Abstract:
In today's highly competitive business world, the ability to predict revenue is crucial for
decision-making and strategic planning. This research aims to develop a predictive model for
revenue rate using machine learning algorithms and economic indicators. The study focuses on
a freight transportation company that operates in multiple countries and relies heavily on
economic indicators such as inflation, interest rates, exchange rates, and gross domestic product
(GDP) to forecast revenue. Data from the International Monetary Fund (IMF) and Trading
Economics data library was collected for 10 different economic indicators. A linear regression
model was developed to determine the significance of these indicators on revenue rate. The
model identified six significant predictors: annual interest rate, GDP annual million, inflation
quarter, quarterly interest rate, CPI quarter, and exchange rate. The model achieved a
coefficient of determination (R2
) of 0.39, indicating that the selected economic indicators
explain 39% of the variance in revenue rate. To make the predictive model more accurate, three
machine learning algorithms were evaluated: linear regression, decision tree, and random forest
regression. The models were evaluated using cross-validation techniques, and the linear
regression model was selected as the best model, achieving a mean absolute error of 6.82. A
dashboard was created using Tableau, which allows the management to input values for
selected economic indicators, and then the model predicts the revenue rate for the company.
The dashboard also includes links to IMF data and Trading Economics data library, providing
additional economic data for further analysis. Overall, this research demonstrates the potential
of machine learning algorithms to predict revenue rate using economic indicators. The study
contributes to the field of revenue prediction models by using machine learning algorithms,
which can produce more accurate and reliable results. Future research could expand on this
study by including additional economic indicators or exploring other machine learning
algorithms to improve the predictive power of the model
