Train delay is one of the foremost problems in the railway systems across the world. According to the TOI newspaper, In India there are about 25.3 million people were used to travel by train in 2006 and this drastically increased year by year. In 2018, every day at least 80 million people in India prefer to travel by trains[1]. Categorically in India, most of the trains unable to run on their scheduled time due to poor signaling and less number of railway tracks. This implies that travellers might get delayed to reach their respective destinations. The aim of this paper is to present the prediction of Train delay in Indian Railways through machine learning techniques to achieve higher accuracy. In the proposed model, we used 3 different machine learning methods (Multivariate regression, Neural Network, and Random Forest) which have been compared with different settings to find the most accurate method. To compare different methods, we consider training time and accuracy of the method over the test data set. Trains in India get delayed frequently, and if we can predict this in advance - it would be a great help for the passengers to plan their journey according to their works.

Train delay, Multivariate Regression, Neural Network, Random Forest

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