How do you handle outliers? How to handle imbalance dataset? Feature engineering techniques?

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Accepted Answer

Outliers can be handled by removing, transforming or imputing them. Imbalanced datasets can be handled by resampling techniques. Feature engineering involves creating new features from existing ones.

Outliers can be removed using statistical methods like z-score or IQR.
Outliers can be transformed using techniques like log transformation or box-cox transformation.
Outliers can be imputed using techniques like mean imputation or regression imputation.
Imbalanced datasets can be handled by oversampling the minority class or undersampling the majority class.
Feature engineering involves creating new features from existing ones using techniques like one-hot encoding, binning, or PCA.
Feature selection techniques like correlation analysis or Lasso regression can be used to select the most important features.

How do you handle outliers? How to handle imbalance dataset? Feature engineering techniques?

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