NPTEL Data Science for Engineers Assignment 8 Answer

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NPTEL Data Science for Engineers Assignment 8 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 Data Science for Engineers Assignment 8 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 Data Science for Engineers Assignment 8 Answer”

NPTEL Data Science for Engineers Assignment

ABOUT THE COURSE :
Learning Objectives :
  1. Introduce R as a programming language
  2. Introduce the mathematical foundations required for data science
  3. Introduce the first level data science algorithms
  4. Introduce a data analytics problem solving framework
  5. Introduce a practical capstone case study
Learning Outcomes:
  1. Describe a flow process for data science problems (Remembering)
  2. Classify data science problems into standard typology (Comprehension)
  3. Develop R codes for data science solutions (Application)
  4. Correlate results to the solution approach followed (Analysis)
  5. Assess the solution approach (Evaluation)
  6. Construct use cases to validate approach and identify modifications required (Creating)
INTENDED AUDIENCE:  Any interested learner
PREREQUISITES:  10 hrs of pre-course material will be provided, learners need to practise this to be ready to take the course.
INDUSTRY SUPPORT:  HONEYWELL, ABB, FORD, GYAN DATA PVT. LTD.

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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Consider the dataset “USArrests.csv”. Answer questions 1 to 4 based on the information given below
This data set contains statistics, in arrests per 100,000 residents for assault, murder, and rape in each of the 50 US states in 1973. Also given is the percent of the population living in urban areas.

• Set the column “States” as index of the data frame while reading the data
• Set the random number generator to set.seed(123)
• Normalize the data using scale function and build the K-means algorithm with the given conditions:
o number of clusters = 4
o nstart=20

1 point
According to the built model, the within cluster sum of squares for each cluster is _________(the order of values in each option could be different):-
 
 
 
 
Ans  – C
1 point
According to the built model, the size of each cluster is _______ (the order of values in each option could be different):-
 
 
 
 
Ans  – B
1 point
The Between Cluster Sum-of-Squares (BCSS) value of the built K-means model is ___________(Choose the appropriate range)
 
 
 
 
Ans  – A
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1 point
The Total Sum-of-Squares value of the built k-means model is________ (Choose the appropriate range)
 
 
 
 
Ans  – D
1 point
Which of the statement is INCORRECT about KNN algorithm?
 
 
 
 
Ans  – B
1 point
K means clustering algorithm clusters the data points based on:-
 
 
 
 
Ans  – C
1 point
The method / metric which is NOT useful to determine the optimal number of clusters in unsupervised clustering algorithms is
 
 
 
 
Ans  – A
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1 point
The unsupervised learning algorithm which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest centroid is
 
 
 
 
Ans  – B
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