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balancedrandomforestclassifier

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We'll use Python to build and evaluate several machine learning models to predict credit risk. Being able to predict credit risk with machine learning algorithms can help banks and financial institutions predict anomalies, reduce risk cases, monitor portfolios, and provide recommendations on what to do in cases of fraud.

  • Updated Mar 5, 2022
  • Jupyter Notebook

I am asked to resample the credit card data since it is not balanced. First, I start to split the data and perform oversampling with RandomOverSampler and SMOTE method, and I undersample with ClusterCentroids algorithm. Then, I utilize the SMOTEENN method to oversample and undersample the data. Finally, I used ensemble models.

  • Updated Jul 21, 2022
  • Jupyter Notebook

The project focuses on handling imbalanced data using techniques like RandomUnderSampler and TomekLinks, while exploring various models such as CART, Random Forest, GBM, and LightGBM. The BalancedRandomClassifier, optimized through hyperparameter tuning, achieved an 80% recall on high-risk customers with an accuracy of 74%.

  • Updated Sep 10, 2024
  • Jupyter Notebook

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