The existing classification system assigns manufacturers to one of four categories: Economy, Volume, Premium, or Luxury. However, as the market evolves and brands offer vehicles outside their traditional niches, it becomes increasingly difficult to assign a single category to a manufacturer. The goal of this thesis was to develop a new classification approach that addresses this by clustering vehicles instead of manufacturers. Two models were developed: one clusters vehicles within countries, and the other clusters vehicles within both countries and segments. To summarize the state of the market, the position of manufacturers, and vehicle characteristics, the cluster parameters were carefully selected. These include price, household income, GDP, manufacturer reputation, automotive expenditures, vehicles bodystyle, and segment. Both clustering models classify cars at the chosen level into six categories. By simulating different scenarios, the models provide deeper insights into market trends. The use of clustering algorithms helps in strategies decision-making processes and allows the results to be applied in other forecasting models.
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