Abstract :

Classifying Iris Flowers Based On Petal And Sepal Measurements Is A Fundamental Task In Botany, Supporting Species Identification And Taxonomy. This Approach Is Valuable For Studying Plant Biodiversity And Evolution. In Horticulture And Agriculture, Accurate Classification Aids Breeding Programs By Identifying Desirable Traits, While In Environmental Science, It Helps Monitor Ecosystems And Guide Conservation Efforts. Beyond Botany, Machine Learning Techniques Used In Classification Can Be Applied To Fields Such As Healthcare, Finance, And Marketing. Traditional Classification Methods Often Rely On Manual Measurements And Expert Judgment. While Effective In Small-scale Settings, These Methods Are Time-consuming, Subjective, And Prone To Inconsistencies. They May Also Struggle With Large Datasets, Subtle Differences Between Species, And Complex Relationships Among Features. Furthermore, Traditional Techniques Often Lack Scalability And Perform Poorly In High-dimensional Spaces. To Address These Limitations, This Work Proposes A Machine Learning-based System For Classifying Iris Flowers. Using Supervised Learning, The Model Automatically Learns Discriminative Patterns From Labeled Data. Features Such As Petal And Sepal Length And Width Are Extracted And Used To Train The Model To Distinguish Between Species. The System Incorporates Cross-validation And Hyperparameter Tuning To Improve Accuracy And Ensure Robustness. Unlike Manual Methods, The Machine Learning Approach Offers Scalability, Consistency, And The Ability To Capture Complex Relationships In The Data. This Results In A More Efficient And Accurate Classification Process, Demonstrating The Broader Potential Of Machine Learning In Scientific And Industrial Applications. Keywords: Iris Flowers, Classification, Machine Learning, KNN, Logistic Regression

Published:

09-6-2025

Issue:

Vol. 25 No. 6 (2025)

Page Nos:

137-144

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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