🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A port of Friday Night Funkin' v0.2.8 made by rebuilding the code via reverse engineering.

Sushi online delivery shop project with typescript / redux toolkit

Interpreting Black-Box Time Series Classifiers using Parameterised Event Primitives

Developing delivery time estimation model using Neural Networks

Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Qt-DAB, a general software DAB (DAB+) decoder with a (slight) focus on showing the signal

A V-slice fork/engine made to have modding in mind. Built off of V-Slice's Source Code

The main idea for this project is explore a Kaggle dataset about ChatGPT reviews using a NLP approach in order to apply ML models for score reviews predictions. I applied LIME algorithm to evaluate explainability to get text and features explanations. I realised a Docker container to set up a Django web application.

A solid foundational understanding of XAI, primarily emphasizing how XAI methodologies can expose latent biases in datasets and reveal valuable insights.

Feature Engineering, Regression, Classification, Model Explanation. My 2 biggest projects exploring the link between economic indicators and U.S. presidential election results.

Performed model evaluation using evaluation metrics such as accuracy, precision, recall, F1-score etc. Then model interpretation using feature importance, SHAP and LIME. Finally , evaluated model robustness and stability through techniques like bootstrapping or Monte Carlo simulations.

Implementation of LIME focused on producing user-centric local explanations for image classifiers.

This repository contains the Python scripts that I have written and run to execute a series of analytic model developments using datasets taken from the book "The Elements of Statistical Elements" by Hastie, Tibshirani, Friedman

Multicycles.org aggregates on one map, more than 300 share vehicles like bikes, scooters, mopeds and cars. Demo APP for the Data Flow API, see https://flow.fluctuo.com

Classifying Travel Mode choice in the Netherlands using KNN, XGBoost, RF and TabNet

A tool for classifying an image into a disaster type, utilizing Python

A library for drawing graphics on the console screen

C# LIME protocol implementation