SHAP as mentioned in Data Analysis section is used for saliency analysis of the model's prediction. SHAP's approach to feature importance offers a fresh perspective compared to traditional methods like permutation and Gini index . While traditional methods focus on the impact of feature permutations on model performance, SHAP evaluates the contribution of each feature directly to predictions. This nuanced distinction enhances our understanding of feature importance, especially in complex models. Additionally, traditional feature importance plots often lack depth, providing limited insight beyond ranking features. In contrast, SHAP's global explanations offer more reliable results, making it a promising alternative for feature importance in various contexts.