MD Raiz, M Akash, A Bharath, N Bunny, T Kavya Sri 03 09 The Store Stock and Sales Analysis and Prediction system is designed to monitor analyze and predict store inventory and sales performance using data analytics and machinelearning techniques Effective stock management is essential for maintaining the right quantity of products avoiding stock shortages reducing excess inventory and improving overall store performance The proposed system collects historical information such as product name category stock quantity sales quantity selling price purchase price sales date revenue and store or region The collected data is cleaned and preprocessed to handle missing values duplicate records and inconsistent information Exploratory data analysis is then performed to identify sales trends fastmoving products slowmoving products seasonal demand and inventory patterns Machinelearning and forecasting algorithms can be applied to historical sales data to predict future product demand and sales The system can estimate the required stock levels for upcoming periods and help identify products that may require restocking Prediction models can be evaluated using metrics such as MAE RMSE MAPE and R to measure their performance An interactive dashboard can display important information such as total sales current stock revenue topselling products lowstock products sales trends predicted demand and inventory status These insights enable store managers to make better decisions regarding purchasing inventory planning product availability and sales strategies Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026