GhadaElkhawaga

PPM_XAI_Comparison

Code of experiments implemented in the paper "Explainability of Predictive Process Monitoring results: Techniques, Experiments and Lessons Learned", comparing XAI methods at different granularities (global/local) with different settings on predictive process monitoring outcomes using process mining event logs

ConsisXAI

ConsisXAI is an implementation of a technique to evaluate global machine learning explainability (XAI) methods based on feature subset consistency

2017-10-26-python_crash_course

data-science-blogs

A curated list of data science blogs

Data-Science-Cheatsheet

docker-python

Kaggle Python docker image

Exploring-the-Deadliness-of-Terrorist-Attacks

flow-analysis-predictions

Process performance prediction using flow analysis techniques

IntegratedGradients

Python implementation of integrated gradients presented in "Axiomatic Attribution for Deep Networks" for explaining any model defined in Keras framework.

Local_rule_basedXAIEvaluation

machine_learning_examples

A collection of machine learning examples and tutorials.

Notebook-Of-Things-I-Do-Not-Know-About

Notebook relating to Machine Learning/Data Science and potentially various other topics.

Predicting-How-Points-End-in-Tennis

CrowdANALYTIX's Data Science Competition

rnnalpha

sklearnflask

Flask API for training and predicting using scikit learn models

stability-predictive-monitoring

This repository contains scripts for predictive monitoring methods (random forest, XGBoost, and LSTM based) and assessing the prediction accuracy and temporal prediction stability.

XAI_predictivemonitoring_Consistency

xgboostExplainer

An R package that makes xgboost models fully interpretable