Preprocessing

Datetime feature engineering transforms temporal data into numerical representations that machine learning models can process. Raw timestamps contain rich…

Categorical variable encoding is the process of converting qualitative data (categories, labels, or discrete values) into numerical representations that…

Feature engineering is the process of transforming raw data into features that better represent the underlying problem to predictive models, resulting in…

Feature scaling is the process of transforming numeric features to a common scale without distorting differences in the ranges of values. Machine learning…

Class imbalance occurs when the distribution of target classes in a classification dataset is significantly skewed, with some classes having many more samples…

Imputation is the process of replacing missing data with substituted values. Unlike deletion methods that discard incomplete observations, imputation preserves…

Missing values are data points that are absent, unknown, or unrecorded in a dataset. They appear as NULL, NaN (Not a Number), empty strings, or special codes…

Outliers are data points that deviate significantly from other observations in a dataset. They can arise from measurement errors, data entry mistakes, natural…

Text preprocessing transforms unstructured natural language into structured, machine-readable formats suitable for analysis and modeling. Raw text contains…