Time Series Driven E-commerce Sales Forecasting and Intelligent Recommendation System

The rapid growth of e-commerce has increased the importance of accurate sales forecasting and personalized product recommendations in data-driven business applications. This paper presents Time Series Based E-commerce Sales Forecasting and Intelligent Recommendation System, a web-based application that integrates machine learning-based sales forecasting with intelligent product recommendation techniques. The forecasting component uses the XGBoost Regressor to predict future daily sales based on historical sales data and engineered temporal features, including lag values, rolling statistics, and calendar-based attributes. The recommendation component implements three independent approaches: K-Nearest Neighbors (KNN)-based collaborative filtering for frequently bought products, content-based filtering using TF-IDF and cosine similarity for identifying similar products, and popularity-based ranking for recommending top-selling products within a selected department. The application is developed using React.js for the frontend and FastAPI for the backend, providing an interactive interface for accessing forecasts and generating product recommendations. MongoDB Atlas is used exclusively for user authentication data, while forecasting and recommendation datasets and model artifacts are maintained separately. The system combines predictive analytics and recommendation techniques within a single web application, providing users with an integrated platform for exploring future sales trends and product suggestions