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https://github.com/makoczoro/credit-default-risk-analysis-eda
This repository contains the detailed EDA Analysis of Home Credit Group Dataset. The analysis aims to find demographic and financial factors associated with higher or lower default risks, providing actionable insights for risk mitigation and improved lending practices
https://github.com/makoczoro/credit-default-risk-analysis-eda
bivariate-analysis correlation-analysis data-preprocessing exploratory-data-analysis exploratory-data-visualizations matplotlib numpy pandas seaborn univariate-analysis
Last synced: 27 days ago
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This repository contains the detailed EDA Analysis of Home Credit Group Dataset. The analysis aims to find demographic and financial factors associated with higher or lower default risks, providing actionable insights for risk mitigation and improved lending practices
- Host: GitHub
- URL: https://github.com/makoczoro/credit-default-risk-analysis-eda
- Owner: makoczoro
- Created: 2025-01-15T15:46:27.000Z (about 1 month ago)
- Default Branch: main
- Last Pushed: 2025-01-22T19:06:04.000Z (30 days ago)
- Last Synced: 2025-01-22T19:23:47.155Z (30 days ago)
- Topics: bivariate-analysis, correlation-analysis, data-preprocessing, exploratory-data-analysis, exploratory-data-visualizations, matplotlib, numpy, pandas, seaborn, univariate-analysis
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Credit Default Risk Analysis EDA đ
Welcome to the **Credit-Default-Risk-Analysis-EDA** repository! Here, you will find a detailed Exploratory Data Analysis (EDA) of the Home Credit Group Dataset. This analysis aims to uncover demographic and financial factors associated with higher or lower default risks, providing valuable insights for risk mitigation and enhancing lending practices.
## Overview âšī¸
In the modern world, assessing credit risks is a crucial aspect of financial decision-making. Understanding the factors that contribute to credit default can help financial institutions make informed decisions regarding lending practices. The Home Credit Group Dataset provides a rich source of data that enables us to explore these factors through statistical analysis and visualization.
## Repository Contents đ
### Data Preprocessing đ ī¸
- Various techniques used to prepare the data for analysis and ensure its quality.
### Univariate Analysis đ
- Statistical analysis of individual variables within the dataset to understand their distribution and characteristics.
### Bivariate Analysis đ
- Examination of relationships between pairs of variables to uncover potential correlations and insights.
### Correlation Analysis đ
- Assessment of the strength and direction of relationships between variables to identify potential predictors of credit default.
### Exploratory Data Visualizations đ
- Visual representations of the data to aid in understanding patterns, trends, and anomalies.
## Tools and Libraries Used đ ī¸
- Matplotlib đ
- NumPy đ§Ž
- Pandas đŧ
- Seaborn đ
## How to Use the Analysis đ
To explore the detailed EDA of the Home Credit Group Dataset, you can download the analysis files from the following link:
[](https://github.com/user-attachments/files/18426772/Application.zip)
Please note that the link provided needs to be launched to access the analysis files. If you encounter any issues with the link, kindly check the "Releases" section of this repository for alternative download options.
## Get in Touch đ§
If you have any questions, feedback, or suggestions regarding the Credit Default Risk Analysis EDA, feel free to reach out to us. Your insights and comments are valuable as we strive to enhance our understanding of credit default risks.
Let's dive into the world of credit risk analysis together and uncover actionable insights to improve lending practices! đ
Happy analyzing! đ
đ [Check out our analysis here!](https://github.com/user-attachments/files/18426772/Application.zip) đ