Rescaling and DIscretizing the Data Preparing Datasets for Machine Learning

In Synthesis AI datasets for machine learning cases, the process of data rescaling is a member of a family of data normalization techniques that try to enhance the quality of a dataset by removing unnecessary dimensions and preventing a situation in which some values are disproportionate to others.

Imagine that you manage a chain of motorcycle dealerships and that the majority of the characteristics in your dataset are either categorical to represent models and body types or have 1-2 digit values, for example, for years of use. But given that the costs are three to four digits, you want to estimate the typical time it will take for the motorcycle to sell in light of its features. Although the price is a crucial factor, you don’t want it to have a greater weight than the other factors.

Min-Max normalization is applicable here. It involves converting numerical values into ranges, such as from 0.0 to 1.0, where 0.0 represents the minimum value and 1.0 is the maximum value, in order to balance the weight of the price attribute with other attributes in a dataset.

Decimal scaling is a little easier method. In order to achieve the same goals, it involves scaling data by shifting a decimal point in either direction.

If you convert numerical values into categorical values, you may occasionally be able to make predictions that are more accurate. This can be done, for instance, by categorizing the complete range of values into various groups.

The age gap between 12 and 15 or 28 and 29 is not very noticeable if you watch client age statistics. These can then be transformed into age groups that are appropriate. Making the numbers categorical makes an algorithm’s job easier and, in turn, increases the accuracy of prediction.

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