Weighted Ensemble simulation framework in Python

History-augmented Markov analysis of weighted ensemble trajectories.

This project used a combination of different machine learning models and optimization techniques to create a powerful binary classification model, ranking in the top 28% of a Kaggle competition. It highlights the use of ensemble learning and hyperparameter tuning to improve model accuracy.

Predicting customer responsiveness to Starbucks app promotions using demographic, behavioral, and offer data. Includes preprocessing, offer-response labeling, EDA insights, and ML models for personalized marketing.