Hello, can you help me to solve this question with code in Python? Thank you

Task 2.2: Get best fitting bias and weights Write the function get_bias_and_weight (x,y, include_bias) that returns w0 (bias) and w1,w2,,wn (weights) that will lead to best fitting line. The argument is used to specify if the model includes a bias term, i.e. has a non-zero bias term. Hence, the function should return w0=0 if it is set to false The function should return w1,,wn as a NumPy matrix with shape (n,1), where n is the number of features (excluding the bias column). We can use the normal equation to get w0,w1,,wn. The normal equation is as follows: w0w1wn=(XTX)1XTy where X is the (augmented for bias) feature matrix and y is the vector of target values. Note: You can use the add_bias_column function for this task. (You do not need to re-define the function in Coursemology. However, you are free to do so if you explicitly want to use your own implementation of the function for this task.) Hint: Consider using numpy. linalg. inv for the matrix inverse. Question 4: Task 2.2: Get best fitting bias and weight View Past An Write the function get_bias_and_weight that returns b and w that will lead to best fitting line. Note: You can use add_bias_column function for this task. Be aware of the w dimension. np.squeeze and np.reshape might be helpful. template.py CS Scanned with CamScanner