GDP Growth Prediction of Bangladesh Using Machine Learning Algorithm

dc.contributor.authorHossain, Amman
dc.contributor.authorHossen, Md
dc.contributor.authorHasan, Md Mahmudul
dc.contributor.authorSattar, Abdus
dc.date.accessioned2022-04-19T05:22:00Z
dc.date.available2022-04-19T05:22:00Z
dc.date.issued2021-03-31
dc.description.abstractThe main objective is to predict GDP Growth by help of other parameters like GDP Per Capita, Inflation Rate, Government Debt, Total Investment, Remittance, Unemployed Rate. The complex relations are obtained by machine learning algorithm among GDP Growth Rate and other parameters to predict GDP Growth Rate that may help everyone to get connected to the field of economy and also to the economist to demonstrate their prediction about the economy. With help of this it is easy to find out the possible way to improve the desire growth of GDP. This project can help to demonstrate our eco-social scenario of future. This project can help to set economic goals for our country and can find out which parameters are most directly related to our GDP Growth and which are less related to our GDP Growth and which are accountable for reducing our GDP Growth. For any country GDP growth is a very important think to follow up. This project will give the analyzed data and we will get proper information to take certain action to keep the growth in higher rate. Through this system it is possible to achieve accurate information about the GDP Growth.
dc.identifier.otherhttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7904
dc.identifier.urihttp://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/7904
dc.language.isoen_US
dc.publisher2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), IEEE
dc.sourceDIU Institutional Repository
dc.subjectGDP growth
dc.subjectML algorithms
dc.subjectRelation
dc.subjectPrediction
dc.subjectComplex
dc.subjectParameters
dc.titleGDP Growth Prediction of Bangladesh Using Machine Learning Algorithm
dc.typeArticle

Files

Collections