Best Practices for Data Cleaning and Preparation

Start with clear objectives for data cleaning to ensure consistency and relevance

Handle missing data by filling gaps or removing incomplete entries

Standardize data formats to maintain uniformity in variables and units

Remove duplicates to avoid skewing results

Handle outliers carefully, validating or correcting unusual data points