Lending Club Loan data analysis
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Updated
Jul 8, 2019 - Jupyter Notebook
Lending Club Loan data analysis
Explanatory Data Analysis and ML model building using Apache Spark and PySpark
Case study to identify risky loan applicants and understand factors that contribute to a loan default.
Building Classification & Prediction model to classify the Loan applicant request as approved or rejected and then predict the Interest rate for Loan Approval.
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.
Loan Default Prediction, Individual Level Loan Data, Machine Learning, Logistic regression, Ridge, LASSO, Gradient Boosting, SVM, Random Forest
Prediction Of Loan Repayment using Sequential Neural Networks on Lending Club Dataset.
LendingClub API interface
To predict whether the loan will be fully funded, based on the scoring of and information related to the application.
Case study to understand driving factors (or driver variables) behind loan default.
Using Machine learning models to classify the risk level of given loans
Master's Project - Classification Models on 'Lending Club' dataset
Repository containing IPython notebooks to Kaggle dataset - All Lending Club loan data
Projects in Data Analysis
Ladder network implementation for python 3.x with tensorflow.
Analysis seeking to reduce the risk of defaults- WORK IN PROGRESS
📘 Detailed Exploratory Data Analysis of Lending Club Loan Data
Project for Machine Learning 1 (classification problem). Prediction of LendingClub loan defaulters. Used classifiers: Logistic Regression, Support Vector Machines (SVM) and Random Forest.
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