Machine Learning From Scratch

Overview

This project is an implementation of various popular machine learning algorithms built entirely from scratch using Python. Each algorithm is implemented in its own file (e.g., DecisionTrees.py) with a corresponding Jupyter notebook (train.ipynb) for testing and training on sample datasets.

Implemented Algorithms:

1. Decision Tree - A tree-based model used for classification and regression tasks.
2. K-Means Clustering - A popular unsupervised learning algorithm used for clustering tasks.
3. K-Nearest Neighbors (KNN) - A simple, instance-based learning algorithm used for classification and regression.
4. Linear Regression - A linear approach to modeling the relationship between a dependent variable and one or more independent variables.
5. Logistic Regression - A statistical method for binary classification tasks.
6. Naive Bayes - A probabilistic classifier based on Bayes' theorem.
7. Principal Component Analysis (PCA) - A dimensionality reduction technique.
8. Perceptron - One of the simplest types of artificial neural networks used for binary classification.
9. Random Forest - An ensemble learning method based on decision trees for classification and regression tasks.

Features

• Each algorithm is implemented without relying on high-level machine learning libraries, focusing on understanding the fundamental principles behind each technique.
• A train.ipynb notebook is provided to demonstrate how to train and test each algorithm with a sample dataset.

Getting Started

Prerequisites

• Python 3.x
• Jupyter Notebook
• Required dependencies are listed in requirements.txt.

Installation

1. Clone the repository:

   git clone https://github.com/KacemMathlouthi/Machine-Learning-From-Scratch.git

2. Navigate to the project directory:

   cd Machine-Learning-From-Scratch

3. Install the dependencies:

   pip install -r requirements.txt

Running the Notebooks

1. Open the train.ipynb notebook in Jupyter, e.g. the KNN training notebook:

   jupyter notebook KNN/train.ipynb

2. Follow the steps to test and train each algorithm on sample datasets.

Configuration

• .gitignore: Lists files and directories to ignore in version control.
• requirements.txt: Contains a list of required Python packages.
• README.md: This file, providing an overview and usage guide for the project.

License

This project is licensed under the MIT License - see the LICENSE file for details.