Logistic Regression

import numpy as np

class LogisticRegression:
    def __init__(self, learning_rate=0.01, num_iterations=10000):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.weights = None
        self.bias = None

    def sigmoid(self, z):
        return 1 / (1 + np.exp(-z))

    def fit(self, X, y):
        m, n = X.shape
        self.weights = np.zeros(n)
        self.bias = 0

        for iteration in range(self.num_iterations):
            # Forward pass
            z = np.dot(X, self.weights) + self.bias
            predictions = self.sigmoid(z)

            # Compute cost
            cost = -(1 / m) * np.sum(y * np.log(predictions) + (1 - y) * np.log(1 - predictions))

            # Backward pass
            dz = predictions - y
            dw = (1 / m) * np.dot(X.T, dz)
            db = (1 / m) * np.sum(dz)

            # Update parameters
            self.weights -= self.learning_rate * dw
            self.bias -= self.learning_rate * db

            # Print cost every 100 iterations
            if iteration % 100 == 0:
                print(f"Iteration {iteration}, Cost: {cost}")

    def predict(self, X):
        z = np.dot(X, self.weights) + self.bias
        predictions = self.sigmoid(z)
        return (predictions >= 0.5).astype(int)

# Example usage:
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Generate synthetic data
np.random.seed(42)
X = np.random.randn(1000, 2)
y = (X[:, 0] + X[:, 1] > 0).astype(int)

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

# Instantiate the logistic regression model
model = LogisticRegression(learning_rate=0.01, num_iterations=100000)

# Train the model
model.fit(X_train, y_train)

# Make predictions on the test data
predictions = model.predict(X_test)

# Print the predictions
print("Predictions:", predictions)

# Calculate accuracy
accuracy = accuracy_score(y_test, predictions)
print("Accuracy:", accuracy)