Bias mitigation in automated loan eligibility process

This project addresses the automation of the student loan eligibility process using a dataset focused on loan approval. Given the risk of inherent bias in machine learning models, particularly towards certain demographic groups, ensuring fairness is paramount. We will experiment with various machine learning models to achieve a balance between accuracy and fairness. By mitigating biases, such as those related to gender, our aim is to promote equitable decision-making and minimize potential discriminatory outcomes.