Explanatory Data Analysis and ML model building using Apache Spark and PySpark
What's up This project was mainly training my self on training ML models 🤖 and also to train on doing EDA 📜 to get the acceptance of the loan.
This project is on a data set from Prosper, which is America’s first marketplace lending platform, with over $7 billion in funded loans. This data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, borrower employment status, borrower credit hi…
Analysis of Loan Data from Prosper
Data analysis project using Prosper Marketplace’s peer-to-peer loan data. Explores interest rates, credit scores, borrower income, and risk trends using Python and Jupyter Notebook.
Analysis of Loan Data from Prosper
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ML analytics on PPP loan forgiveness data for businesses in DC. Completed as a group project for STAT-427: Statistical Machine Learning.
A machine learning project to predict loan default risk using financial and credit history data. Built as part of a team capstone project in master degree at Deakin University.
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