This project uses the K-Nearest Neighbors (KNN) algorithm to classify Iris flowers based on their sepal and petal measurements. The dataset used in this project is the Iris Dataset, which includes 150 samples of Iris flowers, each with four features: sepal length, sepal width, petal length, and petal width.

Made to classify the different species of the Iris flower🌼

Iris flower classification model

This is my project using Extreme Learning Machine (ELM) based on Guang-Bin Huang Paper

• Designed an Iris Flower Classification system for automated species identification using Random Forest, delivering strong predictive performance on the Iris dataset. • Performed EDA and visualized feature relationships using Pandas, Matplotlib, and Seaborn. • Deployed an interactive Streamlit web app for real-time species prediction.

Iris flower has three species; setosa, versicolor, and virginica, which differs according to their measurements. Now assume that you have the measurements of the iris flowers according to their species, and the task is to train a machine learning model that can learn from the measurements of the iris species and classify them.