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%

German Credit Data - 1994

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 ⌛

Machine learning pipeline for multi-class treatment prediction in lung adenocarcinoma (LUAD) using patient-level molecular profiles, featuring ensemble-based model aggregation, benchmarking across diverse classifier architectures, and systematic performance evaluation.

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.

Extreme gradient boosting library ported from XGBoost with subsampling, missing value routing, gamma pruning, and full model serialization.

Tabular classification project with Machine Learning models

Machine Learning Project at Kampus Merdeka Program

A nerdo practices logic living behind ML packages over a notebook dump

Data Science portfolio

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

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.