Predictive Analytics in E-Commerce for CustomerBehavior Forecasting using hybrid Ret-DNN withXGBoost Model

In recent years, electronic (E) - commerce services have rapidly increased in the daily lives of people, which helps them to purchase products online. However, retail platforms have struggled to understand customer behavior and make it difficult to predict their future purchases. To overcome these challenges, this study proposes a hybrid Retail Deep Neural Network (Ret-DNN) with an Extreme Gradient Boosting (XGBoost) model for capturing temporal features and tabular dynamics of retail data. First, data were sourced from a United Kingdom (UK)-based online retailer that contains transactions with almost 500,000 records. Then, the collected data were preprocessed using a series of techniques, such as data cleaning, outlier handling, temporal feature extraction, feature encoding, and z-score normalization, to ensure that the data were ready for model training and testing. Subsequently, the preprocessed data were fed into the Ret-DNN model, which acts as a feature extractor to understand the complete context of customer transactions. Further, the extracted data were fed as input into the XGBoost model, which predicted the final output as the purchase probability of customers. Finally, the proposed RetDNN XGBoost model achieved better results by attaining a Mean Absolute Error (MAE) 0.2193 when compared to the existing Ret-DNN model.