Skip to content
#

easyensembleclassifier

Here are 23 public repositories matching this topic...

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

Credit risk is an inherently unbalanced classification problem, as good loans easily outnumber risky loans. Therefore, you’ll need to employ different techniques to train and evaluate models with unbalanced classes. Using the credit card credit dataset from LendingClub, a peer-to-peer lending services company,

  • Updated Jul 6, 2022
  • Jupyter Notebook

Improve this page

Add a description, image, and links to the easyensembleclassifier topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the easyensembleclassifier topic, visit your repo's landing page and select "manage topics."

Learn more