Job Success Compass 🎯

A modern web application that leverages machine learning to predict job application success. Built with Flask, TypeScript, and custom ML algorithms, it empowers job seekers and employers with actionable insights and a seamless experience.

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🚀 Features

• ML-Powered Predictions: Real-time job application success probability using custom logistic regression (NumPy).
• User Roles: Secure authentication for job seekers, employers, and admins.
• Survey System: Collects and analyzes user data for better predictions.
• Job Offer Management: Employers can post, edit, and manage job offers.
• Interactive Dashboard: Personalized analytics and visualizations.
• Modern UI/UX: Responsive, accessible, and supports dark/light mode.

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🏁 Quick Start

Prerequisites

• Python 3.8+
• Node.js 16+
• npm or bun

Installation

1. Clone the repository

   git clone <your-repo-url>
   cd 2425-11-b-pp-student-practices-assignment-SSPopov21

2. Set up Python environment

   python -m venv venv
   # Windows:
   venv\Scripts\activate
   # macOS/Linux:
   source venv/bin/activate
   pip install -r requirements.txt

3. Install frontend dependencies

   npm install
   # or
   bun install

4. Configure environment Create a .env file in the root with:

   FLASK_APP=run.py
   FLASK_ENV=development
   SECRET_KEY=your-secret-key
   DATABASE_URL=sqlite:///job_success.db
   MAIL_SERVER=smtp.gmail.com
   MAIL_PORT=587
   MAIL_USE_TLS=True
   MAIL_USERNAME=[email protected]
   MAIL_PASSWORD=your-app-password

5. Initialize the database

   flask db upgrade

6. Run the app
   • Backend: flask run
   • Frontend: npm run dev or bun run dev

Visit http://localhost:5000 for the backend and http://localhost:5173 for the frontend (if using Vite).

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🗂️ Project Structure

2425-11-b-pp-student-practices-assignment-SSPopov21/
├── app/           # Flask backend (models, routes, ML, forms, templates)
├── src/           # React/TypeScript frontend (components, pages, utils)
├── migrations/    # Database migrations
├── public/        # Public assets (images, favicon, etc.)
├── requirements.txt
├── run.py         # Flask entry point
└── ...            # Configs, docs, etc.

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🧠 Machine Learning Model

• Algorithm: Custom logistic regression (NumPy)
• Features: Experience, education, skills, industry, job changes, certifications, languages, interview preparation
• Output: Success probability and key influencing factors

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🛠️ Technologies Used

• Backend: Flask, Flask-SQLAlchemy, Flask-Migrate, Flask-Login, Flask-WTF, NumPy
• Frontend: React, TypeScript, Tailwind CSS, Vite
• Database: SQLite (default, via SQLAlchemy)
• Other: Python-dotenv, WTForms

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🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/YourFeature)
3. Commit your changes
4. Push and open a Pull Request

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📬 Contact

Author: Your Name
Email: [email protected]
Project Link: https://github.com/yourusername/job-success-compass

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🙏 Acknowledgments

• Flask, TypeScript, NumPy, React, and all contributors

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