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NPTEL Python for Data Science Assignment 3 Answer

We Discuss About That NPTEL Python for Data Science Assignment 3 Answer

NPTEL Python for Data Science Assignment 3 Answer – Here All The Questions and Answers Provided to Help All The Students and NPTEL Candidate as a Reference Purpose, It is Mandetory to Submit Your Weekly Assignment By Your Own Understand Level.

Are you looking for the Assignment Answers to NPTEL Python for Data Science Assignment 3 Answer? If Yes You are in Our Great Place to Getting Your Solution, This Post Should be help you with the Assignment answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Python for Data Science Assignment 3 Answer”

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NPTEL Python for Data Science Assignment

ABOUT THE COURSE :
The course aims at equipping participants to be able to use python programming for solving data science problems.
INTENDED AUDIENCE : Final Year Undergraduates
PRE-REQUISITES : Knowledge of basic data science algorithms

INTENDED AUDIENCE B.E./Msc (Computer Science)

PRE-REQUISITES Good knowledge of C, Computer Organization and Architecture, x86 Assembly level programming.

Next Week Assignment Answers

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This course can have Associate in Nursing unproctored programming communication conjointly excluding the Proctored communication, please check announcement section for date and time. The programming communication can have a weightage of twenty fifth towards the ultimate score.

Final score = Assignment score + Unproctored programming exam score + Proctored Exam score
  • Assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
  • ( All assignments in a particular week will be counted towards final scoring – quizzes and programming assignments). 
  • Unproctored programming exam score = 25% of the average scores obtained as part of Unproctored programming exam – out of 100
  • Proctored Exam score =50% of the proctored certification exam score out of 100
YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF ASSIGNMENT SCORE >=10/25 AND
UNPROCTORED PROGRAMMING EXAM SCORE >=10/25 AND PROCTORED EXAM SCORE >= 20/50. 
If any one of the 3 criteria is not met, you will not be eligible for the certificate even if the Final score >= 40/100. 

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1. Choose the appropriate command(s) to filter those booking details whose reservation_status are a No-show?

a. data_hotel_ns datahotel. loc[data_hotel.reservation_status=’No-Show’] b. data_hotel_ns = data_hotel[ data _hotel.reservation_status = “No-Show’] c. data hotel_ns = data_hotel.reservation_status.loc[data_hotel.isin([‘No-Show’])] d. data_hotel_ns = data_hotel.loc [data hotel.reservation_status.isin([ No-Show’])]

Answer:- b, d

2. From the same data, find how many bookings were not canceled in the year 2017?

a. 9064
b. 6231
c. 9046
d. None of the above

Answer:- a
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3. From the total bookings that were made in 2017 and not canceled, which month had the highest number of repeated guests?

a. July
b. February
c. January
d. None of the above

Answer:- c

4. Which of the following commands can be used to create a variable Flag, and set the values as Premium when the rating is equal to or greater than 3.25, and otherwise as Regular?

a. dt_cocoa[°Flag’] = [“Premium” if x 3.25 else “Regular” for x in dt_cocoa[‘Rating’ ]] b. dt_cocoa[“Flag’] = [“Premium” if x 3.25 else “Regular” for x in dt_cocoa[ Rating ‘]] c. dt_cocoa[“Flag”] = np.where(dt_cocoa[ “Rating”] < 3.25, “Regular”, “Premium”)
d. None of the above

Answer:- b, c

5. Which instruction can be used to impute the missing values in the column Review Data from the dataframe dt_cocoa by grouping the records company–wise?

Answer:- a

6. After checking the data summary, which feature requires a data conversion considering the data values held?

a. Rating
b. Review Date
c. Company
d. None of the above

Answer:- b
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7What is the maximum average rating for the cocoa companies based out of Guatemala?

a. 4
b. 3.5
c. 3.42
d. None of the above

Answer:- c

8. Which pandas function is used to stack the dataframes vertically?

a. pd.merge()
b. pd.concat()
c. join()
d. None of the above

Answer:- b

9. Of the following set of statements, which of them can be used to extract the column Direction as a separate dataframe?

a. df_weather[[ ‘Direction ‘ ]] b. df_weather.iloc[:,0] c. df_weather.loc[:.[ ‘Direction ‘]] d. None of the above

Answer:-
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10. Which one of these students’ average score across all subjects was the lowest? Which subject has the highest average score across students?

a. Harini, Maths
b. Sathi, Maths
c. Harini, Physics
d. Rekha, Maths

Answer:- b
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