Data Science, Learning by Latent Structures, and Knowledge Discovery

The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery framework provides an orderly partition of the often complex data mining processes to ensure a practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data- and model development-related issues. These issues need to be carefully addressed by allowing a flexible, customized and industry-specific knowledge discovery framework. For this reason, extensions of CRISP-DM are needed. In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. Furthermore, it emphasizes also the need for a detailed business understanding and the interdependencies with the developed models and data quality for fulfilling higher business objectives. Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues.

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PREDICCIÓN DE FUGA DE CLIENTES EN UNA EMPRESA DE DISTRIBUCIÓN DE GAS NATURAL MEDIANTE EL USO DE MINERÍA DE DATOS

Universidad Ciencia y Tecnología ◽

10.47460/uct.v24i106.399 ◽

2020 ◽

Vol 24 (106) ◽

pp. 79-87

Author(s):

Fredy Humberto Troncoso Espinosa ◽

Javiera Valentina Ruiz Tapia

Keyword(s):

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Knowledge Discovery ◽

Data Science ◽

Machine Learning Techniques ◽

Support Vector ◽

Churn Prediction ◽

Customer Churn ◽

Using Data

La fuga de clientes es un problema relevante al que enfrentan las empresas de servicios y que les puede generar pérdidas económicas significativas. Identificar los elementos que llevan a un cliente a dejar de consumir un servicio es una tarea compleja, sin embargo, mediante su comportamiento es posible estimar una probabilidad de fuga asociada a cada uno de ellos. Esta investigación aplica minería de datos para la predicción de la fuga de clientes en una empresa de distribución de gas natural, mediante dos técnicas de machine learning: redes neuronales y support vector machine. Los resultados muestran que mediante la aplicación de estas técnicas es posible identificar los clientes con mayor probabilidad de fuga para tomar sobre estas acciones de retenciónoportunas y focalizadas, minimizando los costos asociados al error en la identificación de estos clientes. Palabras Clave: fuga de clientes, minería de datos, machine learning, distribución de gas natural. Referencias [1]J. 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