lr_lib.py

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris

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

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize and train the Logistic Regression model
logreg = LogisticRegression(max_iter=200)
logreg.fit(X_train, y_train)

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

# Calculate and print accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy on Iris dataset: {accuracy:.4f}')