Credit Risk Modeling – Probability of Default

This project enables learners to apply various data visualization functions & feature engineering techniques to select appropriate features.

Credit Risk Modelling - Probability of Default- Data Treatment & Feature Selection
  • 1 bar graph
    Difficulty: Beginner

    No prior knowledge or experience with data required.

  • Asset 1
    Duration: Approximately 2 hours

Case Overview

  • Credit risk modeling is a useful model in the banking industry aimed at minimizing the credit risk defaulting cases through the use of various machine learning techniques.
  • The core objective of Banking institutions is to lend loans to individuals & companies that need capital. Despite the fact that it is hard to predict who would default, correctly evaluating and managing credit risk can decrease the severity of a loss.
  • Hence, banks can gain an advantage by using credit risk models by understanding the factors which causes such cases and creating a model to predict any future credit default case to prevent losses.
  • Learn to apply numerous machine learning techniques to create an accurate prediction model for credit defaults.

What’s included


Lifetime Access

Access this project for life once completed


Flexible Scheduling

Start learning online immediately, at your own pace


Desktop Only

We recommend completing this project on a desktop

Skills You Will Learn

Data Management

Data Visualization

Business Analysis

Hypothesis Testing

A/B Testing

Data Mining Techniques

Feature Engineering

Machine Learning

Associated Learning Tasks

Case Context

  • To expand the businesses and increase customer attrition, banks need a continual risk management system for their existing customer base that can determine probability of default beforehand.
  • Machine learning and deep learning advancements have made it significantly easier for businesses and individuals to establish their own strong credit default risk prediction models.
  • The purpose is to identify consumers who are at risk of defaulting within the next six months, for which we built a Risk Score to identify high-risk clients and proactively target them with remedial efforts.

  • Firstly, the data visualization techniques are applied to summarize data and determine correlation among various indicators.
  • Duplicated, missing values and outliers are treated, and data sample is updated.
  • VIF was also used to determine the impact of various indicators on the outcome. It is used to determine variables’ contribution to error range in regression.

  • The case provides a step-by-step process of learning and comprehending the various stages and strategies involved with data exploration, data processing, and feature extraction as part of a model development exercise inside a risk framework.
  • The final data set is used to train several models on the Probability of Default.
  • The potential of identifying certain spending habits can be used to determine the persons’ creditworthiness which is extremely beneficial to banks and loan providers when evaluating applications.

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