Predicting Late Delivery Risk in Supply Chains using Machine Learning with EDA, feature engineering, and model explainability

This project applies a Random Forest Classifier to predict whether a student will Pass or Fail based on their features

Customer Churn Prediction System using XGBoost Regressor, built on Telecom Industry dataset

An Analysis and Machine Learning model to understand employee retention and predict churn as part of the Google Advanced Data Analytics Certificate Capstone Project.

VOC-based plant phenotyping using PTR-ToF-MS data from tomato genotypes under different treatments.

Develops 2 machine learning models that predict which customers are likely to leave the service, and to identify the most influential factors contributing to churn.

Projeto de Regressão Avançada para Previsão de Preços de Casas (House Prices). Utiliza XGBoost Otimizado com Feature Engineering e transformação logarítmica do target. Resultado: RMSE de 14,046.04. Destaque para a importância das features de Qualidade Geral e Área Total.

U-M MADS: Milestone Project | ML-powered system that detects and classifies racial bias in news articles using supervised and unsupervised learning techniques, to provide comprehensive bias analysis.

Order‑book liquidity feature engineering + SHAP analysis (XGBoost) with Triple‑Barrier labels; reads OHLCV & depth from SQLite and saves plots to docs/images.

This repository contains code for identifying potential biomarkers of 2022 mpox virus using transcriptomic and machine learning analysis.

End-to-end machine learning pipeline to predict bank customer churn using Decision Tree and Random Forest, with feature engineering, evaluation, and hyperparameter tuning.

Predict telecom customer churn using machine learning models with Python, scikit-learn, and pandas.

scraping premier League 2021-2022 season Data and predicting the result (Win/ Lose) as a classification problem using Gradient Boost and random Forest.

Machine learning for predicting negative cardiac outcomes in patients with atrial fibrillation (AF)

This project compares the effects of Ridge (L2) and Lasso (L1) regression models on clinical data.

A Decision Tree classifier to predict heart disease using the Cleveland dataset.

End-to-end portfolio covering core Machine Learning, Data Engineering (MLOps), and Modern AI (LLM/Agent) development. Demonstrates proficiency in building and deploying robust systems from data acquisition to final deployment.