Implement Linear Regression with Backpropagation

Build a linear-regression model whose parameters are refined through backpropagation. The dataset is provided as a two-dimensional array X and a one-dimensional target array y: each row in X denotes one observation, each column denotes one feature, and the matching entry in y is that observation's target value. Return both the model's predictions and its learned parameters. Use the function signature linear_regression(X, y, learning_rate, epochs) -> (predictions, weights,…