This model predicts the likelihood of a loan applicant defaulting on their loan based on several key features. It uses a Logistic Regression model trained on a simplified dataset for demonstration purposes. This is a basic example and should not be used for real-world loan applications without significant improvements and a much larger, more diverse dataset.
- Model Type: Logistic Regression – A simple and interpretable classification model suitable for binary classification problems (default or no default).
- Credit Score: A scaled value between 0 and 1 representing the applicant's creditworthiness. Higher values indicate better credit.
- Annual Income: Annual income in tens of thousands of dollars.
- Loan Amount: Loan amount requested in tens of thousands of dollars.
- Employment Length: Length of employment in years.
- 0: No Default (Loan repaid successfully)
- 1: Default (Loan not repaid)
A small, synthetic dataset is included for demonstration. This dataset is for illustrative purposes only. Real-world applications require a significantly larger and more representative dataset.
The model's performance is evaluated using accuracy on the training set. While this provides a basic measure of performance, it is crucial to remember that this is a very limited evaluation due to the small dataset size. A more robust evaluation would include techniques like cross-validation and testing on a held-out test set.
- Small Dataset: The model is trained on a small, synthetic dataset, limiting its generalizability and the reliability of the evaluation metrics.
- Simplified Features: Only a few features are considered, while real-world loan applications require a broader range of factors for accurate prediction (e.g., debt-to-income ratio, loan purpose, payment history, etc.).
- Lack of Robust Evaluation: The model's performance is evaluated only using accuracy on the training data, which is not sufficient and can lead to overfitting.
Ensure you have the necessary dependencies installed:
pip install scikit-learn numpyThe notebook (model.ipynb) contains the code for training and using the model. Execute the cells in order to train the model and see its predictions.
Modify the new_data_point variable in the notebook to input new data for prediction. Ensure that the feature order and scaling remain consistent.
- Larger Dataset: Use a substantially larger and more realistic dataset.
- Feature Engineering: Add more relevant features, potentially transforming existing ones for better model performance.
- Model Selection: Experiment with other classification models (e.g., Random Forest, Gradient Boosting) to improve accuracy.
- Robust Evaluation: Use proper evaluation techniques such as cross-validation and a separate test set to assess the model's generalization performance and prevent overfitting.
- Hyperparameter Tuning: Optimize the model's hyperparameters to enhance performance.
This is a basic example for educational purposes. Always use appropriate caution and validation techniques when building and deploying machine learning models for real-world applications, especially those with significant financial implications.