This repository contains an Implementation of SVM's using pandas and sklearn in python.

Overview

Support Vector Machines (SVM) is a powerful algorithm for classification which also finds the best boundary. We explored topics related to Maximum Margin Classifier, Classification with Inseparable Classes, and Kernel Methods.

Contents

• SVM.ipynb: Jupyter Notebook containing the implementation of SVM's using Python.
• data.csv: Sample dataset used in the notebook for demonstration purposes.
• README.md: This file providing an overview of the repository.

Requirements

To run the code in the Jupyter Notebook, you need to have Python installed on your system along with the following libraries:

• NumPy
• pandas
• scikit-learn

You can install these libraries using pip:

pip install numpy pandas scikit-learn

Usage

1. Clone this repository to your local machine:

git clone https://github.com/BaraSedih11/Support-Vector-Machine.git

2. Navigate to the repository directory:

cd SVM

3. Open and run the Jupyter Notebook SVM.ipynb using Jupyter Notebook or JupyterLab.

4. Follow along with the code and comments in the notebook to understand how SVM's is implemented using Python.

Acknowledgements

• scikit-learn: The scikit-learn library for machine learning in Python.
• NumPy: The NumPy library for numerical computing in Python.
• pandas: The pandas library for data manipulation and analysis in Python.