In USA, hospitals are liable for penalties if the 30 day re-admission rate crosses the threshold for the particular clinical condition stipulated under the Affordable Healthcare act. We analyzed past patient history of readmissions. Then, we clustered patients based on clinical, social and behavioral factors like clinical conditions age, gender, associated clinical conditions, weight, life style, ethnicity, economic indicators, geo etc. With this data, we derived a model based on the training set to predict the risk of readmission for a patient. Finally, we tested the model on the testing data set and fine tune for accuracy and ran it across the new patients to predict the readmission risk.