This project implements the ID3 algorithm for reading data stored in multiple data sources. It falls under the broader topic of data mining. Data mining is the reading and processing of useful data from different sources. In essence, the process of finding necessary or useful data contained in a large database is characterized by data mining. In the case of logical results, a decision tree is predominantly used for the analysis. The advantages of using a decision tree are that it is easier to model, analyze and manipulate accordingly. The ID3 algorithm is used to generate a decision tree from a given set of data. The ID3 algorithm builds a decision tree based on the given dataset. Branches and nodes are characterized by specific logical results present in the dataset. The speaker identifies two important terms: information gain and entropy. Entropy comes from information theory and is described as the average of the information contained in each message sent to the recipient. Informally, entropy is intuitively understood as impurity, and information content is directly proportional to entropy. This means that the higher the entropy, the higher the information content. The change in information entropy from one distinct state to another is called information gain. The purpose of building a decision tree is to find the attribute that yields the greatest information gain. The speaker explains that the ID3 algorithm takes the training data and list of attributes as input and returns a decision tree as output. The procedure for the ID3 algorithm can be summarized in the following points. Initially, entropy is calculated for each attribute in the dataset. The attribute with minimum entropy is used as a reference and… in the center of the sheet… It is commonly used by the machine learning community to learn and analyze algorithms and as a source of datasets. The implementation includes an example of “Whether to play tennis”. It consists of various factors such as temperature, humidity and weather conditions. Each attribute is marked with a line number called "rownum". Depending on the combinations of the different factors, a column of “Whether to play tennis” has a binary option of “Yes” or “No”. The speaker then concludes the presentation by stating that this project builds a decision tree using the ID3 algorithm and derives a set of rules. The main focus is on data stored across multiple SQL Server databases. It is also worth mentioning the importance of validating attributes and pruning the decision tree for a complex model. Results may not be consistent if these factors are not taken into account.