The dimensionality of a model is the number of independent attributes in the dataset. In order to reduce the complexity of the model we need to reduce the dimensionality without sacrificing accuracy. When we work in complex multidimensional data, we need to select the features that can improve the accuracy of the technique that we are using. Sometimes, we don't know if the variables are independent or if they share some kind of relationship. We need some criteria to find the best features and reduce the number of variables under consideration. In order to address these problems, we will perform three techniques: feature selection, feature extraction, and dimension reduction.