This project was completed to practice different classification prediction models. The original data and project was created by Udacity, from the Nanodegree 'Predictive Analytics for Business Nanodegree'.

You work for a small bank and are responsible for determining if customers are creditworthy to give a loan to. Your team typically gets 200 loan applications per week and approves them by hand. Due to a financial scandal that hit a competitive bank last week, you suddenly have an influx of new people applying for loans for your bank instead of the other bank in your city. All of a sudden you have nealy 500 loan applications to processs this week. Your manager sees this new influx as a great opportunity and wants you to figure out how to process all of these loan applications within one week. For this project, you will analyze the business problem using the Problem solving framwork and provide a list of creditworthy customers to your manager in the next two days.

Python

pandas, matplotlib.pyplot, seaborn, sklearn, xgboost

Logistic regression model, Decision tree model, Forest model, Boosted forest model, Confusion matrix

credit-data-training.xlsx: This file contains all credit approvals from your past loan applicants the bank has ever completed. customers-to-score.xlsx: This is the new set of customers that you need to score on the classification model you will create. Predicting Default Risk code.ipynb: The python code and results of this project

If this project inspired you, gave you ideas to help with your own project, please consider buying me a coffee.