lendingclub-data
There are 0 repository under lendingclub-data topic.
- Personal-Loan-Status-Prediction-AppLanguage:Jupyter Notebook 2
ArtTucker / Credit_Risk_Analysis
Credit risk analysis using scikit-learn and imbalanced-learn.
Language:Jupyter Notebook 1Asheladia / classification_risky_business
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 models with imbalanced classes. You will see use of the imbalanced-learn and Scikit-learn libraries to build and evaluate models using the two following techniques: Resampling and Ensemble Learning.
Language:Jupyter Notebook 0DiouaneAbdallah / Decision-Trees-Random-Forest-loan-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.
Language:Jupyter Notebook 0Dipti028 / LendingClubCaseStudy
To identify if a person is likely to default or not.
Language:Jupyter Notebook 0SashaFlores / Credit_Risk_Analysis
This project aims to build and evaluate several machine-learning models to predict credit risk using free data from LendingClub.
Language:Jupyter Notebook 0AyanPahari / Gradient-Boosting-on-the-Lending-Club-dataset.
In this Mini Project, we will explore the use of pre-processing methods and Gradient Boosting on the popular Lending Club dataset.
Language:Jupyter Notebookdavidtstill / Machine_Learning_LendingClub
Evaluating several machine-learning models to predict credit risk using LendingClub data.
Language:Jupyter Notebook
