Credit-Report-Analysis

Analyzing borrowers’ risk of defaulting

Background

This project is part of the Data Scientist training program from Practicum by Yandex. More info in link below:

https://practicum.yandex.com/data-scientist

Objective

Prepare a report for a bank’s loan division to determine the likelihood that a customer defaults on a loan. Find out if a customer’s marital status and number of children has an impact on whether they will default on a loan. The bank already has some data on customers’ credit worthiness.

Data description

• children: the number of children in the family
• days_employed: how long the customer has been working
• dob_years: the customer’s ageeducation: the customer’s education level
• education_id: identifier for the customer’s education
• family_status: the customer’s marital status
• family_status_id: identifier for the customer’s marital status
• gender: the customer’s gender
• income_type: the customer’s income type
• debt: whether the customer has ever defaulted on a loan
• total_income: monthly income
• purpose: reason for taking out a loan

Libraries used

• pandas
• NLTK
• WordNetLemmatizer
• SnowballStemmer