GST Collections and Monte Carlo Simulations
Description
This dataset simulates and analyzes the Goods and Services Tax (GST) collection trends for the Indian states of Maharashtra and Karnataka over a specified period. It combines actual historical data with predictions and Monte Carlo simulations to provide a comprehensive view of potential future GST collections. Below is a detailed breakdown of the dataset's structure, purpose, and analysis methods. Dataset Structure Data Fields: Year: This column contains the years from 2017 to 2023, representing the time frame for which GST collection data is available. Maharashtra: This column lists the actual GST collections for Maharashtra for each year in the dataset. The values reflect the financial performance of the state in terms of GST revenue. Karnataka: Similar to the Maharashtra column, this one provides the actual GST collections for Karnataka over the same period. Purpose of the Dataset The primary purpose of this dataset is to analyze historical GST collection trends for Maharashtra and Karnataka, predict future growth using linear regression models, and simulate ideal growth scenarios through Monte Carlo simulations. This analysis can help stakeholders—including policymakers, economists, and business analysts—understand potential future revenue trends and make informed decisions regarding fiscal planning and resource allocation. Analysis Methods Linear Regression Models: Linear regression models are trained separately for Maharashtra and Karnataka using historical data to predict future GST collections from 2024 to 2027. This method provides a straightforward approach to estimating future trends based on past performance. Monte Carlo Simulation: To explore potential ideal growth scenarios, a Monte Carlo simulation is conducted. This involves simulating future GST collections based on defined growth rate ranges (8% to 12% for Maharashtra and 7% to 11% for Karnataka). The simulation runs 10,000 iterations to account for variability in growth rates. The results are used to calculate mean projected collections and confidence intervals (5th and 95th percentiles) for both states. Visualization: The analysis culminates in a comprehensive visualization that includes: Actual and predicted GST collections for both states. Ideal growth trends derived from the Monte Carlo simulation, represented as dashed lines. Confidence intervals shaded around the ideal trends to illustrate potential variability in future collections. Summary In summary, this dataset provides a robust framework for understanding GST collection trends in Maharashtra and Karnataka through historical data analysis, predictive modeling, and simulation techniques. By combining these methods, stakeholders can gain insights into potential future revenue scenarios, assess fiscal health, and plan accordingly. The resulting visualizations effectively communicate these trends, making it easier to interpret complex data and draw actionable conclusions about state revenue projections.
Files
Steps to reproduce
I was not able to add the entire code here, there was a space limitation, therefore I added it to Github. https://github.com/SunilBenedict/Data-Models---Dr.-Sunil-Maria-Benedict/blob/main/README.md