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Globally, the impact of the automotive industry on the economy and our daily existence is enormous, in both directions. Not only do they function as transportation, but far beyond that, vehicles have also become data generating machines. Manufacturers or dealers keep various records and collect statistics, including categories such as model types, production rates, etc.; odometer readings of the distance driven, purchase costs or repair/maintenance expenses, cost factors related to initial or long-term vehicle ownership, engine characteristics such as fuel type/horsepower/overall volume, transmission characteristics at downshift points and more. In this study, association analysis was performed with the Apriori algorithm, one of the data mining methods, using vehicle data from the automobile sector. A total of 634 cars were analyzed based on nine different categories: model, production category, mileage category, price category, engine size category, horsepower category, fuel type, cost category, and transmission type. The data was collected from a publicly accessible source. The Apriori algorithm was used to identify the strongest relationships between these attributes. The results show that vehicles in the high-cost category generally have gasoline fuel type, while vehicles in the new production category are generally in the low range category. In addition, the vehicles in the manual transmission type were found to be mostly gasoline. Vehicles in the low-cost category were generally found to have diesel fuel type. The 634 vehicles analyzed cover a wide range of models and categories. In particular, the strong relationship between fuel type and cost category suggests that gasoline vehicles are usually in the high-cost category. In addition, vehicles in the new production category are in the low-walk category, indicating that these vehicles are generally less used or newer. These findings offer new insights on the automotive industry and consumer behavior and can help vehicle manufacturers and dealers to better understand potential customer preferences. This study highlights the importance of extracting information from large data sets and supporting decision-making processes. [ABSTRACT FROM AUTHOR]