| Sumario: | One of the most crucial procedures is predicting the electricity bill. This is a highly tricky situation with no guarantees. To get around this, we can use machine learning approaches. As a result, anticipating power bills has become a hot study issue. The goal is to predict results with the highest possible accuracy using machine learning techniques. A example dataset will be made available for use. We must analyze the data in this dataset. Supervised Machine Learning Methods are used to analyse the data (SMIT). Data cleaning, data preparation, and data visualization will all be collected using this data analysis. To provide a machine learning-based strategy for accurately estimating the value of the Electricity Price Index by comparing supervise classification machine learning algorithms and predicting outcomes in the form of electricity price increase or stable state. In addition, the performance of several machine learning methods will be compared and discussed. Dataset with evaluation classification report, confusion matrix, and data prioritisation, and the results show that the proposed method is effective. machine learning algorithm technique can be compared to the best accuracy MAE, MSE,R2, and the result shows that the effectiveness of the proposed machine learning algorithm technique can be compared to the best accuracy MAE,MSE,R2.
|
|---|