Iris Flower Classification This repository contains a Python program that uses the Iris flower dataset to classify Iris flowers into three species: Setosa, Versicolor, and Virginica.
The Iris flower dataset is a multivariate data set introduced by the British statistician and biologist Ronald Fisher in his 1936 paper, The Use of Multiple Measurements in Taxonomic Problems. The dataset consists of 150 samples of Iris flowers, with 50 samples each of three species: Setosa, Versicolor, and Virginica. Each sample is described by four features: sepal length, sepal width, petal length, and petal width.
The program in this repository uses the scikit-learn machine learning library to build a decision tree classifier to classify Iris flowers. The program first loads the Iris flower dataset from a CSV file. Then, it splits the dataset into a training set and a test set. The training set is used to train the Logistic Regression classifier. The test set is used to evaluate the accuracy of the classifier.
The program prints the accuracy of the Logistic Regression classifier on the test set. The accuracy is typically around 95%.