There are 3 repositories under lending-club topic.
Lending Club Loan data analysis
Building Classification & Prediction model to classify the Loan applicant request as approved or rejected and then predict the Interest rate for Loan Approval.
Explanatory Data Analysis and ML model building using Apache Spark and PySpark
My personal website and blog where I showcase many of my projects and my progress learning data science.
:blue_book: Detailed Exploratory Data Analysis of Lending Club Loan Data
LendingClub API interface
Projects in Data Analysis
A R wrapper for the Lending Club API. The package allows you to manage the funds in your investor account and to make trading transactions.
Master's Project - Classification Models on 'Lending Club' dataset
Case study to identify risky loan applicants and understand factors that contribute to a loan default.
This is our second project at neuefische DS Bootcamp. Silas Mederer (https://github.com/sls-mdr) and me applied different ML models and for credit default prediction of the P2P platform Lending Club.
Prediction Of Loan Repayment using Sequential Neural Networks on Lending Club Dataset.
Southern Data Science Conference Attempt 2020
A Python wrapper for the Lending Club API.
Mi primer paso en Ciencia de Datos: Default Crediticio LendingClub
This project was made as a final project at Rakamin Academy in collaboration with ID/X Partner.
This app utilizes deep learning to predict the loan status of an applicant, whether it's "Fully Paid" or "Charged Off".
Loan Default Prediction, Individual Level Loan Data, Machine Learning, Logistic regression, Ridge, LASSO, Gradient Boosting, SVM, Random Forest
This repo contains analysis of Lending Club Credit rates and also case study for a client to get a fully funded loan at the lowest credit rate with a desired duration.
Ladder network implementation for python 3.x with tensorflow.
Analyzing credit card risk with machine learning models!
Analysis to understand the driving factors (or driver variables) behind loan default.
Analysis seeking to reduce the risk of defaults- WORK IN PROGRESS
Analyzed LendingClub loan data to determine factors associated with loan default. Built machine learning models to predict probability of default.
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.
Lending Club's loan data analysis using data cleaning/wrangling to predictive modeling
Default Prediction On LendingClub Dataset With Neural Network Classification Models
Default Prediction On LendingClub Dataset With XGBoost & Random Forest Classification Models
Code for classifying whether someone can repay their loan to a banking institution using a supervised learning approach: Binning and Logistic Regression.
Predictive Analysis of Lending Clubs loans to predict whether a loan may default or not using R
Using Machine learning models to classify the risk level of given loans
Case study to understand driving factors (or driver variables) behind loan default.