Project Objective

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.

Foundation

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.

Advantages

• 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.

Potential Improvements

• 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.

Dependencies

This project utilizes the following Python libraries:

• Pandas
• NumPy
• Scikit-learn
• Matplotlib
• Seaborn

Data Source

• Link to the dataset on Kaggle