Using Machine learning models to classify the risk level of given loans

To understand the driving factors (or driver variables) behind loan default, i.e. the variables which are strong indicators of default.

Repository containing IPython notebooks to Kaggle dataset - All Lending Club loan data

Default Prediction On LendingClub Dataset With XGBoost & Random Forest Classification Models

To predict whether the loan will be fully funded, based on the scoring of and information related to the application.

I built and evaluated several machine-learning models to predict credit risk using free data from LendingClub. I employed different techniques for training and evaluating models with imbalanced classes and used the imbalanced-learn and Scikit-learn libraries to build and evaluate models.

Default Prediction On LendingClub Dataset With Neural Network Classification Models

This project builds a model to predict if Lending Club loans will be fully paid or charged off. Using completed loan data, it includes credit history and loan grades but excludes FICO scores and rejected loans due to limited data. Methodologies and results are documented for accuracy and fairness.

Regression analysis to predict the interest rates for lending club.

We analyse unsecured peer to peer lending data with credit history, financial, historical and loan request application information at Lending Club, to study patterns characterizing borrower behaviour on the platform following the data science process. We specifically focus our attention on credit risk and identifying the key drivers behind the d…

Lend to PPDAI borrowers automatically. (Discontinued)

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

A study, analysis and visualization of factors and relationships between those factors which determine the rate of interest and also if a person is a potential loan defaulter by using ML.

Code for classifying whether someone can repay their loan to a banking institution using a supervised learning approach: Binning and Logistic Regression.

Machine learning model for predicting loan repayment using Lending Club data, leveraging TensorFlow for model creation and evaluation.

Using EDA to understand how consumer attributes and loan attributes influence the tendency of default.

Case study to understand driving factors (or driver variables) behind loan default.

Predictive Analysis of Lending Clubs loans to predict whether a loan may default or not using R