Support Vector Machine (SVM) Visualization

This repository contains a Jupyter Notebook designed to visualize and explain the core mechanics of Support Vector Machines (SVM), specifically focusing on the role of Support Vectors.

Project Overview

Support vectors are the training examples that directly influence the position and orientation of the decision boundary. This project demonstrates that removing non-support vectors from the training set results in the exact same model.

Key Features:

• Algorithm: Linear Support Vector Classification (SVC).
• Data Visualization:
  • Custom meshgrid functions to plot decision boundaries.
  • Comparison plots between a model trained on a full dataset vs. a model trained only on support vectors.
• Key Concept: Demonstrates that the model's decision boundary remains identical even when the training set is reduced to only its support vectors.

Installation

1. Clone the repository:

   git clone https://github.com/Joe-Naz01/svm.git
   cd svm
   
   python -m venv .venv
    # Windows: .venv\Scripts\activate
    source .venv/bin/activate
    pip install -r requirements.txt
    jupyter notebook