Data Science portfolio

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%

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.

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.

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

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

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.

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

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

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.