International Research Journal of Engineering and Technology (IRJET) Volume: 11 Issue: 01 | Jan 2024
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
Forecasting of India’s GDP using Various Regression Algorithms Mitali Sarnaik1, Renuka Dad2, Priyanka Shahane3 1Student, Department of Computer Engineering, SCTR’s Pune Institute of Computer Technology, Pune, India 2Student, Department of Computer Engineering, SCTR’s Pune Institute of Computer Technology, Pune, India
3Assistant Professor, Department of Computer Engineering, SCTR’s Pune Institute of Computer Technology, Pune,
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Abstract - The only global metric used to evaluate the
projection has historically been based on econometric models that take historical data and certain economic variables into account. But the complex interactions between an increasing number of variables, along with the nonlinear dynamics of contemporary economies, have exposed shortcomings in traditional approaches. Within this framework, a novel approach to use Machine Learning (ML) appears to be a viable path towards improving GDP prediction accuracy and resilience. This study is a groundbreaking exploration of the merging of modern machine learning techniques with economic understanding, primarily focusing on India, a major global economy with a diverse range of industries, populations, and governmental actions. Our research not only emphasizes the necessity of a modernized method for estimating GDP, but it also provides guidelines for the use of ML models in the field of economics. Within this framework, a novel approach to use Machine Learning (ML) appears to be a viable path towards improving GDP prediction accuracy and resilience. This study is a groundbreaking exploration of the merging of modern machine learning techniques with economic understanding, primarily focusing on India, a major global economy with a diverse range of industries, populations, and governmental actions. Our research not only emphasizes the necessity of a modernized method for estimating GDP, but it also provides guidelines for the use of ML models in the field of economics. Additionally, our research clarifies the interpretability of these models by providing insights into the underlying causes of economic trends—a point that traditional approaches frequently miss. This paper is a call to action for economists, data scientists, and policymakers to create a new alliance between technology and economics, as well as a testament to the potential of machine learning to reshape conventional paradigms. Through this synergy, we set off on a path of transformation that will enable nations with the vision to navigate uncertainty and make wise decisions in a global economy that is becoming more and more complex. Come along as we explore the future of machine learning-based economic forecasting and establish a standard for innovation in GDP forecasting
status of a nation's economy is its gross domestic product (GDP), which is a single figure that expresses the monetary value of all completed products and services produced inside its borders in a specific time period. With India's economy developing at one of the quickest rates in the world, forecasting the country's GDP with precision is crucial for scholars, investors, and politicians alike. By enabling policymakers to make well-informed decisions about fiscal and monetary policies, accurate GDP estimates have the ability to mitigate economic downturns and foster sustainable growth. This study collected, preprocessed, and employed historical economic indicators from various industries, such as agriculture, industry, and services, as features for training and assessing machine learning models. Many techniques, such as Support Vector Machine, Auto Regression, and Linear Regression, were used to forecast India's GDP. fared better than the others in terms of expected accuracy and generalization to new data, according to the trial findings. With any luck, we'll be able to use methods like Multiple Linear Regression, Decision Trees, Random Forests, and Polynomial Regression with more precision. The study's findings will show how machine learning techniques may be used to anticipate India's GDP with a fair degree of accuracy. The results highlight the importance of feature engineering and selection and the need to consider external and internal economic factors. The knowledge gained from this study can help economists and politicians create plans that will promote stability and economic progress. Key Words: Machine Learning, Gross Domestic Product (GDP), Mean Absolute Error, Root Mean Squared Error, Mean Squared Error, Score, Random Forest, Decision Tree, Polynomial Regression, Multiple Linear Regression.
1.INTRODUCTION In a time where data-driven technologies are always evolving, there is going to be a change in the field of economic research and forecasting. The capacity to predict economic indicators with extraordinary accuracy becomes imperative as countries endeavor to manage the intricacies of a fast evolving global environment. The Gross Domestic Product (GDP) is a crucial indicator of a country's economic health that impacts investments, policies, and the general well-being of society. GDP
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