Hisaab: An Integrated Web-Based Machine Learning System for Business Analytics, Revenue Forecasting, and Inventory Optimization

The rapid digitalization of business operations has resulted in the generation of vast amounts of organizational data, creating significant opportunities for improving decision-making through business analytics and machine learning. However, many organizations, particularly small and medium-sized enterprises (SMEs), continue to face challenges in analyzing business performance, accurately forecasting revenue, and managing inventory efficiently. Conventional methods often rely on manual analysis and static reporting, which are insufficient for dynamic business environments.

This paper presents Hisaab, a Machine Learning–Driven Web Platform designed to integrate business analytics, revenue forecasting, and inventory optimization within a unified and intelligent framework. The proposed system utilizes machine learning algorithms to analyze historical sales, financial, and inventory data, enabling organizations to identify trends, forecast future revenue, and predict product demand with improved accuracy. The business analytics module offers interactive dashboards and key performance indicators (KPIs) for real-time monitoring and data-driven decision-making. The revenue forecasting module supports strategic planning and budgeting by generating predictive insights, while the inventory optimization module recommends optimal stock levels to reduce overstocking, stock shortages, and associated operational costs.

Developed as a scalable web-based application, Hisaab provides accessibility, real-time analytics, and intelligent automation for businesses of various sizes. The platform integrates predictive analytics with business intelligence to enhance operational efficiency, improve forecasting accuracy, optimize resource utilization, and support informed managerial decisions. Experimental evaluation demonstrates that the proposed system significantly improves revenue prediction accuracy and inventory management compared with conventional approaches. The findings highlight the potential of integrated machine learning-based business analytics platforms in enabling organizations to achieve sustainable growth, cost reduction, and competitive advantage in today's data-driven business environment.he River Ganga is India's most sacred river, originating at Gangotri in the Himalayas and ultimately reaching the Bay of Bengal through multiple distributaries. After the Farakka Barrage, one major channel flows into Bangladesh as the Padma River, while the distributary flowing through West Bengal is officially known as the Hooghly River perhaps by the European Traders.. Despite this official nomenclature, a significant section of the population continues to identify the river as the Ganga because of its religious, cultural, and historical importance.

This study examines whether the name "Hooghly River" adequately reflects the cultural identity and public perception of the river in West Bengal. Using questionnaires, interviews, Google Forms, and secondary literature, the research analyses people's perceptions, religious practices, and historical evidence associated with the river.