🛡️ Network Security ML MLOps Project

📘 Project Overview

This project is a Phishing URL Detection System developed using Python and deployed following MLOps principles.
The goal is to classify URLs as phishing or legitimate through a fully automated pipeline covering ETL, validation, transformation, model training, tracking, and deployment.

🔑 Key Highlights

• Automated data ingestion, validation, and transformation
• Model training using multiple ML algorithms
• Experiment tracking via MLflow integrated with DagsHub
• AWS S3 for artifact and model storage
• CI/CD pipeline using GitHub Actions
• Containerized deployment with Docker
• Deployed to AWS ECS, and run on EC2 instance for scalable serving
• FastAPI-based REST API for prediction

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⚙️ Tech Stack

• Languages & Libraries: Python 3.10+, pandas, numpy, scikit-learn, mlflow, fastapi, uvicorn
• Database: MongoDB Atlas (as ETL data source)
• Cloud & Deployment: AWS S3, ECS, EC2
• MLOps Tools: MLflow, DagsHub
• Automation: Docker, GitHub Actions

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🧩 Project Architecture

Below are visual representations of the end-to-end workflow:

🗂️ Overall Project Structure

🧠 Data Ingestion

✅ Data Validation

🔄 Data Transformation

🤖 Model Trainer

🚀 Model Deployment

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📦 Python Dependencies

python-dotenv
pandas
numpy
pymongo
certifi
pymongo[srv]
scikit-learn
mlflow
pyaml
dagshub
fastapi
uvicorn
python-multipart