A MACHINE LEARNING APPROACH TO PREDICT BLACK FRIDAY SALES

Abstract

In our study of Black Friday sales prediction, we use Multilayer Perceptron (MLP) models to
explore the complex dynamics of this important shopping occasion. Using a large dataset that includes
demographics of the consumer base, product categories, promotions, and temporal trends, we leverage the
power of the MLP model to assess and forecast sales trends. Because MLP models are flexible, we can capture
ambiguities and minute differences in the sales data, giving us a more nuanced understanding of the dynamics of
Black Friday sales. Our model uses advanced machine-learning algorithms built into the MLP architecture to
learn from past patterns. The programme learns from past sales data to identify underlying trends and patterns,
which improves its ability to anticipate future Black Friday events. Evaluation measures that highlight the
model's capacity to precisely capture temporal dynamics and latent patterns within the sales data are mean
absolute error (MAE) and root mean squared error (RMSE), which act as benchmarks to assess the model's
performance.In the end, our research advances our knowledge of Black Friday customer behaviour and industry
trends, giving merchants useful information to improve their tactics and seize sales opportunities. We give
merchants a strong framework to handle the intricacies of Black Friday by utilising MLP models, empowering
them to make wise decisions and maintain an advantage in the cutthroat retail market.