# 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
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