### 3.1 Quality Grade The **titanic** dataset earns an **A (Excellent)** grade with a 95% overall quality score, reflecting strong structural integrity and near-complete data capture across most passenger attributes. With 92% completeness and perfect referential integrity (structurally guaranteed in this single-table design), the dataset provides a reliable foundation for passenger manifest analysis, survival modeling, and demographic segmentation. Business users can confidently build reports and predictive models on this data, though one significant gap—cabin assignments—requires careful handling in analyses where accommodation details matter. ### 3.2 Key Risk Areas | Risk Type | Detail | Severity | |-----------|--------|----------| | Missing Data | titanic.Cabin (77% null) | High | The **Cabin** column presents the only material quality concern, with 77% of passenger records lacking cabin assignments. This gap likely reflects historical record-keeping practices rather than data collection errors—third-class passengers and crew may not have had assigned cabins documented in the original manifest. Before incorporating cabin data into survival analysis or accommodation-based segmentation, teams must decide whether to exclude this attribute, impute deck information from available patterns, or restrict analysis to the 23% of passengers with known cabin assignments. No referential integrity issues exist, as expected in a single-table passenger manifest. ### 3.3 Remediation Priorities - **Cabin enrichment strategy**: Determine whether the 77% null rate in **titanic.Cabin** represents systematically missing data (e.g., third-class passengers) or random gaps. If systematic, create a "Cabin_Known" flag and analyze the 205 passengers with cabin data as a distinct cohort rather than attempting imputation. - **Document cabin null semantics**: Add metadata clarifying whether missing **Cabin** values mean "not assigned," "not recorded," or "third-class accommodation without individual cabins." This context is essential for analysts building survival models or accommodation reports. - **Validate completeness assumptions**: Confirm that the 92% overall completeness score aligns with business expectations for a historical passenger manifest. If other columns show minor null rates, assess whether they follow similar class-based or ticket-type patterns. - **Establish baseline quality metrics**: Capture current completeness rates by passenger class and ticket type as a reference point. If future data integration adds voyage records or crew manifests, these baselines will help detect quality regression in merged datasets.