Forecasting the Long Term Economics Status of Bangladesh Using Machine Learning Approaches from 2016-2036

  • Minhaz Uddin Department of Computer Science, East Delta University, Chittagong, Bangladesh.
  • Shraboni Rudra Department of Computer Science, East Delta University, Chittagong, Bangladesh.
  • Mohammed Nazim Uddin Department of Computer Science, East Delta University, Chittagong, Bangladesh.
Keywords: Economics, Correlation, Linear Regression, Gradient Boosting, Karl Pearson Coefficient, Statistical Approach


It is a piece of happy news for us that Bangladesh has been now converted to a developing country. The United Nation and World Bank have recognized it. But they have a condition that we need to continue this economic progress till 2024 for getting a permanent recognition. The economic condition depends on many factors like Gross Domestic Product (GDP), Personal Saving, Private Sector Investment, Gross National Income (GNI) per capita, Human Assets Index (HAI) and Economic Vulnerability Index (EVI). This paper portrays the forecast of the long-term economic condition of Bangladesh as an independent variable which is a year and the dependent variables are GDP, private sector investment and personal saving. The living conditions of a country depend on GDP. Personal saving and Private Sector Investment are also important parts of a country’s economy. If we will forecast these attributes properly, then we can determine the economic condition of Bangladesh and living status of the people more accurately. Therefore, we can determine that Bangladesh can fulfil the condition of getting permanent recognized as a developing country. For forecasting these attributes, we proposed a model which consists of Karl Pearson’s coefficient and modified linear regression techniques. For improving performance, we modify linear regression by gradient boosting.  This experiment shows that our model gives us more accurate forecasting about GDP, Private sector investment and Personal Saving.


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