G Praveen, A Nithin, B Abhinav, S Hemanth, B Anjali 03 09 The Online Order Analysis and Prediction for ECommerce system is designed to analyze ecommerce order data and predict future sales customer orders and purchasing trends using data analytics and machinelearning techniques With the rapid growth of online shopping ecommerce platforms generate large volumes of information related to customers products orders payments and deliveries Analyzing this data can help businesses understand customer behavior optimize inventory and improve sales performance The proposed system collects and processes information such as order ID customer details product category product price quantity order date payment method shipping location delivery status and total order value The collected data is cleaned and transformed to handle missing values duplicate records and inconsistent information before analysis The system performs orderwise productwise categorywise customerwise and timebased analysis to identify important sales patterns Machinelearning and statistical techniques can be applied to historical order data to predict future order volume sales revenue product demand and customer purchasing trends The system can also identify frequently purchased products and highdemand categories Interactive dashboards charts graphs and reports are used to display total orders revenue average order value popular products monthly sales trends and prediction results These insights can help ecommerce businesses improve inventory planning marketing strategies sales forecasting and customer management Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026