Testing 6 different machine learning models to determine which is best at predicting credit risk.

This project builds, tunes and evaluates the respective performance of a baseline DecisionTreeClassifier, a GradientBoostingClassifier, and a RandomForestClassifier. These three classifiers are hyperparameter-tuned, and their respective performance compared to select best model for predicting the condition of water-wells in Tanzania.

Credit Card Fraud Detection

L'objectif de ce projet est de développer un classifieur capable de différencier les logiciels malwares des goodwares.

Over- and under-sampled data using four algorithms and compared two machine learning models that reduce bias to identify the most reliable credit risk prediction model.