A Novel Approach for E-Commerce System for Sale Prediction with Denoised Auto Encoder and SVM based Approach

E-commerce, or online shopping, has grown in prominence over the past few years thanks to the proliferation of the Internet. Yet, there are a lot of things that can affect an online store’s success, and if the operators don’t correctly assess their supply and marketing partnerships, they could lose a lot of money. Thus, it is crucial to create a model that can reliably produce high precision sales prediction in order to guarantee the long-term success of e-commerce businesses. The suggested method comprises three stages: preprocessing, Feature selection, and model training. This proposed approach uses zero-phase component analysis and normalization in the preprocessing phase to get rid of noise and inconsistent data. Finally, the model is trained with DAE-SVM after information gain is employed for feature selection. When compared to convolutional neural network and support vector machine models, the proposed model excels.