Application of Decision Tree Mining Algorithm Based on Information Entropy in Intelligent Power Marketing

The rapid development of smart power marketing demands the building of very accurate and efficient predictive models to optimize customer segmentation, demand forecasting, and fraud detection. Traditional classification techniques are generally incapable of dealing with large‐scale, complex energy consumption data sets due to redundant attributes, class imbalance, and overfitting issues. In order to solve these problems, the paper suggests applying the Optimized C5.0 Decision Tree with Entropy‐Based Feature Selection for smart power trading to a higher level. The model proposed not only includes the traditional decision tree classification but also the entropy‐based feature selection, pruning, and advanced techniques in data preprocessing like normalization, imputation of missing data, and Synthetic Minority Over‐Sampling Technique for class imbalance and robustness of classification. The first step in the process of building a decision tree is to find out the entropy, which indicates how uncertain the dataset is. The Information Gain of each attribute is then calculated, and the one with the highest IG is taken as the root node. The data is partitioned repeatedly until a stop criterion is reached, which is where the condition to avoid the model being too complex and thus overfitting comes in. Along with the use of pruning techniques such as Reduced Error Pruning and Gini Index‐based feature scoring, the optimum classification accuracy is achieved. The results of the performance evaluation show that the Optimized C5.0 Decision Tree is better than classical classifiers.