CFXplorer generates optimal distance counterfactual explanations for a given machine learning model.

[NeurIPS 2022] (De-)Randomized Smoothing for Decision Stump Ensembles

Comprehensive benchmark study of feature selection techniques for predictive machine learning models on tabular data. Various feature selection methods are evaluated across different data characteristics and predictive scenarios.

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

German Credit Data - 1994

This project uses EEG data to detect schizophrenia, achieving a robust classifier with LGBM, boasting a ROC AUC of 95.96% and an accuracy of 90%

Project started as submission for MSE course at Praxis Business School, then further worked upon to implement machine learning models with hyperparameter tuning.

This project leverages ML to classify mental health risk signals (potential signs of depression) by analyzing structured profile metadata and unstructured textual data from social platforms, with a focus on user behavior, interactions, and content.

Applied Machine Learning | Fall 2024

Data Science portfolio

In this course, you will get advanced knowledge on Data Mining. This course begins by providing you the complete knowledge about the introduction of Data Mining. This course is a complete package for everyone wanting to pursue a career in data mining.

Project page for "Physics-informed graph neural networks accelerating microneedle simulations towards novelty of micro-nano scale materials discovery" as a part of Romrawin Chumpu's master thesis and publication.

Streamlined toolkit for predicting human phenotypes from UK Biobank using tree-based ensembles and linear models. Load high-dimensional SNP and covariate data, select variants via Random Feature Selection to balance accuracy and runtime, and interpret genetic plus socio-demographic feature contributions with SHAP.

Tree-based models are appealing for price modeling due to their high performance but they can be unstable. Due to competition between insurers, unstable models increase the risk that the overall premium is too small to cover the losses. The thesis propose various strategies for improving the stability of tree-based models.

Usually tree-based and neural network regressors work better for regression tasks than linear regression models, because they can capature complex or subtle non-linear patterns in data.

Data Science Projects.