Sales Performance and Forecasting System

Abstract

The increasing complexity of business operations and the growing volume of transactional data have made it difficult for organizations to derive timely, actionable insights from their sales data using traditional manual methods. This paper presents the design and development of a Sales Performance Analytics and Forecasting System that integrates data preprocessing, feature engineering, and interactive visualization to provide a comprehensive sales intelligence platform. The proposed system processes raw retail sales datasets using Python libraries including Pandas and NumPy, performs systematic data cleaning and transformation, and engineers key features such as total sales computation, temporal attributes, and customer age segmentation. A dynamic Power BI dashboard is developed to visualize sales trends, category-wise performance, and customer segment behavior through interactive charts and key performance indicators. Additionally, a trend-based linear regression forecasting approach is implemented to estimate future sales, enabling data-driven planning and strategic decisionmaking. The system demonstrates how accessible analytics tools can transform raw transactional data into business insights without relying on complex machine learning frameworks.