Iris_flower_classifier

# Importing necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Loading the Iris dataset
iris = load_iris()
X = iris.data  # Features
y = iris.target  # Labels

# Split data into training (80%) and testing (20%) sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train a Decision Tree model
model = DecisionTreeClassifier()
model.fit(X_train, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Model Accuracy: {accuracy:.2f}")

# Test with a new sample (Example input)
samples = [[5.1, 3.5, 1.4, 0.2], 
           [6.0, 2.9, 4.5, 1.5], 
           [7.2, 3.0, 5.8, 1.6]]  # List of multiple samples

predictions = model.predict(samples)

for i, pred in enumerate(predictions):
    print(f"Sample {i+1}: Predicted Species -", iris.target_names[pred])