Leveraging Machine Learning For Predictive Analytics In Ecommerce

Abstract

Predictive analytics is becoming more and more necessary for organizations to use in the quickly changing e-commerce industry in order to predict customer behaviour, optimize marketing campaigns, and improve overall operational efficiency. The goal of this research study is to strengthen predictive analytics in the e-commerce industry by utilizing machine learning approaches.AI is essential for the business analyst's ability to make predictions. AI is a rapidly developing field that is employed in all fields, particularly in data analysis and prediction. In this study, machine learning—a subset of AI—is used for this purpose. The paper's goal is to determine the potential for growth and application of e-commerce in the future. In order to provide a clear explanation, we have used secondary data on the market value of e-commerce as a basis to support the income generated by e-commerce, and online consumers are taken to understand the total contribution of the e-commerce sector in India, then attempted to use Python to determine the forecast for the following years, 2024 to 2026. The current study aims to improve the accuracy of market value projection by using two more factors: the percentage of Indian online shoppers and e-commerce revenue. This paper attempts to offer practical suggestions and best practices for e-commerce practitioners and decision-makers wishing to leverage machine learning for predictive analytics by combining insights from both academic research and industry operations. In the end, the study advances our understanding of e-commerce analytics and establishes the groundwork for more in-depth investigation and creative thinking in this area.