Extreme Gradient Boosting Model-based Forecasting of Big Data Online Sales Record
Keywords: Big Data, E-Commerce, Extreme Gradient Boosting, Forecasting, PySpark.
AbstractNowadays, big data plays a crucial role for many online e-commerce businesses to generate more sales. Big data is a huge collection of data and information which are utilized by many organizations to forecast which products, costs, and advertisements are better to maximize their business profits. This paper aims to apply the extreme gradient boosting (XGBoost) based model to forecast sales growth of online products, specifically books and magazines, from massive datasets present in online shopping. PySpark, as the best suitable and compatible framework, is used for data analysis. The result shows that the proposed model has higher forecasting accuracy with a minimum error rate than other models. A comparative visualization and conclusion are presented in terms of the proposed system's prediction accuracy, error rate, and efficiency.
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