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Credit risk is an inherently unbalanced classification problem, as the number of good loans easily outnumber the number of risky loans. I employed Machine Learning techniques to train and evaluate models with unbalanced classes. I used imbalanced-learn and scikit-learn libraries to build and evaluate models using resampling. I also evaluated the performance of these models and made a recommendation on whether they should be used to predict credit risk.

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balanced-accuracy-scores classification-report cluster-centroids credit-risk random-forest randomoversampler smote-sampling tp-fp

creditloans_machinelearning's Introduction

Hi there πŸ‘‹ , I’m Shaun Wang

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I’m a Student, Developer, Data Analyst, Art Historian

Interested in intersecting Software Development, Art, and Data Analytics.

  • 🌱 I’m currently developing my full-stack skillsets.
  • πŸ”­ I’m currently working on some independent coding projects too, ask me about them if you’re curious!
  • πŸ‘― I’m always down to collaborate on new ideas with other creators.
  • πŸ€” 2022 Goals: Finish my MS in CS Program and Secure a Software Engineer internship.
  • πŸ’¬ Ask me about the intersection between data and culture.

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