In the data-led business situation, it is of great significance to accurately recognize the value of customer traffic for promoting marketing strategy and rationally allocating resources. Traditional random forest model is often unable to deal with unbalanced customer data and redundant feature dimensions, which leads to sub-optimal recognition accuracy. An improved recognition model based on random forest is developed, which integrates weighted feature selection by using information gain ratio and recursive feature elimination. Then SMOTE is used to carry out the balanced sampling strategy, and the improved model also includes the regularized Gini index and dynamic pruning method. In order to prevent the model from over-fitting, the experimental evaluation on public data sets and simulated data sets shows that the recognition accuracy, accuracy and recall rate of the model are significantly improved, which confirms the effectiveness of the model in complex customer value analysis situations.

Wei Wu. Research on Customer Traffic Value Recognition Model Based on Improved Random Forest Algorithm. International Journal of Business Management and Economics and Trade (2024), Vol. 6, Issue 1: 82-93. https://doi.org/10.38007/IJBMET.2025.060109.

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