E-COMMERCE CONSUMPTION ANALYSIS AND PREDICTION

Abstract

The E-Commerce Consumption Analysis and Prediction system is a data-driven application designed to analyze online purchasing behavior and predict future consumption patterns. E-commerce platforms generate large amounts of transactional data through customer orders, products, prices, quantities, payment methods, locations, and purchase dates. Analyzing this information can provide valuable insights into customer preferences and purchasing trends. The proposed system collects and processes e-commerce transaction data containing attributes such as customer ID, product category, product price, quantity, order date, payment method, location, and total purchase value. Data preprocessing techniques are applied to remove duplicate records, handle missing values, correct inconsistent data, and prepare the dataset for analysis. The cleaned data is then used for exploratory and predictive analysis. The system performs consumption analysis to identify frequently purchased products, popular categories, customer spending patterns, revenue trends, and purchasing frequency. It can analyze sales according to product categories, customer segments, geographical locations, and different time periods. Interactive dashboards, charts, graphs, and KPI cards provide an easy way to understand e-commerce consumption behavior. The prediction module uses historical transaction data and selected features to estimate future sales or consumption patterns. Machine Learning techniques can be applied to identify relationships between customer behavior, product demand, purchasing frequency, and transaction value. The prediction results can support inventory planning, sales analysis, and business decision-making. Overall, the proposed system converts raw e-commerce transaction data into meaningful insights and predictive information. It can help businesses understand customer consumption patterns, monitor sales performance, and improve planning. Future enhancements can include personalized product recommendations, customer segmentation, demand forecasting, fraud detection, dynamic pricing analysis, and real-time consumption prediction.