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customer_id
stringlengths
8
8
department
stringclasses
196 values
consumption_style
stringclasses
5 values
locality
stringclasses
679 values
province
stringclasses
23 values
monthly_fee_usd
float64
5
20
SLX83969
department_cat_153
heavy-user
locality_cat_163
Misiones
9.12
UTT27594
department_cat_1
occasional
locality_cat_268
Tucumán
10.15
HER11355
department_cat_1
occasional
locality_cat_102
Buenos Aires
18.93
ZRQ19908
department_cat_142
weekend-viewer
locality_cat_29
Santiago del Estero
7.36
EAT08840
department_cat_142
occasional
locality_cat_90
Córdoba
7.56
KYJ36841
department_cat_1
occasional
locality_cat_1
Santiago del Estero
8.31
FWX15967
department_cat_120
occasional
locality_cat_335
Santa Fe
8.07
YFL75558
department_cat_131
weekend-viewer
locality_cat_630
Buenos Aires
9.77
SYQ06673
department_cat_1
occasional
locality_cat_658
Córdoba
11.06
ULR53254
department_cat_112
weekend-viewer
locality_cat_445
Córdoba
11.55
BVL44253
department_cat_120
occasional
locality_cat_1
Buenos Aires
5.1
UQL42536
department_cat_1
occasional
locality_cat_445
Buenos Aires
7.16
LPH32057
department_cat_142
occasional
locality_cat_15
Córdoba
11.09
NJG43060
department_cat_1
weekend-viewer
locality_cat_667
Buenos Aires
6.77
FYI89056
department_cat_57
occasional
locality_cat_135
Buenos Aires
10.74
TGU54813
department_cat_109
occasional
locality_cat_1
Buenos Aires
9.39
QVZ12279
department_cat_110
occasional
locality_cat_23
Córdoba
11.05
KQG00897
department_cat_1
weekend-viewer
locality_cat_24
Buenos Aires
8.14
FDC92007
department_cat_1
occasional
locality_cat_556
Córdoba
14.1
IEV53432
department_cat_109
occasional
locality_cat_223
Mendoza
9.91
WMF55079
department_cat_1
occasional
locality_cat_589
Córdoba
8.59
LSN05898
department_cat_1
occasional
locality_cat_103
Buenos Aires
10.55
JJX83308
department_cat_142
casual
locality_cat_57
Chaco
8.55
NPG28360
department_cat_109
casual
locality_cat_645
Buenos Aires
14.25
LWJ41058
department_cat_1
occasional
locality_cat_1
Córdoba
10.91
MMQ14097
department_cat_1
casual
locality_cat_224
Córdoba
8.74
XIQ65801
department_cat_1
occasional
locality_cat_654
Córdoba
7.03
RIE90272
department_cat_109
casual
locality_cat_678
Buenos Aires
7.42
JPL63793
department_cat_164
occasional
locality_cat_13
Mendoza
8.98
SBL96047
department_cat_1
occasional
locality_cat_112
Corrientes
8.5
YZL67303
department_cat_1
occasional
locality_cat_1
Buenos Aires
9.19
QJQ91079
department_cat_1
casual
locality_cat_14
Buenos Aires
10.32
AIO31525
department_cat_142
occasional
locality_cat_8
Buenos Aires
8.68
CYV66335
department_cat_1
occasional
locality_cat_1
Córdoba
9.64
HAM36066
department_cat_1
casual
locality_cat_57
Córdoba
7.7
IYI88199
department_cat_1
weekend-viewer
locality_cat_445
Buenos Aires
12.08
REI72685
department_cat_109
occasional
locality_cat_334
Buenos Aires
10.51
YDT32038
department_cat_1
casual
locality_cat_445
Buenos Aires
8.26
RSE17915
department_cat_13
casual
locality_cat_25
Salta
8.52
SZY94877
department_cat_109
occasional
locality_cat_334
Buenos Aires
8.76
QQH57335
department_cat_1
occasional
locality_cat_112
San Juan
10.19
LBG42803
department_cat_109
occasional
locality_cat_24
Buenos Aires
8.09
YLW93736
department_cat_24
occasional
locality_cat_223
Córdoba
11.24
PUA40448
department_cat_158
occasional
locality_cat_556
Neuquén
12.09
ZUL37788
department_cat_1
occasional
locality_cat_46
Córdoba
8.43
YCG05067
department_cat_1
binge-watcher
locality_cat_7
Córdoba
7.53
LOT91986
department_cat_46
occasional
locality_cat_112
Córdoba
8.26
UTK59451
department_cat_109
occasional
locality_cat_390
Córdoba
7.24
DOZ03972
department_cat_109
weekend-viewer
locality_cat_445
Buenos Aires
7.47
CLE00583
department_cat_1
occasional
locality_cat_34
Buenos Aires
9.61
AXI01623
department_cat_1
occasional
locality_cat_368
Córdoba
11.13
QBF53256
department_cat_128
occasional
locality_cat_2
Buenos Aires
7.42
QWP63580
department_cat_1
casual
locality_cat_334
Córdoba
7.11
KNK85635
department_cat_120
occasional
locality_cat_112
Buenos Aires
7.35
ZJF12832
department_cat_46
casual
locality_cat_2
Córdoba
5.1
QKA99390
department_cat_188
casual
locality_cat_645
Córdoba
11.73
EHS45445
department_cat_1
occasional
locality_cat_13
Buenos Aires
8.22
MKZ46008
department_cat_24
occasional
locality_cat_653
Buenos Aires
7.99
LET59307
department_cat_109
occasional
locality_cat_445
Buenos Aires
10.24
TVN82783
department_cat_1
weekend-viewer
locality_cat_334
Buenos Aires
10.38
YYS04882
department_cat_68
weekend-viewer
locality_cat_301
Buenos Aires
7.64
DIE91375
department_cat_1
occasional
locality_cat_223
Buenos Aires
10.18
GAG26035
department_cat_120
occasional
locality_cat_212
Buenos Aires
8.3
SND85365
department_cat_90
weekend-viewer
locality_cat_190
Corrientes
9.48
UKK29832
department_cat_131
casual
locality_cat_445
Córdoba
10.23
DRX95540
department_cat_46
occasional
locality_cat_479
Córdoba
7.66
XFJ27123
department_cat_1
weekend-viewer
locality_cat_112
Buenos Aires
10.16
YRR64967
department_cat_1
casual
locality_cat_14
Buenos Aires
11.36
QKN77130
department_cat_109
occasional
locality_cat_445
Buenos Aires
6.67
YMP33603
department_cat_175
occasional
locality_cat_51
Buenos Aires
11.35
TYS05584
department_cat_153
casual
locality_cat_2
Buenos Aires
10.93
PRY91645
department_cat_109
occasional
locality_cat_101
Buenos Aires
9.97
GCJ95363
department_cat_109
weekend-viewer
locality_cat_1
Buenos Aires
9.14
VSC10416
department_cat_120
occasional
locality_cat_1
Buenos Aires
8.54
IEW36027
department_cat_109
occasional
locality_cat_445
Córdoba
8.43
ZIR84608
department_cat_109
weekend-viewer
locality_cat_223
Buenos Aires
9.45
QUY87956
department_cat_109
weekend-viewer
locality_cat_223
Buenos Aires
9.61
UQZ83677
department_cat_18
occasional
locality_cat_2
Córdoba
8.89
BKT47476
department_cat_79
occasional
locality_cat_112
Buenos Aires
8.81
VSV88678
department_cat_109
occasional
locality_cat_390
Córdoba
8.91
FXW71877
department_cat_1
occasional
locality_cat_616
Santiago del Estero
7.85
TXA76772
department_cat_1
casual
locality_cat_1
Córdoba
13.21
OZV83155
department_cat_109
binge-watcher
locality_cat_201
Buenos Aires
9.14
TKG08174
department_cat_131
occasional
locality_cat_334
Córdoba
7.22
QJW30872
department_cat_120
weekend-viewer
locality_cat_137
Córdoba
7.6
VEP03424
department_cat_109
occasional
locality_cat_334
Córdoba
8.74
FYU15387
department_cat_57
casual
locality_cat_661
Córdoba
7.91
AOX27134
department_cat_164
occasional
locality_cat_212
Córdoba
9.94
SWN47629
department_cat_112
occasional
locality_cat_201
Buenos Aires
9.41
AXL02591
department_cat_109
binge-watcher
locality_cat_346
Buenos Aires
7.34
XFQ19616
department_cat_1
occasional
locality_cat_112
Buenos Aires
9.67
ZJV28549
department_cat_1
weekend-viewer
locality_cat_556
Santa Fe
9.58
LFC46150
department_cat_130
binge-watcher
locality_cat_676
Córdoba
8.67
KAC40761
department_cat_109
occasional
locality_cat_113
Buenos Aires
8.84
ZZG33448
department_cat_1
occasional
locality_cat_334
Tucumán
10.14
XIS42858
department_cat_109
occasional
locality_cat_117
Tucumán
8.22
LOJ99938
department_cat_109
occasional
locality_cat_446
Buenos Aires
10.91
AYR96718
department_cat_114
occasional
locality_cat_568
Buenos Aires
11.58
XQS73547
department_cat_1
occasional
locality_cat_2
Córdoba
8.82
ZWT66958
department_cat_109
weekend-viewer
locality_cat_101
Córdoba
9.68
End of preview. Expand in Data Studio

📦 CUSTOMER PROFILE

Este dataset fue generado completamente de manera sintética, empleando:

  • Modelos estadísticos en Python
  • Algoritmos probabilísticos personalizados
  • Modelos de inteligencia artificial para añadir variabilidad y comportamientos realistas

No contiene datos personales ni información proveniente de individuos reales. Su propósito es estrictamente educativo, académico, experimental y de investigación.


📝 Dataset Summary

Este dataset contiene datos sintéticos diseñados para simular interacciones ficticias de usuarios. Está optimizado para prácticas de análisis de datos, experimentación en ciencia de datos y prototipos de machine learning. No debe utilizarse para aplicaciones comerciales ni para la creación de perfiles reales.


📊 Estructura del Dataset

El dataset incluye las siguientes columnas:

Columna Tipo Descripción
customer_id string Identificador único del cliente.
department string Subdivisión de las provincias.
consumption_style categorical Estilos de hábito de consumo. (occasional, casual, weekend-viewer, binge-watcher, heavy-user.)
locality string Unidad territorial más pequeña.
province string Unidad administrativa principal del país.
monthly_fee_usd float Cuota mensual actual del plan en USD.

Obs: Las columnas numéricas si tienen un valor - significa el número 0.


📂 Formato de los Archivos

El dataset se encuentra formato CSV.


📥 Cómo Cargar el Dataset desde Hugging Face

🔹 Opción 1:

  1. Usando datasets.load_dataset:
from datasets import load_dataset

ds = load_dataset("hpestrellag/customer_profile")
ds
  1. Convertir a Pandas:
import pandas as pd

df = ds["train"].to_pandas()
df.head()

🔹 Opción 2:

Leer el CSV directamente desde Hugging Face Hub

import pandas as pd

df = pd.read_csv("hf://datasets/hpestrellag/customer_profile/customer_profile.csv")
df.head()

🔐 Licencia

Este dataset se publica bajo la licencia:

Creative Commons Atribución-NoComercial 4.0 (CC BY-NC 4.0)

Permisos y restricciones:

✔️ Permitido:

  • Uso académico
  • Investigación
  • Prototipos no comerciales
  • Uso personal o educativo

No permitido:

  • Uso comercial
  • Integración en productos de pago
  • Proyectos que generen ingresos directos o indirectos

🔗 Licencia completa: https://creativecommons.org/licenses/by-nc/4.0/


📚 Cómo Citar Este Dataset

@misc{estrella2025_customer_profile,
  title        = {customer\_profile},
  author       = {{Pavel Estrella G.}},
  year         = {2025},
  publisher    = {Hugging Face Datasets},
  howpublished = {\url{https://huggingface.co/datasets/hpestrellag/customer_profile}},
  note         = {Dataset sintético generado para investigación. Licencia CC BY-NC 4.0.}
}

⚠️ Notas Sobre Datos Sintéticos

  • No representan individuos reales.
  • No fueron obtenidos de personas, empresas o fuentes externas.
  • No contienen información sensible.
  • Se generaron enteramente mediante métodos estadísticos y modelos de IA.
  • No deben utilizarse para tareas que requieran datos reales (marketing, segmentación real, perfiles reales).

👤 Autor

PAVEL ESTRELLA G.

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