The project provides a complete end-to-end workflow for building a binary classifier in Python to recognize the risk of housing loan default. It includes methods like automated feature engineering for connecting relational databases, comparison of different classifiers on imbalanced data, and hyperparameter tuning using Bayesian optimization.

Stochastic AUC Maximization with Deep Neural Networks

Evaluating machine learning methods for detecting sleep arousal, bachelor thesis by Jacob Stachowicz and Anton Ivarsson (2019)

Face recognition, tackling three different "old-school" Computer Vision techniques - as part of the Biometrics System Concepts course @ KU Leuven

Solve complex real-life problems with the simplicity of Keras

Predicting Supreme Court Decisions

This problem is a typical Classification Machine Learning task. Building various classifiers by using the following Machine Learning models: Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), XGBoost (XGB), Light GBM and Support Vector Machines with RBF kernel.

A machine-learning project on detection of fraudulent credit card transactions.

Creating ROC graph for every target on data

Performed feature engineering, cross-validation (5 fold) on baseline and cost-sensitive (accounting for class imbalance) Decision trees and Logistic Regression models and compared performance. Used appropriate performance metrics i.e., AUC ROC, Average Precision and Balanced Accuracy. Outperformed baseline model.

Keras-based Deep Learning to cleanse patient data and predict their chances of survival

Involves analyzing user data to identify patterns and build predictive models that can forecast whether users are likely to stop using the application.

Performance calculation tool for Hateful Memes Challenge

Modelo predictivo de aprendizaje supervisado para detectar clientes en riesgo de cancelar cuenta en Beta Bank.