Sonia2k5/decision_tree_flower

🌸 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:

• setosa
• versicolor
• virginica

---

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])