<![CDATA[Determining car prices is one of the major challenges in the global automotive industry because it is influenced by various factors such as technical specifications, vehicle condition, and market dynamics. This issue becomes more complex as the volume of available data increases, requiring methods capable of performing fast and accurate analysis. This study aims to predict car price levels based on vehicle specifications using a Machine Learning approach, with the Naive Bayes algorithm selected as a solution to simplify the price classification process on large-scale data. The dataset used is the Global Car Sales Analysis from the Kaggle platform, which includes attributes such as Manufacturer, Model, Engine size, Fuel type, Year of manufacture, Mileage, and Price. The research methodology consists of data preprocessing, label encoding for categorical attributes, splitting the dataset into training and testing sets, and applying the Naive Bayes algorithm to classify car prices into three categories: Low, Medium, and High. The results indicate that Naive Bayes is capable of predicting car prices with very strong performance, achieving an accuracy of 96%, precision of 0.97, recall of 0.96, and an F1-score of 0.96. The model performs best on the Low category with an F1-score of 0.98, although performance decreases for the Medium and High categories due to imbalanced class distribution. Further analysis also reveals that Engine size, Year of manufacture, and Mileage are the most influential attributes in determining price. Overall, this study demonstrates that Naive Bayes is an effective method for predicting car prices using global automotive data.]]>
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