FNCE 5352代做、代写Java，Python程序设计、c++编程代写-代写Python编程

UCONN FNCE 5352

Consumer Credit Project

Overview

Banks play a crucial role in market economies. They decide who can get finance and on what terms and

can make or break investment decisions. For markets and society to function, individuals and companies

need access to credit.

Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to

determine whether or not a loan should be granted. This assignment requires students to improve on

the state of the art in credit scoring, by predicting the probability that somebody will experience

financial distress in the next two years.

The goal of this assignment is to build a model that borrowers can use to help make the best financial

decisions.

Files

Files have been posted to

https://github.com/mattmcd71/fnce5352_spring2020/tree/master/Assignments/ConsumerCredit

? DataDictionary.xls – Excel Spreadsheet with description of the data in the test and train files

? ConsumerCred-train.csv – Training data set. This file includes the dependent variable

SeriousDlqin2yrs, as well as the independent variables described in the data dictionary.

? ConsumerCred-test.csv – This is the dataset that you’ll need to predict outcomes for. It does not

include the dependent variable (SeriousDlqin2yrs).

? SampleSubmission.csv – This file is a sample of the file that students will need to submit.

Guidelines

Students can form teams to work on building the model. Each team must submit one or more file

submissions with the probability predictions on the test dataset. Submissions must be emailed to

Tianjiao Zhang. Submissions will be assessed on the AUC (Area under the curve) of the submitted

predictions.