Loan Default Risk Dashboard

Disclaimer: Please note that this dashboard is currently using a subset of the full merged_dataset.csv due to file size limitations for this deployment. The full analysis was conducted on a larger dataset.

This Streamlit application visualizes factors contributing to loan default using a merged customer and financial dataset. The dashboard allows users to explore potential risk indicators through interactive filters and charts.

Overview

The dashboard provides insights into:

• Overall default rate of customers.
• Default rates based on employment type within filtered segments.
• Distribution of Debt-to-Income (DTI) ratio for defaulting and non-defaulting customers.
• Distribution of the Repayment Ratio for defaulting and non-defaulting customers.
• (Optional) Visualization of the model's performance via a confusion matrix.

Features

• Interactive Filters: Adjust the displayed data by age and monthly income using sidebar sliders.
• Key Metrics: Displays the total number of customers and the overall default rate (based on the subset).
• Employment Type Analysis: Bar chart showing the default rate for different employment categories within the filtered data (based on the subset).
• Feature Distributions: Box plots illustrating the distribution of DTI and Repayment Ratio, separated by default status (based on the subset).
• Confusion Matrix (Optional): If confusion_matrix.png is present, it will display the performance of a predictive model trained on the full dataset.
• Key Recommendations: Actionable insights based on the analyzed data.

Repository Contents

• app.py: The main Streamlit application code.
• merged_dataset_sample.csv: A sample of the merged dataset used for this deployed dashboard.
• requirements.txt: Lists the Python libraries required to run the app.
• confusion_matrix.png (Optional): An image of the confusion matrix from your model evaluation (likely trained on the full dataset).
• README.md: This file.

Setup

1. Clone the repository:

   git clone [repository URL]
   cd [repository name]

2. Install the required libraries:

   pip install -r requirements.txt

3. Ensure the sample dataset merged_dataset_sample.csv is in the same directory.

Running the App

To run the Streamlit dashboard, navigate to the repository directory in your terminal and execute:

streamlit run app.py