One of major component of operating system is task scheduling for the optimum utilization of the resources. Round Robin had been an effective task scheduling method so far, but it has certain limitations. It uses static time quantum which sometimes leads to starvation.  The proposed Optimised Round Robin is a modified version of the existing Round Robin scheduling which results in better average time and average turnaround time and overall increase in the performance. The comparative analysis is being done that indicates ORR gives improvement in the system performance.

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This research aims to study the predictive analysis, which is a method of analysis in Machine Learning. Many companies like Ola, Uber etc uses Artificial Intelligence and machine learning technologies to find the solution of accurate fare prediction problem. We are proposing this paper after comparative analysis of algorithms like regression and classification, which are useful for prediction modeling to get the most accurate value. This research will be helpful to those, who are involved in fare forecasting. In previous era, the fare was only dependent on distance, but with the enhancement in technologies the cab’s fare is dependent on a lot of factors like time, location, number of passengers, traffic, number of hours, base fare etc. The study is based on Supervised learning whose one application is prediction, in machine learning.

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