# Importing required libraries
from sklearn.datasets import load_breast_cancer        # Built-in dataset (Breast Cancer)
from sklearn.linear_model import LogisticRegression    # Logistic Regression model
from sklearn.model_selection import train_test_split   # To split data into training & testing
from sklearn.metrics import accuracy_score             # To measure model performance

# Step 1: Load the dataset (features = X, target labels = y)
X, y = load_breast_cancer(return_X_y=True)

# Step 2: Split dataset into training (80%) and testing (20%) sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=23)

# Step 3: Create a Logistic Regression model
# max_iter=10000 ensures the model has enough iterations to converge
clf = LogisticRegression(max_iter=10000, random_state=0)

# Step 4: Train the model using the training data
clf.fit(X_train, y_train)

# Step 5: Predict on the test data and calculate accuracy
acc = accuracy_score(y_test, clf.predict(X_test)) * 100

# Step 6: Print the model accuracy in percentage
print(f"Logistic Regression model accuracy: {acc:.2f}%")

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