Читать книгу Machine Learning Approach for Cloud Data Analytics in IoT - Группа авторов - Страница 82

Rakhi Akhare*, Sanjivani Deokar, Monika Mangla and Hardik Deshmukh

CSED, Lokmanya Tilak College of Engineering, Navi Mumbai, India

Abstract

The retail industry is experiencing a drastic transformation during the past few decades. The technological revolution has further revolutionized the face of the retail industry. As a result, each industry is aiming to obtain a better understanding of its customers in order to formulate business strategies. Formulation of efficient business strategies enables an organization to lure maximum customers and thus obtain a largest portion of market share. In this chapter, authors aim to provide the importance of predictive data analytics in the retail industry. Various approaches for predictive data analytics have been briefly introduced to maintain completeness of the chapter. Finally, authors discuss the employment of machine learning (ML) approaches for predictive data analytics in the retail industry. Various models and techniques have also been presented with pros and cons of each. Authors also present some promising use cases of utilizing ML in retail industry. Finally, authors propose a framework that aims to address the limitations of the existing system. The proposed model attempts to outperform traditional methods of predictive data analytics.

Keywords: Predictive data analytics, retail industry, machine learning, e-business