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Loan Risk Prediction Neural Network and API
LendingClub API interface
To understand how data could be used to minimize the risk of losing money while lending to customers for a Consumer Finance Company by applying Exploratory Data Analysis techniques.The aim is to identify patterns through Univariate/ Bivariate analysis to establish the driving factors that are indicate the borrower's loan defaulting tendency, which may be used for taking actions such as denying the loan, reducing the amount of loan, lending (to risky applicants) at a higher interest rate, etc.
Implmented Decision Trees and Random Forest on Lending club dataset and compared which algorithm performed better .
Basic Artificial Neural Network project to predict if a customer will pay back the loan.
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.
Hands-on data analysis project based on Lending Club
Predicting loan defaults with Scikit-learn tuned models
This is a one layer neural network that predict whether a loan will be defaulted.
Predicting the interest rate based on the credit history of a person
Implemented a Artificial Neural Network on the Lending Club dataset .
In this project, based on the historical data of customers, who has applied for loans, we will identify whether loan should be approved or not. This dataset belongs to Lending Club.
EDA Project case study