We will need a function that returns one bootstrap sample of the regression fit. That is, it resamples the dataset with replacement, then fits a linear regression to the data. Fill in the code below to complete the function

 

def get_one_bootstrap_salary_fit():
   import time
   import pandas as pd
   from sklearn import linear_model
   import numpy as np

   import operator
   from sklearn.linear_model import LinearRegression

 

   '''
   Returns a sklearn.linear_model.LinearRegression model representing
   a fit to a bootstrap-resampled version of salary_df
   '''
   
   #resample the data with replacement (replace=True) to a data frame with
   #the same number of data points (frac=1.0)

   resampled_df = salary_df.sample(frac=1.0, replace=True)

 

   #fit model to resampled data
   X = resampled_df[['YearsExperience']] #[[ ]] subsets so X remains a DataFrame
   y = resampled_df['Salary']       #y should be an array, so we use [ ]
   
   # insert code below using LinearRegression to return a linear regression model
   # with predictor X and outcome variable y

   # YOUR CODE HERE

   #raise NotImplementedError()