Data cleaning involves identifying and rectifying errors, inconsistencies, and gaps in the dataset. Common techniques include data imputation for missing values, normalization for scaling data, and outlier detection for identifying abnormal data points. Preprocessing also involves transforming data into a format suitable for analysis, such as converting categorical data into numerical values.
