Tax optimization is important in financial planning and policy-making helping to secure revenue collection and economic growth. To categorize taxable entities and predict tax collections, the analysis uses machine learning models like Decision Tree Classifiers and Regressors together with statistical techniques like correlation analysis and feature engineering. Information designs, deviations, and patterns can be recognized utilizing visualization instruments such as warm and center thickness gauges (KDE). The comes about appear noteworthy contrasts within the conveyance of Charactive salary, centering on the effect of charge arrangement and financial alter. Changes in property tax also show that different types of properties contribute different amounts of income, hence the need for data-driven tax initiatives. This result shows that Machine learning can improve tax calculation, tax predictions, provide insights for better revenue forecasting, tax policy optimization decision-making and provide personalized tax recommendations. This research also focuses on how artificial intelligence can make tax planning more efficient and exact in Singapore’s evolving tax system.
ML, Tax Optimization, Predictive Analytics, Data-Driven Tax Strategies
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