Cleaning and preparing datasets

Dirty data produces unreliable insights. Before any analysis, datasets need cleaning. Duplicates, missing values, formatting inconsistencies, and errors must be addressed. AI streamlines this tedious but critical process. Describe your data quality issues to AI. It identifies problems like mixed date formats, inconsistent spellings, or outliers. AI suggests cleaning strategies tailored to your specific needs. Some issues need removal, others require imputation or transformation. AI generates cleaning code or formulas you can apply. It explains each step, helping you understand the impact. This transparency ensures you make informed decisions about data modifications. Some cleaning choices affect analysis results significantly.