Purpose of Data Preparation Boundaries
This page explains how data normalization and cleaning are applied to the Cairo residential listings dataset and, equally important, what these processes are not designed to accomplish. The objective is to prevent the assumption that prepared data is complete, verified, or analytically enhanced.
Normalization and cleaning are treated as technical alignment steps, not as methods of validation or interpretation.
Normalization as Structural Alignment
Normalization aligns listing fields into consistent formats so that information can be read and referenced coherently across records. This includes standardizing attribute names, categorical labels, and basic formatting where platforms expose comparable fields.
Normalization does not harmonize meaning across platforms, nor does it resolve inconsistencies in how contributors describe properties.
Cleaning as Error Containment
Cleaning addresses obvious technical issues such as malformed entries, duplicated records, or incomplete fields that prevent basic readability. These steps are applied conservatively to avoid introducing assumptions or inferred corrections.
Cleaning does not correct inaccurate descriptions, misclassified locations, or misleading attributes supplied at the source.
What Cleaning Explicitly Does Not Do
Data preparation does not verify factual accuracy, confirm geographic precision, or reconcile conflicting information across listings. It does not infer missing values, standardize subjective descriptions, or adjust for uneven coverage.
As a result, cleaned data remains a representation of platform-exposed information, not an audited or enriched dataset.
Implications for Interpretation
Normalization and cleaning improve structural readability but do not expand the dataset’s epistemic scope. Prepared data should not be treated as more complete, reliable, or representative than raw listings.
This page therefore establishes clear boundaries to prevent conflating technical preparation with analytical validity.