An Optimized technique for a Sapid Motor pooling Tariff Forecasting System

In this modern era, many challenges are being faced by people like traffic congestion, limitation of parking lots, pollution of environment, etc. Ride sharing is one of the techniques for overcoming such challenges. However, its efficacy in lowering personal automobiles is dependent on its competence while preserving commercial feasibility from the viewpoint of both the vehicle operator and the passenger. Numerous techniques of cluster evaluation have been created which explore for sections of high-ranking intensity in the data, each such area being taken to imply a distinct group. In this research we propose a ride sharing methodology for predicting the Ride sharing fare using the Principal Component Analysis PCA.