A Comprehensive Study on Predicting the Need for Vehicle Maintenance Using Machine Learning

Predicting vehicle maintenance is an important task to reduce downtime and cost. Traditional methods based on mileage and manufacturer direction can lead to maintenance at an early stage or too late. By leveraging machine learning, we can predict maintenance in a better way that can save time and cost effectively. In our paper, we have used machine learning models to predict the maintenance needs based on vehicle features. We have an imbalanced dataset, which contains information about 50,000 vehicles; first, we balanced it using SMOTE (generated new sample points and increased the size up to 80,000). After addressing the imbalance dataset challenge, we have applied several algorithms, including Random Forest, Decision Tree, gradient booster, naïve Bayes and KNN. Decision tree perform maximum performs well on both imbalanced and balanced data samples and achieves an accuracy of 99.97%. These finding highlights the importance of machine learning in predicting vehicle maintenance to save cost and downtime.