This project aims to apply econometric techniques, specifically logistic regression, to build a predictive model which contribute for credit default prediction. Leveraging a dataset sourced from Kaggle, which displays data regarding hypothetical transactions in a fictional bank, the goal is to enhance the assessment of credit risk by accurately identifying clients with a higher probability of default.
Utilizing historical data and borrower-specific characteristics like credit history, income, and age, logistic regression is employed to estimate the likelihood of a client defaulting. This technique allows for a more precise evaluation of potential credit default scenarios.
- Improved Risk Assessment: The model provides a more accurate identification of clients at risk of default, aiding financial institutions in minimizing potential losses.
- Data-Driven Decision Making: Using empirical data enables evidence-based decision-making in credit risk management.
- Feature Engineering: Exploring additional relevant features or refining existing ones could enhance the model's predictive power.
- Model Optimization: Fine-tuning hyperparameters or trying different algorithms may further improve the model's accuracy.
This project utilizes the following Python libraries:
- Pandas
- NumPy
- Scikit-learn
- Matplotlib
- Seaborn