A Novel Approach of Ecommerce for Sales Prediction Using Hybrid ABC and AdaBoost Approach

For online stores to offer speedier shipping times, precise sales forecasting is essential. Online advertising campaigns and the competitive nature of comparable but distinct products are only two of many potential variables that could make accurate sales forecasting more difficult. Typically, these characteristics are disregarded in this approach since traditional methods that rely on time series analysis primarily take sales data into account. Preprocessing, feature selection, and training the model must be carried out in an exact sequence. Data quality concerns, missing information, and product grouping are all part of the preprocessing process. In feature selection, a statistical method known as principal component analysis (PCA) is used to reduce the dimensionality of a dataset that contains multiple linked variables. To train the model, Hybrid-ABC-AdaBoost was utilized. Incredibly, the data show that an accuracy of 97.22 % was reached.