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

I and my team participated in the Amazon ML Challenge, a national-level machine learning competition where we tackled real-world data problems and built predictive models using advanced ML techniques.

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.

Machine Learning Project at Kampus Merdeka Program

Data Science Projects.

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.

Applied Machine Learning | Fall 2024

Data Science portfolio

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.

Orbit Boost is a research-oriented gradient boosting library built from scratch in Python, designed as an experimental alternative to LightGBM, XGBoost, and CatBoost. It introduces oblique projections, BOSS sampling, Newton-style updates, and a ridge-based warm start for improved performance.

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.

🛡️ Detect and report fraudulent activities using advanced modeling techniques to enhance security and protect valuable assets.

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.