Analyzing Credit Default Data Using Descriptive Analysis Techniques

This project is curated to help learners understand the different data mining operations & Machine Learning techniques used to predict credit card default.

Analyze Credit Default Data - Application Of Descriptive Analysis Techniques
  • 2 bar graph
    Difficulty: Intermediate

    Foundational knowledge or experience in statistics or analytics is recommended.

  • Asset 1
    Duration: Approximately 2 hours

Case Overview

  • One of the most widely used tool for presenting data is Descriptive Analysis which is an important machine learning concept for preliminary analysis of the data.
  • Credit Risk is a huge concern in the banking industry due to credit defaulters who fail to pay back their credit on time and hence this puts the pressure on financial institutions to depend on credit risk modeling.
  • An NBFI is struggling to mark profits due to an increase in defaults in the vehicle loan category. Thus it is essential to determine the client’s loan repayment abilities and to understand the relative importance of each parameter contributing to a borrower’s ability to repay the loan.
  • Learn to apply numerous descriptive analysis techniques to analyze  credit default data.

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

Supervised Machine Learning

Business Analysis

Hypothesis Testing

Classification Techniques

Feature Engineering

Associated Learning Tracks

Case Context

  • NFBI is a financial institution that lacks a proper financial banking license. NFBI is currently observing increased cases of faulty vehicle loans and mismanagement in the same category.
  • The companies aim to reduce their losses by analyzing their clients loan repayment abilities.
  • The sector also aims in gathering descriptive analysis of various parameters and how they affect borrowers ability to repay loans.

  • Firstly, the data is treated and preprocessed to remove outliers by establishing a floor and cap for the same.
  • Two-Sample t-Tests were applied which revealed that there is a huge disparity in the credit amount of people with or without cars.
  • Random Forest Classification was performed to analyze the creditworthiness of the borrower.

  • The NFBI industries can use such analysis to detect default vehicle loan possibilities and prevent them in time.
  • The analysis can also help in determining degree of credit risks associated with extending credit to a loan applicant.
  • Fraud Detection Model can also analyze trade data and predict borrowers debt obligations

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