This machine learning project focuses on classifying iris flowers into three species—setosa, versicolor, and virginica—based on four botanical measurements: sepal length, sepal width, petal length, and petal width. Using the well-known Iris dataset, the project involves data preprocessing, model selection, training, and evaluation. Popular algorithms like Decision Trees, Support Vector Machines, and Random Forest are explored. The goal is to create a reliable model, measured by accuracy and other metrics, providing a foundation for broader applications in machine learning. The project utilizes Python libraries such as scikit-learn for implementation and Jupyter Notebooks for documentation and visualization