NoteÂ : – 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 theÂ Â cross validationÂ technique.
Add Python code to perform the following tasks.
Â· Add the appropriate import statements to load the libraries needed and the regression algorithms.
Â· 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.
Â· Define the cross validation function, and use the parameter scoring=’neg_mean_squared_error’.
Â· Call the cross validation function on the six algorithms.
Â· In a comment section, show the validation output value obtained (i.e. the negative mean squared error).
Â· 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.
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