This repository explores various classification algorithms commonly used in machine learning. Each algorithm has its strengths and weaknesses, and the choice of algorithm depends on the specific task and the characteristics of the data.

A perceptron is a fundamental building block of artificial neural networks. It is a simple, single-layer artificial neuron that takes one or more inputs, applies a weighted sum of the inputs, and then applies a threshold to the result. This threshold determines whether the perceptron fires an output of 1 or 0.

Install the required dependencies:

pip install -r requirements.txt

This repository contains code for implementing and evaluating each of the mentioned classification algorithms. It also includes examples of applying these algorithms to various datasets.

Feel free to contribute to this repository by adding more classification algorithms, expanding existing examples, or improving the documentation. Your contributions will help others learn about these powerful machine learning tools.