Electric Power Scam Prediction Using Machine Learning Techniques

Electricity fraud is causing huge losses in both the public and private sectors. The proposition of this paper uses a temporarily controlled learning program to include potential theft data from non-specific data on the existence of multiple sample theft. There are two basic applications, one anticipating the use of power and the other to create a broken application and analysis space. Both are rushing for power protection in creating configuration, redesign, and functionality. Because of the significant costs of energy acquisition, as a restricted measure of energy assets, the efficient and effective use of energy assets is an important part of social and financial development in any country. This work introduces two accounting frameworks for the detection of theft of high- and low-power consumers who rely on machine learning.