Designing a machine learning model is a tricky task. A model may not work in practice although it has high performance on the training data. This article discusses the misuse of a machine learning model that causes the predictions not to work in the real world situation. The other reasons could be overfitting, duplicated samples, and unbiased data. It is always good to use your domain knowledge or talk to some experts and see if your prediction/recommendation results make sense or not.