Using Python, Create a k-fold cross validation code with the number of folds being 10.The raw data needs to be split into training and testing.The number of folds is the number of times you should go through a for loop.Inside of the for loop, index into your raw data to pull out training and testing data based on the fold (think about how the folds are organized and how much data is in each fold, which is based on the total number of folds)Do normal linear regression model fittingKeep track of the R^2 scores of the foldsPrint the average and standard deviation of the R^2 value for the different folds Our libraries that we are importing:# 1a. Importing all the libraries necessary for this assignmentimport seaborn as snsimport pandas as pdimport numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LinearRegression[7] # 1b. Loading the datadata = sns.load_dataset('geyser')Let's check out this dataset.[9] print('data1duration waiting kind3.60079long11.80054 short23.33374long32.28362 short44.53385long2674.11781long2682.15046 short2694.41790long2701.81746 short2714.46774long272 rows x 3 columnsImagine working at Yellowstone National Park as a scientist researching Old Faithful Geyser. The boss wants us to evaluate the predictivenessof the relationship between duration of an eruption in minutes ("duration") and the duration in minutes until the next eruption ("waiting"). Wecollected the data, but the internet in Yellowstone is not very good, so we cannot access the crossvalidation library in python

The python code is written with an example data. you can load your data there. from sklearn.datasets…

Answered: Using Python, Create a k-fold cross…

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Using Python,

Create a k-fold cross validation code with the number of folds being 10.

The raw data needs to be split into training and testing.
The number of folds is the number of times you should go through a for loop.
Inside of the for loop, index into your raw data to pull out training and testing data based on the fold (think about how the folds are organized and how much data is in each fold, which is based on the total number of folds)
Do normal linear regression model fitting
Keep track of the R^2 scores of the folds
Print the average and standard deviation of the R^2 value for the different folds