Credit risk analysis using scikit-learn and imbalanced-learn.

This project aims to build and evaluate several machine-learning models to predict credit risk using free data from LendingClub.

Lending Club's loan data analysis using data cleaning/wrangling to predictive modeling

In this assignment, I have built and evaluate several machine-learning models to predict credit risk using free data from LendingClub. Credit risk is an inherently imbalanced classification problem (the number of good loans is much larger than the number of at-risk loans), so I needed to employ different techniques for training and evaluating mo…

To identify if a person is likely to default or not.

In this Mini Project, we will explore the use of pre-processing methods and Gradient Boosting on the popular Lending Club dataset.

Evaluating several machine-learning models to predict credit risk using LendingClub data.

This project uses lending data from LendingClub.com to determine if potential customers will successfully pay off a loan after entering a lending agreement. Our main goal will be to compare two models: one created using a single decision tree, the other using a random forest.