Data mining techniques for classification using Fischer's iris data set Data sets contain several attributes that are represented by the separate columns in your data set. Usually, there will be one attribute by which your data is categorized, which we call the class. Our goal is to predict the observation’s class by looking at all the observation’s other attributes. This process is known as Classification.
There are many different methods of Classification, which are discussed below. However, with every method, we will first divide our data into training and test sets. We use the training set to build the classification model with the different methods. Then, we use our test set to validate this model.
Classification Methods: -PCA (Principal Component Analysis) -K-Nearest Neighbors (KNN) Classification -LDA (Linear Discriminant Analysis) -Trees