Nowadays, data is growing faster than ever before and this data comes from every sector: businesses, biology, economics, etc. Technology and artificial intelligence allows us to process the large amount of information that is produced from all of these sectors. Data mining refers to the study of pre-existing databases in order to get new insights or information about the data. Data mining uses different techniques to discover patterns and establish relationships to solve problems. Machine Learning uses Data mining techniques and other learning algorithms to build a model of what is happening behind the data so that it can be used to predict future outcomes . The main focus of Machine Learning is the study and design of systems or algorithm that can learn from data . Supervised learning and Unsupervised Learning: Supervised learning is when the algorithm works with input (x) and output (y) variables . Using input and output variables the supervised machine lea
Decision tree learners are powerfull classifiers, which utilizes a tree structure to model the relationship among the features and the potential outcomes. The tree has a root node and decision nodes where choices are made. The choices split the data across branches that indicate the potential outcoumes of a decision. The tree is terminated by leaf nodes (or terminal nodes) that denote the action to be taken as the result of the series of the decisions. In the case of a predictive model, the leaf nodes provide the expected result given the series of events in the tree. After the model is created, many decision trees algorithms output the resulting structure in a human-readable format. This provides tremendous insight into how and why the model works or doesn’t work well for a particular task. This also makes decision trees particularly appropiate for applications in which the classification machanism needs to be transparent for legal reasons, or in case the results needs to be