---
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.

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 Model Overview

- **Algorithm:** Decision Tree Classifier  
- **Library:** Scikit-learn  
- **Dataset:** `sklearn.datasets.load_iris()`  
- **Accuracy:** ~95% (on test data using 70/30 split)

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 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:
- `setosa`
- `versicolor`
- `virginica`

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 Example Code

```python
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])