Implement Linear Regression Backpropagation and Training

Implement Linear Regression Backpropagation and Training Given a training dataset, train a linear-regression model using full-batch gradient descent: [ \hat y = Xw + b ] where X is the feature matrix, w is the weight vector, and b is the bias. The loss is mean squared error: [ L = \frac{1}{n}\sum_{i=1}^{n}(\hat y_i-y_i)^2 ] Implement from scratch: The forward pass; Gradients for w and b; Gradient-descent parameter updates; Output the final parameters after the requested number of epochs. Initialize all parameters to zero. Input First line: n d epochs lr Next n lines: d feature values followed by target y. Output Print the final weights on the first line and the final bias on the second line. Print every floating-point value with 6 decimal places. Constraints 1 <= n <= 10^4 1 <= d <= 100 1 <= epochs <= 10^4 0 < lr <= 1 Example Input 1 1 1 0.1 1 1 Output 0.200000 0.200000