Raw Models Implementation

Welcome to the Raw Models Implementation project! This repository contains implementations of various machine learning models using Python and NumPy. The goal of this project is to provide clear and concise implementations of linear models, decision tree-based models (boosting and bagging), and neural network models, all written using only NumPy for educational purposes.

Table of Contents

1. Introduction
2. Models Implemented
3. Usage
4. Dependencies
5. Contributing
6. License

Introduction

Machine learning is a fascinating field, and understanding the inner workings of different algorithms is crucial for mastering it. This project aims to help individuals grasp the fundamentals by providing implementations of key machine learning models.

Models Implemented

1. Linear Models

• Linear Regression: Simple linear regression using gradient descent.
• Logistic Regression: Binary classification using logistic regression.

2. Decision Tree-Based Models

• Decision Trees: Implementation of a basic decision tree for classification.
• Random Forest: Ensemble method combining multiple decision trees for improved performance.
• Gradient Boosting: Boosting technique using decision trees as weak learners.

3. Neural Network Models

• Feedforward Neural Network (FNN): Implementation of a basic feedforward neural network with customizable architecture.
• Backpropagation: Implementation of the backpropagation algorithm for training neural networks.

Usage

To use any of the implemented models, follow these general steps:

1. Clone the repository:

   git clone https://github.com/Nunyapa/raw_models.git
   cd raw_models

2. Choose the model you want to explore from the appropriate directory.

3. Open the Python script in your favorite editor or Jupyter notebook.

4. Run the script to see the model in action.

Feel free to modify the models and experiment with different datasets.

Dependencies

The implementations in this project rely on the following libraries:

• NumPy

You can install the required dependencies using the following command:

pip install numpy

Contributing

If you'd like to contribute to this project, please follow the contribution guidelines. Contributions, suggestions, and bug reports are highly encouraged!

License

This project is licensed under the MIT License - see the LICENSE file for details. Feel free to use, modify, and distribute the code for educational and non-commercial purposes.