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1
Show your ID card.
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2
Explain your problem statement.
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3
Explain your notebook/project.
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4
How did you approach the problem?
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5
Explain your EDA.
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6
What did you show in the graphs?
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7
Explain the skewness of the target variable.
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8
Explain your preprocessing steps.
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9
Explain your feature engineering.
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10
Explain your model training.
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11
Which model did you use?
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12
How can you improve model performance?
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13
Explain your hyperparameter tuning.
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14
Show your hyperparameter tuning implementation.
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15
Explain your parameter grid.
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16
What do these hyperparameters mean? colsample_bytree learning_rate max_depth n_estimators subsample
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17
What happens if the learning rate is increased?
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18
What happens if the number of estimators is increased?
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19
What happens if the maximum depth is changed?
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20
Show your Pipeline.
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21
What is TSCV (Time Series Cross Validation)?
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22
Why did you use TSCV?
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23
What does the cv parameter mean in TSCV?
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24
Why can your dataset be considered time-series data?
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25
Why do we perform Cross Validation?
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26
Why did you use the rolling mean feature?
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27
What data/features did you not use, and why?
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28
What is the shape of the training dataset?
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29
What is the shape of the test dataset?
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30
Print the first five rows of the training dataset.
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31
Show the basic statistics of the training dataset using describe().
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32
Count the null values in the training dataset.