data science

Note : - Need this document by 11/14/2022 (CST-2:30pm)

Consider the regression analysis on the graduate admissions data set. You can find the code (regression.pdf and simple_validation.pdf) and the data set (graduate-admission.csv) in Modules.

The assignment is to determine which one of the following regression algortihms performs best on the graduate admissions data set using the  cross validation technique.

· KernelRidge

· Ridge

· GradientBoostingRegressor

· ElasticNet

· SVR

· LinearRegression

Add Python code to perform the following tasks.

· Add the appropriate import statements to load the libraries needed and the regression algorithms.

· Load the data, and divide it into training and test sets. The code for this task is exactly the same as the code found in regression.pdf.

· Define the cross validation function, and use the parameter scoring='neg_mean_squared_error'.

· Call the cross validation function on the six algorithms.

· In a comment section, show the validation output value obtained (i.e. the negative mean squared error).

· In a comment section, answer the following question. Based on the cross validation analysis, which model performs best on this data set?

NB: The best algorithm is the one that maximizes the negative mean squared error (since the goal is to minimize the mean squared error).

Add all the code in a file assignment4.py, and upload it.