Analysis of Financial Statements of Previous Years to Forecast Revenue and Reduce Expenses

This research proposes an effective money prediction via a data-driven tool to reduce costs by integrating intelligent data mining techniques. This study plans to improve revenue prediction and find venues for cost stemming by analyzing financial statements for the last 15 years, such as balance sheets and profit and loss reports. Data preprocessing, exploratory data analysis (EDA), correlation analysis, regression analysis, clustering, and time series analysis are well integrated in finding feasible solutions. Model performance is evaluated on multiple metrics, including R-square and mean absolute error while considering computational complexity and efficiency. The findings illustrate how business houses can use such analytical methods to achieve better decision-making, financial sustainability, and increased efficiency. The work also discusses practical limitations, such as data availability and the model’s fitting to dynamic market conditions. The study highlights the importance of continuously monitoring and refining the model for the prediction in a real-world context.