gap between candidate competencies and industry requirements.

Objective

The primary goal of CodePrep is to:

• Analyze resumes and extract relevant skills automatically.
• Identify missing or underdeveloped skills.
• Provide personalized recommendations such as courses, certifications, and learning resources.
• Match users with suitable job opportunities based on their skills and experience.
• Improve employability and career growth through AI-driven insights.

Literature Survey

The project is based on research in:

1. Resume Parsing and Job Domain Prediction – Using NLP techniques and machine learning classifiers to extract resume information and predict job domains.
2. Deep Learning-Based Skill Extraction – Employing transformer models such as BERT and RoBERTa for accurate skill identification from resumes and job descriptions.
3. Semantic Job Matching (Resume2Vec) – Using embedding-based representations and similarity measures for improved job recommendations.
4. Skill Extraction and Classification Research – Leveraging modern datasets and context-aware models for better skill recognition and classification.

Methodology

The development process follows a structured approach:

1. Data Collection and Preprocessing
   • Gather resumes, job descriptions, and skill taxonomies.
   • Apply text preprocessing techniques such as tokenization, stop-word removal, and lemmatization.
2. Resume Parsing
   • Extract information such as education, experience, certifications, and skills using NLP techniques.
3. Skill Extraction and Classification
   • Use pre-trained language models (e.g., BERT, RoBERTa) to identify and categorize technical and soft skills.
4. Skill Gap Analysis
   • Compare user skills with industry requirements to identify missing competencies.
5. Recommendation Generation
   • Suggest courses, certifications, learning resources, and career development paths.
6. Job Matching
   • Match user profiles with job descriptions using AI and machine learning algorithms.

Proposed System

CodePrep provides an end-to-end AI-driven solution that:

• Processes unstructured resumes and job descriptions.
• Extracts and classifies skills automatically.
• Identifies strengths and weaknesses in user profiles.
• Generates personalized recommendations.
• Suggests relevant job opportunities based on skill compatibility.

System Architecture and Modules

The platform consists of several integrated modules:

• Resume Parsing Module
• Skill Extraction Module
• Skill Gap Analysis Module
• Recommendation Engine
• Job Matching System
• Skill Testing Module
• Interactive Dashboard
• Admin Module for job posting and system management

The workflow begins with resume upload, followed by skill extraction, gap analysis, recommendation generation, and job matching. Results are presented through a user-friendly dashboard.

Results and Performance

Testing showed that the platform successfully:

• Extracts skills, education, experience, and certifications from resumes.
• Identifies skill gaps accurately.
• Provides personalized learning recommendations.
• Matches users with relevant job opportunities.
• Conducts skill assessments and generates performance feedback.
• Handles incomplete or invalid data reliably.

The integration of AI and ML improves the accuracy, efficiency, and reliability of skill extraction and job matching while maintaining low response times.

Conclusion

The CodePrep – AIML-powered Skill Extraction and Skill Building platform has been successfully designed, developed, and tested. The system effectively integrates Artificial Intelligence and Machine Learning techniques to provide an intelligent solution for career development. It focuses on key functionalities such as resume parsing, skill extraction, skill gap analysis, personalized recommendations, job matching, skill testing, and administrative management. The platform successfully addresses the challenges faced by job seekers in identifying their skills, understanding missing competencies, and finding suitable job opportunities. By automating resume analysis and using machine learning algorithms, CodePrep provides accurate and personalized insights that help users improve their professional profiles

References

[1] N. K. Gupta, S. Sharma, and R. Kumar, “Automated Resume Parsing and Skill Extraction Using Natural Language Processing,” International Journal of Computer Applications, vol. 182, no. 3, pp. 25–31, 2021. [2] A. Joshi, P. R. Awar, and M. S. Patil, “AI-Based Skill Recommendation System for Career Development,” Proceedings of the 2022 IEEE International Conference on Artificial Intelligence, pp. 112–118, 2022. [3] T. Wolf et al., “HuggingFace’s Transformers: State-of-the-Art Natural Language Processing,” Journal of Machine Learning Research, vol. 21, no. 140, pp. 1–8, 2020. [4] J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of NAACL-HLT 2019, pp. 4171–4186, 2019. [5] Russell, S., & Norvig, P. Artificial Intelligence: A Modern Approach. Pearson Education. (Used for understanding core Artificial Intelligence concepts and problem-solving techniques) [6] Bishop, C. M. Pattern Recognition and Machine Learning. Springer. (Reference for machine learning algorithms used in classification and prediction tasks) [7] Jurafsky, D., & Martin, J. H. Speech and Language Processing. (Used for Natural Language Processing techniques applied in resume parsing) [8] Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning. MIT Press. (Reference for deep learning concepts relevant to AI-based systems) [9] Scikit-learn Documentation https://scikit-learn.org (Used for implementing machine learning models in the system) [10] TensorFlow Documentation – https://www.tensorflow.org (Reference for building and training AI/ML models) [11] Python Official Documentation – https://docs.python.org(Used for backend development and data processing) [12] Resume Parsing and NLP Techniques – Various research papers and online journals (Used for implementing resume analysis and skill extraction module) [13] Job Recommendation Systems using Machine Learning – IEEE Research Papers (Reference for designing job matching algorithms)

Copyright

Copyright © 2026 Prof. N. L. Bhale, Omkar Gaikwad, Palash Aware , Sagar Chavhan, Devyani Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.