Linear Support Vector Classifier (SVC) Visualization This repository contains Python code to visualize the decision boundaries created by a linear Support Vector Classifier (SVC) on the Iris dataset.

Dependencies: *numpy *matplotlib *scikit-learn You can install the dependencies using pip: pip install numpy matplotlib scikit-learn Usage: 1.Clone the repository: git clone https://github.com/your-username/linear-svc-visualization.git 2.Navigate to the repository directory: cd linear-svc-visualization 3.Run the Python script: python linear_svc_visualization.py Description: The code performs the following tasks: *Loads the Iris dataset. *Prepares the data by selecting only the first two features (sepal length and sepal width). *Initializes and trains a linear Support Vector Classifier (SVC) using scikit-learn. *Creates a mesh grid to visualize the decision boundaries. *Plots the decision boundaries and data points using matplotlib. Result: The script generates a plot showing the decision boundaries created by the linear SVC to classify the Iris dataset based on sepal length and sepal width.