In many industries inclusive of automotive vehicle industry, predictive maintenance has become more important. It is hard to\ndiagnose failure in advance in the vehicle industry because of the limited availability of sensors and some of the designing exertions.\nHowever with the great development in automotive industry, it looks feasible today to analyze sensor�s data along with machine\nlearning techniques for failure prediction. In this article, an approach is presented for fault prediction of four main subsystems of\nvehicle, fuel system, ignition system, exhaust system, and cooling system. Sensor is collected when vehicle is on the move, both in\nfaulty condition (when any failure in specific system has occurred) and in normal condition.The data is transmitted to the server\nwhich analyzes the data. Interesting patterns are learned using four classifiers, Decision Tree, Support Vector Machine,
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