A skill-based student progress and career recommendation system that bridges the gap between online certifications and real-world career opportunities.
SkillBridge is an integrated platform that automatically extracts skills from resumes and course certificates, evaluates competency levels, and recommends personalized career paths — all within a single unified system.
Built as a mini-project for the Bachelor of Engineering in Computer Science and Business Systems at BMS Institute of Technology & Management, Bengaluru (VTU), 2024-25.
- 📄 Resume Parsing — Upload a PDF resume; skills, education, and experience are extracted automatically
- 🎓 Course Management — Add completed courses with progress, grades, and hours spent
- 🧠 Skill Proficiency Scoring — Proficiency computed using a weighted formula:
Score = 0.5 × Progress + 0.3 × Grade + 0.2 × Duration - 🔍 Semantic Job Matching — NLP-powered matching using spaCy embeddings and cosine similarity
- 📊 Career Recommendations — Top job roles ranked by a composite match score (0–100)
- 🕳️ Skill Gap Analysis — Identifies missing skills and suggests targeted courses
- 📈 Analytics Dashboard — Visualizes skill distribution, course progress, and job readiness
- 📧 Email Notifications — Sends career insights and recommendations to your inbox via SendGrid
| Layer | Technology |
|---|---|
| Frontend | Streamlit |
| Backend | Python 3.8+ |
| NLP | spaCy, NLTK |
| Machine Learning | scikit-learn |
| Resume Parsing | PyPDF2, pytesseract (OCR) |
| Database | SQLite (dev) / MySQL (production) |
| Visualization | Plotly, Matplotlib |
| Email Service | SendGrid |
| Authentication | bcrypt |
| Data Processing | pandas, NumPy |
- Python 3.8+
- pip
- A SendGrid account (for email features)
- Clone the repository
git clone https://github.com/NandithaKale/SkillBridge.git
cd SkillBridge
- Install dependencies
pip install -r requirements.txt
-
Set up environment variables
Create a
.envfile in the root directory:
SENDGRID_API_KEY=your_sendgrid_api_key_here
- Run the application
streamlit run app.py
The system follows a 4-layer architecture:
- Presentation Layer — Streamlit web dashboard
- Business Logic Layer — Python modules: AuthManager, ResumeParser, CareerPredictor, EmailService
- Data Processing Layer — NLP engine (spaCy) for semantic skill analysis
- Persistence Layer — Relational database storing users, courses, jobs, and recommendations
- Skills are extracted from resumes and course certificates using NLP
- Proficiency scores are computed per skill based on progress, grade, and time invested
- User skills are semantically matched against job requirements (threshold: cosine similarity ≥ 0.65)
- A composite match score is calculated:
MatchScore = 40 × Coverage + 50 × AvgProficiency + 10 × ImportanceScore - Jobs are ranked and displayed with skill gap analysis and learning path suggestions
| Name | USN |
|---|---|
| D. Nanditha Kale | 1BY23CB012 |
| Rishika Gitta | 1BY23CB043 |
| Sai Kavyashree S. | 1BY23CB048 |
| Sanjana Athani | 1BY24CB403 |
Guide: Dr. Vishwa Kiran S., Professor & Head of Department, CSBS — BMSIT&M
- Integration with LinkedIn, GitHub, and digital badge platforms
- Transformer-based embeddings (BERT/GPT) for improved matching
- Expanded skill taxonomy using ESCO / ONET standards
- Mobile application for on-the-go career guidance
- Feedback loops for continuous personalization
This project was developed for academic purposes under VTU, 2024-25.