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easy-ensemble-classifier

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Data preparation, statistical reasoning and machine learning are used to solve an unbalanced classification problem. Different techniques are employed to train and evaluate models with unbalanced classes.

  • Updated May 19, 2021
  • Jupyter Notebook

Using machine learning (ML) models to predict credit risk using data typically analysed by peer-to-peer lending services. Resampling data with SMOTE, Cluster Centroids, SMOTEENN and applying ensemble learning classifiers: Balanced Random Forest Classifier and Easy Ensemble Classifier.

  • Updated Dec 7, 2020
  • Jupyter Notebook

About Six different techniques are employed to train and evaluate models with unbalanced classes. Algorithms are used to predict credit risk. Performance of these different models is compared and recommendations are suggested based on results. Topics

  • Updated Oct 6, 2022
  • Jupyter Notebook

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