The main purpose of this paper is exploring the fact that how to use a machine learning model in order to perform predictive maintenance on Automobile. Maintenance and Care play a key role in the smooth and safe running of your motorcycle. The goal is to predict when the automobile require service or maintenance. If the model runs successfully, it gives us enough data about determining what the problem is and not only providing the necessary solutions but also ordering the parts and scheduling the people necessary to repair it. The innovative solutions of Predictive Maintenance recursively monitor, evaluates and report the component and system conditions in the vehicle. Various techniques are discussed and tested, such as linear and quantile regression. The primary aim of the system is to increase the vehicle’s efficiency due to the observed and supervised driving behavior which is able to minimize the fuel consumptions and exhaust. Based on received data from the various connected vehicle and transmitting it to the cloud i.e. Azure where the processing of the data takes place, errors are predicted and fixed before time and with less damage of vehicle whereby reducing the overall cost of maintenance

Predictive maintenance, machine learning automobiles, 2-wheelers, Internet of Things, AZURE

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