Banking sector offers a large variety of financial services out of which the significant amenities include payment services, custodial and safety support, loan application assistance and credit card options. Machine Learning and Data Science has a vast application in the field of Banking sector. These domains play significant role in the field of loan prediction which helps in fraud detection, risk management system, maintaining efficiency, transparency and helping in continuous improvement. Due to the increasing rate of loan defaults, it’s a difficult task for the banking authorities to assess the loan requests and deal with the risks of customers defaulting the loan. An automatic loan prediction system is developed using machine learning models where the machine learns and predicts whether to approve the loan or not based on the eligibility criteria. Six prediction models including Logistic Regression Model, Decision tree, Random Forest, Support Vector Machine, K-Nearest Neighbor Classifier and XG-Boost algorithm have been performed. A comparative analysis is carried out between the models where the performance metrics for each model is calculated and compared based on which the best suitable model for predicting the loan approval status is concluded.