Prediction of Electricity Bill using Supervised Machine Learning Technique

Generally, predicting how the Electricity cost will function is one of the most demanding tasks, and predicting the price can be the most critical process ever. This task is very complex and has uncertainties. One of the most interesting (or perhaps most profitable) time-series data and machine learning techniques is to prevent this problem. Subsequently, estimating power costs has turned into a significant area of research. The objective is to pick the most effective way to figure out how to utilize electrical design to anticipate the best outcomes. AI Technology dissected the informational collection to acquire data about changes, single-variable examination, twofold factor and different breaks down; What is missing is significant clinical consideration, data approval, admittance to clearing/getting ready data, and admittance to all information given. The principal objective of this program is to show the machine how to foresee power costs utilizing prescient arrangements. Reasonableness and cost can be anticipated to expand power costs or be constrained by economic government and AI calculations. Likewise, think about and assess the presence of various calculations. Informational indexes and stage assessment reports, disarray, and handling networks for crisis data are found, and the outcomes show that the exhibition of the proposed AI calculation can measure up to the proper calculation: MAE, MSE, R2.