Proctor mlp_level1_viva3

Why did you choose this preprocessing strategy instead of another?

Did you consider any alternative preprocessing techniques?

Did you consider any alternative preprocessing techniques?

If you had more time, what would you do to improve your model/project?

Did you refer to any research papers, books, or other external resources while building the project?

Show your Kaggle competition submissions.

Create a preprocessing pipeline using: Mean imputation for numerical columns Mode (most frequent) imputation for categorical columns.

Modify an existing pipeline to replace Median imputation with Mode.

Fill categorical columns using SimpleImputer(strategy="most_frequent").

Fill categorical columns with mode using Pandas (without SimpleImputer).

Import the California Housing dataset.

Perform an 80:20 train-test split.

Load the Iris dataset and train an SGDClassifier.

Load the Iris dataset and train SGDClassifier without train_test_split.

Filter rows where: RegionCode == "Florida" AND TargetValue > 50000

Why did you choose this preprocessing strategy?

Did you consider alternative preprocessing methods?

If you had more time, how would you improve the model/project?

Did you use research papers/books/external resources?

Show your Kaggle competition submissions.

Create preprocessing pipeline with mean (numerical) and mode (categorical) imputation.

Replace median imputation with mode imputation.

Fill categorical columns using SimpleImputer(strategy="most_frequent").

Fill categorical columns using Pandas mode.

Load California Housing dataset and perform an 80:20 train-test split.

Train an SGDClassifier on the Iris dataset (with/without train-test split).

Filter a DataFrame where RegionCode == "Florida" and TargetValue > 50000.