Readmissions to a hospital after procedures are costly and considered to be an indication of poor quality. As Per the Affordable Care Act of 2010, hospitals may be reimbursed at a reduced rate for patients readmitted to a hospital within 30 days of discharge. In this project, we used statistical and machine-learning methods to analyze the Nationwide Inpatient Sample dataset provided by HCUP (Healthcare Cost and Utilization Project) to identify various clinical, demographic and socio-economic factors that play crucial roles in predicting the revenue loss due to readmissions. Three medical conditions, namely chronic obstructive pulmonary disorder (COPD), total hip arthroplasty (THA), and total knee arthroplasty (TKA) have been primarily used for this purpose. Our analysis builds on both non-parametric and parametric statistical models and machine learning techniques such as Decision Tree, Gradient Boosting, Logistic Regression and Neural Networks. We evaluated and compared these models based on Area under ROC (AUC) and misclassification rate. By including visual analytics, this analysis not only enables the hospitals to compute the loss of revenue but also monitors their quality of service in a real-time fashion.