Analysis and preparation of a data set on energy consumption of buildings in machine learning models.

The first, and at the same time one of the most important tasks in building a predictive system is the task of analyzing the original data set. The next task is to process the data according to the purpose and objectives of the predictive system. These two tasks are often not given enough attention and, as a consequence, the developer does not get the desired result as a result of building the system. This paper discusses methods and algorithms for data analysis and processing that can help in building a predictive system. The data set from the machine learning competition "ASHRAE - Great Energy Predictor III" is used as input data. The analysis phase focuses on constructing graphs and drawing conclusions from them. The paper shows how to use graphical information to find anomalies in the data and eliminate them. Examples of graphs that provide information useful for building machine learning models are given. The data processing phase describes the transformations performed on the data. It considers the transformations that lead to positive results as well as those that lead to negative results. [ABSTRACT FROM AUTHOR]