🚀 E2E Job Recommender System

Welcome to the E2EJobRecommender repository! This project is a robust, end-to-end job recommendation system designed to match users with the most relevant job opportunities using advanced algorithms and scalable infrastructure.

🌟 Features

• Personalized Job Recommendations: Tailors job suggestions to each user's profile, preferences, and skillset.
• End-to-End Pipeline: From data ingestion and preprocessing to model training and recommendation serving.
• Modular Architecture: Organized codebase with clear separation of concerns for easy customization and extension.
• Dockerized Deployment: Easily spin up the system using Docker and Docker Compose for consistent environments.
• Interactive Notebooks: Jupyter notebooks for exploratory data analysis, model evaluation, and prototyping.
• Logging & Artifacts: Comprehensive logging and artifact management for experiment tracking and debugging.

🏗️ Project Structure

├── app/                # Application logic (APIs, interfaces)
├── artifacts/          # Model artifacts, checkpoints, etc.
├── logs/               # Log files
├── notebooks/          # Jupyter notebooks for prototyping & EDA
├── src/                # Source code for core modules
├── main.py             # Main entry point to launch the server
├── requirements.txt    # Python dependencies
├── docker-compose.yaml # Docker Compose configuration
├── Dockerfile          # Docker image build file
├── setup.py, pyproject.toml # Packaging and build files

🚦 Getting Started

1. Prerequisites

• Python (version as per .python-version)
• Docker & Docker Compose

2. Installation

Clone the repository:

git clone https://github.com/Kra09-kp/E2EJobRecommender.git
cd E2EJobRecommender

Install dependencies:

pip install -r requirements.txt

3. Running the Server

Start the application using MCP:

mcp dev main.py

• This will launch the job recommender server.
• Stop the server with Ctrl+C.

Tip: If you encounter a timeout error while fetching jobs, increase the request timeout value in the server UI's configuration section.

Or, use Docker Compose:

docker-compose up --build

📝 Notebooks

Check the notebooks/ directory for sample data analysis, model experiments, and prototyping.

📄 License

This project is licensed under the MIT License.

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Built with ❤️ by Kra09-kp