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Python for Data Science NPTEL Assignment Solutions Week 4
Q1. The power consumption of an individual house in a residential complex has been recorded for the previous year. This data is analysed to predict the power consumption for the next year. Under which type of machine learning problem does this fall under?
a. Classification
b. Regression
c. Reinforcement Learning
d. None of the above
Answer: b. Regression
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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
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?
a. kNN
b. Random Forest
c. Logistic Regression
d. Linear regression
Answer: d. Linear regression
Q5. The plot shown below denotes the percentage distribution of the target column values within the train_data dataframe. Which of the following options are correct?
a. Yes > 20, No > 60
b. No > 70, Yes > 20
c. Yes > 30, No > 70
d. Yes > 70, No > 30
Answer: b. No > 70, Yes > 20
Q6. After applying logistic regression, what is/are the correct observations from the resultant confusion matrix?
a. True Positive = 29, True Negative = 94
b. True Positive = 94, True Negative = 29
c. False Positive = 5, True Negative = 94
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
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?
a. 2.00
b. 0.50
c. 1.06
d. 0.75
Answer: c. 1.06
Q10. X and Y are two variables that have a strong linear relationship. Which of the following statements are incorrect?
a. There cannot be a negative relationship between the two variables
b. The relationship between the two variables is purely causal.
c. One variable may or may not cause a change in the other variable.
d. The variables can be positively or negatively correlated with each other.
Answer: a. There cannot be a negative relationship between the two variables
c. One variable may or may not cause a change in the other variable.
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