Abstract
Data plays a significant role in studying different problems and suggesting optimized solutions. Data is a critical resource in this modern era, but with the availability of vast amounts of data, it is highly impossible for humans to identify the patterns and relationships among the data and make informed decisions. Machines can learn from the given enormous amount of data and can make predictions with the help of specific algorithms. Machine learning is a subfield of Artificial Intelligence that allows computer systems to learn from the data to make decisions on the related unseen data. The performance of the machine learning algorithms improves with increased exposure to data. With the numerous practical applications of machine learning in the real world, it is used in all fields, such as medicine, entertainment, education, banking, etc. The question that arises with these machine learning models is how fair the model is in making the decisions. The model might be biased if it decides based on sensitive variables for specific problems. This project aims to understand the model's fairness and mitigate the bias caused by these sensitive variables.