The main objective is to recognize flower species quickly and precisely. This is very useful in various fields, such as biodiversity conservation, environmental research, and crop management. With a good classification model, researchers or practitioners can identify flower species just by measuring their shape, making it easier to make decisions on protecting and managing these species.
This dataset contains data about three types of Iris flowers: Setosa, Versicolor, and Virginica. There are 150 samples with four features measured, namely sepal length and width and petal length and width. This analysis aims to create a model that can recognize flower types based on these traits. I'm using the dataset from scikit-learn library. Building a classification model with the Random Forest algorithm.
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