Unified E-Commerce Platform Aggregator using NLP and Machine Learning Techniques

The study proposed the integration of Natural Language Processing (NLP) with Machine Learning (ML) techniques like ARIMA (Autoregressive Integrated Moving Average) and LSTM (Long Short-Term Memory) to develop a smart predictive system for e-commerce platforms. NLP extracts insights from customer feedback, product descriptions, and social media sentiments to understand customer preferences and market trends. ARIMA, a time-series forecasting model, predicts demand fluctuations based on historical sales data, enabling better stock management, while LSTM captures long-term dependencies in sequential data for more accurate forecasting and product recommendations. By combining these techniques, the system enhances decision-making, optimizes pricing strategies, and improves customer engagement through personalized recommendations. Additionally, it helps businesses maintain an efficient inventory system by reducing overstocking and stockouts, ultimately increasing profitability. This approach not only benefits businesses but also enhances the shopping experience by making e-commerce platforms more adaptive and responsive to changing market demands. The integration of NLP, ARIMA, and LSTM ensures a data-driven strategy that leverages AI and ML to transform the e-commerce industry through predictive analytics and intelligent decision-making.