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
L'objectif de ce projet est de développer un classifieur capable de différencier les logiciels malwares des goodwares.
Testing 6 different machine learning models to determine which is best at predicting credit risk.
Prédire si un individu est atteint ou non de maladie cardiaque.
Prédire si un invidu est positif au COVID19 ou non.
Credit Card Fraud Detection
Data preparation, Statistical reasoning, Machine Learning
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
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