Data Cleaning and Joining Attribute/Transactional Data

In terms of Synthesis AI datasets for machine learning, assumed or approximated values are more appropriate for an algorithm than plain absent ones. There are ways to more accurately guess which value is missing or get around the problem even if you don’t know the exact value. Choosing the best strategy also greatly depends on the data and domain you have:

1. Replace missing values using dummy values, such as n/a for categorical categories or 0 for numerical values.

2. Replace the missing numerical numbers with mean values.

3. The most prevalent items can also be used to fill in for categorical values.

Data cleansing can be automated if you utilize machine learning as a service platform. For instance, Amazon ML will do it entirely on its own, but Azure Machine Learning lets you choose from different techniques. To learn more about the systems on the market, take a look at our comparison of MLaaS solutions.

Your data set may contain some complex values, and breaking them out into separate components can enable you to identify more precise correlations. In fact, this procedure is the antithesis of data reduction because it requires adding new attributes based on the already-existing ones.

For instance, if the day of the week affects your sales success, separating the day from the date as a separate categorical variable may give the algorithm more pertinent data.

These kinds of data may be present in various data sources or logs that you maintain. In order to increase their predictive power, both categories can benefit from one another. For instance, if you are monitoring sensor readings from machinery to enable predictive maintenance, you are probably generating logs of transactional data. However, you can add attributes like the equipment model, the batch, or its location to search for relationships between equipment behavior and its attributes.

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