NPTEL Python for Data Science Week 4 Assignment Answers 2025
1. 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.
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2. Which of the following are multiclass classification problems?
- Classifying emails as spam or not spam.
- Classifying a person’s blood type as A, B, AB, or O.
- Predicting the price of a second-hand car.
- Classifying a movie genre into Drama, Comedy, Action, or Thriller.
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3. If a linear regression model achieves zero training error, can we say that all the data points lie on a straight line in the feature space?
- Yes
- No
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4. 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
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5. 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
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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 data?
- 60 – 79
- 90 – 95
- 30 – 59
- 80 – 89
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7. How are categorical variables preprocessed before model building?
- Standardization
- Dummy variables
- Correlation
- None of the above
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8. A regression model with the function y=80+4.5x was built to understand the impact of temperature x on ice cream sales y. The temperature this month is 10 degrees more than the previous month. What is the predicted difference in ice cream sales?
- 56 units
- 45 units
- 80 units
- None of the above
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9. X and 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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10. 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
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