https://github.com/aymane-maghouti/machine-learning-from-scratch

This project implements various machine learning algorithms from scratch using Python and NumPy, without relying on external libraries such as TensorFlow, Keras, or scikit-learn. The implemented algorithms include classification, regression, clustering, and basic neural network models.
https://github.com/aymane-maghouti/machine-learning-from-scratch

classification clustering from-scratch machine-learning-algorithms neural-network regression

Last synced: 9 months ago
JSON representation

This project implements various machine learning algorithms from scratch using Python and NumPy, without relying on external libraries such as TensorFlow, Keras, or scikit-learn. The implemented algorithms include classification, regression, clustering, and basic neural network models.

• Host: GitHub
• URL: https://github.com/aymane-maghouti/machine-learning-from-scratch
• Owner: aymane-maghouti
• Created: 2023-08-17T10:50:12.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2023-08-22T12:53:09.000Z (almost 3 years ago)
• Last Synced: 2024-11-16T09:13:10.573Z (over 1 year ago)
• Topics: classification, clustering, from-scratch, machine-learning-algorithms, neural-network, regression
• Language: Python
• Homepage: https://aymane-maghouti.github.io
• Size: 8.7 MB
• Stars: 3
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Machine Learning Algorithms from Scratch

This project implements various machine learning algorithms from scratch using Python and NumPy, without relying on external libraries such as TensorFlow, Keras, or scikit-learn. The implemented algorithms include classification, regression, clustering, and basic neural network models.

## Table of Contents

- [Algorithms Implemented](#algorithms-implemented)

  - [Classification](#classification)

    - [K-Nearest Neighbors (KNN)](#k-nearest-neighbors-knn)

    - [Logistic Regression](#logistic-regression)

    - [Naive Bayes](#naive-bayes)

  - [Regression](#regression)

    - [Linear Regression](#linear-regression)

  - [Clustering](#clustering)

    - [K-Means Clustering](#k-means-clustering)

  - [Neural Networks](#neural-networks)

    - [Perceptron](#perceptron)

    - [Neural Network](#neural-network)

- [Project Structure](#project-structure)

- [Getting Started](#getting-started)

- [Usage](#usage)

- [Contributions and Feedback](#contributions-and-feedback)

- [Contact](#contact)

## Algorithms Implemented

### Classification

1. **K-Nearest Neighbors (KNN)**

   - The KNN algorithm classifies data points based on the majority class of their k-nearest neighbors in the feature space.

2. **Logistic Regression**

   - Logistic Regression is a binary classification algorithm that models the probability of a data point belonging to a particular class using a logistic function.

3. **Naive Bayes**

   - The Naive Bayes algorithm is a probabilistic classifier based on Bayes' theorem with the assumption of independence between features.

### Regression

4. **Linear Regression**

   - Linear Regression is used for predicting continuous numerical values by fitting a linear relationship between the input features and the target variable.

### Clustering

5. **K-Means Clustering**

   - K-Means is an unsupervised clustering algorithm that partitions data points into 'k' clusters based on their similarity in the feature space.

### Neural Networks

6. **Perceptron**

   - The Perceptron is a basic neural network unit that learns to classify inputs into two categories using a weighted sum and a threshold activation function.

7. **Neural Network**

   - This implementation represents a simple feedforward neural network with customizable architecture, including multiple layers and activation functions.

## Project Structure

- **/algorithms**

  - Contains implementation files for each algorithm (e.g., `KNN`, `KMeans`, ...).

- **Example.py**

  - An example usage script that demonstrates how to use each implemented algorithm on sample datasets.

- **README.md**

  - The main documentation file (this file) that provides an overview of the project, algorithms, and instructions for usage.

Otherwise, this is the repository tree:

``` bash

├───Classification

│   ├───KNN

│   │   └───KNN.py

│   │   └───Example.py

│   ├───LogisticRegression

│   │   └───LogisticRegression,py

│   │   └───Example.py

│   └───Naive Bayes

│       └───NaiveBayes.py

│   │   └───Example.py

├───Clustering

│   └───KMeans

│       └───KMeans.py

│   │   └───Exampl.py

├───Neural Network

│   ├───Neural Network

│   │   └───NeuralNetwork.py

│   │   └───train_dataset.csv

│   └───Perceptron

│       └───Perceptron.py

│   │   └───Example.py

└───Regression

    └───LinearRegression

│   │   └───LinearRegression.py

        └───Example.py

```

## Getting Started

1. Clone this repository: `git clone https://github.com/aymane-maghouti/Machine-Learning-From-Scratch.git`

2. Navigate to the project directory: `cd Machine-Learning-From-Scratch`

3. Install required packages: `pip install numpy matplotlib pandas`

4. Run the example script: `python Example.py`

## Usage

Each algorithm's implementation is contained within its respective file in the `/algorithms` directory. To use a specific algorithm, import the corresponding module in your code.

Example:

``` python 

from KNN import KNN

#Create KNN classifier

knn = KNN(k=3)

knn.fit(X_train, y_train)

predictions = knn.predict(X_test)

```

## Contributions and Feedback

If you find any issues, bugs, or ways to improve the implementations, please feel free to open an issue or pull request.

## Contact

By Aymane Maghouti