Credit Risk Detection

Overview

This project focuses on detecting credit risk using various data science models and visualizations. The dataset used is german_credit_data.csv, which is stored in the dataset folder.

Files and Folders

• dataset/german_credit_data.csv: The model training and evaluation dataset.
• outputs/using_matplotlib/: Contains visualizations created using Matplotlib.
• outputs/using_yellowbrick/: Contains visualizations created using Yellowbrick.

Models and Visualizations

Python Files

1. using_matplotlib.py:

   • Models Used: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree, Random Forest, Naive Bayes, Neural Network.
   • Visualizations: Includes various plots such as Confusion Matrix, ROC Curve, Precision-Recall Curve, and others using Matplotlib.

2. using_yellowbrick.py:

   • Models Used: K-Nearest Neighbors (KNN).
   • Visualizations: Includes ROC Curve, Precision-Recall Curve, and others using Yellowbrick.

Installation

To run the code, first install the required packages. Use the following command:

pip install -r requirements.txt