import numpy as np

class LinearRegression:
    def __init__(self, learning_rate=0.01, num_iterations=1000):
        self.learning_rate = learning_rate
        self.num_iterations = num_iterations
        self.weights = None
        self.bias = None

    def fit(self, X, y):
        # Add a column of ones to X for the bias term
        X = np.column_stack((np.ones(len(X)), X))

        # Initialize weights and bias
        self.weights = np.zeros(X.shape[1])
        self.bias = 0

        # Gradient Descent
        for _ in range(self.num_iterations):
            predictions = self.predict(X)
            errors = predictions - y

            # Update weights and bias
            self.weights -= self.learning_rate * (1 / len(X)) * np.dot(errors, X)
            self.bias -= self.learning_rate * (1 / len(X)) * np.sum(errors)

    def predict(self, X):
        if self.weights is None or self.bias is None:
            raise Exception("Model not trained. Call fit() first.")

        return np.dot(X, self.weights) + self.bias

# Example usage:
# Assuming X and y are your input features and target variable, respectively
X = np.array([1, 2, 3, 4, 5])
y = np.array([2, 4, 5, 4, 5])

# Reshape X to a column vector
X = X.reshape(-1, 1)

# Create and train the linear regression model
model = LinearRegression()
model.fit(X, y)

# Make predictions
new_data = np.array([6, 7, 8])
new_data = new_data.reshape(-1, 1)
predictions = model.predict(np.column_stack((np.ones(len(new_data)), new_data)))

print("Predictions:", predictions)