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SVM Powered Flower Species Classification

PEETHALA BINDHU PRIYA, PADALA SAI BHAVANI

Assistant Professor, 2MCA Final Semester, Master of Computer Applications, Sanketika Vidya Parishad Engineering College, Vishakhapatnam, Andhra Pradesh, India

Abstract

The Iris flower classification problem is a classic example of multi-class classification used to demonstrate the effectiveness of machine learning algorithms. This project applies supervised learning techniques to accurately predict the species of an Iris flower based on four key features: sepal length, sepal width, petal length, and petal width. Using the well-known Iris dataset, we implemented and evaluated several machine learning models, including Logistic Regression, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and Decision Trees. The dataset was preprocessed and split into training and testing sets to assess each model’s performance. Among the evaluated models, the Support Vector Machine achieved the highest accuracy, demonstrating its robustness in handling linearly separable classes. The results highlight the capability of machine learning algorithms in solving simple classification problems effectively and provide a foundation for more complex pattern recognition tasks in the field of artificial intelligence.

IndexTerms: Iris flower classification, Multi-class classification, Supervised learning,  Machine learning,Logistic Regression,  k-Nearest Neighbors (k-NN),  Support Vector Machines (SVM), Decision Trees,  Feature selection, Sepal length.