E-ticaret sektörü için yapay zeka tabanli satiş i̇ptal/i̇ade tahminleme yazilimi

In today's rapidly evolving digital landscape, e-commerce has moved far beyond serving as a simple sales channel and has become a core component of strategic business planning. This study aims to develop machine learning based classification models for order cancellation and return prediction. These models were built using a real-world dataset of 11,000 transaction records collected from an e-commerce company between May 2020 and August 2024. 10,000 records were used for training the models, while the remaining 1,000 were reserved for independent testing. The training dataset was balanced across three classes: completed, canceled, and returned orders. Two distinct modeling approaches were adopted. The first approach utilized a multi-class classification framework to predict all three outcomes in a single model. The second approach employed separate binary classification models for cancellations and returns, allowing for more targeted predictions. To construct these models, five machine learning algorithms were applied: Logistic Regression (LR), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Deep Neural Network (DNN). To enhance model performance, four feature selection strategies were evaluated: minimum Redundancy Maximum Relevance (mRMR), Relief-F, F-Classification, and a Hybrid method combining the three. Among these, Relief-F and Hybrid approach yielded the most robust results, particularly when used in conjunction with RF and XGBoost.The results indicated that the binary classification approach produced superior predictive accuracy and sensitivity compared to the multi-class approach. SVM models demonstrated strong performance in binary classification scenarios, whereas LR and DNN models underperformed. Overall, the integration of advanced feature selection techniques with RF and XGBoost significantly improved forecasting capabilities, offering e-commerce businesses a valuable tool for optimizing operational planning and decision-making processes.