python for data science nptel assignment 4 solutions

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

NPTEL Python For Data Science Assignment Answers

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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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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.

More Weeks of Python for Data Science: Click here

More Nptel Courses: Click here

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.

Python for Data Science NPTEL All weeks: https://progies.in/answers/nptel/python-for-data-science

More NPTEL course answers: https://progies.in/answers/nptel

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

Answers will be Uploaded Shortly and it will be Notified on Telegram, So  JOIN NOW

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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Prof. Raghunathan Rengaswamy IIT Madras

*Additional GST and optional Exam fee are applicable.

Description

Additional information, certification process, course details.

• Reviews (8)

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

INTENDED AUDIENCE

Final Year Undergraduates

PRE-REQUISITES

Knowledge of basic data science algorithms

ABOUT THE INSTRUCTOR

Prior to joining IIT Madras as a professor, Prof.Rengaswamy was a professor of Chemical Engineering and Co-Director of the Process Control and Optimization Consortium at Texas Tech University, Lubbock, USA. He was also a professor and associate professor at Clarkson University, USA and an assistant professor at IIT Bombay. His major research interests are in the areas of fault detection and diagnosis and development of data science algorithms for manufacturing industries.

1. Join the course Learners may pay the applicable fees and enrol to a course on offer in the portal and get access to all of its contents including assignments. Validity of enrolment, which includes access to the videos and other learning material and attempting the assignments, will be mentioned on the course. Learner has to complete the assignments and get the minimum required marks to be eligible for the certification exam within this period.

COURSE ENROLMENT FEE: The Fee for Enrolment is Rs. 1000 + GST

2. Watch Videos+Submit Assignments After enrolling, learners can watch lectures and learn and follow it up with attempting/answering the assignments given.

3. Get qualified to register for exams A learner can earn a certificate in the self paced course only by appearing for the online remote proctored exam and to register for this, the learner should get minimum required marks in the assignments as given below:

CRITERIA TO GET A CERTIFICATE Assignment score = Score more than 50% in at least 3/4 assignments. Exam score = 50% of the proctored certification exam score out of 100 Only the e-certificate will be made available. Hard copies will not be dispatched.”

4. Register for exams The certification exam is conducted online with remote proctoring. Once a learner has become eligible to register for the certification exam, they can choose a slot convenient to them from what is available and pay the exam fee. Schedule of available slot dates/timings for these remote-proctored online examinations will be published and made available to the learners.

EXAM FEE: The remote proctoring exam is optional for a fee of Rs.1500 + GST. An additional fee of Rs.1500 will apply for a non-standard time slot.

5. Results and Certification After the exam, based on the certification criteria of the course, results will be declared and learners will be notified of the same. A link to download the e-certificate will be shared with learners who pass the certification exam.

CERTIFICATE TEMPLATE

Week 1: BASICS OF PYTHON SPYDER (TOOL) • Introduction Spyder • Setting working Directory • Creating and saving a script file • File execution, clearing console, removing variables from environment, clearing environment • Commenting script files • Variable creation • Arithmetic and logical operators • Data types and associated operations

Week 2: Sequence data types and associated operations • Strings • Lists • Arrays • Tuples • Dictionary • Sets • Range

NumPy and Array

Week 3: • Pandas dataframe and dataframe related operations on Toyota Corolla dataset Reading files Exploratory data analysis Data preparation and preprocessing • Data visualization on Toyoto Corolla dataset using matplotlib and seaborn libraries Scatter plot Line plot Bar plot Histogram Box plot Pair plot •Control structures using Toyota Corolla dataset if-else family for loop for loop with if break while loop •Functions

Week 4: CASE STUDY

•Regression Predicting price of pre-owned cars •Classification Classifying personal income

8 reviews for Python for Data Science

Vikas Kumar – April 24, 2022

BRAHMA REDDY – May 10, 2022

VERY INFORMATIVE .

sailendra reddy (verified owner) – August 1, 2022

till now good going

Laxman Tulshiram sultane – September 17, 2022

Myself dream course

DEEPJYOTISH – October 5, 2022

Excellent 👍👍👍👍👍👍 Course

kartik – November 23, 2022

Thibu abiliya.k – April 11, 2023

Bowvithra. K – April 11, 2023

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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

Course Name: Python for Data Science

• About Course
• Certificate Type
• Toppers list
• Registration

Course abstract

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

Course Instructor

PROF. RAGHUNATHAN RENGASAMY

Teaching assistant(s),  course duration : sep-oct 2020,   view course,  syllabus,  enrollment : 20-may-2020 to 21-sep-2020,  exam registration : 14-sep-2020 to 02-nov-2020,  exam date : 18-dec-2020,   course statistics will be published shortly, certificate eligible, certified category count, successfully completed, participation.

Category : Successfully Completed

Category : Elite

Category : Silver

Category : Gold

Final score calculation logic.

• Assignment Score = Average of best 3 out of 4 assignments.
• Final Score(Score on Certificate)= 50% of Exam Score +25%Unproctored programming exam score+ 25% of Assignment Score NOTE: We have taken best assignment score and unproctored Score from Both Jan and July course

PAMIR ROY 96%

ABHINAND RAJAGOPAL 93%

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, DESIGN AND MANUFACTURING, KANCHEEPURAM

OMKAR SUNIL DALVI 92%

RAMRAO ADIK INSTITUTE OF TECHNOLOGY

SUJEETH SARAVAN RAJ S 92%

ADITYA MAHESH CHANDAK 91%

VIT UNIVERSITY-VELLORE

RUPIREDDY NAVADEEP REDDY 90%

INSTITUTE OF AERONAUTICAL ENGINEERING

MD JAFFAR MOLLAH 90%

UNIVERSITY OF MADRAS

SHATADRU ROY CHOWDHURY 90%

Jadavpur University

DR SHRISHAIL SHARAD GAJBHAR 90%

WALCHAND INSTITUTE OF TECHNOLOGY

SANJU SEBASTIAN 90%

ST. JOSEPH`S COLLEGE OF ENGINEERING & TECHNOLOGY

RAJKUMAR LAKSHMANAMOORTHY 89%

NIRO PROJECTS

NIKHIL SHETTY 89%

YASWANTH MOOGILIPURI 89%

NMAM INSTITUTE OF TECHNOLOGY

SACHIN MINOCHA 89%

GALGOTIAS UNIVERSITY

KUMAR RAMANAND 89%

SRIVATSAVA GANAPATHY 89%

Foretell Business Solutions Private Limited

SUNIL KUMAR BANOTH 88%

Indian Institute of Technology Madras

VIGNESH S 88%

SANDEEP RAGHUWANSHI 88%

SAMRAT ASHOK TECHNOLOGICAL INSTITUTE

SHARAYU MILINDKUMAR MALI 88%

SOU. VENUTAI CHAVAN POLYTECHNIC (SVCP)

RAMLA M 88%

S.R.M. INSTITUTE OF SCIENCE AND TECHNOLOGY

R ARUN KUMAR 88%

SRI SIVASUBRAMANIYA NADAR COLLEGE OF ENGINEERING

CHETNA AGARWAL 88%

ARCHIT NITIN SHINDE 88%

EzyTax Solutions

ANOOP A NAIR 88%

Indian Institute of Science Education and Research, Thiruvananthapuram (IISER-Tvm)

N KAUSIK 88%

BRUNDAVANAM DINESH KUMAR 88%

Indian Institute of Technology,Madras

R SUBHA 88%

Sir M Visvesvaraya Institute of Technology

RUBY UPADHYAY 88%

TATARAO MOKA 88%

GANESH M BALGI 87%

SHREE VIDYADHIRAJ POLYTECHNIC,KUMTA

VMS PRAKASH YERUBANDI 87%

Power Grid Corporation of India Limited

M BADRI NARAYANAN 87%

SANKAR DE 87%

Fi-Tek Pvt Ltd

ARUN KUMAR 87%

MLV Textile and Engineering College, Bhilwara

SHRUTI SACHDEVA 87%

Netaji Subhas University of Technology

SIDHARTH S PRAKASH 87%

AKASH RAO 87%

SUSHODHAN SUDHIR VAISHAMPAYAN 87%

Tata Consultancy Services

AMIT CHAUHAN 87%

NAGASURESH THAVVA 87%

SAMAPRIYA TRIVEDI 87%

SHALINI 87%

Powergrid Corporation of India Ltd

DR. AJITKUMAR SHITOLE 87%

INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY

RAVINA KUMAWAT 86%

University of Rajasthan, Jaipur

MARIA PHILNA ARUJA 86%

SURESHSRINI S K 86%

KANIMOZHI SELVI C S 86%

KONGU ENGINEERING COLLEGE

ANUP DEDHIA 86%

DHIRAJ MUKESH JHA 86%

GOVT.COLLEGE OF ENGINEERING

DHEERAJ KUMAR 86%

EXICOM TELE-SYSTEMS LTD.

PATEL FALGUNIBEN NARENDRABHAI 86%

SARDAR VALLABHBHAI PATEL INSTITUTE OF TECHNOLOGY

SRISHTI VASHISHTHA 86%

North Cap University

BASAWARAJ VITHALREDDY 86%

ROHIT MONDAL 86%

Cypress Semiconductor Technology India Private Limited.

ABHISHEK RATHI 86%

NITIN MALIK 86%

THE NORTHCAP UNIVERSITY

BIKAS CHOUDHARY 86%

KAKADIYA KAMAL KHODABHAI 86%

DEVANG PATEL INSTITUTE OF ADVANCE TECHNOLOGY AND RESEARCH

ABHISEK SAHA 86%

University of Hyderabad

MOHAMMED INAYATHULLA 86%

MALLA REDDY ENGINEERING COLLEGE (AUTONOMOUS)

SUMANTHA NTS 86%

POWER RESERCH AND DEVELOPMENT CONSULTANT PVT. LTD

RAJKUMAR PORWAL 85%

SHRI GOVINDRAM SEKSARIA INSTITUTE OF TECHNOLOGY AND SCIENCES

MANOJ KUMAR SAJJA 85%

N.M.A.M. Institute of Technology

LUCKY SHROTRIYA 85%

SATHYA S 85%

R.M.K.Engineering College

VISHAL R 85%

Anna university, CEG, guindy

ANUKUL KUMAR SUMAN 85%

SUMAN GUPTA 85%

Teoco Software Pvt Ltd

MITHILESH M 85%

VIT UNIVERSITY CHENNAI

ADITYA PANDEY 85%

A SRINIVAS RAO 85%

KESHAV MEMORIAL INSTITUTE OF TECHNOLOGY (KMIT)

ANNAM SAI SHIVA KARTHIK 85%

SRI VASAVI ENGINEERING COLLEGE

SRIYA DATLA 85%

ABHINAV PANDEY 85%

ANKUSH PATEL 85%

POWERGRID Corporation of India Ltd.

SHILPA J SHETTY 85%

SRISHTI 85%

The NorthCap University

HARMAN SINGH 85%

GURU NANAK DEV ENGINEERING COLLEGE

ABHINAV SINGH 85%

THAKUR COLLEGE OF ENGINEERING AND TECHNOLOGY

VIDYARANI M KATIGAR 85%

GOGTE INSTITUTE OF TECHNOLOGY

SANGEETHA B 85%

ATIF FARIDI 85%

Eliting Bots

MADHUR MUNJAL 84%

Infoedge India

SUPRATEEK SHUKLA 84%

Laminaar Aviation Infotech (India) Pvt. Ltd.

SUNNY KUMAR AKARAPU 84%

NITIN BALIRAM SAWANT 84%

VIKASH KUMAR SINGH 84%

Tata Consultance Services

KALAIVANI K S 84%

DARSHAN MANISH KALE 84%

PIMPRI CHINCHWAD COLLEGE OF ENGINEERING & RESEARCH

DR MANISHA SATISH DIVATE 84%

Usha Pravin Gandhi College of Arts, Science and Commerce

AKSHAT MANTRY 84%

UNIVERSITY OF ENGINEERING & MANAGEMENT (UEM)

JAI NARESH SHAH 84%

PRAMOD SHRIRANG GOLE 84%

SSNC Globeop Financial services India pvt. lmtd

RIKDEV MANDAL 84%

ADITYA GUPTA 84%

INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, UNA (HP)

TABINDA 84%

SSM COLLEGE OF ENGINEERING

RAJEE A M 84%

JAGANATHAN ANNAMALAI PARAMESHWARAN 84%

MADHAVAN V 84%

GOPINATH VENKATESAN 84%

TEJASKUMAR RASIKLAL GHADIYALI 84%

UCCC & SPBCBA & SDHG COLLEGE OF BCA & IT

ADITYA MANOJ RAKSHIT 84%

RAJIV GANDHI COLLEGE OF ENGINEERING & RESERCH

SACHIN KUMAR JINDAL 84%

UTTAM INSTITUTE OF MANAGEMENT STUDIES

AKASH SINGH 84%

DHRUMIN NATVARBHAI PATEL 84%

LEELABEN DASHRATHBHAI RAMDAS PATEL (LDRP) INSTITUTE OF TECHNOLOGY AND RESEARCH

SACHIN KUMAR 84%

Ameriprise India LLP

BADAGALA ADARSH 84%

GMR INSTITUTE OF TECHNOLOGY

TARLEEN KAUR 84%

SHAVETA 84%

VAISHALI KALRA 84%

The Northcap University

VARSHA P 84%

MALNAD COLLEGE OF ENGINEERING

BHAVNA MITTAL 84%

BAL BHARATI PUBLIC SCHOOL, SECTOR-12, DWARKA, NEW DELHI

NAYAN RANJAN PAUL 84%

KMBB COLLEGE OF ENGINEERING AND TECHNOLOGY

Enrollment Statistics

Total enrollment: 41911, registration statistics, total registration : 2531, assignment statistics, score distribution graph - legend, assignment score: distribution of average scores garnered by students per assignment., exam score : distribution of the final exam score of students., final score : distribution of the combined score of assignments and final exam, based on the score logic..

Python for Data Science

Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout.

• Reading files
• Exploratory data analysis
• Data preparation and preprocessing
• Scatter plot
• if-else family
• for loop with if break
• Predicting price of pre-owned cars
• Classifying personal income

Books and references

Instructor bio.

Prof. Ragunathan Rengasamy

Course certificate.

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