tree-based-models

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 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%

Detects anomalies using the Isolation Forest algorithm, with clear visual comparison between original data and anomaly-marked data in an unsupervised learning setup.

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

German Credit Data - 1994

Tabular classification project with Machine Learning models

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.

For this project, we will analyze publicly available data from LendingClub.com, which connects borrowers needing money with investors. The goal is to create a model that predicts the likelihood of borrowers repaying their loans. We will focus on Lending Club's data from 2007-2010 to classify and determine the repayment behavior pre-2016.

Data-variance capture ability of Composition-descriptors while predicting the band gap (primarily semiconductor family choosen)

Machine Learning Project at Kampus Merdeka Program

Data Science portfolio

Notebooks that document my process of: cleaning NHL data, features engineering, and training models to predict NHL playoff teams in the 2025-2026 season

Exploring key features that lead to language endangerment and leveraging them within modern machine learning methods to predict a language's endangerment level.

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

Automated reasoning 🤖 for CoT prompting 💬 using explainability attributes from tree-based 🌳 models for binary classification on tabular datasets