Exploring common techniques for data preparation
After the data experimentation phase, you should have gathered enough knowledge to start preprocessing the data. This process is also often referred to as feature engineering. When coming from multiple sources, such as applications, databases, or warehouses, as well as external sources, your data cannot be analyzed or interpreted immediately.
It is, therefore, of imminent importance to preprocess data before you choose a model to interpret your problem. In addition to this, there are different steps involved in data preparation, which depend on the data that is available to you, such as the problem you want to solve, and with that, the ML algorithms that could be used for it.
You might ask yourself why data preparation is so important. The answer is that the preparation of your data might lead to improvements in model accuracy when done properly. This could be due to the relationships within your data that have been simplified...
