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https://github.com/dagiteferi/credit-scoring-model
https://github.com/dagiteferi/credit-scoring-model
Last synced: 8 days ago
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- Host: GitHub
- URL: https://github.com/dagiteferi/credit-scoring-model
- Owner: dagiteferi
- Created: 2025-01-22T17:14:22.000Z (24 days ago)
- Default Branch: main
- Last Pushed: 2025-01-22T17:36:07.000Z (24 days ago)
- Last Synced: 2025-01-22T18:31:15.467Z (24 days ago)
- Language: Python
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Credit Scoring Model for Bati Bank
## Table of Contents
- [Introduction](#introduction)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Features](#features)
- [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)
- [Feature Engineering](#feature-engineering)
- [Model Training and Evaluation](#model-training-and-evaluation)
- [API Development](#api-development)
## Introduction
In a world where financial inclusion is critical, providing underserved communities with access to credit becomes paramount. This project aims to develop a comprehensive and effective credit scoring system for Bati Bank, leveraging data from an eCommerce platform to enable a buy-now-pay-later service for customers. The journey encompasses understanding credit scoring methodologies, exploratory data analysis (EDA), feature engineering, model training, and evaluation.
## Project Structure
```plaintext
dagiteferi-credit-scoring-model/
├── README.md
├── requirements.txt
├── credit_scoring/
│ ├── db.sqlite3
│ ├── manage.py
│ ├── urls.py
│ ├── credit_scoring/
│ │ ├── __init__.py
│ │ ├── asgi.py
│ │ ├── settings.py
│ │ ├── urls.py
│ │ ├── views.py
│ │ ├── wsgi.py
│ │ └── templates/
│ │ └── predict.html
│ ├── scoring_api/
│ │ ├── __init__.py
│ │ ├── admin.py
│ │ ├── apps.py
│ │ ├── models.py
│ │ ├── serializers.py
│ │ ├── tests.py
│ │ ├── urls.py
│ │ ├── views.py
│ │ └── migrations/
│ │ └── __init__.py
│ └── webapp/
│ ├── __init__.py
│ ├── admin.py
│ ├── apps.py
│ ├── models.py
│ ├── tests.py
│ ├── urls.py
│ ├── views.py
│ ├── migrations/
│ │ └── __init__.py
│ └── templates/
│ └── home.html
├── models/
│ ├── Logistic Regression_best_model.pkl
│ └── Random Forest_best_model.pkl
├── notebooks/
│ ├── README.md
│ ├── Exploratory_Data_Analysis.ipynb
│ ├── Feature_Engineering.ipynb
│ ├── Modelling.ipynb
│ └── __init__.py
├── scripts/
│ ├── README.md
│ ├── Eda.py
│ ├── Feature_Engineering.py
│ ├── Modelling.py
│ ├── __init__.py
│ └── file_structure.py
├── src/
│ └── __init__.py
├── tests/
│ ├── __init__.py
│ └── test_api.py
└── .github/
└── workflows/
└── unittests.yml
```
## Installation
Clone the repository
```bash
git clone https://github.com/your-repo/dagiteferi-credit-scoring-model.git
```
Navigate to the project directory
```bash
cd dagiteferi-credit-scoring-model
```
Create a virtual environment
```bash
python3 -m venv venv
```
Activate the virtual environment
```bash
# On Windows
venv\Scripts\activate
```
```bash
# On macOS/Linux
source venv/bin/activate
```
Install the required dependencies
```bash
pip install -r requirements.txt
```
## Usage
Running the Django Application
Navigate to the credit_scoring directory
```bash
cd credit_scoring
```
Apply the database migrations
```bash
python manage.py migrate
```
Run the Django development server
```bash
python manage.py runserver
```
Making Predictions
The API endpoint /api/predict/ can be used to make predictions. Use the following sample payload:
```json
{
"ProviderId": 4,
"ProductCategory": 2,
"Amount": 0.050426,
"Value": 0.076352,
"PricingStrategy": 2,
"FraudResult": 0,
"Total_Transaction_Amount": 0.165893,
"Average_Transaction_Amount": -0.074327,
"Transaction_Count": -1,
"Std_Transaction_Amount": 0.145069,
"Transaction_Hour": 19,
"Transaction_Day": 15,
"Transaction_Month": 12,
"Transaction_Year": 2025,
"CurrencyCode_WOE": 0.0,
"ProviderId_WOE": 3.137005,
"ProductId_WOE": 1.645067,
"ProductCategory_WOE": 1.620379,
"Recency": 2265,
"RFMS_score": -0.042337,
"ProductId_1": false,
"ProductId_2": false,
"ProductId_3": false,
"ProductId_4": false,
"ProductId_5": false,
"ProductId_6": false,
"ProductId_7": false,
"ProductId_8": false,
"ProductId_9": false,
"ProductId_10": false,
"ProductId_11": false,
"ProductId_12": false,
"ProductId_13": false,
"ProductId_14": false,
"ProductId_15": false,
"ProductId_16": false,
"ProductId_17": false,
"ProductId_18": false,
"ProductId_19": false,
"ProductId_20": false,
"ProductId_21": false,
"ProductId_22": false,
"ChannelId_ChannelId_2": true,
"ChannelId_ChannelId_3": false,
"ChannelId_ChannelId_5": true,
"TransactionHour": 15,
"TransactionDay": 11,
"TransactionMonth": 5,
"TransactionWeekday": 1,
"ProductId": 1
}
```
## Features
Exploratory Data Analysis (EDA): Understand data distributions, identify outliers, and visualize correlations.
Feature Engineering: Create aggregate features, extract new features, encode categorical variables, handle missing values, normalize numerical features, and calculate Weight of Evidence (WoE).
Model Training and Evaluation: Train multiple machine learning models, perform hyperparameter tuning, evaluate models, and save the best models.
API Development: Develop a REST API for real-time credit scoring predictions.
## Exploratory Data Analysis (EDA)
The EDA process involves the following steps:
Data Loading and Overview: Load and inspect the dataset.
Missing Values Identification: Check for missing values.
Numerical Features Distribution: Analyze the distribution of numerical features.
Skewness Analysis: Check for skewness in numerical features.
Correlation Analysis: Understand the relationships between features.
Outlier Detection: Identify and handle outliers.
## Feature Engineering
Feature engineering involves several tasks:
Create Aggregate Features: Summarize transaction data for each customer.
Extract Features: Derive new features from existing data.
Encode Categorical Variables: Convert categorical variables into numerical format.
Handle Missing Values: Ensure the dataset is clean.
Normalize/Standardize Numerical Features: Enhance model performance.
Construct RFMS Scores and Labels: Calculate RFMS scores and assign labels.
Calculating Weight of Evidence (WoE): Transform categorical variables
## Model Training and Evaluation
Data Preprocessing: Clean and prepare the data.
Split the Data: Divide data into training and testing sets.
Train the Models: Train multiple models and compare performance.
Define Hyperparameter Grids: Set up grids for hyperparameter tuning.
Perform Grid Search: Find the best hyperparameters.
Evaluate the Models: Assess model performance.
Save the Best Models: Retain the best-performing models.
## API Development
Developing the REST API involves the following steps:
Set Up Django Project: Create and configure the Django project.
Develop API Endpoints: Create