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

Credit Card Fraud Detection

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.

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

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.