Many algorithms for imbalanced data support binary and multiclass classification only. This approach is made for mulit-label classification (aka multi-target classification). 🌻

Classification and Oversampling Algorithms Comparison, using Deep Feature Synthesis and Feature Selection with RFE

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

Data preparation, Statistical reasoning, Machine Learning

Credit Card Fraud Detection

Prédire si un individu est atteint ou non de maladie cardiaque.

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

Prédire si un invidu est positif au COVID19 ou non.

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.

Machine-learning models to predict credit risk using free data from LendingClub. Imbalanced-learn and Scikit-learn libraries to build and evaluate models by using Resampling and Ensemble Learning