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.

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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:

Output

The model returns the predicted species as one of the following:

• setosa
• versicolor
• virginica

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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])