nptel python for data science assignment 4 answers 2023

Python for Data Science NPTEL | Week 4

Session: JAN-APR 2024/ JULY-DEC 2023

Course name: Python For Data Science

Course Link: Click Here

These are NPTEL Python for Data Science Assignment 4 Answers

Q1. Which of the following are regression problems? Assume that appropriate data is given. Predicting the house price. Predicting whether it will rain or not on a given day. Predicting the maximum temperature on a given day. Predicting the sales of the ice-creams.

Answer: a, c, d

Q2. 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 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. Yes No

Answer: Yes

Q4. Which of the following machine learning techniques would NOT be appropriate to solve the problem given in the problem statement? kNN Random Forest Logistic Regression Linear regression

Answer: Linear regression

Q5. After applying logistic regression, what is/are the correct observations from the resultant confusion matrix? True Positive = 29, True Negative = 94 True Positive = 94, True Negative = 29 False Positive = 5, True Negative = 94 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? 60 – 79 90 – 95 30 – 59 80 – 89

Answer: 90 – 95

Q7. How are categorical variables preprocessed before model building? Standardization Dummy variables Correlation None of the above

Answer: Dummy variables

Q8. A multiple linear regression model is built on the Global Happiness Index dataset ‘GHI_Report.csv’. What is the RMSE of the baseline model? 2.00 0.50 1.06 0.75

Answer: 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? 156 mm 15.6 mm -156 mm None of the above

Answer: 156 mm

Q10. X nd Y are two variables that have a strong linear relationship. Which 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 negatively correlated with each other.

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Session: JAN-APR 2023

Course Name: Python for Data Science

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.

Q2. Which of the followings 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.

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

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.

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

Prepare the data by following the 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.

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

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

The Global Happiness Index report contains the Happiness Score data with multiple features (namely the Economy, Family, Health, and Freedom) that could affect the target variable value. Prepare the data by following the steps given 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

Q8. 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

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.

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Session: JULY-DEC 2022

Course name: Python for Data Science

Link to Enroll: Click Here

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 __________. 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? a. 156 mm b. 15.6 mm c. -156 mm d. None of the above

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?

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 d. None of the above

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 c. 30 – 59 d. 80 – 89

Answer: b. 90 – 95

Q8. How are categorical variables preprocessed before model building? a. Standardization b. Dummy variables c. Correlation d. None of the above

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? 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 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 

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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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{Week 4} NPTEL Python For Data Science Assignment Answer 2023

NPTEL Python For Data Science Assignment Answer

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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[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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The course aims at equipping participants to be able to use python programming for solving data science problems

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Python for data science was very useful for my studies and also it is easy to understand

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.

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Nptel data Science for Engineers Assignment 4 Answers 2023| Data Science for Engineers | Study2night

NPTEL Data Science for Engineers Assignment 4 Answers 2023?  In this article we will discuss about the answers for Week 4 assignment of Data science for Engineers. All these answers are make it as reference.I am confident in providing these answers.

Also Read:  NPTEL Data Science for Engineers Assignment 3 Answers 2023  

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NPTEL Data Science for Engineers Assignment 4 Answers 2023 

Last Date: 22-02-2023

You can find the answers for Data Science for Engineers Assignment 4 Answers 2023 below

Q1.  Let f(x)=x^3+6x^2−3x−5. Select the correct options from the given options below : 

a. −2+√5 will give the maximum for f(x).

b. −2+√5 will give the minimum for f(x). 

c. The stationary points of f(x) are −2+√5 and −2+√5 

d. The stationary points of f(x) are -4 and 0

Answer: [ c , b ]  

                Reason : f '' (x) at   −2+√5 is > 0. Hence, it is called as local minima. 

             To find the stationary points of f(x), we need to find the values of x where f'(x) = 0.

f(x) = x^3 + 6x^2 - 3x - 5

f'(x) = 3x^2 + 12x - 3

Consider f'(x) = 0, we get:    3x^2 + 12x - 3 = 0

Dividing on both sides by 3:  x^2 + 4x - 1 = 0

Using the quadratic formula, we get:

x= -b ± sqrt(4*a*c - 2*a)

x = (-4 ± sqrt(16 + 4))/2 = (-4 ± sqrt(20))/2

x = -2 ± sqrt(5)

So the stationary points of f(x) are -2 + sqrt(5) and -2 - sqrt(5). 

To determine whether each point is a maximum or minimum , we need to look at the sign of f''(x) at each point.

f''(x) = 6x + 12

At x = -2 + sqrt(5), we have,

f''(-2 + sqrt(5)) = 6(-2 + sqrt(5)) + 12 = -6 + 6sqrt(5) > 0

So -2 + sqrt(5) is a local minimum.

At x = -2 - sqrt(5), we have,

f''(-2 - sqrt(5)) = 6(-2 - sqrt(5)) + 12 = -6 - 6sqrt(5) < 0

So -2 - sqrt(5) is a local maximum.

From the following information given below, answer the below questions Q2 and Q3.

Consider the following Optimization problem. 

                                                         maxxϵRf(x) , where                                     f(x)=x^4+7x^3+5x^2−17^x+3

Let x∗ be the maximizer of f(x)

Q2.  What is the second order sufficient condition for x∗ to be the maximize of the function f(x)? 

a. 4x^3 + 21x^2 + 10x - 17 = 0

b. 12x^2 + 42x + 10 = 0

c. 12x^2 + 42x + 10 > 0

d. 12x^2 + 42x + 10 < 0 

Answer:   [ c]   12x^2 + 42x + 10 > 0 

Q3. Find the value of x*

Answer:   [ c ] -1.43  

Q4. Let f(x) = 2Sin x , 0≤x≤2π . Select the correct the options from the given following . 

a. π/2 is the global maximum of f(x).

b. π is the global minimum of f(x). 

c. 3π/2 is the global maximum of f(x). 

d. 3π/2 is the global maximum of f(x). 

Answer:   [ a, d ]  

Using the following information answer the following questions Q5, Q6, Q7,Q8. 

Let f(x) = 2x1^2 + 3x1x2 + 3x2^2 + x1 + 3x2

Q5. Find the gradient of f(x). 

Conclusion:

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