This is a repository containing code examples of Support Vector Machines (SVM) implementation in Python using Scikit-learn.

• Introduction
• Dependencies
• Usage
• Support Vector Machines
• Conclusion

Support Vector Machines is a powerful machine learning algorithm used for classification and regression analysis. SVM is based on the idea of finding a hyperplane that best separates the data into different classes.

The code in this repository is written in Python 3. It uses the following dependencies:

• NumPy
• Scikit-learn
• Matplotlib

You can install these dependencies using pip:

pip install numpy
pip install scikit-learn
pip install matplotlib

To use this code, simply clone the repository:

git clone https://github.com/Sam-ops09/Support-Vector-Machine.git

Then navigate to the cloned directory and run the desired Python file. For example:

cd Support-Vector-Machine
python svm_classification.py

The repository contains two examples of SVM implementation:

1. SVM Classification: This example shows how to implement SVM for classification analysis. It uses a dataset of iris flowers and classifies them into three different species.

2. SVM Regression: This example shows how to implement SVM for regression analysis. It uses a dataset of housing prices and predicts the prices based on different features.

Support Vector Machines is a powerful machine learning algorithm used for classification and regression analysis. The implementation of SVM using Scikit-learn in Python is straightforward and easy to use. This repository provides code examples for SVM implementation, which can be used as a starting point for more complex projects.