Enhancing Sustainable AI with Efficient Machine Learning Models: An Iris Classification Perspective
Keywords:
Logistic Regression, Classification, Support Vector Machines, Machine Learning, Random Forest, Iris dataset, K-Nearest NeighboursAbstract
The earth's biodiversity is abundant. Approximately 360000 species contribute to the earth's ecology by forming a healthy biome. In terms of size, form, and colour, some of them are physically identical. As a result, identifying any species is challenging. Setosa is first, Versicolor is second, and Virginica is third, these are the three subspecies of the flower species known as Iris. As the Iris dataset is regularly accessible, we chose to use it. There are three classes of fifty examples each in the Iris flower dataset. Machine learning is used in the Iris dataset to determine the subspecies of iris blossoms. The work focuses on the automatic identification of floral classes by machine learning techniques with a high degree of accuracy as opposed to approximation. Pre-processing, dataset partitioning, and classification utilizing Random Forest, K-Nearest Neighbours, Logistic Regression, and Support Vector Machine are the four models involved in putting this strategy into practice.
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