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Python for Data Science : Solution for Assignment 4 released !!

Dear Learner, The Solution for Assignment 4 have been uploaded for the course "Python for Data Science". The solution set can be accessed using the following link: Assignment 4 solution:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=56&lesson=140 Please use the discussion forum if you have any queries. Thanks & Regards, NPTEL Team.

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Python for Data Science : Solution for Assignment 3 released !!

Dear Learner, The Solution for Assignment 3 have been uploaded for the course "Python for Data Science". The solution set can be accessed using the following link: Assignment 3 solution:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=41&lesson=125 Please use the discussion forum if you have any queries. Thanks & Regards, NPTEL Team.

Week 4 Feedback Form: Python for Data Science!!

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Python for Data Science : Week 4 Supplementary Materials!!

Dear Learners, Please go through the lectures in "Supplementary material for week 4",  as there will be questions asked in the assignment for week 4 as well as in the final exam. Have fun learning. Thanks & Regards NPTEL team

Python for Data Science : Solution for Assignment 1&2 released !!

Dear Learner, The Solution for Assignment 1&2 have been uploaded for the course "Python for Data Science". The solution set can be accessed using the following link: Assignment 1 solution:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=18&lesson=123 Assignment 2 solution:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=30&lesson=124 Please use the discussion forum if you have any queries. Thanks & Regards, NPTEL Team.

Python for Data Science : Assignment 4 is live now!!

Dear Learners, The lecture videos for Week 4  have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=56&lesson=57 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-4  for Week-4  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=56&assessment=134 Assignment-4  for Week-4  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=56&assessment=139 The assignment has to be submitted on or before Wednesday,[21/02/2024], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Week 3 Feedback Form: Python for Data Science!!

Python for data science: reminder for assignment 1 & 2 deadline.

Dear Learners, The Deadline for Assignments 1 & 2 will close on Wednesday,[07/02/2024], 23:59 IST. Kindly submit the assignments before the deadline. Thanks and Regards, -NPTEL Team

Python for Data Science : Assignment 3 is live now!!

Dear Learners, The lecture videos for Week 3 have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=41&lesson=42 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-3  for Week-3  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=41&assessment=133 Assignment-3  for Week-3  is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=41&assessment=137 The assignment has to be submitted on or before Wednesday,[14/02/2024], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Week 2 Feedback Form: Python for Data Science!!

Reminder: nptel: exam registration is open now for jan 2024 courses.

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Python for Data Science : Assignment 2 is live now!!

Dear Learners, The lecture videos for Week 2 have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=30&lesson=31 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-2 for Week-2 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=30&assessment=132 Assignment-2 for Week-2 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=30&assessment=136 The assignment has to be submitted on or before Wednesday,[07/02/2024], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Week 1 Feedback Form: Python for Data Science!!

Python for data science : assignment 1 is live now.

Dear Learners, The lecture videos for Week 1 have been uploaded for the course "Python for Data Science" . The lectures can be accessed using the following link:   Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=18&lesson=19 The other lectures in this week are accessible from the navigation bar to the left. Please remember to login into the website to view contents (if you aren't logged in already). Practice Assignment-1 for Week-1 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=18&assessment=131 Assignment-1 for Week-1 is also released and can be accessed from the following link Link:  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=18&assessment=135 The assignment has to be submitted on or before Wednesday,[07/02/2024], 23:59 IST. As we have done so far, please use the discussion forums if you have any questions on this module. Note : Please check the due date of the assignments in the announcement and assignment page if you see any mismatch write to us immediately. Thanks and Regards, -NPTEL Team

Python for Data Science : Assignment 0 is live now!!

Dear Learners, We welcome you all to this course "Python for Data Science" . The assignment 0 has been released. This assignment is based on a prerequisite of the course. You can find the assignment in the link :  https://onlinecourses.nptel.ac.in/noc24_cs54/unit?unit=16&assessment=130 Please note that this assignment is for practice and it will not be graded. Thanks & Regards   -NPTEL Team

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NPTEL Python for Data Science Assignment 4 Answers 2023

NPTEL Python for Data Science Assignment 4 Answers 2023:-  All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

NPTEL Python For Data Science Week 4 Assignment Answer 2023

1. Which of the following are regression problems? Assume that appropriate data is given.

• Predicting the house price.
• Predicting w h ether it will rain or not on a given day.
• Predicting the maximum temperature on a g iven day.
• Predicting the sales of the ice-creams.

2. Which of the followings are binary classification problems?

• Predicting whether a patient is diagnosed with cancer or not.
• Predicting whether a team will win a tournament or not.
• Predicting the price of a second-hand car.
• Classify web text into one of the follow in g categories: Sports, Entertainment, or Technology.

3. If a linear regression model achieves zero training error, can we say that all the data points lie on a hyperplane in the (d+1)-dimensional space? Here, d is the nu m ber of features.

Read the information given below and answer the questions from 4 to 6: Data Description: An automotive service chain is launching its new grand service station this weekend. They offer to service a wide variety of cars. The current capacity of the station is to check 315 cars thoroughly per day. As an inaugural offer, they claim to freely check all cars that arrive on their launch day, and report whether they need servicing or not!

Unexpectedly, they get 450 cars. The servicemen will not work longer than the working hours, but the data analysts have to!

Can you save the day for the new service station?

How can a data scientist save the day for them?

He has been given a data set, ‘ ServiceTrain.csv ’ that contains some attributes of the car that can be easily measured and a conclusion that if a service is needed or not.

Now for the cars they cannot check in detail, they measure those attributes and store them in ‘ ServiceTest.csv ’

Problem Statement:

Use machine learning techniques to identify whether the cars require service or not

Read the given datasets ‘ ServiceTrain.csv ’ and ‘ ServiceTest.csv ’ as train data and test data respectively and import all the required packages for analysis.

4. Which of the following machine learning techniques would NOT be ap p ropriate to solve the problem given in the problem statement?

• Random Forest
• Logisti c Regression
• Linear regression

5. After applying logistic regression, what is/are the correct observat ion s from the resultant confusion matrix?

• True Positive = 29, True Negative = 94
• True Positive = 94, Tr u e Negative = 29
• False Positive = 5, True Negative = 94
• None of the above

Prepare the data by following th e steps given below, and answer questions 6 and 7.

• Encode categorical variable, Service – Yes as 1 and No as 0 for both the train and test datasets.
• Split the set of independent features and the dependent feature on both the train and test datasets.
• Set random_state for the instance of the logistic regression class as 0.

6. The logistic regression model built between the input and output variables is checked for its prediction accuracy of the test data. What is the accuracy range (in %) of the predictions made over test dat a ?

• 60 – 79
• 90 – 9 5

7. How are categorical variables preprocessed before m odel building?

• Standardization
• Dummy var i ables
• Correlation

The Global Happiness Index report contains the Happiness Score data w i th multiple features (namely the Economy, Family, Health, and Freedom) that could affect the target variable value.

Prepare the data by following the steps g iven below, and answer question 8

• Split the set of independent features and the dependent feature on the given dataset
• Create training and testing data from the set of independent features and dependent feature by splitting the original data in the ratio 3:1 respectively, and set the value for random_state of the training/test split method’s instance as 1

8. A multiple linear regression model is built on the Global Happiness Index dat a set ‘GHI_Report.csv’. What is the RMSE of the baseline model?

9. A regression model with the following function y=60+5.2x was built to understand the impact of humidity (x) on rainfall (y). The humidity this week is 30 more than the previous week. Wh a t is the predicted difference in rainfall?

10. X and Y are two variables that have a strong linear relationship. Whi c h of the following statements are incorrect?

• There cannot be a negative relationship between the two variables.
• The relationship between the two variables is purely causal.
• One variable may or may not cause a change in the other variable.
• The variables can be positively or negativel y correlated with each other

About Python For Data Science

The course aims at equipping participants to be able to use python programming for solving data science problems. CRITERIA TO GET A CERTIFICATE Average assignment score = 25% of the average of the best 3 assignments out of the total 4 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 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.

NPTEL Python for Data Science Assignment 4 Answers July 2022

1. The power consumption of an individual house in a residential complex has been recorded for the previous year. This data is analysed to predict the power consumption for the next year . Under which type of machine learning problem does this fall under? a. Classification b. Regression c. Reinforcement Learning d. None of the above

2. A dataset contains data collected by the Tamil Nadu Pollution Control Board on environmental conditions (154 variables) from one of their monitoring stations. This data is further analyzed to understand the most significant factors that affect the Air Quality Index. The predictive algorithm that can be used in this situation is _______ _ ___. a. Logistic Regression b. Simple Linear Regression c. Multiple Linear Regression d . None of the above

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3. A regression model with the following function y = 60 + 5.2x was built to understand the impact of humidity (x) on rainfall (y). The humidity this week is 30 more than the previous week . What is the predicted difference in rainfall? a. 156 mm b. 15.6 mm c. -156 mm d. None of the above

4. Which of the following machine learning techniques would NOT be appropriate to solve the problem given in the problem statement? a. kNN b. Random Forest c . Logistic Regression d. Linear regression

5. The plot shown below denotes the percentage distribution of the target column values within the train_data dataframe. Which of the following options are correct?

6. After applying logistic regression, what is/are the correct observations from the resultant confusion matrix? a. True Positive = 29, True Negative = 94 b. True Positive = 94, True Negative = 29 c. False Positive = 5 , True Negative = 94 d. None of the above

👇 For Week 04 Assignment Answers 👇

7. The logistic regression model built between the input and output variables is checked for its prediction accuracy of the test data. What is the accuracy range (in %) of the predictions made over test data? a. 60 – 79 b. 90 – 95 c. 30 – 59 d. 80 – 89

8. How are categorical variables preprocessed before model building? a. Standardization b. Dummy variables c. Correlation d. None of the above

9. A multiple linear regression model is built on the Global Happiness Index dataset “GHI_Report.csv”. What is the RMSE of the baseline model? a. 2.00 b. 0.50 c. 1.06 d. 0.75

10. X and Y are two variables that have a strong linear relationship. Which of the following statements are incorrect? a. There cannot be a negative relationship between the two variables. b. The relationship between the two variables is purely causal. c. One variable may or may not cause a change in the other variable . d. The variables can be positively or negatively correlated with each other.

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NPTEL Python for Data Science Assignment 4 Answers Jan 2022

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

(A) 5, Rs. >= 10,000 (B) 4, Rs. < 1000 (C) 5, Rs. < 1000 (D) 4, ‘1000 <= Rs. < 5,000’

Answer:- (C) 5, Rs. < 1000

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

(A) 35.54  (B) 38.44  (C) 33.00  (D) None of the above

Answer:- (B) 38.44 

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 ________.

(A) Less than 50%  (B) Between 50% and 60%  (C) Greater than 70%  (D) None of the above

Answer:- (C) Greater than 70% 

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?

(A) 11.11%  (B) 3.7%  (C) 20%  (D) 39.2%

Answer:- (C) 20% 

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

(A) 64%  (B) 78%  (C) 76.4%  (D) None of the above

Answer:- (A) 64% 

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?

(A) 1.99  (B) 0.85  (C) 1.06  (D) 0.33

Answer:- (C) 1.06 

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

(A) 55.63  (B) 45.81  (C) 75.59  (D) 81.46

Answer:- (D) 81.46

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

(A) Linear Regression assumes that there exists a linear relationship between the independent variable and dependent variable.  (B) The error terms are assumed to be independent and normally distributed.  (C) The percentage of variation in the dependent variable as explained by the independent variable/variables is expressed by R-squared value.  (D) Residuals are the product of the predicted value and the actual observed value.

Answer:- (A), (B), (C)

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

(A) Logistic Regression doesn’t require a linear relationship between the dependent and independent variables.  (B) The value of the logistic function being a probability will range between 0 and 1.  (C) Cost function of Logistic Regression is also called as the Log Loss function.  (D) The dependent variable can be of both numerical or categorical type just like the independent variables.

Answer:- (C) Cost function of Logistic Regression is also called as the Log Loss function. 

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

(A) Standardization  (B) Dummy variables  (C) Correlation  (D) None of the above

Answer:- (B) Dummy variables 

Disclaimer :- We do not claim 100% surety of solutions, these solutions are based on our sole expertise, and by using posting these answers we are simply looking to help students as a reference, so we urge do your assignment on your own.

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NPTEL Python for Data Science Assignment 4 Answers 2022:-  All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

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Python for Data Science | NPTEL | Week 4 Answers

This set of MCQ(multiple choice questions) focuses on the  Python for Data Science NPTEL Week 4 Answers

You should practice these questions to improve fundamentals of Data Science needed for various interviews (like company interview, campus interview, walk-in interview), entrance exams, placements and other competitive exams. All the questions in this particular section are based on only “ Python for Data Science NPTEL Week 4 Answers “.

Course layout

Week 1 : Basics of Python Spyder Week 2: Sequence data types & associated operations Week 3: Data frames Week 4:  Case study

NOTE:  You can check your answer immediately by clicking show answer button. Moreover, this set of “Python for Data Science NPTEL Week 4 Answers” contains 10 questions.

Now, start attempting the quiz.

Python for Data Science NPTEL Week 4 Answers

Q1. Which of the following are regression problems? Assume that appropriate data is given.

a) Predicting the house price. b) Predicting whether it will rain or not on a given day. c) Predicting the maximum temperature on a given day. d) Predicting the sales of the ice-creams.

Answer: a), c), d)

Q2 . Which of the following are binary classification problems?

a) Predicting whether a patient is diagnosed with cancer or not. b) Predicting whether a team will win a tournament or not. c) Predicting the price of a second-hand car. d) Classify web text into one of the following categories: Sports, Entertainment, or Technology.

Answer: a), b)

Q3. If a linear regression model achieves zero training error, can we say that all the data points lie on a hyperplane in the (d+1)-dimensional space? Here, d is the number of features.

a) Yes b) No

Answer: a) Yes

Q4. Which of the following machine learning techniques would NOT be appropriate to solve the problem given in the problem statement?

a) kNN b) Random Forest c) Logistic Regression d) Linear regression

Answer: d) Linear regression

Q5. After applying logistic regression, what is/are the correct observations from the resultant confusion matrix?

a) True Positive = 29, True Negative = 94 b) True Positive = 94, True Negative = 29 c) False Positive = 5, True Negative = 94 d) None of the above

Answer: a), c)

Q6. The logistic regression model built between the input and output variables is checked for its prediction accuracy of the test data. What is the accuracy range (in %) of the predictions made over test data?

a) 60 -79 b) 90 – 95 c) 30 – 59 d) 80 – 89

Answer: b) 90 – 95

Q7. How are categorical variables preprocessed before model building?

a) Standardization b) Dummy variables c) Correlation d) None of the above

Answer: b) Dummy variables

Q8. A multiple linear regression model is built on the Global Happiness Indes dataset ‘GHI_Report.csv’. What is the RMSE of the baseline model?

a) 2.00 b) 0.50 c) 1.06 d) 0.75

Answer: c) 1.06

Q9. A regression model with the following function y = 60 + 5.2x was built to understand the impact of humidity (x) on rainfall (y). The humidity this week is 30 more than the previous week. What is the predicted difference in rainfall?

a) 156 mm b) 15.6 mm c) -156 mm d) None of the above

Answer: a) 156 mm

Q10. X and Y are two variables that have a strong linear relationship. Which of the following statements are incorrect?

a) There cannot be a negative relationship between the two variables. b) The relationship between the two variables is purely causal. c) One variable may or may not cause a change in the other variable. d) The variables can be positively or negatively correlated with each other.

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

a) 5, Rs. >= 10,000 b) 4, Rs. < 1000 c) 5, Rs. < 1000 d) 4, ‘1000 <= Rs. < 5,000’

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

a) 35.54 b) 38.44 c) 33.00 d) None of the above

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 ________.

a) Less than 50% b) Between 50% and 60% c) Greater than 70% d) None of the above

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?

a) 11.11% b) 3.7% c) 20% d) 39.2%

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

a) 64% b) 78% c) 76.4% d) None of the above

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?

a) 1.99 b) 1.06 c) 0.85 d) 0.33

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

a) 55.63 b) 45.81 c) 75.59 d) 81.46

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

a) Linear Regression assumes that there exists a linear relationship between the independent variable and dependent variable. b) The error terms are assumed to be independent and normally distributed. c) The percentage of variation in the dependent variable as explained by the independent variable/variables is expressed by R-squared value. d) Residuals are the product of the predicted value and the actual observed value.

Answer: a), b), c)

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

a) Logistic Regression doesn’t require a linear relationship between the dependent and independent variables. b) The value of the logistic function being a probability will range between 0 and 1. c) Cost function of Logistic Regression is also called as the Log Loss function. d) The dependent variable can be of both numerical or categorical type just like the independent variables.

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

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NPTEL Python for Data Science Assignment 4 Answers 2022

• by QuizXp Team
• August 24, 2022 August 24, 2022

NPTEL Python for Data Science Assignment 4 Answers :- Hello students in this article we are going to share NPTEL Python for Data Science assignment week 4 answers. All the Answers provided below to help the students as a reference, You must submit your assignment at your own knowledge.

Below you can find NPTEL Python for Data Science Assignment 4 Answers

NPTEL Python for Data Science Assignment 4 Answers 2022:-

Q1. The power consumption of an individual house in a residential complex has been recorded for the previous year. This data is analysed to predict the power consumption for the next year. Under which type of machine learning problem does this fall under?

a. Classification b. Regression c. Reinforcement Learning d. None of the above

Answer : b. Regression

Q2. A dataset contains data collected by the Tamil Nadu Pollution Control Board on environmental conditions (154 variables) from one of their monitoring stations. This data is further analyzed to understand the most significant factors that affect the Air Quality Index. The predictive algorithm that can be used in this situation is ___________.

Answer : c. Multiple Linear Regression

Q3. A regression model with the following function y = 60 + 5.2x was built to understand the impact of humidity (x) on rainfall (y). The humidity this week is 30 more than the previous week. What is the predicted difference in rainfall?

Answer: a. 156 mm

Q4. Which of the following machine learning techniques would NOT be appropriate to solve the problem given in the problem statement?

Answer: d. Linear regression

Q5. The plot shown below denotes the percentage distribution of the target column values within the train_data dataframe. Which of the following options are correct?

Answer: b. No > 70, Yes > 20

Q6. After applying logistic regression, what is/are the correct observations from the resultant confusion matrix?

Answer: b. True Positive = 94, True Negative = 29

Q7. The logistic regression model built between the input and output variables is checked for its prediction accuracy of the test data. What is the accuracy range (in %) of the predictions made over test data?

Answer: b. 90 – 95

Q8. How are categorical variables preprocessed before model building?

Answer: b. Dummy variables

Q9. 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. 1.06

Q10. X and Y are two variables that have a strong linear relationship. Which of the following statements are incorrect?

Answer: a. There cannot be a negative relationship between the two variables

c. One variable may or may not cause a change in the other variable.

For More NPTEL Answers:-  CLICK HERE

Disclaimer: We do not claim 100% surety of answers, these answers are based on our sole knowledge, and by posting these answers we are just trying to help students, so we urge do your assignment on your own.

if you have any suggestions then comment below or contact us at  [email protected]

If you found this article Interesting and helpful, don’t forget to share it with your friends to get this information.NPTEL Python for Data Science Assignment 4 Answers 2022

[Week 1-4] NPTEL Python For Data Science Assignment Answers 2023

NPTEL Python For Data Science Assignment Answers

Table of Contents

NPTEL Python For Data Science Week 4 Assignment Answer 2023

1. Which of the following are regression problems? Assume that appropriate data is given.

• Predicting the house price.
• Predicting w h ether it will rain or not on a given day.
• Predicting the maximum temperature on a g iven day.
• Predicting the sales of the ice-creams.

2. Which of the followings are binary classification problems?

• Predicting whether a patient is diagnosed with cancer or not.
• Predicting whether a team will win a tournament or not.
• Predicting the price of a second-hand car.
• Classify web text into one of the follow in g categories: Sports, Entertainment, or Technology.

3. If a linear regression model achieves zero training error, can we say that all the data points lie on a hyperplane in the (d+1)-dimensional space? Here, d is the nu m ber of features.

Read the information given below and answer the questions from 4 to 6: Data Description: An automotive service chain is launching its new grand service station this weekend. They offer to service a wide variety of cars. The current capacity of the station is to check 315 cars thoroughly per day. As an inaugural offer, they claim to freely check all cars that arrive on their launch day, and report whether they need servicing or not!

Unexpectedly, they get 450 cars. The servicemen will not work longer than the working hours, but the data analysts have to!

Can you save the day for the new service station?

How can a data scientist save the day for them?

He has been given a data set, ‘ ServiceTrain.csv ’ that contains some attributes of the car that can be easily measured and a conclusion that if a service is needed or not.

Now for the cars they cannot check in detail, they measure those attributes and store them in ‘ ServiceTest.csv ’

Problem Statement:

Use machine learning techniques to identify whether the cars require service or not

Read the given datasets ‘ ServiceTrain.csv ’ and ‘ ServiceTest.csv ’ as train data and test data respectively and import all the required packages for analysis.

4. Which of the following machine learning techniques would NOT be ap p ropriate to solve the problem given in the problem statement?

• Random Forest
• Logisti c Regression
• Linear regression

5. After applying logistic regression, what is/are the correct observat ion s from the resultant confusion matrix?

• True Positive = 29, True Negative = 94
• True Positive = 94, Tr u e Negative = 29
• False Positive = 5, True Negative = 94
• None of the above

Prepare the data by following th e steps given below, and answer questions 6 and 7.

• Encode categorical variable, Service – Yes as 1 and No as 0 for both the train and test datasets.
• Split the set of independent features and the dependent feature on both the train and test datasets.
• Set random_state for the instance of the logistic regression class as 0.

6. The logistic regression model built between the input and output variables is checked for its prediction accuracy of the test data. What is the accuracy range (in %) of the predictions made over test dat a ?

• 60 – 79
• 90 – 9 5

7. How are categorical variables preprocessed before m odel building?

• Standardization
• Dummy var i ables
• Correlation

The Global Happiness Index report contains the Happiness Score data w i th multiple features (namely the Economy, Family, Health, and Freedom) that could affect the target variable value.

Prepare the data by following the steps g iven below, and answer question 8

• Split the set of independent features and the dependent feature on the given dataset
• Create training and testing data from the set of independent features and dependent feature by splitting the original data in the ratio 3:1 respectively, and set the value for random_state of the training/test split method’s instance as 1

8. A multiple linear regression model is built on the Global Happiness Index dat a set ‘GHI_Report.csv’. What is the RMSE of the baseline model?

9. A regression model with the following function y=60+5.2x was built to understand the impact of humidity (x) on rainfall (y). The humidity this week is 30 more than the previous week. Wh a t is the predicted difference in rainfall?

10. X and Y are two variables that have a strong linear relationship. Whi c h of the following statements are incorrect?

• There cannot be a negative relationship between the two variables.
• The relationship between the two variables is purely causal.
• One variable may or may not cause a change in the other variable.
• The variables can be positively or negativel y correlated with each other

NPTEL Python For Data Science Week 3 Assignment Answer 2023

1. Which of the following is the correct approach to fill missing values in case of categorical variable?

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

• df_cars[[‘Type’]]
• df_cars.iloc[[:, 1]
• df_cars.loc[:, [‘Type’]]

3. The method df_cars.describe() will give description of which of the following column?

• Price (in lakhs)
• All of the above

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

• pd.concat()

5. Which of the following are libraries in Python?

6. Which of the following variable have null values?

• Review Date

7. Which of the following countries have maximum locations of cocoa manufacturing companies?

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

• Review date
• Bean origin

9. What is the maximum rating of chocolates?

• [bool, int, float, float, str]
• [str, int, float, float, str]
• [bool, int, float, int, str]
• [bool, int, int, float, str]

NPTEL Python For Data Science Week 2 Assignment Answer 2023

1. Which of the following object does not support ind e xing?

• dict i onary

2. Given a NumPy array, arr = np.array([[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]), what is the output of the command, print(arr[0][1])?

• [[1 2 3] [4 5 6] [7 8 9]

3. What is the output of the following code?

• [2, 3, 4, 5]
• [1, 2, 3, 4]
• Will throw an error: Set objects are no t iterable.

5. Which of the following code gives output My friend’s house is in Chennai?

6. Let t1=(1,2, “tuple”,4) and t2=(5,6,7). Which of the follo w ing will not give any error after the execution?

• t1.append(5)
• x=t2[t1[1]]
• t3=(t1 , t2)
• t3=(list(t1), list(t2))

7. Let d={1:“Pyhton”,2:[1,2,3]}. Which among the fol l owing will not give the error after the execution?

• d[2].append(4)
• d.update({‘one’ : 22})

8. Wh i ch of the following data type is immutable?

9. student = {‘name’: ‘Jane’, ‘age’: 25 , ‘courses’: [‘Math’, ‘Statistics’]} Which among the following will return {‘name’: ‘Jane’, ‘age’: 26, ‘courses’: [‘Math’ , ‘Statistics’], ‘phone’: ‘123-456’}?

• student.update({‘age’ : 26})
• student.update({‘age’ : 26, ‘phone’: ‘123-456’}) 
• student[‘phone’] = ‘123-456’

[‘M’, ‘A’, ‘H’, ‘E’, ‘S’, ‘H’] [‘m’, ‘a’, ‘h’, ‘e’, ‘s’ , ‘h’] [‘M’, ‘a’, ‘h’, ‘e’, ‘s’, ‘h’] [‘m’, ‘A’, ‘H’, ‘E’, ‘S’, ‘H’]

NPTEL Python For Data Science Week 1 Assignment Answer 2023

• Error: Invalid operation, unsupported operator ‘*’ used between ‘int’ and ‘str’

• Code will throw an error.

4. Which of the following variable names are INVALID in Python?

• variable_ 1

5. While naming the variable, use of any special character other than unders c ore(_) ill throw which type of error?

• Syntax error
• Value er r or
• Index error

6. Let x = “Mayur”. Which of the following commands converts the ‘x’ to float datatype?

• str(float,x)
• x.flo a t()
• Cannot convert a string to float data type

7. Which Python library is commonly used for data wrangling and manipulation?

9. Given two variables, j = 6 and g = 3.3. If both normal division and floor division operators were used to divide j by g, what would be the data type of the value obtained from the operations?

• float, float

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Programming for Data Science

Teaching data scientists the tools they need to use computers to do data science

Advanced Python for Data Science Assignment 4

• Work in groups of three using the repositories you created in Assignment 3.
• Conduct three code reviews of the nbody_opt.py code. Two team members will act as reviewers and the third (the repository owner) the reviewee.
• Using the Issues tracker on GitHub, each reviewer is to open at least two issues against a reviewee’s repository (four issues total).
• Use the checklist as a guide as to what issues to open. Issues can cover the same topic, provided they apply to different code. i.e. each code review must comprise four distinct issues.
• Each reviewee should address the four issues opened against their code, then commit the changes, one commit per issue.

Note: if you are unable to form a group of three, join an existing group to make four members. Each team member will need to open four issues in such a way that each team member’s repository still receives four issues in total.

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Python for data science assignment solutions week 4 2022.

Are you looking for help in Python for Data Science NPTEL week 4 assignment answers? So, here in this article, we have provided Python for Data Science week 4 assignment answer’s hint.

Python for Data Science NPTEL Assignment Solutions Week 4

Q1. The power consumption of an individual house in a residential complex has been recorded for the previous year. This data is analysed to predict the power consumption for the next year. Under which type of machine learning problem does this fall under?

a. Classification b. Regression c. Reinforcement Learning d. None of the above

Answer : b. Regression

For instant notification of any updates, Join us on telegram .

Q2. A dataset contains data collected by the Tamil Nadu Pollution Control Board on environmental conditions (154 variables) from one of their monitoring stations. This data is further analyzed to understand the most significant factors that affect the Air Quality Index. The predictive algorithm that can be used in this situation is ___________.

a. Logistic Regression

b. Simple Linear Regression

c. Multiple Linear Regression

d. None of the above

Answer : c. Multiple Linear Regression

Q3. A regression model with the following function y = 60 + 5.2x was built to understand the impact of humidity (x) on rainfall (y). The humidity this week is 30 more than the previous week. What is the predicted difference in rainfall?

Answer: a. 156 mm

Q4. Which of the following machine learning techniques would NOT be appropriate to solve the problem given in the problem statement?

b. Random Forest

c. Logistic Regression

d. Linear regression

Answer: d. Linear regression

Q5. The plot shown below denotes the percentage distribution of the target column values within the train_data dataframe. Which of the following options are correct?

a. Yes > 20, No > 60

b. No > 70, Yes > 20

c. Yes > 30, No > 70

d. Yes > 70, No > 30

Answer: b. No > 70, Yes > 20

Q6. After applying logistic regression, what is/are the correct observations from the resultant confusion matrix?

a. True Positive = 29, True Negative = 94

b. True Positive = 94, True Negative = 29

c. False Positive = 5, True Negative = 94

Answer: b. True Positive = 94, True Negative = 29

Q7. The logistic regression model built between the input and output variables is checked for its prediction accuracy of the test data. What is the accuracy range (in %) of the predictions made over test data?

a. 60 – 79

b. 90 – 95

d.  80 – 89

Answer: b. 90 – 95

Q8. How are categorical variables preprocessed before model building?

a. Standardization

b. Dummy variables

c. Correlation

Answer: b. Dummy variables

Q9. 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. 1.06

Q10. X and Y are two variables that have a strong linear relationship. Which of the following statements are incorrect?

a. There cannot be a negative relationship between the two variables

b. The relationship between the two variables is purely causal.

c. One variable may or may not cause a change in the other variable.

d. The variables can be positively or negatively correlated with each other.

Answer: a. There cannot be a negative relationship between the two variables

Disclaimer: These answers are provided only for the purpose to help students to take references. This website does not claim any surety of 100% correct answers. So, this website urges you to complete your assignment yourself.

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Python for Data Science NPTEL Assignment Solutions Week 3

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