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.

• 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.

• 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.

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

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: [email protected]
• Technical Support: [email protected]
• Business Inquiries: [email protected]

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