{
    "action": "final",
    "content": "## Distinct Values in Categorical Columns\n\nBased on the analysis of the AI job dataset, here are the distinct value counts for each categorical column:\n\n| Column | Distinct Values |\n|--------|----------------|\n| **job_title** | 20 |\n| **company_name** | 16 |\n| **industry** | 15 |\n| **education_required** | 4 |\n| **employment_type** | 4 |\n| **experience_level** | 4 |\n| **company_size** | 3 |\n| **remote_ratio** | 3 |\n| **salary_currency** | 3 |\n\n### Key Observations\n\n- **High cardinality columns**: `job_title` (20), `company_name` (16), and `industry` (15) have the most variety, reflecting the diverse nature of AI job roles, employers, and sectors.\n\n- **Low cardinality columns**: `company_size`, `remote_ratio`, and `salary_currency` each have only 3 distinct values, making them useful for broad categorical groupings.\n\n- **Medium cardinality columns**: `education_required`, `employment_type`, and `experience_level` each have 4 distinct values, providing balanced segmentation for analysis.\n\nThis cardinality distribution suggests the dataset captures a good mix of granular details (job titles, companies) and standardized categories (employment types, experience levels) for comprehensive job market analysis."
}