Forecasting for e-commerce sales using supervised machine learning algorithms

The burgeoning landscape of e-commerce relies significantly on predictive analytics to drive operational efficiency and strategic decision-making. This thesis delves into the theoretical underpinnings of machine learning algorithms, showcasing their evolution and pivotal role in facilitating the growth of online commerce. At its core, this research centers on forecasting sales patterns through the analysis of an extensive e- commerce dataset. Forecasting stands as a linchpin for various critical functions within e-commerce enterprises. Its multifaceted applications encompass inventory management, ensuring optimal stock levels and streamlined deliveries, financial planning through astute asset management, dynamic pricing strategies, and the enhancement of customer satisfaction via efficient delivery operations. Furthermore, forecasting plays a pivotal role in refining marketing endeavors, enabling tailored campaigns and judicious budget allocation. The integration of machine learning algorithms fortifies these functionalities. Central to this research is the foundational task of sales prediction in the e- commerce realm, with a specific emphasis on integrating campaign variables. Leveraging six diverse machine learning algorithms, the study aims to discern the most accurate and explicable model. Remarkably, the investigation identifies LGBM as the most suitable algorithm. Notably, the inclusion of campaign variables, an aspect seldom explored in prior studies concerning forecasting, yields intriguing insights. However, contrary to initial presumptions, the SHAP analysis reveals a lesser influence of campaign variables on the model's interpretability. Acknowledging this limitation, the study highlights the potential for augmenting model interpretability by employing clustering algorithms to effectively represent variables, as outlined in the limitations section.