Portfolio of projects

Loan Defaulter's Prediction using Statistical Analysis

A project to compare machine learning algorithms for a loan default dataset using MATLAB.

Uni-variate and Bi-variate analysis to understand the driving factor behind loan default

This repository contains all material related to the project done as a part of the course Introduction to Data Analytics (MS4610) in the Fall 2020 semester.

Clustering bank loan customers using KMeans clustering and predicting their loan statuses using XGBClassifier. The prediction model is explained with SHAP values.

Underwriting Loan Doc Generator: Business and Real Estate Investment DL App

2nd Place Overall in Tartan Data Science Cup S-17

은행 대출 채무 이행 예측 솔루션

The project entails building a model that predicts if someone who seeks a loan might be a defaulter or a non-defaulter. We have several independent variables like, checking account balance, credit history, purpose, loan amount etc. Ensemble Models such as Bagging, AdaBoosting, GradientBoost, XGBoost, Random Forest etc will be used for the modelling

Loan Default Detector App built with XGBoost, FastApi, Docker and Streamlit

Decision_Trees_and_Random_Forests

learning project about comparing various models for loan acceptance predictance and improving accuracy with the Decision Tree classifier model

Analyzed credit loan data from Kaggle. with 132 variables and 300000+ records. The aim is to find significant factors that contribute to the loan default

Goal is to determine whether client (Lending Club) should invest in P2P loans.

Loan Default Prediction Dataset from Kaggle and default prediction using machine learning techniques

Data Science Challenge from Coursera Project : Loan Default Prediction

Predicting Loan Defaulters using various Classification Algorithms using Python (Numpy, Pandas, Sklearn, Matplotlib, Seabon)

Supervised Learning Practice on Borrowers Behaviour