https://github.com/thecoderpinar/ml-perceptron-project

This project demonstrates the implementation of the Perceptron algorithm for binary classification tasks. It includes various advanced features such as data augmentation, feature engineering, and deep learning techniques to enhance model performance and robustness.
https://github.com/thecoderpinar/ml-perceptron-project

artificialintelligence binaryclassification dataanalysis datascience deep-learning jupyter-notebook machinelearning opensource perceptronalgorithm programming python

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This project demonstrates the implementation of the Perceptron algorithm for binary classification tasks. It includes various advanced features such as data augmentation, feature engineering, and deep learning techniques to enhance model performance and robustness.

• Host: GitHub
• URL: https://github.com/thecoderpinar/ml-perceptron-project
• Owner: ThecoderPinar
• License: mit
• Created: 2023-10-17T17:37:05.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-17T17:52:32.000Z (over 1 year ago)
• Last Synced: 2024-01-30T02:40:20.284Z (12 months ago)
• Topics: artificialintelligence, binaryclassification, dataanalysis, datascience, deep-learning, jupyter-notebook, machinelearning, opensource, perceptronalgorithm, programming, python
• Language: Jupyter Notebook
• Homepage:
• Size: 847 KB
• Stars: 2
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine Learning Perceptron Project

![Perceptron Image](https://miro.medium.com/v2/resize:fit:645/0*LJBO8UbtzK_SKMog)

Welcome to the Machine Learning Perceptron Project repository! 🚀

This project is an in-depth exploration of the Perceptron algorithm for binary classification tasks. By integrating advanced machine learning techniques and a comprehensive data preprocessing pipeline, we aim to provide a holistic understanding of the algorithm's capabilities and limitations.

## Project Overview

The primary objectives of this project include:

- Implementing the Perceptron algorithm from scratch and analyzing its performance on various datasets.

- Incorporating data augmentation techniques to enhance the robustness of the model.

- Conducting feature engineering and selection to optimize the model's predictive capabilities.

- Leveraging deep learning frameworks for comparison and performance evaluation.

## Features

✨ Data Augmentation with Noise Addition: Enhancing the generalization capacity of the model.

✨ Automatic Feature Selection: Selecting the most informative features to avoid overfitting.

✨ Dimensionality Reduction with PCA: Simplifying complex data representations for better visualization.

✨ Kernel-Based Methods: Exploring non-linear feature mappings for intricate pattern recognition.

✨ Deep Learning Techniques with TensorFlow: Implementing a neural network for comparison and analysis.

## Usage

To run the project, follow these steps:

1. Clone the repository: `git clone https://github.com/ThecoderPinar/ML-Perceptron-Project.git`

2. Install the required dependencies: `pip install -r requirements.txt`

3. Execute the main script: `Perceptron_Algorithm_Implementation.ipynb`

Feel free to explore the codebase and experiment with different parameters to observe the algorithm's behavior.

## Contributors

A big thank you to all the contributors who have dedicated their time and expertise to enrich this project.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

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For more detailed insights and discussions, please refer to the [Wiki](https://github.com/your-username/ML-Perceptron-Project/wiki) section. Your feedback and contributions are highly appreciated!

If you have any questions or suggestions, feel free to reach out. Let's learn and grow together! 🌟

#MachineLearning #PerceptronAlgorithm #DataScience #GitHubProjects