btrinh69 / Loan-default-prediction

This project builds a machine learning model which predicts possible defaulters based on their historic data

Loan-default-prediction

This project builds a machine learning model which predicts possible defaulters based on their historic data. The main model is Catboost Classifier.

Context

An organization wants to predict who possible defaulters are for the consumer loans product. They have data about historic customer behavior based on what they have observed. Hence when they acquire new customers they want to predict who is riskier and who is not.

Data

The following values were provided at the time of the loan application.

Column	Description	Type
income	Income of the user	int
age	Age of the user	int
experience	Professional experience of the user in years	int
profession	Profession	string
married	Whether married or single	string
house_ownership	Owned or rented or neither	string
car_ownership	Does the person own a car	string
risk_flag	Defaulted on a loan	string
currentjobyears	Years of experience in the current job	int
currenthouseyears	Number of years in the current residence	int
city	City of residence	string
state	State of residence	string

Task

Build a model to predict possible defaulters based on the data provided. The evaluation metrics are:

• roc_auc_score
• accuracy

Files

• full_notebook.ipynb: The notebook which performs all parts of the tasks ranging from EDA, model development, and error analysis. This notebook can be used to reproduce the results
• full_notebook.html: The html version of the full notebook which is used to display the results (pandas-profiling and pipeline diagram) without having to run the notebook again
• data: the directory which contains the data files