Data Preprocessing

Data preprocessing is a very critical step for developing ML solutions as it helps make sure that the model is not trained on biased data. It has the capability to improve a model's performance, and it is often the reason why the same algorithm for the same data problem works better for a programmer that has done an outstanding job preprocessing the dataset.

For the computer to be able to understand the data proficiently, it is necessary to not only feed the data in a standardized way but also make sure that the data does not contain outliers or noisy data, or even missing entries. This is important because failing to do so might result in the algorithm making assumptions that are not true to the data. This will cause the model to train at a slower pace and to be less accurate due to misleading interpretations of data.

Moreover, data preprocessing does not end there. Models do not work the same way, and each one makes different assumptions. This means...