🌸 Iris Classifier ML Pipeline — Complete Tutorial & Instructions Manual

1. 1Install required libraries using pip
2. 2Load the Iris dataset using scikit-learn
3. 3Preprocess the data by encoding categorical variables
4. 4Split the data into training and testing sets
5. 5Train a classifier model using scikit-learn
6. 6Evaluate the model's performance using metrics such as accuracy and precision

Data scientists and machine learning engineers can use this tutorial to learn how to build and deploy a complete ML pipeline, while software engineers can learn how to integrate ML models into their applications

💡 A well-structured ML pipeline is crucial for building and deploying accurate machine learning models

🚀 Build a complete Iris Classifier ML pipeline with Python and scikit-learn! 🌸