hello, please, I need to create the MARS algorithm Multivariate Adaptive Regression Splines (MARS) using machine learning with more than one column for time series, find the r2 score and split the data to train and test. and R2 for test data using python please

In the language R: Which of the following functions in the broom library can generate a data frame of component-level information (e.g., coefficients, t-statistics) from a linear regression model? Select one: A. glance() B. augment() C. round_df() D. tidy()

In R, write a function that produces plots of statistical power versus sample size for simple linear regression. The function should be of the form LinRegPower(N,B,A,sd,nrep), where N is a vector/list of sample sizes, B is the true slope, A is the true intercept, sd is the true standard deviation of the residuals, and nrep is the number of simulation replicates. The function should conduct simulations and then produce a plot of statistical power versus the sample sizes in N for the hypothesis test of whether the slope is different than zero. B and A can be vectors/lists of equal length. In this case, the plot should have separate lines for each pair of A and B values (A[1] with B[1], A[2] with B[2], etc). The function should produce an informative error message if A and B are not the same length. It should also give an informative error message if N only has a single value. Demonstrate your function with some sample plots. Find some cases where power varies from close to zero to near…

TODO: Lienar Regression with least Mean Squares (LMS) Optimize the model through gradient descent. *Please complete the TODOs. * !pip install wget import osimport randomimport tracebackfrom pdb import set_traceimport sysimport numpy as npfrom abc import ABC, abstractmethodimport traceback from util.timer import Timerfrom util.data import split_data, feature_label_split, Standardizationfrom util.metrics import msefrom datasets.HousingDataset import HousingDataset class BaseModel(ABC):    """ Super class for ITCS Machine Learning Class"""     @abstractmethod    def fit(self, X, y):        pass     @abstractmethod    def predict(self, X):        pass class LinearModel(BaseModel):    """        Abstract class for a linear model         Attributes        ==========        w       ndarray                weight vector/matrix    """     def __init__(self):        """            weight vector w is initialized as None        """        self.w = None     # check if the matrix is 2-dimensional. if…