import pandas as pddf = pd.read_csv("../../../../delete/medical_insurance.csv")
df.head()| person_id | age | sex | region | urban_rural | income | education | marital_status | employment_status | household_size | ... | liver_disease | arthritis | mental_health | proc_imaging_count | proc_surgery_count | proc_physio_count | proc_consult_count | proc_lab_count | is_high_risk | had_major_procedure | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 75722 | 52 | Female | North | Suburban | 22700.0 | Doctorate | Married | Retired | 3 | ... | 0 | 1 | 0 | 1 | 0 | 2 | 0 | 1 | 0 | 0 |
| 1 | 80185 | 79 | Female | North | Urban | 12800.0 | No HS | Married | Employed | 3 | ... | 0 | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
| 2 | 19865 | 68 | Male | North | Rural | 40700.0 | HS | Married | Retired | 5 | ... | 0 | 0 | 1 | 1 | 0 | 2 | 1 | 0 | 1 | 0 |
| 3 | 76700 | 15 | Male | North | Suburban | 15600.0 | Some College | Married | Self-employed | 5 | ... | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
| 4 | 92992 | 53 | Male | Central | Suburban | 89600.0 | Doctorate | Married | Self-employed | 2 | ... | 0 | 1 | 0 | 2 | 0 | 1 | 1 | 0 | 1 | 0 |
5 rows × 54 columns