This project focuses on training a machine learning model to classify iris flowers based on their measurements. The iris flower dataset contains measurements of three different species: setosa, versicolor, and virginica. These species exhibit variations in their measurements, making them distinguishable from each other.
The goal of this project is to develop a machine learning model that can accurately classify iris flowers based on their measurements. By learning from a labeled dataset, the model will be able to generalize and predict the species of new iris flowers based on their measurements.
While the Scikit-learn library provides a built-in dataset for iris flower classification, this project utilizes a separately downloaded dataset. This dataset offers the same measurements for iris flowers and enables the implementation of machine learning algorithms for iris flower classification.
By leveraging machine learning techniques, this project aims to contribute to the field of flower classification and provide a practical solution for automating the identification of iris species based on their physical attributes.