Prepare DataFrames for GradingDo not make changes to the below codeAfter completing all data preparation tasks, run the following four cells to prepare your DataFrame for grading by:1. Outputting the features and response DataFrames (you do not need to print).2. Using the NumPy around() function to round all the values in both DataFrames to ZERO decimal places. You are calling these features_round andresponse_round , respectively.3. Computing the sum of every column for both features_round and response_round , and saving those values as features_final andresponse_finalFinally, you are printing your final answer using the print() function.Be sure to run all cells of your notebook prior to submitting, so that all output is rendered, visible and there are no error messages.In [ ]:featuresIn [ ]:I responseIn [ ]:I features_round = np.around (features, decimals=0)features_final = features_round.sum(axis=0)print(features_final)In [ ]: N response_round = np.around (response, decimals=0)response_final = response_round.sum(axis=0)print(response_final) DTSC670: Foundations of Machine Learning ModelsModule 1Assignment 2: COVID-19 Data WranglingName:The purpose of this assignment is to hone your data wrangling skills. Your task for this assignment is to perform the data preparation as instructed in theDTSC670_Assignment_2 pdf listed in Brightspace. After performing all the data preparation tasks outlined in the document, run the code in the "PrepareDataFrames for Grading" section.You are supplied an Excel file called BrazilcOVIDData.xlsx - be sure to put the data file in the same directory as this Jupyter Notebook. Please note that itmay take around 5 minutes to read-in all of the data in this file.In [ ]:N ### ENTER CODE HERE #### Insert as many cells as you need, but be sure your code is very neat and very well documented.In [ ]:N # Get Final Features DataFrame# features = ### ENTER CODE HERE #### Get Final Response DataFrame# response = ### ENTER CODE HERE ###

Note: Code run successfully as above problem describe. I have provided source and output screenshot…

Assignment 2: COVID-19 Data Wrangling Name: The purpose of this assignment is to hone your data wrangling skills. Your task for this assignment is to perform the data preparation as instructed in the DTSC670_Assignment_2 pdf listed in Brightspace. After performing all the data preparation tasks outlined in the document, run the code in the "Prepare DataFrames for Grading" section. You are supplied an Excel file called BrazilcOVIDData.xlsx - be sure to put the data file in the same directory as this Jupyter Notebook. Please note that it may take around 5 minutes to read-in all of the data in this file. ]: ### ENTER CODE HERE ### # Insert as many cells as you need, but be sure your code is very neat and very well documented. ]: # Get Final Features DataFrame # features = ### ENTER CODE HERE ### # Get Final Response DataFrame # response = ### ENTER CODE HERE ###

Assignment 2: COVID-19 Data Wrangling Name: The purpose of this assignment is to hone your data wrangling skills. Your task for this assignment is to perform the data preparation as instructed in the DTSC670_Assignment_2 pdf listed in Brightspace. After performing all the data preparation tasks outlined in the document, run the code in the "Prepare DataFrames for Grading" section. You are supplied an Excel file called BrazilcOVIDData.xlsx - be sure to put the data file in the same directory as this Jupyter Notebook. Please note that it may take around 5 minutes to read-in all of the data in this file. ]: ### ENTER CODE HERE ### # Insert as many cells as you need, but be sure your code is very neat and very well documented. ]: # Get Final Features DataFrame # features = ### ENTER CODE HERE ### # Get Final Response DataFrame # response = ### ENTER CODE HERE ###

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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