Machine Learning Algorithms from Scratch

Welcome to Pynot, a repository dedicated to implementing machine learning algorithms from scratch.

This repository contains implementations of various machine learning algorithms written from scratch in Python. The aim is to provide a clear understanding of how these algorithms work under the hood.

Table of Contents

• Introduction
• Algorithms Implemented
• Installation
• Usage
• Comparisons
• Contributing

Introduction

This project is designed for educational purposes to help you understand the inner workings of popular machine learning algorithms. By implementing these algorithms from scratch, you will gain a deeper insight into their mechanics, which is often abstracted away by high-level libraries such as scikit-learn or TensorFlow.

Algorithms Implemented

The following algorithms are currently implemented in this repository:

1. Linear Regression
2. Logistic Regression
3. Decision Tree
4. Random Forest
5. Neural Networks
6. Support Vector Machine (SVM) - On Going
7. K-Nearest Neighbors (KNN) - On Going
8. Principal Component Analysis (PCA) - On Going

Each algorithm is implemented in its own module with a corresponding example script demonstrating its usage.

Installation

To get started, clone the repository and install the required dependencies.

git clone https://github.com/omartarekmoh/ML_From_Scratch.git
cd ML_From_Scratch
pip install -r requirements.txt

Usage

Each algorithm can be used by importing the respective module. Below is an example of how to use the Linear Regression implementation.

from pyatch.linear_models.LinearRegression import LinearRegression
import numpy as np

# Generate some example data
X = np.array([[1, 1], [1, 2], [2, 2], [2, 3]])
y = np.dot(X, np.array([1, 2])) + 3

# Initialize and train the model
model = LinearRegression()
model.fit(X, y)

# Make predictions
predictions = model.predict(X)
print(predictions)

Comparisons

You can find comparison scripts for each algorithm in the comparisons directory. These scripts demonstrate how to use the algorithms on various datasets and it's preformace against the scikit-learn algorithms. To run a comparison

This can be used with any model that was made

1. Navigate to the comparisons directory within the project.
2. Run the *model_name*_comparison.py script.

cd comparisons
python linear_regression_comparison.py

Contributing

Contributions are welcome! If you would like to add a new algorithm, improve existing implementations, or fix bugs, please open a pull request. Make sure to follow the contribution guidelines.

1. Fork the repository
2. Create a new branch (git checkout -b feature/your-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature/your-feature)
5. Open a pull request

Contributors

• @omartarekmoh
• @omarghazy56