hi

from sklearn.datasets import load_iris

from sklearn.model_selection import train_test_split

from sklearn.linear_model import LogisticRegression

from sklearn.metrics import accuracy_score, precision_score, recall_score

# Load example dataset

data = load_iris()

X = data.data # Features

y = data.target # Labels

# Split data

X_train, X_test, y_train, y_test = train_test_split(

X, y, test_size=0.2, random_state=42

)

# Create and train model

model = LogisticRegression(max_iter=1000)

model.fit(X_train, y_train)

# Predict

y_pred = model.predict(X_test)

# Metrics

print("Accuracy:", accuracy_score(y_test, y_pred))

print("Precision:", precision_score(y_test, y_pred, average="macro"))

print("Recall:", recall_score(y_test, y_pred, average="macro"))