{
    "action": "final",
    "content": "## Column Classification for Titanic Dataset\n\nBased on the metadata inspection, here's how the 16 columns classify:\n\n### **Identifiers** (2 columns)\n- **PassengerId** — unique passenger identifier (883 distinct values out of 891 rows)\n- **Ticket** — ticket number (752 distinct values; some passengers shared tickets)\n\n### **Categorical Dimensions** (5 columns)\n- **Sex** — gender (2 values: female, male)\n- **Pclass** — passenger class (3 values: 1st, 2nd, 3rd class)\n- **Embarked** — port of embarkation (3 values: C, Q, S; 2 nulls)\n- **Cabin** — cabin number (163 distinct values, but 687 nulls — 77% missing)\n- **Survived** — survival outcome (2 values: 0=died, 1=survived)\n\n### **Numeric Metrics** (3 columns)\n- **Age** — passenger age in years (range 0.42 to 80.0; 177 nulls)\n- **Fare** — ticket fare paid (range 0.0 to 512.33; 283 distinct values)\n- **SibSp** — number of siblings/spouses aboard (range 0 to 8)\n- **Parch** — number of parents/children aboard (range 0 to 6)\n\nNote: SibSp and Parch could also be treated as categorical (only 7 distinct values each), but they represent counts and are best used as numeric measures.\n\n### **Dates/Times** (1 column)\n- **_ingestion_timestamp** — when the data was loaded (all rows share the same timestamp: 2026-08-11 23:14:58+00)\n\n### **Text Attributes** (1 column)\n- **Name** — passenger name (1045 distinct values, essentially unique)\n\n### **System Metadata** (4 columns)\n- **_batch_id**, **_source_file**, **_source_system** — internal tracking fields (all constant across the dataset)\n\n---\n\n**Key observations:**\n- No meaningful temporal dimension exists for analysis (the ingestion timestamp is constant)\n- High missingness in Age (20%) and especially Cabin (77%) will affect analyses using those fields\n- The dataset is well-suited for survival prediction modeling with Sex, Pclass, Age, Fare, and family size (SibSp + Parch) as features"
}