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.

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

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.

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 Mpox virus using transcriptomic data and machine learning analysis.

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

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

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.