Abstract
People produce huge amount of data in daily life. By collecting and analyzing those data, many researchers want to improve human life or to replace human labor, such as predict economic circumstances and identify diseases. With the rapidity of computing speed and the substantial increase of storage space, it is an important issue to develop data analysis excellently. Those data can be complex and in multi-dimensions, so it increases the difficulty for analyzing data. As a data analysis technique, principal component analysis can retain most information out of data and, at the same time, reduce dimension effectively. In this thesis, we have used principal components to represent four data sets: 8OX data, colon cancer data, breast cancer data, and wine data. For presenting visual examples, the results of our experiments are shown in two and three-dimension plots by using MATLAB tools. At the end, we will discuss a notice on usage of PCA and its possible solution to obtain accurate results.