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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.

License: MIT License

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

ml-perceptron-project's Introduction

Machine Learning Perceptron Project

Perceptron Image

Welcome to the Machine Learning Perceptron Project repository! ๐Ÿš€

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

โœจ Data Augmentation with Noise Addition: Enhancing the generalization capacity of the model. โœจ Automatic Feature Selection: Selecting the most informative features to avoid overfitting. โœจ Dimensionality Reduction with PCA: Simplifying complex data representations for better visualization. โœจ Kernel-Based Methods: Exploring non-linear feature mappings for intricate pattern recognition. โœจ 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 file for details.


For more detailed insights and discussions, please refer to the 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! ๐ŸŒŸ

#MachineLearning #PerceptronAlgorithm #DataScience #GitHubProjects

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