Network Security ML Project

📌 About

This repository contains my learning journey through the end-to-end implementation of a Machine Learning project in Network Security.

I am following along with Krish Naik Sir's implementation to understand how a real-world ML project is built from scratch, from data ingestion all the way to deployment.

🎯 What I'm Learning

• Data Ingestion
• Data Validation
• Data Transformation
• Exploratory Data Analysis
• Feature Engineering
• Model Training
• Model Evaluation
• Model Selection
• ML Pipelines
• MongoDB Integration
• Logging & Exception Handling
• Configuration Management
• CI/CD
• Docker
• AWS Deployment
• End-to-End ML Project Structure

🧠 Learning Goal

The main goal of this project is to understand how an ML project works end-to-end, rather than just training a model in a notebook.

I am learning how to structure, develop, test, deploy, and maintain a production-oriented Machine Learning project.

🛠️ Tech Stack

• Python
• NumPy
• Pandas
• Scikit-learn
• MongoDB
• Docker
• Git & GitHub
• AWS
• CI/CD

📚 Learning Source

I am following along with Krish Naik Sir's Network Security ML project/tutorial as a learning reference.

This repository documents my implementation, experiments, and understanding as I progress through the project.

🚀 Status

🚧 Currently Learning & Building

This project is a work in progress and will be updated as I learn and implement each stage of the pipeline.

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Learning by building. Understanding by implementing. 🚀