Explainable AI (XAI) methods are meant to explain and interpret the black box ML/DL models. We use SHAP (SHapley Additive exPlanations) as a technique for feature importance analysis . SHAP is a model-agnostic method and does not require model retraining . SHAP excels with data like time series and 1D datasets due to its ability to capture temporal dependencies and sequential patterns.