Data Mining Application Forecast of Business Trends of Electronic Products

Abstract

Sales forecasting is a pressing concern for companies amid rising consumer demand and intensifying competition, compounded by declining sales due to growing socio-economic challenges. Currently, many companies are having difficulty selling products due to a lack of management systems. To assist that, data mining techniques are introduced but it is difficult to evaluate the data and it is practically impossible to accurately forecast large amounts of data. However, data mining remains an important management tool that supports early decisions to increase profits, innovate business trends and improve sales by generating intelligence from the company's data resources. In this article, the research object chosen is the data of a nationwide electronics company. Their sales volume data for consumer electronics was used and applied to this study. The study used a "clustering" algorithm to group data based on the unique characteristics of each product, region, season, and time to estimate the amount of goods sold in the past, thereby predicting the amount of goods that will be exported. Password is sold in the following years and look for market trends. For each group, the results obtained with k = 3 show that the number of elements in each cluster is 771422, 11874, and 312, respectively. Combined with the "regression tree" algorithm for cluster partitioning and using the protocol Evaluate MSE and RMSE to evaluate the accuracy of the model, a result of 43065.66 Sales forecasting results show that the model's accuracy is close to realistic accuracy and depends on seasonal factors that are really important to some people. Based on the above results, the business's marketing campaigns and strategies will be deployed and achieve high results.