Paper Key : IRJ************466
Author: Pawar Akanksha Dattatraya,Bhagat Poonam Shashikant,Tekale Pranjal Shivdas,Tupe Manisha Nana
Date Published: 13 Nov 2024
Abstract
The "Sales Forecasting" project is intended to empower retail shop owners to help streamline shop operations by capitalizing on the prowess of machine learning towards sales trend prediction and management of inventory. This application is meant to fit historical sales data into the Time Series Analysis and Regression models toward producing accurate sales forecasting. The system interacts with Firebase for the deployment of the machine learning model and for storing the facility for inventory and sales data. The mobile application, built using Flutter, has been designed to give shop owners much more comfort and ease in handling their inventory, viewing sales statistics, and having alerts on low stock levels. Some of the features added include product addition or update and deletion, detailed sales statistics: daily, monthly, and yearly sales trends, top-selling items, and categories. It will be giving real-time analysis of the inventory data so that the owners may have actionable insights for maintaining stock levels and maximizing the potential of sales.Firebase Authentication is used for safe login and authorization to ensure the privacy of data as well as safety measures with respect to the users. The two major flows of the project include data preparation, model training, database integration, and app development, centered on the experience of an efficient user interface. Overall, the "Sales Forecasting" project provides powerful, AI-based solutions aimed at making shop owners better decision makers, able to estimate the amounts they could be selling in the future, reduce problems with the stock inventory, and prevent going out of stock due to timely alerts and real-time analytics.
DOI Requested