E-COMMERCE ORDER ANALYSIS SYSTEM

Abstract

The rapid growth of online shopping platforms has generated a massive amount of transactional data that can be used to understand customer behavior, product performance, and business trends. Analyzing this data helps organizations make informed decisions, optimize inventory management, and improve customer satisfaction. Traditional methods of analyzing sales data mainly rely on manual reports and basic statistics, which often fail to provide deep insights into purchasing patterns and revenue trends. This project presents an ECommerce Order Analysis System developed using Python and data visualization techniques. The system analyzes historical order data to identify sales patterns, customer purchasing behavior, and product performance. By applying data processing and analytical techniques, the system generates meaningful insights through interactive visualizations and statistical summaries. The proposed system processes order datasets containing information such as order ID, product category, product name, quantity, price, customer ID, and order date. Using Python libraries such as Pandas, Matplotlib, and Seaborn, the system performs data preprocessing, statistical analysis, and visualization. Various charts and graphs such as revenue trends, category sales distribution, customer analysis, and product performance metrics are generated to help businesses understand their sales performance. The project demonstrates how data analytics techniques can transform raw order data into meaningful business insights. The developed system provides a scalable and efficient solution for analyzing e commerce sales data and supports data-driven decision making for business growth.