Comparative Analysis of use of Machine Learning Algorithm for Prediction of Sales

Abstract

Predicting sales holds immense importance for businesses, particularly for large retailers. It significantly influences decision-making processes and their subsequent execution. For big retail stores engaged in marketing and sales, accurate sales prediction is crucial for effectively allocating resources, estimating sales revenue, and devising future strategies. It empowers these stores to grasp market trends, make informed decisions, and adapt to evolving conditions, ensuring long-term viability and prosperity. Given the dynamic nature of trends and market conditions, traditional forecasting methods face significant challenges in keeping pace with rapid changes. This is where the application of machine learning algorithms becomes indispensable, especially when dealing with large and complex datasets. This paper delves into the exploration of various machine learning algorithms and models to predict sales, conducting comparative analyses to assess their performance.The overarching aim is to identify the most suitable algorithm or combination of algorithms through comprehensive analysis. Furthermore, the paper scrutinizes how the characteristics of the dataset impact the performance of these algorithms, offering valuable insights for selecting the most appropriate machine learning algorithm based on performance analysis. By leveraging these insights, businesses can enhance their sales prediction capabilities and make informed decisions, thereby maximizing their success in the marketplace.