52.52. Multiple Linear Regression

Implement **Multiple Linear Regression** using gradient descent for models with multiple features: **y = Xw + b**.

Gradient Descent Update Rules:

y_pred = X @ w + b

dw = (1/n) * (X^T @ (y_pred - y)) * 2

db = (1/n) * sum(2 * (y_pred - y))

w = w - learning_rate * dw

b = b - learning_rate * db

Where X is a matrix of shape (n_samples, n_features) and w is a vector of shape (n_features,).

Your class should have:

Example:

X = [[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]]

y = [5, 8, 11, 14, 17] (i.e., y = x1 + 2*x2 + 1... approximately)

model = MultipleLinearRegression(learning_rate=0.01, n_iterations=1000)

model.fit(X, y)

model.predict([[6, 7]]) # Should return value close to 20

**Explanation:** With multiple features, the model learns a weight for each feature and a single bias term.

Constraints: