In this era, many business organizations and financial institutions are mostly conducted based on their large volumes of data. To analyze large volumes of data there are many techniques. In the big data, data mining process, Machine Learning is an arising strategy to investigate the huge volumes of information and is utilized to settle on basic choices for the business and financial association. It is also used in many sectors like medical, sentiment analysis for social media comments, banking sector and so on where it first learns the data and then performs the predictive analysis. In today's world, technology is progressing and with the progression of technologies many organizations are trying to adapt their business model with many technologies. For banking and financial sectors credit risk is a great threat and to predict the credit worthiness of a customer there are many techniques that exist. In this research, we have explored the dataset of bank which contains the information of credit defaulter client and applied some supervised Machine Learning algorithms to predict the credit ability of a customer and the clients who covered the highest chance of short-dated credit recovery. Also, we have performed feature scaling techniques so that many machine learning models can behave much better and perform well. We analyzed our dataset and dropped those unnecessary features that do not affect our model performance. Then we applied the final necessary features to those Machine Learning algorithms, calculated the accuracy and compared the accuracy with other classifier algorithms. Compared with other classifiers, we have observed that Random Forest performs better and it gives 89% accuracy to the prediction of credit recovery.