<![CDATA[The rapid advancements in machine learning techniques have significantly impacted various industries, including the automotive sector. This study explores the implementation of machine learning algorithms to classify the condition of car engines based on an automotive vehicle engine health dataset. The primary objective of this research is to develop a reliable predictive model to facilitate proactive maintenance and reduce unexpected failures. Several algorithms, including Support Vector Machine (SVM), Naive Bayes, and K-Nearest Neighbor (KNN), were evaluated. The dataset was preprocessed and split into 80% training data and 20% test data. Model performance was assessed using metrics such as accuracy, precision, recall, and F1-score. The results indicate that Naive Bayes outperformed the other models, achieving an accuracy of 66% and a precision of 82%. This study demonstrates the potential of machine learning in predictive maintenance applications and highlights the importance of selecting appropriate algorithms and preprocessing techniques. Future work will focus on expanding the dataset and exploring ensemble methods to further enhance model accuracy and reliability.]]>
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