from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
import pandas as pd
import warnings
warnings.filterwarnings("ignore")

# Load the Iris dataset
iris = load_iris()
X = iris.data
y = iris.target

# Convert to DataFrame for readability
df = pd.DataFrame(X, columns=iris.feature_names)
df['target'] = y

# Split the data
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Initialize and train Random Forest
rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
rf_model.fit(X_train, y_train)

# Predict on test data
y_pred = rf_model.predict(X_test)

# Evaluate model
accuracy = accuracy_score(y_test, y_pred)
print(f" Model Accuracy: {accuracy:.2f}")
print("\n Classification Report:\n", classification_report(y_test, y_pred, target_names=iris.target_names))

# Test sample prediction
sample = X_test[0].reshape(1, -1)
prediction = iris.target_names[rf_model.predict(sample)[0]]

print(f"\nSample Flower Features: {X_test[0]}")
print(f" Predicted Species: {prediction}")

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