https://github.com/udacity-machinelearning-internship/support-vector-machine

Implementing SVM's using pandas and sklearn in python
https://github.com/udacity-machinelearning-internship/support-vector-machine

numpy pandas sklearn support-vector-machines svm

Last synced: 9 months ago
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Implementing SVM's using pandas and sklearn in python

• Host: GitHub
• URL: https://github.com/udacity-machinelearning-internship/support-vector-machine
• Owner: Udacity-MachineLearning-Internship
• Created: 2024-05-21T20:21:08.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-22T10:41:47.000Z (over 1 year ago)
• Last Synced: 2025-01-21T08:24:10.225Z (11 months ago)
• Topics: numpy, pandas, sklearn, support-vector-machines, svm
• Language: Jupyter Notebook
• Homepage:
• Size: 6.84 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          


![Support_Vector_Machine](https://github.com/BaraSedih11/Support-Vector-Machine/assets/98843912/2499f646-0994-43b4-a62d-a852adaeaf3d)

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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:

```bash

pip install numpy pandas scikit-learn

```

## Usage

1. Clone this repository to your local machine:

```bash

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

```

2. Navigate to the repository directory:

```bash

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](https://scikit-learn.org/): The scikit-learn library for machine learning in Python.

- [NumPy](https://numpy.org/): The NumPy library for numerical computing in Python.

- [pandas](https://pandas.pydata.org/): The pandas library for data manipulation and analysis in Python.