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- W4387237537 abstract "Every year in the United States, the 4th Thursday of November is commemorated as Thanksgiving Day. The next day which is a Friday is known as Black Friday. This day is the busiest day in terms of shopping because all major retailers and e-commerce websites offer massive amounts of discounts and deals. Hence, this sale is termed the Black Friday Sale. There is a lot of potentials to make a profit even after such discounts if the sales patterns from previous years’ data are analyzed properly. Investigating various demographics of customers and analyzing the purchase amount spent by each customer on various products, there is a need to find out patterns for this behavior. Therefore, we utilized classical and modern artificial intelligence and machine learning techniques such as Linear Regression, Neural Networks, Gradient Boosting Trees and AutoML, to make predictions on the available test data to find a model for the most accurate predictions. We used a Graphical Processing Unit (GPU)-based high-performance computing environment to analyze the performance of various artificial intelligence and machine learning techniques for e-commerce applications. Since the dataset contains information about various demographics and backgrounds of the customers, we encoded the data in an easy-to-understand format and reduce the bias for each algorithm. Furthermore, various techniques of feature engineering are used, and new features are generated from existing features by grouping the target variable purchase, for each sub-category of that feature. Erroneous predictions are also handled; ultimately, the model performed well on unseen test data. Finally, this study can help researchers to find the best model with more accurate predictions for e-commerce applications." @default.
- W4387237537 created "2023-10-02" @default.
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- W4387237537 date "2023-01-01" @default.
- W4387237537 modified "2023-10-16" @default.
- W4387237537 title "GPU Based AI for Modern E-Commerce Applications: Performance Evaluation, Analysis and Future Directions" @default.
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- W4387237537 doi "https://doi.org/10.1007/978-3-031-30101-8_3" @default.
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