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| # Predicción batch – POST /predict/batch | |
| # Banco Ripley · Modelo Predictiva Early | |
| # URL base: https://nicolasitmeet-predictiva-early.hf.space | |
| # | |
| # Envía hasta 500 registros en una sola llamada bajo la clave "records". | |
| # Nota: los campos "serie" y "PERIODO" son ignorados por el pipeline; puedes omitirlos. | |
| BASE_URL="https://nicolasitmeet-predictiva-early.hf.space" | |
| curl -s -X POST "${BASE_URL}/predict/batch" \ | |
| -H "Content-Type: application/json" \ | |
| -d '{ | |
| "records": [ | |
| { | |
| "MESES_ANT_RCC": 111, | |
| "EDAD": 54, | |
| "GENERO": "M", | |
| "FLAG_ENTIDAD_PRINCIPAL": 0, | |
| "GRADO_INSTRUCCION": "SECUNDARIA", | |
| "DEPARTAMENTO": "LIMA", | |
| "PROVINCIA": "LIMA", | |
| "DISTRITO": "MIRAFLORES", | |
| "FLAG_TC_MODELOS": 0, | |
| "FLAG_MES": 1, | |
| "CONTAR_COMP": 12, | |
| "MARCA_HP": 0, | |
| "MARCA_SEG_VIDA": 0, | |
| "MARCA_DIF": 1, | |
| "MARCA_CONV": 0, | |
| "SITUACION_LABORAL": "INDEPENDIENTE", | |
| "ESTADO_CIVIL": "1", | |
| "MAX_ATRASO3": 0, | |
| "MAX_ATRASO6": 0, | |
| "MAX_ATRASO12": 0, | |
| "UTIL_TARJ": 0.45, | |
| "UTIL_EFEC": 0.10, | |
| "RATIO_CONS": 0.30 | |
| }, | |
| { | |
| "MESES_ANT_RCC": 24, | |
| "EDAD": 28, | |
| "GENERO": "F", | |
| "FLAG_ENTIDAD_PRINCIPAL": 1, | |
| "GRADO_INSTRUCCION": "UNIVERSITARIA", | |
| "DEPARTAMENTO": "AREQUIPA", | |
| "PROVINCIA": "AREQUIPA", | |
| "DISTRITO": "CERCADO", | |
| "FLAG_TC_MODELOS": 1, | |
| "FLAG_MES": 0, | |
| "CONTAR_COMP": 3, | |
| "MARCA_HP": 0, | |
| "MARCA_SEG_VIDA": 1, | |
| "MARCA_DIF": 0, | |
| "MARCA_CONV": 1, | |
| "SITUACION_LABORAL": "DEPENDIENTE", | |
| "ESTADO_CIVIL": "2", | |
| "MAX_ATRASO3": 2, | |
| "MAX_ATRASO6": 3, | |
| "MAX_ATRASO12": 3, | |
| "UTIL_TARJ": 0.92, | |
| "UTIL_EFEC": 0.75, | |
| "RATIO_CONS": 0.88 | |
| } | |
| ] | |
| }' | python3 -m json.tool | |