A novel approach to improve the efficiency of transaction in online railway ticket booking system using anomaly or outlier detection method comparing with KNN algorithm.

By examining user ratings using a machine learning classifier technique, this study aims to improve the transaction efficiency of new online railway ticket booking systems. The KNN method and the anomaly/outlier detection algorithm are the subjects of this essay. We evaluated the methods using a dataset consisting of nine thousand records. Each has been through N= 10 iterations in the programming exercise. method using a variety of attributes in order to achieve a high percentage of correctness. Using Anomaly or Outlier Detection Method with a greater accuracy of 92.73 above KNN Algorithm accuracy of 88.41 and a significant value of P=0.001, this study demonstrates an increase in the effectiveness of the online railway ticket booking system. The outcome demonstrated that the Anomaly Methods That Are More Effective Than KNN Algorithm. [ABSTRACT FROM AUTHOR]