Enhance Outlier Detection Test Coverage and Edge Case Handling

[smog-root]

Issue: #473

Note: Add all the labels and Update it to the leaderboard SWOC leaderboard

This PR improves the test cases for the outlier detection functions (remove_outliers_iqr, remove_outliers_zscore) by adding additional checks and edge cases. The following updates have been

1. Single Outlier:

• Tests were added to verify that a single outlier (e.g., 120000 in salary data) is correctly removed by both IQR and Z-score methods.

• Checks the length of the DataFrame before and after outlier removal to ensure that only the outlier is

2. No Outliers Scenario:

• A test was added to handle cases where there are no outliers. It ensures that the DataFrame remains unchanged when no outliers are

3. Multiple Outliers:

• A new test case was added to handle and verify the removal of multiple outliers from the dataset.

• The function now ensures that all outliers are correctly identified and

4. Data Integrity Checks:

• For each test, we validate the data integrity by ensuring that outliers are removed without affecting other data points.

Benefits:

• Increased test coverage for edge cases, ensuring robustness of the outlier detection

• Improved validation of the correctness of the remove_outliers_iqr and remove_outliers_zscore functions under various data conditions.

Changes:

• Added test_no_outliers to handle cases without any
• Added test_multiple_outliers to check removal of multiple
• Enhanced test_remove_outliers_iqr and test_remove_outliers_zscore to assert correct behavior and data integrity.

Testing:

• All existing tests along with new ones have been executed successfully.

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[pavitraag]

@UTSAVS26 its good to go for merge