COMPREHENSIVE EXAMINATION OF GROSS DOMESTIC PRODUCT
Author(s):
Dr. D. Anusha, A. Nanditha, N. Manoj, S. Teja Shree Ram, V. Sravani
Keywords:
Economy, Gross Domestic Product, Linear Regression, Decision Tree Regression.
Abstract
Gross Domestic Product (GDP) is a measure of the value of goods and services produced in a country over a period of time. It's a broad measure of a country's economic health. A country’s GDP depends on various factors, from micro industries like cafes and saloons to macro and major industries like IT, Banking and Tourism, all of them contribute in the growth of the economy of a country. Countries around the world collect data on consumption, investment, government spending and net exports to measure the GDP of that country. This makes GDP a universal measurement. If economy is healthy GDP growth expands, if economy is in bad shape GDP contracts. In the recent years ‘recession’ has become a trending topic, two consecutive quarters of negative GDP growth can lead to recession, it can largely affect the country’s economy in all the sectors leading to issues such as Unemployment, Business Closures, Decreased sales and profits, Housing prices, etc. To overcome these issues governments design various ways, goals and set up policies for a better economic growth. This project’s main aim is to identify the factors that are affecting the GDP by reviewing the previous economic movements and predicting how economic changes can amend patterns of previous trends. A more accurate prediction can help the country to build a strong economy. This research-based project also calculates the importance of features affected for the calculation of the GDP. It is very useful for the viewer to view the GDP growth of the different countries and also, they can see the best and worst predict performance in form of data visualizations such as graphs and plots by using python Matplotlib and Seaborn libraries. We used mathematical models and simple ML algorithms like Linear Regression, Random Forest Regression and Decision Tree Regression to predict future developments. To evaluate the model’s performance, Mean Squared Error (MSE) and Mean Squared Logarithmic Error (MSLE) are used.
Article Details
Unique Paper ID: 164225

Publication Volume & Issue: Volume 10, Issue 12

Page(s): 320 - 324
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