Data Transmission and Machine Learning with Client-Server Model

Introduction

This repository contains code for demonstrating data transmission and machine learning using a client-server model. In this project, we create a client-server architecture where the client generates random data and sends it to the server. The server then processes the received data, trains a machine learning model, and evaluates its performance.

Learning Objectives

• Understand how the client-server model works.
• Learn how to transmit data between client and server using sockets.
• Train a machine learning model on server-side using received data.
• Evaluate the performance of the trained model.

Components

• serverAI.py: This file contains the code for the server side. It listens for connections from clients, receives data, processes it, trains a machine learning model, and evaluates its performance.
• clientAI.py: This file contains the code for the client side. It generates random data and sends it to the server at regular intervals.

Usage

1. Clone this repository to your local machine:

   git clone https://github.com/Halildeniz/data-transmission-and-machine-learning.git

2. To install the required libraries, run the following command:

   $ pip install -r requirements.txt

3. Navigate to the project directory:

   $ cd data-transmission-and-machine-learning

4. In a separate terminal, start the client by running serverAI.py

   $ python serverAI.py

5. In a separate terminal, start the client by running clientAI.py:

   $ python clientAI.py

The client will generate random data and send it to the server, which will then process the data, train a machine learning model, and evaluate its performance

Contributing

Contributions are welcome! Please feel free to open an issue or submit a pull request with any improvements or additional features.

🔗 Links:

• LinkedIn : https://www.linkedin.com/in/halil-ibrahim-deniz/
• Instagram : https://www.instagram.com/deniz.halil333/
• YouTube: https://www.youtube.com/c/HalilDeniz
• Personal Website: https://denizhalil.com/
• Instagram (Production Brain): https://www.instagram.com/production.brain/
• YouTube (Production Brain): https://www.youtube.com/@ProductionBrain1
• Discord (Production Brain): https://discord.gg/nGBpfMHX4u
• Additional Resources:
  • Machine Learning in Network Security: Preventing Cyber Attacks
  • Machine Learning in Cybersecurity: An Artificial Neural
  • Assessing Password Strength with Machine Learning in Python
  • Harnessing Machine Learning for Enhanced Cybersecurity
  • Machine Learning with Python: Top Libraries and Their Examples
  • Beginning Your Journey in Programming and Cybersecurity - Navigating the Digital Future
  • What Is Artificial Intelligence? A Comprehensive Overview

🎁 Donation Links:

• Buy Me a Coffee: https://www.buymeacoffee.com/halildeniz
• Patreon: patreon.com/denizhalil

📧 Contacts:

• General Queries: halildeniz313@gmail.com
• Technical Support: deniz.halil3363@hotmail.com
• Business Inquiries: halildeniz@denizhalil.com

License

This project is licensed under the MIT License. See the LICENSE file for details.