In python,

In a regularized regression model (ridge regression), the target function to be minimized is f(beta) = summation from i=1 to n (yi - beta*xi)^2 + lambda*beta^2 where xi and yi are the observed predictor and response values, n is the number of observations, lambda is a given hyper-parameter, and beta is the target parameter to be estimated. Use the gradient descent method with a fixed learning rate 0.0001 and tolerance 0.0001 to find beta, respectively when lambda = 1, 10, 100. The observed data are as follows, i.e., n = 6, x1 = 10, y1 = 32, x6 = 22, y6 =72, etc

i	x	y
1	10	32
2	13	40
3	17	46
4	18	62
5	20	54
6	22	72