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This project aims to predict bank customer churn using a dataset derived from the Bank Customer Churn Prediction dataset available on Kaggle. The dataset for this competition has been generated from a deep learning model trained on the original dataset, with feature distributions being similar but not identical to the original data.
AI applications can be found in various real-world systems, including vehicle system design and real-time car accident prediction. There is an increasing need to better explain AI-driven processes, especially in terms of potential legal disputes that might result from AI decisions. This analysis addresses this explainability and legal issues.
This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of gravelly soils. This model is developed using LightGBM and SHAP.