Fiscal Revenue Prediction Model Source Code

The fiscal revenue prediction model source code analyzes relationships between fiscal revenue and key economic indicators such as national income, industrial output, agricultural output, total population, employed population, and fixed asset investment. The implementation typically involves data preprocessing (handling missing values, normalization), feature selection using correlation analysis, and building regression models (such as multiple linear regression or time series analysis) trained on historical datasets. To elaborate, the model employs statistical techniques and machine learning algorithms to quantify the impact of each factor on fiscal revenue. The code structure may include modules for data loading, exploratory data analysis (EDA), model training with libraries like scikit-learn or statsmodels, and validation using metrics like R-squared and RMSE. By identifying trends and patterns in historical data, the model generates revenue forecasts with measurable accuracy. Moreover, fiscal revenue prediction is critical for financial planning and policy formulation. Accurate models enable governments and organizations to optimize budget allocation, investment strategies, and resource management. The source code provides a reproducible framework for analysts to customize variables, retrain models with new data, and integrate uncertainty analysis (e.g., confidence intervals) to support data-driven decision-making.