<![CDATA[Determining car prices is a crucial aspect of the automotive industry that requires accurate data analysis for strategic decision-making. This study aims to compare the performance of the Random Forest and C4.5 algorithms in classifying car prices based on specific features, such as technical specifications, production year, and market conditions. The dataset used in this study consists of [mention the size and source of the dataset if available], analyzed using a cross-validation approach to ensure the accuracy of the results. The performance of both algorithms is evaluated based on several metrics, including accuracy, precision, recall, and F1-score. The results show that the Random Forest algorithm consistently outperforms the C4.5 algorithm across most evaluation metrics, achieving an accuracy of [best Random Forest accuracy] compared to [best C4.5 accuracy]. These findings indicate that the Random Forest algorithm is more effective in handling multivariate data complexity and providing more reliable predictions. The conclusions of this study highlight the potential of Random Forest as the primary method for car price classification, especially in scenarios requiring high accuracy levels. This research also contributes to a comparative understanding of decision-tree-based algorithms for applications in the automotive industry and opens opportunities for further research into developing more adaptive and efficient models.]]>
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