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I'll use various techniques to train and evaluate a model based on loan risk. I will use a dataset of historical lending activity from a peer-to-peer lending services company to build a model that can identify the creditworthiness of borrowers.
Our group chose this question to bring attention to the little knowledge that young loan applicants have. Based on our findings in our models we explore: Which age group is the least likely to apply for loans? Which group is most likely to default on loans?
Credit risk analysis determines a borrower's ability to meet debt obligations and the lender's aim when advancing credit. The goal is to identify patterns that indicate if a person is unlikely to repay the loan or labeled as a bad risk through automated machine learning algorithms.
This work is about "Loan Default Prediction" which is one of the most important and critical problems faced by banks and other financial institutions as it has a huge effect on profit
Classification and regression models for predicting the level of risk associated with extending credit to a borrower and the basic EPS amount respectively.
This repository contains projects related to data mining. Data mining finds valuable information hidden in large volumes of data and it is the analysis of data and the use of software techniques for finding patterns and regularities in sets of data.
A data analysis project to classify whether an applicant is capable of paying a home loan by using 4 machine learning models (Logistic Regression, SVM, Random Forest and LGBM) and 1 deep learning model (DeepFM). We also drew some insights from the best model that can be useful for analysts in bank.