metadata
license: mit
language:
- en
🌸 Iris Flower Species Predictor
This is a simple yet effective machine learning model that predicts the species of an Iris flower (setosa, versicolor, or virginica) using four key features:
- Sepal length (cm)
- Sepal width (cm)
- Petal length (cm)
- Petal width (cm)
The model is built using a Decision Tree Classifier from scikit-learn, trained on the classic Iris dataset.
Model Overview
- Algorithm: Decision Tree Classifier
- Library: Scikit-learn
- Dataset:
sklearn.datasets.load_iris() - Accuracy: ~95% (on test data using 70/30 split)
How to Use
Input
You need to provide these four numerical inputs:
| Feature | Type | Example |
|---|---|---|
| Sepal length | Float | 5.1 |
| Sepal width | Float | 3.5 |
| Petal length | Float | 1.4 |
| Petal width | Float | 0.2 |
Output
The model returns the predicted species as one of the following:
setosaversicolorvirginica
Example Code
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
# Load and train model
iris = load_iris()
X, y = iris.data, iris.target
model = DecisionTreeClassifier()
model.fit(X, y)
# Predict
sample = [[5.1, 3.5, 1.4, 0.2]]
predicted_class = model.predict(sample)[0]
print("Predicted species:", iris.target_names[predicted_class])