NOTE: This talk focuses on Explainable AI, and building an interactive application with Shiny

We demonstrate a pure Python solution for exploring and understanding datasets using state-of-the-art machine learning and explainable AI techniques. Our application features a reactive dashboard built with Shiny, specifically designed for the daily work of data scientists.

The tool provides insights into data rapidly and effortlessly through an interactive dashboard. It facilitates data preprocessing, interactive exploratory data analysis, on-demand model training, evaluation, and interpretation. It further renders dynamic, annotated, and interactive visualizations. This allows to pinpoint critical elements and relations as root causes in a haystack of features, compressing a full day's work into under an hour.

Utilizing Plotly for dynamic visualizations, along with Scikit-learn, CatBoost, SHAP values, and MLflow for experiment tracking, married with shiny reactive dashboard, we facilitate quick and easy data preprocessing and exploration, model training and evaluation, together with explainable AI.