Predicting Financial Performance in the Romanian Transportation Sector: A Machine Learning Approach

Since the beginning of the 21st century, Romania has experienced economic growth, driven by the liberalization of various industries. This shift has led to increased corporate profits, which in turn have contributed to a rise in Gross Domestic Product (GDP) and higher salaries for employees. Machine Learning and Artificial Intelligence have emerged as some of the most impactful technologies in recent years, due to their ability to analyze vast amounts of data and automate complex tasks. This research focuses on applying Machine Learning and Artificial Intelligence algorithms to analyze the most representative transportation companies in Romania. The study explores various financial indicators, including shareholders’ equity, liabilities, number of employees, turnover, net profit, fixed assets, and current assets, as well as location-related data such as county, city, and date of establishment. A comprehensive data analysis approach has been implemented, beginning with data cleaning, followed by exploratory data analysis to identify patterns and correlations between variables through interactive visualizations. Furthermore, multiple Machine Learning algorithms have been developed to predict the net profit of these companies based on independent features, with model performance evaluated using specific metrics.