https://github.com/kshula/debt_analysis
Debt Analysis and Prediction with Machine learning
https://github.com/kshula/debt_analysis
Last synced: 3 months ago
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Debt Analysis and Prediction with Machine learning
- Host: GitHub
- URL: https://github.com/kshula/debt_analysis
- Owner: kshula
- Created: 2024-05-09T09:04:44.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-09T11:22:03.000Z (about 2 years ago)
- Last Synced: 2024-05-10T11:57:59.579Z (about 2 years ago)
- Language: Python
- Homepage:
- Size: 1.78 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: readme.md
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README
# Debt Analysis and Prediction with Machine learning
This project is a Streamlit web application designed for debt service analysis and prediction. It includes features for evaluating lender classification using K-Nearest Neighbors (KNN) and predicting debt service using Random Forest and Gradient Boosting models.
## Features
### 1. Lender Classification with KNN
The application includes a functionality to perform lender classification based on debt characteristics using the K-Nearest Neighbors (KNN) algorithm. The key steps involved are:
- Loading and preprocessing the debt data.
- Applying KNN to classify lenders based on debt stock and interest arrears.
- Visualizing the classification results using interactive plots.
### 2. Debt Service Prediction
The application offers debt service prediction using two machine learning models: Random Forest and Gradient Boosting. The prediction process involves:
- Splitting the dataset into training and testing sets.
- Training the models on the training data.
- Evaluating model performance using accuracy and R2 score.
- Generating future predictions for debt service over specified periods.
## How to Use
1. **Installation**
Ensure you have Python installed. Clone this repository and navigate to the project directory.
```bash
git clone https://github.com/kshula/debt_analysis.git
cd debt
```
Install the required Python packages using pip and the provided requirements.txt file.
```bash
Copy code
pip install -r requirements.txt
```
## Running the Application
Start the Streamlit web app by running the following command in your terminal.
```bash
Copy code
streamlit run main.py
```
This will launch the web application in your default web browser.
## Navigation
Home: Displays an overview of debt service over time.
Model Accuracy: Evaluates model performance on debt service data.
Predictions: Generates future predictions for debt service using selected models.
Debt Analysis: Machine learning KNN Analysis
## File Structure
main.py: Main Python script containing Streamlit application code.
data/: Directory containing dataset files used by the application.
requirements.txt: List of Python packages required for the project.
## Contributors
Kampamba Shula