Predicting Budget Revenues of the Republic of Congo: Multiple Linear Regression Approach

Alongside political, legal and financial suggestions, prognosticating as part of a state's budget planning process endures a substantial element. An act that furnishes for and authorizing what the State hopes to recover as earnings along with what it intends to bear as expenditure for a calendar year and, however, which may be subject to revision during its implementation in reaction to the current economic situation; the state budget remains tactic for the extant, quality, functioning and organization of its Administration. As an oil-producing country, the Republic of Congo, which must, among other things, have the financial means to meet these ambitions, does not escape, like other countries selling hydrocarbons, the preponderance of revenues from this sector in its budget forecasts. In view of the unpredictability of international oil markets on which government revenues are largely dependent, the use of artificial intelligence would disclose ornaments in data volumes and model interdependent systems to generate outcomes synonymous with enhanced decision-making efficiency and value for money. In this article, we will have to use Machine Learning to create the prediction model using secondary data from international organizations and official annals of the Government of the Republic of Congo, that is, the annual price of a barrel of oil and the budget foresees of state incomes and expenditures entered in various initial and altering finance laws between the years 1980-2019. This representation is based on the multiple linear retrogression algorithms that will ascertain the linear relationship between a dependent variable and independent or explanatory variables. This will also concede us to approximate the foretell of the value “state revenues” from the values “oil prices” and “state expenses”. As a result of the evaluation, the coefficient of determination (R2) of the performance of the dummy based on the test data is 99%. Finally, the stereotype will be practiced on a web interface granting users to enter the new independent data and then click a button to illustrate the result of the predictions of the dependent value.