The purpose of data augmentation is to prepare data for machine learning.
Data must be presented in a specific format in order to be used in machine learning. Thus, it is often necessary to transform data from its original form into something that can be read by a computer.
Additionally, the data may need to be reshaped to reflect desired properties. Rain analysis is an example of this. The amount of rain will vary depending on the season, but if the data is presented in seconds, the value will keep rising and will not be useful. If you supply the date as a month and day number, you will be unable to determine that month 1 follows month 12. As a result, you must use cyclical functions to reflect this.
All of the parameters one plans to give to the model must be taken into account.
A useful augmentation of the data must be based on clearly defined and described variables, so that there is no doubt about what they mean.
With an augmentation, you can use the augmented data to create a machine learning model after the augmentation has been made. The model can then be used to predict one or more of the augmented data points.
Q-analytics process when we do a data analysis is:
As part of the introductory meeting, we will discuss the scope of the analysis, what goals you have, and how you envision your ideal solutions. In addition, we will suggest ways to turn your idea into a reality. 1-2 hours duration.
During this phase, access is granted to the data. It is possible that we receive the data and analyze it in our office. The information can also be analyzed on-site with you if it is extra sensitive. Depending on how and what data we need to access, the time period may vary.
We analyze the data and set up the final product for you. It takes approximately 1-2 weeks, depending on the scope and the goals.
You will receive the final product, in which we will present our findings, explain how we arrived at the results, and hand over the functions we created to augment the data. This takes approx. 1-2 hours.