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import numpy as np import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.datasets import make_classification from sklearn.metrics import accuracy_score, classification_report, confusion_matrix X, y = make_classification(n_samples=1000, n_features=10, n_classes=2, random_state=42) X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) scaler = StandardScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) model = LogisticRegression() model.fit(X_train, y_train) y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) conf_matrix = confusion_matrix(y_test, y_pred) report = classification_report(y_test, y_pred) print(f"Accuracy: {accuracy:.2f}") print("Confusion Matrix:\n", conf_matrix) print("Classification Report:\n", report)