import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import pickle

# Load data
data = load_iris()
X = pd.DataFrame(data.data, columns=data.feature_names)
y = data.target

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Train model
model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)

# Save model
with open("model.pkl", "wb") as f:
    pickle.dump(model, f)