Classifying Iris Using SVM

# Introduction

In this project, you will learn how to classify the iris dataset using a Support Vector Classifier (SVC) model. The iris dataset is a classic machine learning dataset that contains information about different species of irises, including their sepal length, sepal width, petal length, and petal width.

## 🎯 Tasks

In this project, you will learn:

- How to import the required libraries and load the iris dataset
- How to split the dataset into training and testing sets
- How to create and train a Support Vector Classifier model
- How to make predictions using the trained model
- How to evaluate the model's performance using accuracy score and classification report

## 🏆 Achievements

After completing this project, you will be able to:

- Use the scikit-learn library to work with the iris dataset
- Split a dataset into training and testing sets
- Create and train a Support Vector Classifier model
- Make predictions using a trained model
- Evaluate a model's performance using accuracy score and classification report