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1
Show your ID card.
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2
Introduction.
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3
Explain your notebook/project.
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4
How many models did you train?
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5
On how many models did you perform Hyperparameter Tuning?
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6
Why did you perform Hyperparameter Tuning only on those models?
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7
Which model took the longest to train? Why?
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8
Explain Gradient Descent.
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9
Explain Gradient Descent on a whiteboard.
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10
Why did you use Accuracy instead of F1-Score or ROC-AUC?
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11
Draw and explain the Confusion Matrix.
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12
Write and explain the formulas for Precision, Recall, F1-Score, and Accuracy.
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13
How did you perform feature engineering on date/time features?
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14
Which hyperparameters did you tune?
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15
What time series model would you use?
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16
What more could you have done to improve the project?
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17
How did you handle class imbalance?
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18
What does the C parameter in Logistic Regression represent?
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19
Why did you use Median instead of Mean (or vice versa) for imputation?
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20
Which imputation technique would you use for a right-skewed feature?
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21
Which model did you use for the final submission?
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22
Show your Kaggle leaderboard score and rank.
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23
Show your Kaggle submissions.
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24
Questions based on your notebook implementation.
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25
Suggestions on improving the notebook/project.
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26
Asked if you have any questions for the proctor.