Coding challenge for a job interview examining the predictors of vehicle accident severity using GB Road Safety Data

Frontend for ShapEmotionsCorrectionAPI

In this project we predict credit card defaults using classification models.

Use machine learning to find out what drives sales and predict sales

XAI analytics to understand the working of SHAP values

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.

Android malware detection using machine learning.

No-code Machine learning (Pre-alpha)

In this repository you will fine explainability of machine learning models.

Code for my thesis about SHAP. Implementation of DecisionTree, SVM, BERT on 2 Datasets Imdb and Argument Mining

Code for EACL Workshop paper Can BERT eat RuCoLA? Topological Data Analysis to Explain

ML implementations in Multi-scale model for lignin biosynthesis in Populus Trichocarpa

Predict probability of default on credit

Determining Feature Importance by Integrating Random Forest and SHAP in Python

XGB - SHAP XAI

👨‍💻 This repository shows how machine learning and SHAP can be leveraged to understand the reasons of production downtime ⌛

Predicting NBA game outcomes using schedule related information. This is an example of supervised learning where a xgboost model was trained with 20 seasons worth of NBA games and uses SHAP values for model explainability.

This repository is associated with interpretable/explainable ML model for liquefaction potential assessment of soils. This model is developed using XGBoost and SHAP.

Understanding the limitations of Gassmann's fluid substitution model using explainable ML

Getting explanations for predictions made by black box models.