Update README.md

Author: hyunjoonbok

README.md

@@ -89,9 +89,9 @@ In this notebook, we will use the subset of the Freddie Mac Single-Family datase
 
 
    #### Explainable Machine Learning with SHAP
-   [Understand Classification Model with SHAP](https://github.com/hyunjoonbok/Python-Projects/blob/master/%5BExplainable%20Machine%20Learning%5D%20SHAP%20Decision%20Plots%20in%20Depth.ipynb) \
-   [Understand Regression Model with SHAP](https://github.com/hyunjoonbok/Python-Projects/blob/master/%5BExplainable%20Machine%20Learning%5D%20Understand%20Regression%20Model%20with%20SHAP%20(XGBoost).ipynb) \
-   [SHAP Decision Plots in Depth](https://github.com/hyunjoonbok/Python-Projects/blob/master/%5BExplainable%20Machine%20Learning%5D%20Understand%20Sentiment%20Analysis%20Model%20with%20SHAP%20(Logistic%20Regression).ipynb)
+   [Understand Classification Model with SHAP](https://github.com/hyunjoonbok/Python-Projects/blob/master/GeneralML/ExplainableML/%5BExplainable%20Machine%20Learning%5D%20Detailed%20Bar%20Plots%20and%20Waterfall%20Plots%20in%20SHAP.ipynb) \
+   [Understand Regression Model with SHAP](https://github.com/hyunjoonbok/Python-Projects/blob/master/GeneralML/ExplainableML/%5BExplainable%20Machine%20Learning%5D%20Understand%20Regression%20Model%20with%20SHAP%20(XGBoost).ipynb) \
+   [SHAP Decision Plots in Depth](https://github.com/hyunjoonbok/Python-Projects/blob/master/GeneralML/ExplainableML/%5BExplainable%20Machine%20Learning%5D%20SHAP%20Decision%20Plots%20in%20Depth.ipynb)
    <p>
 
 SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. Here, we look at the implementation of Tree SHAP, a fast and exact algorithm to compute SHAP values for trees and ensembles of trees. We have 3 different basic examples (regression / classifcation / more in-depth graphics) that can be applied to visualizaing the model.