This project aims to classify credit scores using machine learning techniques. The provided Jupyter Notebook (Model.ipynb) contains code for training and evaluating various classification models on a dataset of credit scores.
Credit scoring is a crucial task in financial institutions to assess the creditworthiness of individuals or businesses. This project focuses on building classification models to predict credit scores based on various features such as income, debt, and credit history.
The dataset used in this project is not included in this repository. However, you can use any dataset containing relevant features and credit score labels. Ensure the dataset is preprocessed and cleaned before using it with the provided code.
To run the code in the Jupyter Notebook, ensure you have Python installed along with the following libraries:
- numpy
- pandas
- seaborn
- plotly
- matplotlib
- scikit-learn
You can install these libraries using pip:
You can install these libraries via pip:
pip install numpy pandas seaborn plotly scikit-learn matplotlib- Clone this repository:
git clone https://github.com/fuadh246/Credit-score-classification.git- Navigate to the project directory:
cd Credit-score-classification- Open and run the
Model.ipynbJupyter Notebook using Jupyter or any compatible environment.
The notebook includes implementations of various machine learning models for credit score classification, including:
- Logistic Regression
- K-Neighbors Classifier
- Decision Trees
- Linear SVC
- Random Forest
Feel free to experiment with other models or custom implementations according to your requirements.
The performance of each model is evaluated using metrics such as accuracy, precision, recall, and F1-score. Additionally, visualizations such as confusion matrices and ROC curves are provided for further analysis.