This repository contains the implementation of a credit scoring model designed to predict the creditworthiness of individuals based on historical financial data. The project utilizes various classification algorithms to build and evaluate the model's performance.

• Introduction
• Objectives
• Dataset
• Steps
• Installation
• Usage
• Model Evaluation
• Contributing
• License

Credit scoring is a crucial task in the financial industry, helping lenders assess the risk of lending to potential borrowers. This project aims to develop a machine learning model to predict the creditworthiness of individuals by analyzing historical financial data.

• Preprocess the dataset by handling missing values and encoding categorical variables.
• Split the data into training and testing sets.
• Train various classification algorithms on the training data.
• Evaluate the model's performance using appropriate classification metrics.
• Select the best-performing model for predicting creditworthiness.

The dataset contains historical financial information about individuals, including features such as credit history, income, debt levels, and other relevant attributes. The target variable indicates whether an individual is creditworthy.

1. Preprocessing:

   • Handle missing values.
   • Encode categorical variables.

2. Data Splitting:

   • Split the data into training and testing sets.

3. Model Training:

   • Train various classification algorithms such as logistic regression, decision tree, random forest, and others on the training data.

4. Model Evaluation:

   • Evaluate the models' performance using classification metrics such as accuracy, precision, recall, and F1-score.
   • Select the best-performing model based on evaluation metrics.

To run the project, you need to have Python and Jupyter Notebook installed. Install the required packages using the following command:

pip install -r requirements.txt

1. Clone the repository:

   git clone https://github.com/your-username/credit-scoring-model.git
   cd credit-scoring-model

2. Install the dependencies:

   pip install -r requirements.txt

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open the notebook credit_scoring_model.ipynb and follow the instructions to run the code and train the model.

The performance of the model is evaluated using the following classification metrics:

• Accuracy: The ratio of correctly predicted instances to the total instances.
• Precision: The ratio of correctly predicted positive observations to the total predicted positives.
• Recall: The ratio of correctly predicted positive observations to all the observations in the actual class.
• F1-score: The weighted average of Precision and Recall.

These metrics help in assessing the model's ability to predict creditworthiness accurately and reliably.

Contributions are welcome! If you have any ideas or improvements, feel free to submit a pull request. Please ensure your contributions adhere to the repository's guidelines.

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to explore the code and use the provided models as a basis for further research and development in credit scoring. If you encounter any issues or have suggestions for improvement, please open an issue or submit a pull request.