Finsearch_Credit_Score

Credit Score Analysis Model

This repository contains a simple Python script that demonstrates the creation of a basic credit score analysis model using synthetic data and the XGBoost classifier. This README.md file provides an overview of the code and how to run it.

Requirements

Before running the code, make sure you have the following Python libraries installed:

NumPy pandas scikit-learn XGBoost

You can install these libraries using pip:

pip install numpy pandas scikit-learn xgboost

Usage

#Clone this repository to your local machine: git clone https://github.com/RSM009/Finsearch_Credit_Score/

Navigate to the project directory:

cd <repository-directory>

Run the credit_score_analysis.py script:

python credit_score_analysis.py

Code Overview

#The credit_score_analysis.py script follows these main steps:

#Data Generation: It generates synthetic data using scikit-learn's make_classification function, creating a dataset with 1000 samples and 10 features.

#Data Preprocessing: The script splits the data into training and testing sets, standardizes the numerical features using scikit-learn's StandardScaler, and prepares the data for model training.

#Model Training: It uses the XGBoost classifier from the xgboost library to train a credit score prediction model on the preprocessed data.

#Model Evaluation: The script makes predictions on the test set and evaluates the model's performance using metrics like accuracy, confusion matrix, and a classification report.

#Model Saving: It saves the trained model as 'credit_score_model.pkl' using scikit-learn's joblib.

Output

After running the script, you will see the following output in the terminal:

Accuracy: The accuracy score of the model on the test data. Confusion Matrix: A matrix showing the true positive, true negative, false positive, and false negative values. Classification Report: Detailed statistics on precision, recall, F1-score, and support for each class. The trained model is also saved as 'credit_score_model.pkl' for future use.

Please note that this is a simplified example for educational purposes. Real-world credit scoring models involve more complex data preprocessing, feature engineering, and regulatory compliance.

License This project is licensed under the MIT License. Feel free to use and modify the code for your purposes.