This project uses various machine learning algorithms to classify the Iris flower dataset, a classic and simple dataset commonly used for testing algorithms. It includes 150 samples of iris flowers, each with four features (sepal length, sepal width, petal length, petal width) and one of three classes (Iris-setosa, Iris-versicolor, Iris-virginica).
Introduction
The goal of this project is to classify iris flowers into three species using their physical characteristics. We will explore and compare different machine learning models to find the most effective one for this classification task.
Dataset
The Iris dataset is available from the UCI Machine Learning Repository and is included in this project. The dataset consists of 150 samples, each with four features:
Sepal Length
Sepal Width
Petal Length
Petal Width
Each sample belongs to one of three classes:
Iris-setosa
Iris-versicolor
Iris-virginica
Algorithms Used
The following machine learning algorithms are implemented and compared in this project:
Logistic Regression
K-Nearest Neighbors (KNN)
Support Vector Machines (SVM)
Naive Bayes
Decision Tree