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1
Why did you choose this preprocessing strategy instead of another?
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2
Did you consider any alternative preprocessing techniques?
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3
Did you consider any alternative preprocessing techniques?
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4
If you had more time, what would you do to improve your model/project?
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5
Did you refer to any research papers, books, or other external resources while building the project?
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6
Show your Kaggle competition submissions.
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7
Create a preprocessing pipeline using: Mean imputation for numerical columns Mode (most frequent) imputation for categorical columns.
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8
Modify an existing pipeline to replace Median imputation with Mode.
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9
Fill categorical columns using SimpleImputer(strategy="most_frequent").
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10
Fill categorical columns with mode using Pandas (without SimpleImputer).
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11
Import the California Housing dataset.
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12
Perform an 80:20 train-test split.
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13
Load the Iris dataset and train an SGDClassifier.
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14
Load the Iris dataset and train SGDClassifier without train_test_split.
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15
Filter rows where: RegionCode == "Florida" AND TargetValue > 50000
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16
Why did you choose this preprocessing strategy?
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17
Did you consider alternative preprocessing methods?
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18
If you had more time, how would you improve the model/project?
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19
Did you use research papers/books/external resources?
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20
Show your Kaggle competition submissions.
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21
Create preprocessing pipeline with mean (numerical) and mode (categorical) imputation.
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22
Replace median imputation with mode imputation.
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23
Fill categorical columns using SimpleImputer(strategy="most_frequent").
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24
Fill categorical columns using Pandas mode.
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25
Load California Housing dataset and perform an 80:20 train-test split.
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26
Train an SGDClassifier on the Iris dataset (with/without train-test split).
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27
Filter a DataFrame where RegionCode == "Florida" and TargetValue > 50000.