This tool utilizes a dataset of loan applicants to develop a logistic regression model for predicting credit default risk.
A dataset containing information about previous loan applicants and whether they honored the payment or not was used. The objective was to create a logistic regression model to identify patterns indicating whether a person is likely to default.
Financial institutions can use this tool to assess the creditworthiness of loan applicants by analyzing historical data. It aids in decision-making processes regarding loan approvals, helping to minimize the risk of default.
Ensure that you have access to the Kaggle dataset and the required Python libraries for data analysis.
Clone the repository using git clone https://github.com/kajinmo/CreditScoreModeling.git
Install necessary Python libraries as specified in the requirements.txt
Load the dataset from Kaggle for analysis