# Untitled

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python

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^{}

import numpy as np from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score def sigmoid(z): return 1 / (1 + np.exp(-z)) def compute_cost(X, y, weights): m = len(y) h = sigmoid(np.dot(X, weights)) cost = -(1/m) * np.sum(y * np.log(h) + (1 - y) * np.log(1 - h)) return cost def gradient_descent(X, y, weights, learning_rate, iterations): m = len(y) cost_history = [] for i in range(iterations): h = sigmoid(np.dot(X, weights)) gradient = np.dot(X.T, (h - y)) / m weights -= learning_rate * gradient cost = compute_cost(X, y, weights) cost_history.append(cost) return weights, cost_history def predict(X, weights): return sigmoid(np.dot(X, weights)) >= 0.5 # Model Training Example weights = np.zeros(X_train_scaled.shape[1]) weights, cost_history = gradient_descent(X_train_scaled, y_train, weights, learning_rate=0.01, iterations=1000) # Model Prediction Example y_pred = predict(X_test_scaled, weights) accuracy = accuracy_score(y_test, y_pred) precision = precision_score(y_test, y_pred) recall = recall_score(y_test, y_pred) f1 = f1_score(y_test, y_pred)

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