🧭 PATH FINDER AI-Assisted Career Guidance System for Engineering Students 📌 Overview
PATH FINDER is a career suggestion system designed to help engineering students across all branches identify suitable career paths based on their:
Personal interests and current skills
Work environment preferences
Return on investment (ROI) considerations
Academic performance and support system
The system uses a static career database and a rule-based AI agent to generate personalized career recommendations and highlight skill gaps along with a career preparation framework.
This project is currently in its MVP (Minimum Viable Product) stage and is built using Python and Streamlit.
🎯 Target Users
Engineering students (any branch)
Students exploring:
Private sector jobs
Government jobs
Higher studies
Entrepreneurship
Bank / PSU jobs
⚙️ System Architecture (MVP) User Interface (Streamlit) ↓ User Profile Builder ↓ Static Career Database ↓ AI Career Agent (Rule-based reasoning) ↓ Career Recommendation + Skill Gap Analysis
🧠 Core Features
Career recommendations based on user profile
Skill matching between user and career requirements
Identification of missing skills
Career preparation focus suggestions
Interactive web interface using Streamlit
📁 Project Structure PATH_FINDER/ │ ├── app.py # Streamlit UI ├── agent.py # Career recommendation agent ├── career_db.py # Static career database ├── scoring.py # Career matching logic ├── requirements.txt └── README.md
🚀 How It Works
The user enters their skills and preferences through the Streamlit interface.
The AI agent compares the user profile with predefined career paths.
Each career path is scored based on skill match and preferences.
The system recommends the best-matched career.
Missing skills and preparation focus are displayed.
🛠️ Installation & Setup 1️⃣ Clone or download the project git clone cd PATH_FINDER
2️⃣ Install dependencies pip install -r requirements.txt
3️⃣ Run the application streamlit run app.py
🧪 Technologies Used
Python
Streamlit
Rule-based AI agent architecture
📈 Current Limitations
Uses a static career database
No machine learning or LLM integration yet
Limited career categories
No persistence of user data
🔮 Future Enhancements
Interest profiling (RIASEC / O*NET-based)
LLM-powered conversational career agent
Real O*NET skill mappings
Personalized learning paths
Scholarship and financial planning awareness
Mentor recommendation system
🤝 Contribution
This project is under active development. Suggestions, feedback, and contributions are welcome.
👤 Author
Adila Shaharban B.Tech Computer Science & Engineering Government College of Engineering, Kannur
📝 License
This project is developed for educational and research purposes.
💡 Why this README is important
Shows clarity of thinking
Makes your project interview-ready
Helps future collaborators understand your system
Signals that you build real engineering projects