NPTEL Python for Data Science Assignment 4 Answers 2022

NPTEL Python for Data Science Assignment 4

Are you looking for the Answers to NPTEL Python for Data Science Assignment 4? This article will help you with the answer to the National Programme on Technology Enhanced Learning (NPTEL) Course “NPTEL Python for Data Science Assignment 4”. So read the complete article carefully.

What is Python for Data Science?

Python for Data Science is a fun-filled course where Domain Certification helps learners to gain expertise in a specific Area/Domain. This can be helpful for learners who wish to work in a particular area as part of their job or research or for those appearing for some competitive exam or becoming job ready or specialising in an area of study.  

Every domain will comprise Core courses and Elective courses. Once a learner completes the requisite courses per the mentioned criteria, you will receive a Domain Certificate showcasing your scores and the domain of expertise.


Average assignment score = 25% of the average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF THE AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Below you can find the answers for NPTEL Python for Data Science Assignment 4

Assignment No.Answers
Python for Data Science Assignment 1 Click Here
Python for Data Science Assignment 2 Click Here
Python for Data Science Assignment 3 Click Here
Python for Data Science Assignment 4 Click Here

NPTEL Python for Data Science Assignment 4 Answers:-

Q1. How many unique values are present in the Sbal feature; also, what is the most frequent value within Sbal?

Answer:- c

Q2. Find the average age of those customers who have a credit history [Chist] wherein the dues are not paid earlier.

Answer:- b

Q3. A Logistic Regression model is built in which none of the features used are standardized. The train to test proportion is 75:25 and the random state is set to 1. The accuracy of the model is ________.

Answer:- c

Q4. Import StandardScaler() from the sklearn.preprocessing package to standardize the features. Use the same train-test proportion and the random state should be set to 1. After standardizing the logistic regression model, by what percentage has the misclassified samples changed?

Answer:- c

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Q5. When KNN classification is applied on the same standardized data at the optimal value for k nearest neighbours, the accuracy achieved is ______.

Answer:- a

Q6. A multiple linear regression model is built on the Global Happiness Index dataset “GHI_Report.csv”. What is the rmse of the baseline model?

Answer:- c

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Q7. From the multiple linear regression model built on the GHI index, we get an R-squared value of _______ on the test data subset.

Answer:- d

Q8. Which of the following statement/s about Linear Regression is / are true?

Answer:- a,b,c

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Q9. Which of the following statements is inaccurate about Logistic Regression?

Answer:- c

Q10. In a KNN model, by which means do we handle categorical variables?

Answer:- b

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